Grid and Cooperative Computing: Second International Workshop, GCC 2003, Shanghai, China, December 7-10, 2003, Revised Papers, Part II

Lecture Notes in Computer Science Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris ...

Author: Minglu Li | Xian-He Sun | Qianni Deng | Jun Ni

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Lecture Notes in Computer Science Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen

Editorial Board Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen University of Dortmund, Germany Madhu Sudan Massachusetts Institute of Technology, MA, USA Demetri Terzopoulos New York University, NY, USA Doug Tygar University of California, Berkeley, CA, USA Moshe Y. Vardi Rice University, Houston, TX, USA Gerhard Weikum Max-Planck Institute of Computer Science, Saarbruecken, Germany

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Berlin Heidelberg New York Hong Kong London Milan Paris Tokyo

Minglu Li Xian-He Sun Qianni Deng Jun Ni (Eds.)

Grid and Cooperative Computing Second International Workshop, GCC 2003 Shanghai, China, December 7-10, 2003 Revised Papers, Part II

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Volume Editors Minglu Li Qianni Deng Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai 200030, P.R. China E-mail: {li-ml, deng-qn}@cs.sjtu.edu.cn Xian-He Sun Illinois Institute of Technology Department of Computer Science Stuart Building, Chicago, IL 60616, USA E-mail: [email protected] Jun Ni University of Iowa Department of Computer Science Iowa City, IA 52242, USA E-mail: [email protected]

Library of Congress Control Number: 2004104848 CR Subject Classification (1998): C.2, D.4, D.2, H.4, H.3, H.5.2-3, I.2 ISSN 0302-9743 ISBN 3-540-21993-5 Springer-Verlag Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable to prosecution under the German Copyright Law. Springer-Verlag is a part of Springer Science+Business Media springeronline.com c Springer-Verlag Berlin Heidelberg 2004 Printed in Germany Typesetting: Camera-ready by author, data conversion by PTP-Berlin, Protago-TeX-Production GmbH Printed on acid-free paper SPIN: 10999332 06/3142 543210

Preface

Grid and cooperative computing has emerged as a new frontier of information technology. It aims to share and coordinate distributed and heterogeneous network resources for better performance and functionality that can otherwise not be achieved. This volume contains the papers presented at the 2nd International Workshop on Grid and Cooperative Computing, GCC 2003, which was held in Shanghai, P.R. China, during December 7–10, 2003. GCC is designed to serve as a forum to present current and future work as well as to exchange research ideas among researchers, developers, practitioners, and users in Grid computing, Web services and cooperative computing, including theory and applications. For this workshop, we received over 550 paper submissions from 22 countries and regions. All the papers were peer-reviewed in depth and qualitatively graded on their relevance, originality, significance, presentation, and the overall appropriateness of their acceptance. Any concerns raised were discussed by the program committee. The organizing committee selected 176 papers for conference presentation (full papers) and 173 submissions for poster presentation (short papers). The papers included herein represent the forefront of research from China, USA, UK, Canada, Switzerland, Japan, Australia, India, Korea, Singapore, Brazil, Norway, Greece, Iran, Turkey, Oman, Pakistan and other countries. More than 600 attendees participated in the technical section and the exhibition of the workshop. The success of GCC 2003 was made possible by the collective efforts of many people and organizations. We would like to express our special thanks to the Ministry of Education of P.R. China and the municipal government of Shanghai. We also thank IBM, Intel, Platform, HP, Dawning and Lenovo for their generous support. Without the extensive support from many communities, we would not have been able to hold this successful workshop. Moreover,our thanks go to Springer-Verlag for its assistance in putting the proceedings together. We would like to take this opportunity to thank all the authors, many of whom traveled great distances to participate in this workshop and make their valuable contributions. We would also like to express our gratitude to the program committee members and all the other reviewers for the time and work they put into the thorough review of the large number of papers submitted. Last, but not least, our thanks also go to all the workshop staff for the great job they did in making the local arrangements and organizing an attractive social program. December 2003

Minglu Li, Xian-He Sun Qianni Deng, Jun Ni

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Conference Committees

Honorary Chair Qinping Zhao (MOE, China)

Steering Committee Guojie Li (CCF, China) Weiping Shen (Shanghai Jiao Tong University, China) Huanye Sheng (Shanghai Jiao Tong University, China) Zhiwei Xu (IEEE Beijing Section, China) Liang-Jie Zhang (IEEE Computer Soceity, USA) Xiaodong Zhang (NSF, USA)

General Co-chairs Minglu Li (Shanghai Jiao Tong University, China) Xian-He Sun (Illinois Institute of Technology, USA)

Program Co-chairs Qianni Deng (Shanghai Jiao Tong University, China) Jun Ni (University of Iowa, USA)

Panel Chair Hai Jin (Huazhong University of Science and Technology, China)

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Program Committee Members Yaodong Bi (University of Scranton, USA) Wentong Cai (Nanyang Technological University, Singapore) Jian Cao (Shanghai Jiao Tong University, China) Jiannong Cao (Hong Kong Polytechnic University, China) Guo-Liang Chen (University of Science and Technology of China, China) Jian Chen (South Australia University, Australia) Xuebin Chi (Computer Network Information Center, CAS, China) Qianni Deng (Shanghai Jiao Tong University, China) Xiaoshe Dong (Xi’an Jiao Tong University, China) Joseph Fong (City University of Hong Kong) Yuxi Fu (Shanghai Jiao Tong University, China) Guangrong Gao (University of Delaware, Newark, USA) Yadong Gui (Shanghai Supercomputing Center, China) Minyi Guo (University of Aizu, Japan) Jun Han (Swinburne University of Technology, Australia) Yanbo Han (Institute of Computing Technology, CAS, China) Jinpeng Huai (Beihang University, China) Weijia Jia (City University of Hong Kong) ChangJun Jiang (Tongji University, China) Hai Jin (Huazhong University of Science and Technology, China) Francis Lau (University of Hong Kong) Keqin Li (State University of New York, USA) Minglu Li (Shanghai Jiao Tong University, China) Qing Li (City University of Hong Kong) Xiaoming Li (Peking University, China) Xinda Lu (Shanghai Jiao Tong University, China) Junzhou Luo (Southeast University, China) Fanyuan Ma (Shanghai Jiao Tong University, China) Dan Meng (Institute of Computing Technology, CAS, China) Xiangxu Meng (Shandong University, China) Jun Ni (University of Iowa, USA) Lionel M. Ni (Hong Kong University of Science & Technology) Yi Pan (Georgia State University, USA) Depei Qian (Xi’an Jiao Tong University, China) Yuzhong Qu (Southeast University, China) Hong Shen (Advanced Institute of Science & Technology, Japan) Xian-He Sun (Illinois Institute of Technology, USA) Huaglory Tianfield (Glasgow Caledonian University, UK) Weiqin Tong (Shanghai University, China) Cho-Li Wang (University of Hong Kong) Frank Wang (London Metropolitan University, UK) Jie Wang (Stanford University, USA) Shaowen Wang (University of Iowa, USA) Xingwei Wang (Northeastern University, China)


Jie Wu (Florida Atlantic University, USA) Zhaohui Wu (Zhejiang University, China) Nong Xiao (National University of Defense Technology, China) Xianghui Xie (Jiangnan Institute of Computing Technology, China) Chengzhong Xu (Wayne State University, USA) Zhiwei Xu (Institute of Computing Technology, CAS, China) Guangwen Yang (Tsinghua University, China) Laurence Tianruo Yang (St. Francis Xavier University, Canada) Qiang Yang (Hong Kong University of Science & Technology) Jinyuan You (Shanghai Jiao Tong University, China) Haibiao Zeng (Sun Yat-Sen University, China) Ling Zhang (South China University of Technology, China) Xiaodong Zhang (NSF, USA and College of William and Mary, USA) Wu Zhang (Shanghai University, China) Weimin Zheng (Tsinghua University, China) Aoying Zhou (Fudan University, China) Wanlei Zhou (Deakin University, Australia) Jianping Zhu (University of Akron, USA) Hai Zhuge (Institute of Computing Technology, CAS, China)

Organization Committee Xinda Lu (Chair) (Shanghai Jiao Tong University, China) Jian Cao (Shanghai Jiao Tong University, China) Ruonan Rao (Shanghai Jiao Tong University, China) Meiju Chen (Shanghai Jiao Tong University, China) An Yang (Shanghai Jiao Tong University, China) Zhihua Su (Shanghai Jiao Tong University, China) Feilong Tang (Shanghai Jiao Tong University, China) Jiadi Yu (Shanghai Jiao Tong University, China)

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Will Globus dominate Grid computing as Windows dominated in PCs? If not, what will the next Grid toolkits looks like? Panel Chair Hai Jin, Huazhong University of Science and Technology, China [email protected]

Panelists Wolfgang Gentzsch, Sun Microsystems, Inc., USA [email protected] Satoshi Matsuoka, Tokyo Institute of Technology, Japan [email protected] Carl Kesselman, University of Southern California, USA [email protected] Andrew A. Chien, University of California at San Diego, USA [email protected] Xian-He Sun, Illinois Institute of Technology, USA [email protected] Richard Wirt, Intel Corporation, USA [email protected] Zhiwei Xu, Institute of Computing Technology, CAS, China [email protected] Francis Lau, University of Hong Kong [email protected] Huaglory Tianfield, Glasgow Caledonian University, UK [email protected]

Table of Contents, Part II

Session 6: Advanced Resource Management, Scheduling, and Monitoring Synthetic Implementations of Performance Data Collection in Massively Parallel Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chu J. Jong, Arthur B. Maccabe

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GMA+ – A GMA-Based Monitoring and Management Infrastructure for Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chuan He, Zhihui Du, San-li Li

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A Parallel Branch–and–Bound Algorithm for Computing Optimal Task Graph Schedules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Udo H¨ onig, Wolfram Schiffmann

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Selection and Advanced Reservation of Backup Resources for High Availability Service in Computational Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . Chunjiang Li, Nong Xiao, Xuejun Yang

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An Online Scheduling Algorithm for Grid Computing Systems . . . . . . . . . . Hak Du Kim, Jin Suk Kim

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A Dynamic Job Scheduling Algorithm for Computational Grid . . . . . . . . . . Jian Zhang, Xinda Lu

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An Integrated Management and Scheduling Scheme for Computational Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ran Zheng, Hai Jin

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Multisite Task Scheduling on Distributed Computing Grid . . . . . . . . . . . . . Weizhe Zhang, Hongli Zhang, Hui He, Mingzeng Hu

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Adaptive Job Scheduling for a Service Grid Using a Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yang Gao, Hongqiang Rong, Frank Tong, Zongwei Luo, Joshua Huang

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Resource Scheduling Algorithms for Grid Computing and Its Modeling and Analysis Using Petri Net . . . . . . . . . . . . . . . . . . . . . . . Yaojun Han, Changjun Jiang, You Fu, Xuemei Luo

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Architecture of Grid Resource Allocation Management Based on QoS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaozhi Wang, Junzhou Luo

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An Improved Ganglia-Like Clusters Monitoring System . . . . . . . . . . . . . . . . Wenguo Wei, Shoubin Dong, Ling Zhang, Zhengyou Liang Effective OpenMP Extensions for Irregular Applications on Cluster Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Minyi Guo, Jiannong Cao, Weng-Long Chang, Li Li, Chengfei Liu

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A Scheduling Approach with Respect to Overlap of Computing and Data Transferring in Grid Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Changqin Huang, Yao Zheng, Deren Chen A Deadline and Budget Constrained Cost-Time Optimization Algorithm for Scheduling Dependent Tasks in Grid Computing . . . . . . . . . 113 Haolin Feng, Guanghua Song, Yao Zheng, Jun Xia A Load Balancing Algorithm for Web Based Server Grids . . . . . . . . . . . . . . 121 Shui Yu, John Casey, Wanlei Zhou Flexible Intermediate Library for MPI-2 Support on an SCore Cluster System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Yuichi Tsujita Resource Management and Scheduling in Manufacturing Grid . . . . . . . . . . 137 Lilan Liu, Tao Yu, Zhanbei Shi, Minglun Fang A New Task Scheduling Algorithm in Distributed Computing Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Jian-Jun Han, Qing-Hua Li GridFerret: Grid Monitoring System Based on Mobile Agent . . . . . . . . . . . 145 Juan Fang, Shu-Jie Zhang, Rui-Hua Di, He Huang Grid-Based Resource Management of Naval Weapon Systems . . . . . . . . . . . 149 Bin Zeng, Tao Hu, ZiTang Li A Static Task Scheduling Algorithm in Grid Computing . . . . . . . . . . . . . . . 153 Dan Ma, Wei Zhang A New Agent-Based Distributed Model of Grid Service Advertisement and Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Dan Ma, Wei Zhang, Hong-jun Zhang IMCAG: Infrastructure for Managing and Controlling Agent Grid . . . . . . . 161 Jun Hu, Ji Gao A Resource Allocation Method in the Neural Computation Platform . . . . 166 Zhuo Lai, Jiangang Yang, Hongwei Shan


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An Efficient Clustering Method for Retrieval of Large Image Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 Yu-Xiang Xie, Xi-Dao Luan, Ling-Da Wu, Song-Yang Lao, Lun-Guo Xie Research on Adaptable Replication Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . 174 Dong Zhao, Ya-wei Li, Ming-Tian Zhou Co-operative Monitor Web Page Based on MD5 . . . . . . . . . . . . . . . . . . . . . . . 179 Guohun Zhu, YuQing Miao Collaboration-Based Architecture of Flexible Software Configuration Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Ying Ding, Weishi Zhang, Lei Xu The Research of Mobile Agent Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Xiaobin Li, Aijuan Zhang, Jinfei Sun, Zhaolin Yin Research of Information Resources Integration and Shared in Digital Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Xiaofeng Zhou, Zhijian Wang, Ping Ai Scheduling Model in Global Real-Time High Performance Computing with Network Calculus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Yafei Hou, ShiYong Zhang, YiPing Zhong CPU Schedule in Programmable Routers: Virtual Service Queuing with Feedback Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Tieying Zhu Research on Information Platform of Virtual Enterprise Based on Web Services Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Chao Young, Jiajin Le A Reliable Grid Messaging Service Based on JMS . . . . . . . . . . . . . . . . . . . . 207 Ruonan Rao, Xu Cai, Ping Hao, Jinyuan You A Feedback and Investigation Based Resources Discovery and Management Model on Computational Grid . . . . . . . . . . . . . . . . . . . . . 211 Peng Ji, Junzhou Luo Moment Based Transfer Function Design for Volume Rendering . . . . . . . . . 215 Huawei Hou, Jizhou Sun, Jiawan Zhang Grid Monitoring and Data Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Yi Chi, Shoubao Yang, Zheng Feng An Economy Driven Resource Management Architecture Based on Mobile Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Peng Wan, Wei-Yong Zhang, Tian Chen

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Decentralized Computational Market Model for Grid Resource Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 Qianfei Fu, Shoubao Yang, Maosheng Li, Junmao Zhun A Formal Data Model and Algebra for Resource Sharing in Grid . . . . . . . . 231 Qiujian Sheng, Zhongzhi Shi An Efficient Load Balance Algorithm in Cluster-Based Peer-to-Peer System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 Ming-Hong Shi, Yong-Jun Luo, Ying-Cai Bai Resource Information Management of Spatial Information Grid . . . . . . . . . 240 Deke Guo, Honghui Chen, Xueshan Luo An Overview of CORBA-Based Load Balancing . . . . . . . . . . . . . . . . . . . . . . . 244 Jian Shu, Linlan Liu, Shaowen Song Intelligence Balancing for Communication Data Management in Grid Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250 Jong Sik Lee On Mapping and Scheduling Tasks with Synchronization on Clusters of Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 Bassel R. Arafeh An Efficient Load Balancing Algorithm on Distributed Networks . . . . . . . . 259 Okbin Lee, Sangho Lee, Ilyong Chung

Session 7: Network Communication and Information Retrieval Optimal Methods for Object Placement in En-Route Web Caching for Tree Networks and Autonomous Systems . . . . . . . . . . . . . . . . . . . . . . . . . 263 Keqiu Li, Hong Shen A Framework of Tool Integration for Internet-Based E-commerce . . . . . . . 271 Jianming Yong, Yun Yang Scalable Filtering of Well-Structured XML Message Stream . . . . . . . . . . . . 279 Weixiong Rao, Yingjian Chen, Xinquan Zhang, Fanyuan Ma Break a New Ground on Programming in Web Client Side . . . . . . . . . . . . . 287 Jianjun Zhang, Mingquan Zhou An Adaptive Mixing Audio Gateway in Heterogeneous Networks for ADMIRE System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294 Tao Huang, Xiangning Yu Kernel Content-Aware QoS for Web Clusters . . . . . . . . . . . . . . . . . . . . . . . . . 303 Zeng-Kai Du, Jiu-bin Ju


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A Collaborative Multimedia Authoring System . . . . . . . . . . . . . . . . . . . . . . . 311 Mee Young Sung, Do Hyung Lee Research of Satisfying Atomic and Anonymous Electronic Commerce Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Jie Tang, Juan-Zi Li, Ke-Hong Wang, Yue-Ru Cai Network Self-Organizing Information Exploitation Model Based on GCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Yujun Liu, Dianxun Shuai, Weili Han Admire – A Prototype of Large Scale E-collaboration Platform . . . . . . . . . 335 Tian Jin, Jian Lu, XiangZhi Sheng A Most Popular Approach of Predictive Prefetching on a WAN to Efficiently Improve WWW Response Times . . . . . . . . . . . . . . . . . . . . . . . . 344 Christos Bouras, Agisilaos Konidaris, Dionysios Kostoulas Applications of Server Performance Control with Simple Network Management Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352 Yijiao Yu, Qin Liu, Liansheng Tan Appcast – A Low Stress and High Stretch Overlay Protocol . . . . . . . . . . . . 360 V. Radha, Ved P Gulati, Arun K Pujari Communication Networks: States of the Arts . . . . . . . . . . . . . . . . . . . . . . . . . 372 Xiaolu Zuo DHCS: A Case of Knowledge Share in Cooperative Computing Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380 Shui Yu, LeYun Pan, Futai Zou, FanYuan Ma Improving the Performance of Equalization in Communication Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388 Wanlei Zhou, Hua Ye, Lin Ye Moving Communicational Supervisor Control System Based on Component Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396 Song Yu, Yan-Rong Jie A Procedure Search Mechanism in OGSA-Based GridRPC Systems . . . . . 400 Yue-zhuo Zhang, Yong-zhong Huang, Xin Chen An Improved Network Broadcasting Method Based on Gnutella Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404 Zupeng Li, Xiubin Zhao, Daoyin Huang, Jianhua Huang Some Conclusions on Cayley Digraphs and Their Applications to Interconnection Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 Wenjun Xiao, Behrooz Parhami

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Multifractal Characteristic Quantities of Network Traffic Models . . . . . . . . 413 Donglin Liu, Dianxun Shuai Network Behavior Analysis Based on a Computer Network Model . . . . . . 418 Weili Han, Dianxun Shuai, Yujun Liu Cutting Down Routing Overhead in Mobile Ad Hoc Networks . . . . . . . . . . 422 Jidong Zhong, Shangteng Huang Improving Topology-Aware Routing Efficiency in Chord . . . . . . . . . . . . . . . 426 Dongfeng Chen, Shoubao Yang Two Extensions to NetSolve System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430 Jianhua Chen, Wu Zhang, Weimin Shao A Route-Based Composition Language for Service Cooperation . . . . . . . . . 434 Jianguo Xing To Manage Grid Using Dynamically Constructed Network Management Concept: An Early Thought . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438 Zhongzhi Luan, Depei Qian, Weiguo Wu, Tao Liu Design of VDSL Networks for the High Speed Internet Services . . . . . . . . . 442 Hyun Yoe, Jaejin Lee The Closest Vector Problem on Some Lattices . . . . . . . . . . . . . . . . . . . . . . . . 446 Haibin Kan, Hong Shen, Hong Zhu Proposing a New Architecture for Adaptive Active Network Control and Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 450 Mahdi Jalili-Kharaajoo, Alireza Dehestani, Hassan Motallebpour A Path Based Internet Cache Design for GRID Application . . . . . . . . . . . . 455 Hyuk Soo Jang, Kyong Hoon Min, Wou Seok Jou, Yeonseung Ryu, Chung Ki Lee, Seok Won Hong On the Application of Computational Intelligence Methods on Active Networking Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 Mahdi Jalili-Kharaajoo

Session 8: Grid QoS Grid Computing for the Masses: An Overview . . . . . . . . . . . . . . . . . . . . . . . . 464 Kaizar Amin, Gregor von Laszewski, Armin R. Mikler A Multiple-Neighborhoods-Based Simulated Annealing Algorithm for Timetable Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474 He Yan, Song-Nian Yu


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Lattice Framework to Implement OGSA: Its Constructs and Composition Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . 482 Hui Liu, Minglu Li, Jiadi Yu, Lei Cao, Ying Li, Wei Jin, Qi Qian Moving Grid Systems into the IPv6 Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 490 Sheng Jiang, Piers O’Hanlon, Peter Kirstein MG-QoS: QoS-Based Resource Discovery in Manufacturing Grid . . . . . . . . 500 Zhanbei Shi, Tao Yu, Lilan Liu An Extension of Grid Service: Grid Mobile Service . . . . . . . . . . . . . . . . . . . . 507 Wei Zhang, Jun Zhang, Dan Ma, Benli Wang, YunTao Chen Supplying Instantaneous Video-on-Demand Services Based on Grid Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513 Xiao-jian He, Xin-huai Tang, Jinyuan You A Grid Service Lifecycle Management Scheme . . . . . . . . . . . . . . . . . . . . . . . . 521 Jie Qiu, Haiyan Yu, Shuoying Chen, Li Cha, Wei Li, Zhiwei Xu An OGSA-Based Quality of Service Framework . . . . . . . . . . . . . . . . . . . . . . . 529 Rashid Al-Ali, Kaizar Amin, Gregor von Laszewski, Omer Rana, David Walker A Service Management Scheme for Grid Systems . . . . . . . . . . . . . . . . . . . . . . 541 Wei Li, Zhiwei Xu, Li Cha, Haiyan Yu, Jie Qiu, Yanzhe Zhang A QoS Model for Grid Computing Based on DiffServ Protocol . . . . . . . . . . 549 Wandan Zeng, Guiran Chang, Xingwei Wang, Shoubin Wang, Guangjie Han, Xubo Zhou Design and Implementaion of a Single Sign-On Library Supporting SAML (Security Assertion Markup Language) for Grid and Web Services Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557 Dongkyoo Shin, Jongil Jeong, Dongil Shin Performance Improvement of Information Service Using Priority Driven Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565 Minji Lee, Wonil Kim, Jai-Hoon Kim HH-MDS: A QoS-Aware Domain Divided Information Service . . . . . . . . . . 573 Deqing Zou, Hai Jin, Xingchang Dong, Weizhong Qiang, Xuanhua Shi Grid Service Semigroup and Its Workflow Model . . . . . . . . . . . . . . . . . . . . . . 581 Yu Tang, Haifang Zhou, Kaitao He, Luo Chen, Ning Jing A Design of Distributed Simulation Based on GT3 Core . . . . . . . . . . . . . . . 590 Tong Zhang, Chuanfu Zhang, Yunsheng Liu, Yabing Zha

XVIII


A Policy-Based Service-Oriented Grid Architecture . . . . . . . . . . . . . . . . . . . 597 Xiangli Qu, Xuejun Yang, Chunmei Gui, Weiwei Fan Adaptable QOS Management in OSGi-Based Cooperative Gateway Middleware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604 Wei Liu, Zhang-long Chen, Shi-liang Tu, Wei Du Design of an Artificial-Neural-Network-Based Extended Metacomputing Directory Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608 Haopeng Chen, Baowen Zhang

Session 9: Algorithm, Economic Model, Theoretical Model of the Grid Gridmarket: A Practical, Efficient Market Balancing Resource for Grid and P2P Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 612 Ming Chen, Guangwen Yang, Xuezheng Liu A Distributed Approach for Resource Pricing in Grid Environments . . . . . 620 Chuliang Weng, Xinda Lu, Qianni Deng Application Modelling Based on Typed Resources . . . . . . . . . . . . . . . . . . . . . 628 Cheng Fu, Jinyuan You A General Merging Algorithm Based on Object Marking . . . . . . . . . . . . . . . 636 Jinlei Jiang, Meilin Shi Charging and Accounting for Grid Computing System . . . . . . . . . . . . . . . . . 644 Zhengyou Liang, Ling Zhang, Shoubin Dong, Wenguo Wei Integrating New Cost Model into HMA-Based Grid Resource Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652 Jun-yan Zhang, Fan Min, Guo-wei Yang CoAuto: A Formal Model for Cooperative Processes . . . . . . . . . . . . . . . . . . 660 Jinlei Jiang, Meilin Shi A Resource Model for Large-Scale Non-hierarchy Grid System . . . . . . . . . 669 Qianni Deng, Xinda Lu, Li Chen, Minglu Li A Virtual Organization Based Mobile Agent Computation Model . . . . . . . 677 Yong Liu, Cong-fu Xu, Zhaohui Wu, Wei-dong Chen, Yun-he Pan Modeling Distributed Algorithm Using B . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683 Shengrong Zou Multiple Viewpoints Based Ontology Integration . . . . . . . . . . . . . . . . . . . . . . 690 Kai Zhang, Yunfa Hu, Yu Wang


XIX

Automated Detection of Design Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694 Zhixiang Zhang, Qing-Hua Li Research on the Financial Information Grid . . . . . . . . . . . . . . . . . . . . . . . . . . 698 Jiyue Wen, Guiran Chang RCACM: Role-Based Context-Awareness Coordination Model for Mobile Agent Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 702 Xin-huai Tang, Yaying Zhang, Jinyuan You A Model for Locating Services in Grid Environment . . . . . . . . . . . . . . . . . . . 706 Erfan Shang, Zhihui Du, Mei Chen A Grid Service Based Model of Virtual Experiment . . . . . . . . . . . . . . . . . . . 710 Liping Shen, Yonggang Fu, Ruimin Shen, Minglu Li Accounting in the Environment of Grid Society . . . . . . . . . . . . . . . . . . . . . . 715 Jiulong Shan, Huaping Chen, GuoLiang Chen, Haitao Tian, Xin Chen A Heuristic Algorithm for Minimum Connected Dominating Set with Maximal Weight in Ad Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 719 Xinfang Yan, Yugeng Sun, Yanlin Wang Slice-Based Information Flow Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723 Wan-Kyoo Choi, Il-Yong Chung

Session 10: Semantic Grid and Knowledge Grid Semantic Rule Service Model: Enabling Intelligence on Grid Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 727 Qi Gao, HuaJun Chen, Zhaohui Wu, WeiMing Lin CSCW in Design on the Semantic Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 736 Dazhou Kang, Baowen Xu, Jianjiang Lu, Yingzhou Zhang SIMON: A Multi-strategy Classification Approach Resolving Ontology Heterogeneity – The P2P Meets the Semantic Web . . . . . . . . . . 744 LeYun Pan, Liang Zhang, Fanyuan Ma SkyEyes: A Semantic Browser for the KB-Grid . . . . . . . . . . . . . . . . . . . . . . . 752 Yuxin Mao, Zhaohui Wu, HuaJun Chen Toward the Composition of Semantic Web Services . . . . . . . . . . . . . . . . . . . . 760 Jinghai Rao, Xiaomeng Su A Viewpoint of Semantic Description Framework for Service . . . . . . . . . . . 768 Yuzhong Qu

XX


A Novel Approach to Semantics-Based Exception Handling for Service Grid Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 778 Donglai Li, Yanbo Han, Haitao Hu, Jun Fang, Xue Wang A Semantic-Based Web Service Integration Approach and Tool . . . . . . . . . 787 Hai Zhuge, Jie Liu, Lianhong Ding, Xue Chen A Computing Model for Semantic Link Network . . . . . . . . . . . . . . . . . . . . . 795 Hai Zhuge, Yunchuan Sun, Jie Liu, Xiang Li A Semantic Web Enabled Mediator for Web Service Invocation . . . . . . . . . 803 Lejun Zhu, Peng Ding, Huanye Sheng A Data Mining Algorithm Based on Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . 807 Xue-bai Zang, Xiong-fei Li, Kun Zhao, Xin Guan Prototype a Knowledge Discovery Infrastructure by Implementing Relational Grid Monitoring Architecture (R-GMA) on European Data Grid (EDG) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 811 Frank Wang, Na Helian, Yike Guo, Steve Thompson, John Gordon

Session 11: Data Remote Access, Storage, and Sharing The Consistency Mechanism of Meta-data Management in Distributed Storage System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 815 Zhaofu Wang, Wensong Zhang, Kun Deng Link-Contention-Aware Genetic Scheduling Using Task Duplication in Grid Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 822 Wensheng Yao, Xiao Xie, Jinyuan You An Adaptive Meta-scheduler for Data-Intensive Applications . . . . . . . . . . . 830 Xuanhua Shi, Hai Jin, Weizhong Qiang, Deqing Zou Dynamic Data Grid Replication Strategy Based on Internet Hierarchy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 838 Sang-Min Park, Jai-Hoon Kim, Young-Bae Ko, Won-Sik Yoon Preserving Data Consistency in Grid Databases with Multiple Transactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 847 Sushant Goel, Hema Sharda, David Taniar Dart: A Framework for Grid-Based Database Resource Access and Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 855 Chang Huang, Zhaohui Wu, Guozhou Zheng, Xiaojun Wu An Optimal Task Scheduling for Cluster Systems Using Task Duplication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 863 Xiao Xie, Wensheng Yao, Jinyuan You


XXI

Towards an Interactive Architecture for Web-Based Databases . . . . . . . . . . 871 Changgui Chen, Wanlei Zhou Network Storage Management in Data Grid Environment . . . . . . . . . . . . . . 879 Shaofeng Yang, Zeyad Ali, Houssain Kettani, Vinti Verma, Qutaibah Malluhi Study on Data Access Technology in Information Grid . . . . . . . . . . . . . . . . 887 YouQun Shi, ChunGang Yan, Feng Yue, Changjun Jiang GridTP Services for Grid Transaction Processing . . . . . . . . . . . . . . . . . . . . . 891 Zhengwei Qi, Jinyuan You, Ying Jin, Feilong Tang FTPGrid: A New Paradigm for Distributed FTP System . . . . . . . . . . . . . . . 895 Liutong Xu, Bo Ai Using Data Cube for Mining of Hybrid-Dimensional Association Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 899 Zhi-jie Li, Fei-xue Huang, Dong-qing Zhou, Peng Zhang Knowledge Sharing by Grid Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 903 Bangyong Liang, Juan-Zi Li, Ke-Hong Wang A Security Access Control Mechanism for a Multi-layer Heterogeneous Storage Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 907 Shiguang Ju, Héctor J. Hern´ andez, Lan Zhang Investigating the Role of Handheld Devices in the Accomplishment of Grid-Enabled Analysis Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 913 Ashiq Anjum, Arshad Ali, Tahir Azim, Ahsan Ikram, Julian J. Bunn, Harvey B. Newman, Conrad Steenberg, Michael Thomas

Session 12: Computer-Supported Cooperative Work and Cooperative Middleware A TMO-Based Object Group Model to Structuring Replicated Real-Time Objects for Distributed Real-Time Applications . . . . . . . . . . . . . 918 Chang-Sun Shin, Su-Chong Joo, Young-Sik Jeong Fuzzy Synthesis Evaluation Improved Task Distribution in WfMS . . . . . . . 927 Xiao-Guang Zhang, Jian Cao, Shensheng Zhang A Simulation Study of Job Workflow Execution Models over the Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 935 Yuhong Feng, Wentong Cai, Jiannong Cao An Approach to Distributed Collaboration Problem with Conflictive Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 944 Jingping Bi, Qi Wu, Zhongcheng Li

XXII


Temporal Problems in Service-Based Workflows . . . . . . . . . . . . . . . . . . . . . . . 954 Zhen Yu, Zhaohui Wu, ShuiGuang Deng, Qi Gao iCell: Integration Unit in Enterprise Cooperative Environment . . . . . . . . . 962 Ruey-Shyang Wu, Shyan-Ming Yuan, Anderson Liang, Daphne Chyan The Availability Semantics of Predicate Data Flow Diagram . . . . . . . . . . . 970 Xiaolei Gao, Huaikou Miao, Shaoying Liu, Ling Liu Virtual Workflow Management System in Grid Environment . . . . . . . . . . . 978 ShuiGuang Deng, Zhaohui Wu, Qi Gao, Zhen Yu Research of Online Expandability of Service Grid . . . . . . . . . . . . . . . . . . . . . 986 Yuan Wang, Zhiwei Xu, Yuzhong Sun Modelling Cooperative Multi-agent Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 994 Lijun Shan, Hong Zhu GHIRS: Integration of Hotel Management Systems by Web Services . . . . . 1002 Yang Xiang, Wanlei Zhou, Morshed Chowdhury Cooperative Ants Approach for a 2D Navigational Map of 3D Virtual Scene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1010 Jiangchun Wang, Shensheng Zhang Workflow Interoperability – Enabling Online Approval in E-government . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1018 Hua Xin, Fu-ren Xue A Multicast Routing Algorithm for CSCW . . . . . . . . . . . . . . . . . . . . . . . . . . . 1022 Xiong-fei Li, Dandan Huan, Yuanfang Dong, Xin Zhou A Multi-agent System Based on ECA Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . 1026 Xiaojun Zhou, Jian Cao, Shensheng Zhang A Hybrid Algorithm of n-OPT and GA to Solve Dynamic TSP . . . . . . . . . 1030 Zhao Liu, Lishan Kang The Application Research of Role-Based Access Control Model in Workflow Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1034 Baoyi Wang, Shaomin Zhang, Xiaodong Xia Research and Design of Remote Education System Based on CSCW . . . . . 1038 Chunzhi Wang, Miao Shao, Jing Xia, Huachao Chen Data and Interaction Oriented Workflow Execution . . . . . . . . . . . . . . . . . . . 1042 Wan-Chun Dou, Juan Sun, Da-Gang Yang, Shi-Jie Cai


XXIII

XCS System: A New Architecture for Web-Based Applications . . . . . . . . . 1046 Yijian Wu, Wenyun Zhao A PKI-Based Scalable Security Infrastructure for Scalable Grid . . . . . . . . 1051 Lican Huang, Zhaohui Wu A Layered Grid User Expression Model in Grid User Management . . . . . . 1055 Limin Liu, Zhiwei Xu, Wei Li A QoS-Based Multicast Algorithm for CSCW in IP/DWDM Optical Internet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1059 Xingwei Wang, Hui Cheng, Jia Li, Min Huang, Ludi Zheng An Evolutionary Constraint Satisfaction Solution for over the Cell Channel Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1063 ¨ Ahmet Unveren, Adnan Acan

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1067

Table of Contents, Part I

Vega Grid: A Computer Systems Approach to Grid Research . . . . . . . . . . . Zhiwei Xu

1

Problems of and Mechanisms for Instantiating Virtual Organizations . . . . Carl Kesselman

2

Grid Computing: The Next Stage of the Internet . . . . . . . . . . . . . . . . . . . . . . Irving Wladawsky-Berger

3

Making Grid Computing Real for High Performance and Enterprise Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Richard Wirt

4

Grid Computing for Enterprise and Beyond . . . . . . . . . . . . . . . . . . . . . . . . . . Songnian Zhou

5

Semantic Grid: Scientific Issues, Methodology, and Practice in China . . . . Hai Zhuge

6

Grid Computing, Vision, Strategy, and Technology . . . . . . . . . . . . . . . . . . . . Wolfgang Gentzsch

7

Towards a Petascale Research Grid Infrastructure . . . . . . . . . . . . . . . . . . . . . Satoshi Matsuoka

8

The Microgrid: Enabling Scientific Study of Dynamic Grid Behavior . . . . . Andrew A. Chien

9

On-Demand Business Collaboration Enablement with Services Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liang-Jie Zhang

10

Session 1: Grid Application Multidisciplinary Design Optimization of Aero-craft Shapes by Using Grid Based High Performance Computational Framework . . . . . . . . Hong Liu, Xi-li Sun, Qianni Deng, Xinda Lu

11

A Research on the Framework of Grid Manufacturing . . . . . . . . . . . . . . . . . . Li Chen, Hong Deng, Qianni Deng, Zhenyu Wu

19

Large-Scale Biological Sequence Assembly and Alignment by Using Computing Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Shi, Wanlei Zhou

26

XXVI


Implementation of Grid-Enabled Medical Simulation Applications Using Workflow Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Junwei Cao, Jochen Fingberg, Guntram Berti, Jens Georg Schmidt

34

C 2 : A New Overlay Network Based on CAN and Chord . . . . . . . . . . . . . . . . Wenyuan Cai, Shuigeng Zhou, Linhao Xu, Weining Qian, Aoying Zhou

42

An Engineering Computation Oriented Visual Grid Framework . . . . . . . . . Guiyi Wei, Yao Zheng, Jifa Zhang, Guanghua Song

51

Interaction Compatibility: An Essential Ingredient for Service Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jun Han

59

A Distributed Media Service System Based on Globus Data-Management Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiang Yu, Shoubao Yang, Yu Hong

67

Load Balancing between Heterogeneous Computing Clusters . . . . . . . . . . . Siu-Cheung Chau, Ada Wai-Chee Fu

75

“Gridifying” Aerodynamic Design Problem Using GridRPC . . . . . . . . . . . . Quoc-Thuan Ho, Yew-Soon Ong, Wentong Cai

83

A WEB-GIS Based Urgent Medical Rescue CSCW System for SARS Disease Prevention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaolin Lu

91

MASON: A Model for Adapting Service-Oriented Grid Applications . . . . . Gang Li, Jianwu Wang, Jing Wang, Yanbo Han, Zhuofeng Zhao, Roland M. Wagner, Haitao Hu

99

Coordinating Business Transaction for Grid Service . . . . . . . . . . . . . . . . . . . 108 Feilong Tang, Minglu Li, Jian Cao, Qianni Deng Conceptual Framework for Recommendation System Based on Distributed User Ratings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Hyun-Jun Kim, Jason J. Jung, Geun-Sik Jo Grid Service-Based Parallel Finite Element Analysis . . . . . . . . . . . . . . . . . . . 123 Guiyi Wei, Yao Zheng, Jifa Zhang The Design and Implementation of the GridLab Information Service . . . . 131 Giovanni Aloisio, Massimo Cafaro, Italo Epicoco, Daniele Lezzi, Maria Mirto, Silvia Mocavero Comparison Shopping Systems Based on Semantic Web – A Case Study of Purchasing Cameras . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Ho-Kyoung Lee, Young-Hoon Yu, Supratip Ghose, Geun-Sik Jo


XXVII

A New Navigation Method for Web Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Jie Yang, Guoqing Wu, Luis Zhu Application Availability Measurement in Computational Grid . . . . . . . . . . 151 Chunjiang Li, Nong Xiao, Xuejun Yang Research and Application of Distributed Fusion System Based on Grid Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Yu Su, Hai Zhao, Wei-ji Su, Gang Wang, Xiao-dan Zhang An Efficient and Self-Configurable Publish-Subscribe System . . . . . . . . . . . 159 Tao Xue, Boqin Feng The Implementation of the Genetic Optimized Algorithm of Air Craft Geometry Designing Based on Grid Computing . . . . . . . . . . . . . . . . . . 164 Xi-li Sun, Xinda Lu, Qianni Deng Distributed Information Management System for Grid Computing . . . . . . 168 Liping Niu, Xiaojie Yuan, Wentong Cai The Design of Adaptive Platform for Visual-Intensive Applications over the Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 Hui Xiang, Bin Gong, Xiangxu Meng, Xianglong Kong Maintaining Packet Order for the Parallel Switch . . . . . . . . . . . . . . . . . . . . . 176 Yuguo Dong, Binqiang Wang, Yunfei Guo, Jiangxing Wu Grid-Based Process Simulation Technique and Support System . . . . . . . . . 180 Hui Gao, Li Zhang Some Grid Automata for Grid Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 Hao Shen, Yongqiang Sun The Cooperation of Virtual Enterprise Supported by the Open Agent System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 Zhaolin Yin, Aijuan Zhang, Xiaobin Li, Jinfei Sun The Granularity Analysis of MPI Parallel Programs . . . . . . . . . . . . . . . . . . . 192 Wei-guang Qiao, Guosun Zeng NGG: A Service-Oriented Application Grid Architecture for National Geological Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 Yu Tang, Kaitao He, Zhen Xiang, Yongbo Zhang, Ning Jing Integration of the Distributed Simulation into the OGSA Model . . . . . . . . 200 Chuanfu Zhang, Yunsheng Liu, Tong Zhang, Yabing Zha An Extendable Grid Simulation Environment Based on GridSim . . . . . . . . 205 Efeng Lu, Zhihong Xu, Jizhou Sun

XXVIII


The Architecture of Traffic Information Grid . . . . . . . . . . . . . . . . . . . . . . . . . 209 Zhaohui Zhang, Qing Zhi, Guosun Zeng, Changjun Jiang Construction Scheme of Meteorological Application Grid (MAG) . . . . . . . . 213 Xuesheng Yang, Weiming Zhang, Dehui Chen OGSA Based E-learning System: An Approach to Build Next Generation of Online Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 Hui Wang, Xueli Yu, Li Wang, Xu Liu Multimedia Delivery Grid: A Novel Multimedia Delivery Scheme . . . . . . . . 221 ZhiHui Lv, Jian Yang, ShiYong Zhang, YiPing Zhong The System for Computing of Molecule Structure on the Computational Grid Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Yongmei Lei, Weimin Xu, Bingqiang Wang An Efficient Parallel Crawler in Grid Environment . . . . . . . . . . . . . . . . . . . . 229 Shoubin Dong, Xiaofeng Lu, Ling Zhang, Kejing He The Development and Application of Numerical Packages Based on NetSolve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Haiying Cheng, Wu Zhang, Yunfu Shen, Anping Song Grid-Based Biological Computation Service Environment . . . . . . . . . . . . . . 237 Jing Zhu, Guangwen Yang, Weimin Zheng, Tao Zhu, Meiming Shen, Li’an Qiao, Xiangjun Liu CIMES: A Collaborative Image Editing System for Pattern Design . . . . . . 242 Xianghua Xu, Jiajun Bu, Chun Chen, Yong Li Campus Grid and Its Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 Zhiqun Deng, Guanzhong Dai The Realization Methods of PC Cluster Experimental Platform in Linux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Jiang-ling Zhang, Shi-jue Zheng, Yang Qing Coarse-Grained Distributed Parallel Programming Interface for Grid Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Yongwei Wu, Qing Wang, Guangwen Yang, Weiming Zheng User Guided Parallel Programming Platform . . . . . . . . . . . . . . . . . . . . . . . . . 259 Yong Liu, Xinda Lu, Qianni Deng A High-Performance Intelligent Integrated Data Services System in Data Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262 Bin Huang, Xiaoning Peng, Nong Xiao, Bo Liu


XXIX

Architecting CORBA-Based Distributed Applications . . . . . . . . . . . . . . . . . . 266 Min Cao, Jiannong Cao, Geng-Feng Wu, Yan-Yan Wang Design of NGIS: The Next Generation Internet Server for Future E-society . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 Chong-Won Park, Myung-Joon Kim, Jin-Won Park Video-on-Demand System Using Multicast and Web-Caching Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 SeokHoon Kang

Session 2: Peer to Peer Computing PeerBus: A Middleware Framework towards Interoperability among P2P Data Sharing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Linhao Xu, Shuigeng Zhou, Keping Zhao, Weining Qian, Aoying Zhou Ptops Index Server for Advanced Search Performance of P2P System with a Simple Discovery Server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 Boon-Hee Kim, Young-Chan Kim Improvement of Routing Structure in P2P Overlay Networks . . . . . . . . . . . 292 Jinfeng Hu, Yinghui Wu, Ming Li, Weimin Zheng Overlay Topology Matching in P2P Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 300 Yunhao Liu, Xiao Li, Lionel M. Ni, Yunhuai Liu Effect of Links on DHT Routing Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 308 Futai Zou, Liang Zhang, Yin Li, Fanyuan Ma A Peer-to-Peer Approach to Task Scheduling in Computation Grid . . . . . . 316 Jiannong Cao, Oscar M.K. Kwong, Xianbing Wang, Wentong Cai Efficient Search in Gnutella-Like “Small-World” Peer-to-Peer Systems . . . 324 Dongsheng Li, Xicheng Lu, Yijie Wang, Nong Xiao Dominating-Set-Based Searching in Peer-to-Peer Networks . . . . . . . . . . . . . 332 Chunlin Yang, Jie Wu GFS-Btree: A Scalable Peer-to-Peer Overlay Network for Lookup Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 Qinghu Li, Jianmin Wang, Jiaguang Sun An Approach to Content-Based Approximate Query Processing in Peer-to-Peer Data Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348 Chaokun Wang, Jianzhong Li, Shengfei Shi A Hint-Based Locating and Routing Mechanism in Peer-to-Peer File Sharing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356 Hairong Jin, Shanping Li, Tianchi Ma, Liang Qian

XXX


Content Location Using Interest-Based Subnet in Peer-to-Peer System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 Guangtao Xue, Jinyuan You, Xiaojian He Trust and Cooperation in Peer-to-Peer Systems . . . . . . . . . . . . . . . . . . . . . . . 371 Junjie Jiang, Haihuan Bai, Weinong Wang A Scalable Peer-to-Peer Lookup Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 Haitao Chen, Chuanfu Xu, Zunguo Huang, Huaping Hu, Zhenghu Gong Characterizing Peer-to-Peer Traffic across Internet . . . . . . . . . . . . . . . . . . . . 388 Yunfei Zhang, Lianhong Lei, Changjia Chen Improving the Objects Set Availability in the P2P Environment by Multiple Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396 Kang Chen, Shuming Shi, Guangwen Yang, Meiming Shen, Weimin Zheng PBiz: An E-business Model Based on Peer-to-Peer Network . . . . . . . . . . . . 404 Shudong Chen, Zengde Wu, Wei Zhang, Fanyuan Ma P2P Overlay Networks of Constant Degree . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 Guihai Chen, Chengzhong Xu, Haiying Shen, Daoxu Chen An Efficient Contents Discovery Mechanism in Pure P2P Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420 In-suk Kim, Yong-hyeog Kang, Young Ik Eom Distributed Computation for Diffusion Problem in a P2P-Enhanced Computing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428 Jun Ni, Lili Huang, Tao He, Yongxiang Zhang, Shaowen Wang, Boyd M. Knosp, Chinglong Lin Applications of Peer to Peer Technology in CERNET . . . . . . . . . . . . . . . . . . 436 Chang-ji Wang, Jian-Ping Wu PSMI: A JXTA 2.0-Based Infrastructure for P2P Service Management Using Web Service Registries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 440 Feng Yang, Shouyi Zhan, Fuxiang Shen CIPS-P2P: A Stable Coordinates-Based Integrated-Paid-Service Peer-to-Peer Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446 Yunfei Zhang, Shaolong Li, Changjia Chen, Shu Zhang A Multicast Routing Algorithm for P2P Networks . . . . . . . . . . . . . . . . . . . . 452 Tingyao Jiang, Aling Zhong Leveraging Duplicates to Improve File Availability of P2P Storage Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456 Min Qu, Yafei Dai, Mingzhong Xiao


XXXI

Distributing the Keys into P2P Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 Shijie Zhou, Zhiguang Qin, Jinde Liu SemanticPeer: An Ontology-Based P2P Lookup Service . . . . . . . . . . . . . . . . 464 Jing Tian, Yafei Dai, Xiaoming Li Authentication and Access Control in P2P Network . . . . . . . . . . . . . . . . . . . 468 Yuqing Zhang, Dehua Zhang Methodology Discussion of Grid Peer-Peer Computing . . . . . . . . . . . . . . . . . 471 Weifen Qu, Qingchun Meng, Chengbing Wei PipeSeeU: A Scalable Peer-to-Peer Multipoint Video Conference System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475 Bo Xie, Yin Liu, Ruimin Shen, Wenyin Liu, Changjun Jiang

Session 3: Grid Architectures Vega Grid: A Computer Systems Approach to Grid Research . . . . . . . . . . . 480 Zhiwei Xu, Wei Li RB-GACA: A RBAC Based Grid Access Control Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487 Weizhong Qiang, Hai Jin, Xuanhua Shi, Deqing Zou, Hao Zhang GriDE: A Grid-Enabled Development Environment . . . . . . . . . . . . . . . . . . . 495 Simon See, Jie Song, Liang Peng, Appie Stoelwinder, Hoon Kang Neo Information Grid Toolkit: Infrastructure of Shanghai Information Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503 Xinhua Lin, Qianni Deng, Xinda Lu On-Demand Services Composition and Infrastructure Management . . . . . . 511 Jun Peng, Jie Wang GridDaen: A Data Grid Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519 Nong Xiao, Dongsheng Li, Wei Fu, Bin Huang, Xicheng Lu Research on Security Architecture and Protocols of Grid Computing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 529 Xiangming Fang, Shoubao Yang, Leitao Guo, Lei Zhang A Multi-agent System Architecture for End-User Level Grid Monitoring Using Geographic Information Systems (MAGGIS): Architecture and Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536 Shaowen Wang, Anand Padmanabhan, Yan Liu, Ransom Briggs, Jun Ni, Tao He, Boyd M. Knosp, Yasar Onel An Architecture of Game Grid Based on Resource Router . . . . . . . . . . . . . . 544 Yu Wang, Enhua Tan, Wei Li, Zhiwei Xu

XXXII


Scalable Resource Management and Load Assignment for Grid and Peer-to-Peer Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552 Xuezheng Liu, Ming Chen, Guangwen Yang, Dingxing Wang Research on the Application of Multi-agent Technology to Spatial Information Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 560 Yan Ren, Cheng Fang, Honghui Chen, Xueshan Luo An Optimal Method of Diffusion Algorithm for Computational Grid . . . . . 568 Rong Chen, Yadong Gui, Ji Gao A Reconfigurable High Availability Infrastructure in Cluster for Grid . . . . 576 Wen Gao, Xinyu Liu, Lei Wang, Takashi Nanya An Adaptive Information Grid Architecture for Recommendation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 584 M. Lan, W. Zhou Research on Construction of EAI-Oriented Web Service Architecture . . . . 592 Xin Peng, Wenyun Zhao, En Ye GridBR: The Challenge of Grid Computing . . . . . . . . . . . . . . . . . . . . . . . . . . 601 S.R.R. Costa, L.G. Neves, F. Ayres, C.E. Mendon¸ca, R.S.N. de Bragan¸ca, F. Gandour, L.V. Ferreira, M.C.A. Costa, N.F.F. Ebecken Autonomous Distributed Service System: Basic Concepts and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608 H. Farooq Ahmad, Kashif Iqbal, Hiroki Suguri, Arshad Ali ShanghaiGrid in Action: The First Stage Projects towards Digital City and City Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 616 Minglu Li, Hui Liu, Changjun Jiang, Weiqin Tong, Aoying Zhou, Yadong Gui, Hao Zhu, Shui Jiang, Ruonan Rao, Jian Cao, Qianni Deng, Qi Qian, Wei Jin Spatial Information Grid – An Agent Framework . . . . . . . . . . . . . . . . . . . . . 624 Yingwei Luo, Xiaolin Wang, Zhuoqun Xu Agent-Based Framework for Grid Computing . . . . . . . . . . . . . . . . . . . . . . . . . 629 Zhihuan Zhang, Shuqing Wang A Hierarchical Grid Architecture Based on Computation/Application Metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633 Wan-Chun Dou, Juan Sun, Da-Gang Yang, Shi-Jie Cai A Transparent-to-Outside Resource Management Framework for Computational Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637 Ye Zhu, Junzhou Luo


XXXIII

A Service-Based Hierarchical Architecture for Parallel Computing in Grid Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641 Weiqin Tong, Jingbo Ding, Jianquan Tang, Bo Wang, Lizhi Cai A Grid Computing Framework for Large Scale Molecular Dynamics Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645 WenRui Wang, GuoLiang Chen, HuaPing Chen, Shoubao Yang Principle and Framework of Information Grid Evaluation . . . . . . . . . . . . . . 649 Hui Li, Xiaolin Li, Zhiwei Xu, Ning Yang Manufacturing Grid: Needs, Concept, and Architecture . . . . . . . . . . . . . . . . 653 Yushun Fan, Dazhe Zhao, Liqin Zhang, Shuangxi Huang, Bo Liu Developing a Framework to Implement Security in Web Services . . . . . . . 657 Fawaz Amin Alvi, Shakeel A. Khoja, Zohra Jabeen

Session 4: Grid Middleware and Toolkits Computing Pool: A Simplified and Practical Computational Grid Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661 Peng Liu, Yao Shi, San-li Li Formalizing Service Publication and Discovery in Grid Computing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 669 Chuliang Weng, Xinda Lu, Qianni Deng An Improved Solution to I/O Support Problems in Wide Area Grid Computing Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 677 Bin Wang, Ping Chen, Zhuoqun Xu Agora: Grid Community in Vega Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685 Hao Wang, Zhiwei Xu, Yili Gong, Wei Li Sophisticated Interaction – A Paradigm on the Grid . . . . . . . . . . . . . . . . . . . 692 Xingwu Liu, Zhiwei Xu A Composite-Event-Based Message-Oriented Middleware . . . . . . . . . . . . . . 700 Pingpeng Yuan, Hai Jin An Integration Architecture for Grid Resources . . . . . . . . . . . . . . . . . . . . . . . 708 Minglu Li, Feilong Tang, Jian Cao Component-Based Middleware Platform for Grid Computing . . . . . . . . . . . 716 Jianmin Zhu, Rong Chen, Guangnan Ni, Yuan Liu Grid Gateway: Message-Passing between Separated Cluster Interconnects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 724 Wei Cui, Jie Ma, Zhigang Huo

XXXIV


A Model for User Management in Grid Computing Environments . . . . . . . 732 Bo Chen, Xuebin Chi, Hong Wu GSPD: A Middleware That Supports Publication and Discovery of Grid Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 738 Feilong Tang, Minglu Li, Jian Cao, Qianni Deng, Jiadi Yu, Zhengwei Qi Partially Evaluating Grid Services by DJmix . . . . . . . . . . . . . . . . . . . . . . . . . 746 Hongyan Mao, Linpeng Huang, Yongqiang Sun Integrated Binding Service Model for Supporting Both Naming/Trading and Location Services in Inter/Intra-net Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 754 Chang-Won Jeong, Su-Chong Joo, Sung-Kook Han Personal Grid Running at the Edge of Internet . . . . . . . . . . . . . . . . . . . . . . . 762 Bingchen Li, Wei Li, Zhiwei Xu Grid Workflow Based on Performance Evaluation . . . . . . . . . . . . . . . . . . . . . 770 Shao-hua Zhang, Yu-jin Wu, Ning Gu, Wei Wang Research on User Programming Environment in Grid . . . . . . . . . . . . . . . . . . 778 Ge He, Donghua Liu, Zhiwei Xu, Lin Li, Shengliang Xu The Delivery and Accounting Middleware in the ShanghaiGrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 786 Ruonan Rao, Baiyan Li, Minglu Li, Jinyuan You Applying Agent into Web Testing and Evolution . . . . . . . . . . . . . . . . . . . . . . 794 Baowen Xu, Lei Xu, Jixiang Jiang Experiences on Computational Program Reuse with Service Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 799 Ping Chen, Bin Wang, Guoshi Xu, Zhuoqun Xu Research and Implementation of the Real-Time Middleware in Open System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803 Jian Peng, Jinde Liu, Tao Yang An Object-Oriented Petri Nets Based Integrated Development Environment for Grid-Based Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 809 Hongyi Shi, Aihua Ren Some Views on Building Computational Grids Infrastructure . . . . . . . . . . . 813 Bo Dai, Guiran Chang, Wandan Zeng, Jiyue Wen, Qiang Guo Research on Computing Grid Software Architecture . . . . . . . . . . . . . . . . . . . 817 Changyun Li, Gansheng Li, Yin Li


XXXV

Research on Integrating Service in Grid Portal . . . . . . . . . . . . . . . . . . . . . . . . 821 Zheng Feng, Shoubao Yang, Shanjiu Long, Dongfeng Chen, Leitao Guo GSRP: An Application-Level Protocol for Grid Environments . . . . . . . . . . 825 Zhiqiang Hou, Donghua Liu, Zhiwei Xu, Wei Li Towards a Mobile Service Mechanism in a Grid Environment . . . . . . . . . . . 829 Weiqin Tong, Jianquan Tang, Liang Jin, Bo Wang, Yuwei Zong Mobile Middleware Based on Distributed Object . . . . . . . . . . . . . . . . . . . . . . 833 Song Chen, Shan Wang, Ming-Tian Zhou

Session 5: Web Security and Web Services On the Malicious Participants Problem in Computational Grid . . . . . . . . . 839 Wenguang Chen, Weimin Zheng, Guangwen Yang Certificate Validation Scheme of Open Grid Service Usage XKMS . . . . . . . 849 Namje Park, Kiyoung Moon, Sungwon Sohn, Cheehang Park Distributed IDS Tracing Back to Attacking Sources . . . . . . . . . . . . . . . . . . . 859 Wu Liu, Hai-Xin Duan, Jian-Ping Wu, Ping Ren, Li-Hua Lu The Study on Mobile Phone-Oriented Application Integration Technology of Web Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 867 Luqun Li, Minglu Li, Xianguo Cui Group Rekeying Algorithm Using Pseudo-random Functions and Modular Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 875 Josep Pegueroles, Wang Bin, Miguel Soriano, Francisco Rico-Novella Semantics and Formalizations of Mission-Aware Behavior Trust Model for Grids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 883 Minglu Li, Hui Liu, Lei Cao, Jiadi Yu, Ying Li, Qi Qian, Wei Jin Study on a Secure Access Model for the Grid Catalogue . . . . . . . . . . . . . . . 891 Bing Xie, Xiao-Lin Gui, Qing-Jiang Wang Modeling Trust Management System for Grids . . . . . . . . . . . . . . . . . . . . . . . . 899 Baiyan Li, Wensheng Yao, Jinyuan You Avoid Powerful Tampering by Malicious Host . . . . . . . . . . . . . . . . . . . . . . . . 907 Fangyong Hou, Zhiying Wang, Zhen Liu, Yun Liu Secure Grid-Based Mobile Agent Platform by Instance-Oriented Delegation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 916 Tianchi Ma, Shanping Li

XXXVI


Authenticated Key Exchange Protocol Secure against Offline Dictionary Attack and Server Compromise . . . . . . . . . . . . . . . . . . . . . . . . . . . 924 Seung Bae Park, Moon Seol Kang, Sang Jun Lee StarOTS: An Efficient Distributed Transaction Recovery Mechanism in the CORBA Component Runtime Environment . . . . . . . . . . . . . . . . . . . . 932 Yi Ren, Jianbo Guan, Yan Jia, Weihong Han, Quanyuan Wu Web Services Testing, the Methodology, and the Implementation of the Automation-Testing Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 940 Ying Li, Minglu Li, Jiadi Yu Composing Web Services Based on Agent and Workflow . . . . . . . . . . . . . . . 948 Jian Cao, Minglu Li, Shensheng Zhang, Qianni Den Structured Object-Z Software Specification Language . . . . . . . . . . . . . . . . . . 956 Xiaolei Gao, Huaikou Miao, Yihai Chen Ontology-Based Intelligent Sensing Action in Golog for Web Service Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 964 Zheng Dong, Cong Qi, Xiao-fei Xu The Design of an Efficient Kerberos Authentication Mechanism Associated with Directory Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 972 Cheolhyun Kim, Yeijin Lee, Ilyong Chung A Multi-agent Based Architecture for Network Attack Resistant System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 980 Jian Li, Guo-yin Zhang, Guo-chang Gu Design and Implementation of Data Mapping Engine Based on Multi-XML Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 984 Yu Wang, Liping Yu, Feng Jin, Yunfa Hu Research on the Methods of Search and Elimination in Covert Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 988 Chang-da Wang, Shiguang Ju, Dianchun Guo, Zhen Yang, Wen-yi Zheng Design and Performance of Firewall System Based on Embedded Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 992 Yuan-ni Guo, Ren-fa Li OGSA Security Authentication Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 996 Hongxia Xie, Fanrong Meng Detecting Identification of a Remote Web Server via Its Behavioral Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1000 Ke-xin Yang, Jiu-bin Ju


XXXVII

Access Control Architecture for Web Services . . . . . . . . . . . . . . . . . . . . . . . . . 1004 Shijin Yuan, Yunfa Hu Formalizing Web Service and Modeling Web Service-Based System Based on Object Oriented Petri Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1008 Xiaofeng Tao, Changjun Jiang Report about Middleware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1012 Beibei Fan, Shisheng Zhu, Peijun Lin Grid Security Gateway on RADIUS and Packet Filter . . . . . . . . . . . . . . . . . 1017 Jing Cao, BingLiang Lou A Security Policy Implementation Model in Computational GRID . . . . . . . 1021 Feng Li, Junzhou Luo An Approach of Building LinuxCluster-Based Grid Services . . . . . . . . . . . . 1026 Yu Ce, Xiao Jian, Sun Jizhou Dynamic E-commerce Security Based on the Web Services . . . . . . . . . . . . . 1030 Gongxuan Zhang, Guowei Zuo Standardization of Page Service Using XSLT Based on Grid System . . . . . 1034 Wanjun Zhang, Yi Zeng, Wei Dong, Guoqing Li, Dingsheng Liu Secure Super-distribution Protocol for Digital Rights Management in Unauthentic Network Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1039 Zhaofeng Ma, Boqin Feng X-NIndex: A High Performance Stable and Large XML Document Query Approach and Experience in TOP500 List Data . . . . . . . . . . . . . . . . 1043 Shaomei Wu, Xuan Li, Zhihui Du The Analysis of Authorization Mechanisms in the Grid . . . . . . . . . . . . . . . . 1047 Shiguang Ju, Zhen Yang, Chang-da Wang, Dianchun Guo Constructing Secure Web Service Based on XML . . . . . . . . . . . . . . . . . . . . . . 1051 Shaomin Zhang, Baoyi Wang, Lihua Zhou ECC Based Intrusion Tolerance for Web Security . . . . . . . . . . . . . . . . . . . . . 1055 Xianfeng Zhang, Feng Zhang, Zhiguang Qin, Jinde Liu Design for Reliable Service Aggregation in an Architectural Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1059 Xiaoli Zhi, Weiqin Tong The Anatomy of Web Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1063 Hongbing Wang, Yuzhong Qu, Junyuan Xie

XXXVIII


Automated Vulnerability Management through Web Services . . . . . . . . . . . 1067 H.T. Tian, L.S. Huang, J.L. Shan, G.L. Chen Optimizing Java Based Web Services by Partial Evaluation . . . . . . . . . . . . . 1071 Lin Lin, Linpeng Huang, Yongqiang Sun An XML Based General Configuration Language: XGCL . . . . . . . . . . . . . . . 1075 Huaifeng Qin, Xingshe Zhou Modification on Kerberos Authentication Protocol in Grid Computing Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1079 Rong Chen, Yadong Gui, Ji Gao A Distributed Honeypot System for Grid Security . . . . . . . . . . . . . . . . . . . . . 1083 Geng Yang, Chunming Rong, Yunping Dai Web Security Using Distributed Role Hierarchy . . . . . . . . . . . . . . . . . . . . . . . 1087 Gunhee Lee, Hongjin Yeh, Wonil Kim, Dong-Kyoo Kim User Authentication Protocol Based on Human Memorable Password and Using ECC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1091 Seung Bae Park, Moon Seol Kang, Sang Jun Lee New Authentication Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1095 Seung Bae Park, Moon Seol Kang, Sang Jun Lee Web Proxy Caching Mechanism to Evenly Distribute Transmission Channel in VOD System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1099 Backhyun Kim, Iksoo Kim, SeokHoon Kang

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1103

Synthetic Implementations of Performance Data Collection in Massively Parallel Systems Chu J. Jong1 and Arthur B. Maccabe2 1 2

School of Information Technology, Illinois State University, IL, USA [email protected] Department of Computer Science, University of New Mexico, NM USA [email protected]

Abstract. Most performance tools that run on Massively Parallel (MP) systems do not scale up as the number of nodes increases. We studied the scalability problem of MP system performance tools and proposed a solution, replacing the two-level data collection structure by hierarchal one. To demonstrate that hierarchical data collection structure solves the scalability problem, we synthesized an implementation model to implement the performance data collection in MP systems. This paper presents our synthetic implementation results. Keywords: Data collection, response time, split node, performance knee.

1

Introduction

Complex applications, such as genetic analysis, material simulation, and climate modeling[8], require high performance and high computation resources to generate results. MP systems, built with thousands processors and huge amount of memory, meet these applications’ needs1 . The effectiveness of using the MP system computation power mainly depends on the user’s knowledge and sometimes on the compilers or system performance tools. In the past years, developers have made significant efforts to parallel performance tools. Recent work showed that many areas, such as reducing overheads[3], improving accuracies[10], minimizing perturbations[9], and increasing convenience[5], have been improved except the tool’s scalability. We use the response time, time between issuing a user command and a reply, to measure the performance of a tool. The results of performance tools are based on the performance data collection and processing. Two main factors contribute to the response time and they are: effective processor utilization and balanced point[6]. In MP systems, when the workload incurred by workers exceeds the 1

ASCI project: The Red had 9,298 Pentium Xeon processors; the Blue-Pacific composed 1464 RS/6000 nodes with 4 CPUs; the Blue-Mountain had 256 MIPS R10000 processors; and the BlueGene/L will have 130,000 advanced microprocessors to achieve 367 teraflops.

M. Li et al. (Eds.): GCC 2003, LNCS 3033, pp. 1–9, 2004. c Springer-Verlag Berlin Heidelberg 2004

2

C.J. Jong and A.B. Maccabe

server’s balanced point, the server starts thrashing and that causes exponential processor utilization drop and response time increase. Since most performance tools use two-level structure (one server and n workers) to collect data, the workload of the server is proportional to the number of workers. When the number of workers reaches the server’s limit, thrashing occurs. A k level hierarchical data collection structure (one server, n ¯ workers, and two or more worker-servers) allows worker-servers share the workload to prevent server thrashing. Thus increases the total number of workers significantly. In theory, the maximum numbers of workers can be handled by a single server without thrashing are: m = min{ ttws , bbws , ppws } in a two-level data collection k structure and m ˆ = ¯ q (m ¯ similar to m) in a k level hierarchical data q=1 m ts bs collection structure. tw , bw , and ppws are ratios of network throughput, memory size, and processing power between server and workers respectively. Although some systems provide large ratios[4] to accommodate more workers, m and m ¯ are bounded by the system capacities.

2

Previous Work – Response Time Simulation

We built a time driven event simulator to simulate the response time behaviors for both data collection structures. From our simulation results, we concluded that a hierarchical data collection structure eliminates the response time knee (exponential increasing) that two-level data collection structures cannot avoid[7].

3

Response Time Implementation

We used a small cluster of 8 Pentium III 500 MHz PCs to construct our implementation model. We wrote MPI implementation programs in C run on Linux with customized kernel modules. We used a node splitting mechanism, p-split, to produce a larger virtual node cluster. The p-split is a virtualization mechanism that splits a physical node into a number of virtual nodes. The basic principle of p-split is to divide resources of a physical node into p equivalent slices and group one slice from each divided resource to form a virtual node (processor). Theoretically the value of p can be arbitrary chosen, the reality is that the more virtual processors, the higher the system overheads and the heavier the channel workload. We applied p-split to seven PCs. Virtual processors are workers or workerservers, the un-split PC is the server. Applying p = 8 to p-split seven PCs, we built a virtual processor cluster that is equivalent to an one-order magnitude scale down cluster of a 56 500-MHz single CPU workers and a one-GHz quad CPU server connected by a one-Gigabit network cluster. 3.1

System Modules

Our customized kernel modules allows us to have better control on the system resources. Our pseudo application, pseudo data generating/collection, and pseudo

Synthetic Implementations of Performance Data Collection

3

data processing allows us to emulate a system without side effects. Following are descriptions of our modules. System Structure. All PCs are connected by a 100-megabit Ethernet. The server processor is the un-split PC, the worker and worker-server processors are virtual processors. Both workers and worker-servers are evenly distributed among seven PCs, no two worker-servers reside in the same PC, and no two children from the same parent reside in the same PC too. System Control Units. Memory Management Unit (MMU) - Locks a block of memory for processors. It uses paging to handle memory page faults and all memory requests have to go through it. Processor Management Unit (PMU) Controls CPU cycles consumed by processors. It suspends a process when that process reaches its CPU cycle limit. Perturbation Control Unit (PCU) - Controls application perturbation rate. Implementation Software. Processors - One server processor and many worker and/or worker-server processors. A 8:1 computation ratio between server and worker (worker-server) processor and a 8:4:1 memory ratio among server, worker-servers, and workers. – Server: Runs on the server processor. Major functions are: to interact with the user, set system topology, monitor process behaviors, and fulfill the user’s request. Server collects the response time and calculates global statistics, such as data gathering time, data processing time, data processing rate, and channel time. – Worker: Runs on worker processor. Worker calculates local statistics, such as response time, data collection time, data processing time, data processing rate, and channel time. – Worker-Server: Runs on worker-server processor. Worker-server calculates non-local statistics, such as worker-server response time, data gathering time, data processing time, data processing rate, and channel time. Application Process - Runs on all processors, it emulates the computation and I/O behavior of different applications. Data Processing Process - Invoked by data generating/collection process after data gathering completed. It processes data by reading and writing every byte at least once. It provides a hook to allow different data access methods to be used, such as sequential, offset, indexed, link list, scattered among pages or segments. The size of data access range from a single byte to several hundred bytes. Data Generating/Collection Process Invoked by performance tool process. It performs three different tasks, data generating, data gathering, and data forwarding. It generates performance data and puts them in the data buffer with other data gathered from its children. After data processing is completed, it sends the result to its parent.

4


3.2

The Implementations

The two-level structure has two kinds of processes: server and worker, the server can have up to 56 child workers. The hierarchical structure has three kinds of processes: server, worker-server, and worker, the server has seven child workerservers, and each worker-server can have up to seven child workers. Communications are restricted to the processes having direct parent and child relations. Following are logical actions of each of them: – Processor has no parent - server. To get a user request, parse the request, send data collection command, allocate data processing buffers, wait for all data and then process data, and present results to the user. – Processor has no children - worker. To wait for data collection command, allocate data buffers, store data in the buffer, and send data to its parent. – Processor has both parent and children - worker-server. To wait for data collection command, allocate data buffer, send data collection command, allocate data processing buffers, store data in the buffer, wait for all data and then process data, and sends results to its parent.

4

Implementation Results

Implementations start from one server and 7 workers (worker-servers) and increment one worker per physical node at a time (to eliminate impacts from unbalanced communication delay) until the total worker number reaches to 56.

Fig. 1. Server Response Time of Two-Level Structure


5

Fig. 2. Server Data Processing Rate of Two-Level Structure

4.1

Server of Two-Level Structure

Figure 1 shows the response time of the two-level data collection structure. Horizontal scale is the number of workers and vertical scale is the response time in milliseconds. Labels indicate data sizes in kilobytes collected by each worker. The total amount of data gathered by the server equals the data size multiplied by the number of workers. Figure 2 shows the data processing rates. The 256K rate drops from 2183 bytes/ms at 48 workers to 355 bytes/ms at 50 workers and the 384K rate drops from 2187 bytes/ms at 32 workers to 406 bytes/ms at 33 workers. The rate drops indicate thrashings. Table 1. Average Worker-server Data Processing Time (ms/kilobytes) # Worker Processors 512K Data Collection 384K Data Collection 256K Data Collection 128K Data Collection 64K Data Collection 32K Data Collection 16K Data Collection 8K Data Collection 4K Data Collection

8 0.31 0.31 0.30 0.30 0.30 0.30 0.30 0.30 0.30

15 0.39 0.33 0.35 0.32 0.30 0.30 0.30 0.31 0.32

22 0.38 0.36 0.35 0.33 0.34 0.30 0.31 0.31 0.31

29 0.39 0.39 0.38 0.33 0.42 0.31 0.30 0.31 0.33

36 0.39 0.37 0.37 0.35 0.39 0.30 0.31 0.32 0.34

43 0.38 0.42 0.38 0.36 0.54 0.32 0.30 0.76 0.31

50 0.41 0.52 0.41 0.36 0.31 0.34 0.31 0.30 0.30

57 0.36 0.35 0.35 0.33 0.38 0.31 0.30 0.30 0.30

6


Fig. 3. Average Worker Server Response Time

4.2

Worker-Server of Hierarchical Structure

Figure 3 shows the average worker-server response times, the sums of the workerserver data gathering times and the worker-server data processing times, of the hierarchical data collection structure. The worker-server data gathering time is the longest time between when a worker-server issues a data collection command and receives all data packets. The average worker-server data processing times are shown in table 1. Except one value, 0.76 ms from 8K data size, most of them are in the range between 0.30 ms/KByte and 0.42 ms/Kbyte. Either of gathering time or processing time that start non-linear incrementation will make the response time non-linear. Table 1 indicated that none of the worker-servers were thrashing. Larger values were caused by the delay from the Linux process management. Actually, thrashing occurred in the virtual node subsystem due to the TCP/IP stack and buffer contention. 4.3

Server of Hierarchical Structure

Figure 4 shows the normalized (filtering outliers and replacing subsystem by values generated from algorithms based on the data sizes) response times of the hierarchical data collection structure. Graphs are data sizes of 4, 8, 16, 32, 64, 128, 256, 384, and 512 kilobytes. The overall data collected by all processors equals the data size multiplied by the total number of processors. Figure 5 shows the corresponding data processing rates. All rates are in the range between 2700 bytes/ms and 3300 bytes/ms. They showed no evidence of the server being saturated, and thrashing did not occur.


7

Fig. 4. Normalized Server Response Time

Fig. 5. Server Data Processing Rate

5

Discussion

In figure 1, 256K and 384K graphs start non-linear incrementation when the total data collected by the server reaches 12 megabytes. The same graphs in figure 2 also showed a non-linear decrementing at a total data size of 12 megabytes. From the theory of thrashing [2][1], when all main memory are used, swapping and waiting occurs. The 12 megabyte main memory was the upper limit we set up for the server.

8


In figure 3, some response time start non-linear incrementation after 50 processors regardless of the data size. However, from table 1, we know that all data processing times (except the 8K) are in the range between 0.30 ms/Kbyte to 0.42 ms/Kbyte. That means that none of the worker-servers reach their thrashing point. The large response time delays must caused by their data gathering times. Although applying p = 8 to p-split creates enough virtual nodes, it causes the sub-node system thrashing since the increasing workload reaches the system’s balanced point. The only way to prevent the sub-node system thrashing is not to increase the number of p, but to add physical nodes (PCs) to the system. The normalized hierarchical structure graphs in figure 4 shows that the response times are increased linearly in terms of the total processors. The overall data size does not affect the response time even after it reaches the memory limit. Figure 5 shows flat server data processing rates across all data sizes. That means that the server never reaches its balance point and thrashing has been totally eliminated. We also noticed that larger data size has slightly higher data processing rate. The constant data packet overhead in favors larger data collection size

6

Conclusion and Future Work

The results of our implementations shows that the hierarchical data collection structure eliminates the response time knee that a two-level data collection structure cannot avoid. The scalability has been improved in orders of magnitude with a minor network delay penalty per level. Both structures collectes the same overall amount of data, however, the amont of data gathered by two servers are not the same. In fact, the server in the hierarchical data collection structure receives quality data instead of quantity data. We are currently porting our implementation model to a larger cluster. We are also working on instrumentation and performance data presentation. We plan to enhance our virtualization mechanisms to provide a better development environment. Our long term goal is to produce a system that helps developers develop better MP system performance tools.

References 1. W. C. L. A. Alderson and B. Randell. Thrashing in a multiprogrammed paging system. Operating Systems Techniques by Hoare and Perrott, pages 152–167, 1972. 2. P. J. Denning. The working set model for program behavior. Communication of the ACM, (5):323–333, May 1968. 3. G. Eisenhauer, B. Schroeder, K. Schwan, V. Martin, and J. Vetter. DataExchange: High Performance Communication in Distributed Laboratories. Ninth International Conference on Parallel and Distributed Computing and Systems, October 1997. 4. D. A. R. et al. Scalable Performance Analysis: The Pablo Performance Analysis Environment. IEEE Scalable Performance Libraries Conference, IEEE Service Center, Piscaataway, N.J., 1993.


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5. W. Gu, G. Eisenhauer, and K. Schwan. Falcon: On-line Monitoring and Steering of Parallel Programs. Concurrency: Practice and Experience, 1995. 6. P. B. Hansen. Operating System Principles. Prentice-Hall, Inc., Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1973. 7. C. J. Jong and A. B. Maccabe. A simulator for performance tools in massively parallel system. In Postceedings of The International Conference on Parallel and Distributed Processing Techniques and Applications, June 2003. 8. T. Kindler, K. Schwan, D. Silva, M. Trauner, and F. Alyea. A Paraller Spectral Model for Atmospheric Transport Processes. Concurrency: Practice and Experience, 8:639–666, November 1996. 9. C. Liao, M. Martonosi, and D. W. Clark. Performance Monitoring in a MyrinetConnected Shrimp Cluster. In Symposium on Parallel and Distributed Tools, pages 21–29, P.O. Box 12114 Church Street Station, New York, N.Y. 10257, August 1998. The Association for Computing Machinery. 10. R. L. Ribler, J. S. Vetter, H. Simitci, and D. A. Reed. Autopilot: Adaptive Control of Distributed Applications. Proceedings of the 7th IEEE Symposium on HighPerformance Distributed Computing, Chicago, IL, July 1998.

GMA+ – A GMA-Based Monitoring and Management Infrastructure for Grid Chuan He, Zhihui Du, and Sanli Li Grid Research Group, Department of Computer Science and Technology, Tsinghua University, Beijing, China [email protected]

Abstract. In this paper, we investigate many monitoring and management tools in Grid and other distributed systems. After focusing on the advantage and disadvantage of GMA (Grid Monitoring Architecture), we propose a new monitoring and management schema for Grid --GMA+. In GMA+, we add a close loop feedback structure to GMA and provides interfaces which match Grid Service standards for all its components and defines metadata for monitoring events. It is a novel infrastructure for Grid monitoring and management with highly modularity, usability and scalability.

1 Introduction Nowadays, single PC and even HPCs can not satisfy the explosive need of high performance computing. It needs to connect various heterogeneous resources which are distributed in physical location with high speed network to solve some huge problems. Grid [1] is the extension of traditional distributed computing technology, it waves resources in LAN or WAN and constructs dynamic VO(Virtual Organization) to implement secure and coordinated sharing of resources between persons, organizations and resources. The target of Grid monitoring and management is monitoring resources in Grid for fault detection, performance analysis, performance tuning, load balancing and scheduling. Compared with traditional distributed systems, Grid is more complicated in architecture, larger in scale and more distributed in physical location. So it is more urgent to construct a monitoring and management system with high performance, high scalability and high stabilization to do automatic management in Grid environment.

2 Related Works There have already been many monitoring tools in traditional distributed systems. However, those existing tools can not completely meet the needs of Grid monitoring and management.

M. Li et al. (Eds.): GCC 2003, LNCS 3033, pp. 10–17, 2004. © Springer-Verlag Berlin Heidelberg 2004

GMA+ – A GMA-Based Monitoring and Management Infrastructure for Grid

11

NetLogger [2], Paradyn [3], AIMS [4], Gloperf [5] and SPI [6] collect data from distributed systems and then analyze the data using special tools. Those tools generate monitoring data with unique format, which constrains their cooperation with other systems. Globus HBM [7] periodically sends “Heart Beat” to a centralized collection system in order to do fault detection in a distributed environment. The centrality of HBM make it difficult for extending, what’s more, HBM relies on local information so that it can not make forecast and management from the whole point of view. NWS [8] and DSRT [9] only monitor some specific resources (network bandwidth, CPU) but are not adaptive for new resources. RMON [10] and Autopilot [11] implement intelligent and dynamic control by using technology like reactive circuits. JAMM [12] is another similar system, but it only can be used in Java program. The shortcoming of those systems is that they are bound to given programming languages. So they can’t cooperate with other systems. SNMP-based tools are not suitable for Grid monitoring and management which runs on WAN and requires high performance. In addition, those toolkits do not provide application level monitoring.

3 GMA GMA [13] is an architecture presented and supported by GGF. It aims to constitute standards for grid monitoring and make existing systems interoperate. 3.1 Architecture GMA first adopts a producer-consumer model in Grid monitoring. All monitoring information are events which are base on timestamp for storing and transferring.

Fig. 1. Grid Monitoring Architecture

12

C. He, Z. Du, and S. Li

Fig. 1. shows the architecture of GMA. GMA is consisted of four key components: 1. Sensors. A sensor is any program which can generate time-stamped monitoring events. For example, we have sensors that monitor CPU usage, memory usage, and network usage. 2. Producers. Producers is the source of performance events. Producers send events to consumers through their interfaces and one producer can provide several independent interfaces for sending different events. 3. Consumers. Applications who receive and consume monitoring events. Consumers can not only run at the same computer with the producer, but can run at remote computers also. 4. Directory Service. The directory service in Grid likes a registry. Producers and consumers publish their location information in the directory service. When a producer and a consumer find each other through directory services, the monitoring procedure runs only between the producer and the consumer, but has no relationship with the directory service at all. All monitoring information in GMA are events. Another important contribution of GMA is that it abstracts three event passing patterns between producers and consumers. They are: Publishing/subscribing pattern, Query/response pattern and Notification pattern. The protocol between producers and consumers are not defined in GMA, it can be implemented in many ways. For example: using SOAP/HTTP, LDAP or XML/BXXP. What’s more, we also can use one protocol in controlling but another distinct protocol in transmission under different situations. 3.2 Current Status of GMA and Its Limitations R-GMA [14] makes use of relation database to offer directory service instead of LDAP. But it can not be suitable for huge data transaction, especially when facing outburst of information. GridMon [15] is the first prototype of Grid monitoring in China. Although it applies GMA thoughts partially, it is not entirely base on GMA model. In fact, GridMon uses LDAP as directory service and only realizes the query/ response model. It defines a scalable format for monitoring events, yet it is too simple to serve a complex system like Grid. GMA presents a highly scalable and flexible architecture for grid monitoring. However, there are still some limitations in GMA that should be improved in the following ways: 1. In GMA, the directory service should be universal enough to support LDAP, relation database and P2P distributed directory storage rather than restricted to LDAP. 2. There is no definition of the metadata model of monitoring information in GMA system. Using XML schema is a good way to define it and make the system more suitable for monitoring various kinds of new resources. 3. Producers and consumers in GMA system need to provide standard external interfaces. Moreover, considering that Grid is a wide area system, we should encapsulate those interfaces as Web Service or Grid Service proposed in OGSA [16].


13

4. GMA only offers monitoring functions but does not integrate monitoring with management. In a practical system, GMA ought to be joined into the structure like close loop feedback in order to combine monitoring and management together.

4 GMA+ 4.1 Architecture According to the shortages of GMA mentioned above, we give an improved Grid Monitoring system—GMA+ based on GMA. The architecture of GMA+ is showed in Fig. 2. We classify all events in GMA+ into 2 types: M-Event for monitoring events and C-Event for controlling events. We also add controller and actuator as 2 new parts.

Fig. 2. The architecture of GMA+

Actually, the controller consists of a consumer, a producer and its controlling logic. The controller can analyze various events, then it will generate new M-Events and CEvents, in the end, it will send corresponding C-Events to the actuator. The actuator receives the evens and adjusts the status of Grid resources dynamically. Typically, the functions of the controller can be: validating the monitoring events, classifying the events, decision-making system and etc. In GMA+, many controllers can be connected as a chain, so sensor->producer-> controller…->controller->actuator forms a local closed loop structure. Fig. 3. shows three different types of controller: controller1 filters M-Event; controller2 analyzes the receiving C-Events and M-Events and generates C-Events for controlling; controller3 is a more complicated one for decision-making. 4.2 Components in GMA+ The interfaces of all GMA+ components follow Grid Service Specification in OGSA. Grid Services are more suitable for use in a Grid environment than Web Services,

14


Fig. 3. Different Controllers in GMA+

because Web Services are mostly persistent, while services in Grid are mainly volatile. Another important reason is that Grid Service has a soft lifetime management to release useless resources automatically, but Web Service can not solve that problem easily. In a word, adopting Grid Service in Grid monitoring and management has many benefits such as standardization, easy to distribute, easy for cooperating, independent for platforms and programming languages. 4.2.1 Sensors We implement four different types of sensors in GMA+. They are host sensors, network sensors, process sensors and application sensors. In a real Grid, there may be more kinds of sensors than what we have defined. For extension, we have given the metadata model for all events in GMA+, encapsulated sensors with Grid Service interface and defined the programming model for how to develop a new sensor. Those all guarantee that new sensors could be added into GMA+ easily. 4.2.2 Consumers GMA+ offers many kinds of querying and analyzing tools as consumers. They mainly contains: 1. Storing archives. Although the monitoring events in Grid is mainly useful in short period of time, it still needs a storing archive to keep those events for later use. We implement a distributed storage system to archive the monitoring events in GMA+ using LDAP and RDBMS. 2. Real-time monitoring tools. The tools are used to monitor real-time data. 3. Analyzing tools. These tools are used to analyze the data which is kept in the storing archives. 4. Query and analysis portal. GMA+ also provides a web portal to do query and analysis. In GMA+, we define several types of users with different privileges to do monitoring and management.


15

4.2.3 Controller and Actuator In GMA+, we just implement two simple controllers: C-Controller and A-Controller to demonstrate the usage of controllers and actuators. C-Controller is used to classify monitoring events. A-Controller is used to analyze the monitoring events of computers (the computing resources in Grid). If the load or temperature of CPU exceeds the threshold, A-Controller will notify the corresponding actuator to report an alarm. Fig. 4. shows the collaboration sequence of sensors, controllers and actuators.

Fig. 4. Sequence diagram of sensors, controllers and actuators

4.3 Directory Service There are some traditional directory systems such as LDAP, DNS, DEC’s GNS [17], Intentional Naming Systems [18] and Active Names [19]. Nevertheless, they may meet similar problems when used directly in the Grid. 1. These directory services can not serve for the storage of a great number of data; as a result they can not satisfy the extension of Grid. 2. These services are not suitable for Grid monitoring data which needs to be updated frequently. They are only fit for the frequently-asked queries. 3. These services are not able to support queries related with multi-objects, for example, function “Join” in SQL. Recently, more methods were put forward to solve the directory service problems, such as using RDBMS [20]. But we have to see that there are not any mature solutions. As all components in GMA+ are Grid Services, the directory service in GMA+ is used to index Grid Services. We use WSIL to implement the directory. WSIL has a lightweight, distributed model: the XML documents which describe Web Services can be stored at any location; independent documents can connect with others through URL. Every component in GMA+ (producer, consumer, controller, sensor, and actuator) contains three parts: Grid Service interfaces, service entity and service description in WSIL. The distributed WSIL documents in the network construct a logical directory service. Fig. 5. shows the architecture of directory service in GMA+.

16


Fig. 5. Directory Service using WSIL

4.4 Metadata and Protocol Monitoring and management events are widely used in GMA+. So it is important to define the metadata of events with high scalability. We use XML Schema to define the metadata for various events. The metadata of events do not rely on transferring protocol. In GMA+, we use SOAP as the transfer protocol. SOAP is a lightweight protocol base on HTTP and XML. It is a protocol supporting RPC through HTTP and has many benefits such as: high scalability, easy to understand and easy to deploy.

5 Next Step Work In the future, we prepare to add more adapters and sensors in GMA+ in order to monitor and manage more Grid resources. And we also plan to implement more complicated controllers and actuators with powerful algorithm in order to do intelligent control and management in Grid.

References 1. 2.

3.

Foster and C. Kesselman, “The Grid: Blueprint for a New Computing Infrastructure”, Morgan Kaufmann Publishers, San Francisco, CA, 1999. Brian Tierney, William Jonston, Brian Crowley, Gary Hoo, Chris Brooks, and Dan Gunter, “The NetLogger Methodology for High Performance Distributed Systems Performance Analysis”, Proc. of IEEE High Performance Distributed Computing Conference (HPDC7), July 1998. Barton P. Miller, Jonathan M. Cargille, R. Bruce Irvin, Krishna Kunchithapadam, Mark D. Callaghan, Jeffrey K. Hollingsworth, Karen L. Karavanic, and Tia Newhall, “The Paradyn Parallel Performance Measurement Tool”, IEEE Computer, 28(11), November 1995, pp. 37–46.

GMA+ – A GMA-Based Monitoring and Management Infrastructure for Grid 4.

5.

6.

7. 8. 9. 10.

11.

12. 13.

14. 15.

16.

17. 18.

19.

20.

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Jerry C. Yan, “Performance Tuning with AIMS—An Automated Instrumentation and Monitoring System for Multicomputers”, Proc. of the Twenty-Seventh Hawaii Int. Conf. on System Sciences, Hawaii, January 1994. Craig A. Lee, Rich Wolski, Ian Foster, Carl Kesselman, and James Stepanek, “A Network Performance Tool for Grid Environments”, Proc. of SC’99, Portlan, Oregon, Nov. 13–19, 1999. Devesh Bhatt, Rakesh Jha, Todd Steeves, Rashmi Bhatt, and David Wills, “SPI: An Instrumentation Development Environment for Parallel/Distributed Systems”, Proc. of Int. Parallel Processing Symposium, April 1995. http://www.globus.org/hbm/heartbeat_spec.html R. Wolski et al., “The Network Weather Service: A Distributed Resource Performance Forecasting Service for Metacomputing”, Journal of Future Generation Systems, 1998. H. Chu and K. Nahrstedt, “CPU Service Classes for Multimedia Applications,” Proc. of IEEE Multimedia Computing and Applications, Florence, Italy, June 1999. Clifford W. Mercer and Ragunathan Rajkumar, “Interactive Interface and RTMach Support for Monitoring and Controlling Resource Management”, Proceedings of Real-Time Technology and Applications Symposium, Chicago, Illinois, May 15-17, 1995, pp. 134– 139. J.S. Vetter and D.A. Reed, “Real-time Monitoring Adaptive Control and Interactive Steering of Computational Grids”, The International Journal of High Performance Computing Applications (2000) Chris Brooks, Brian Tierney, and William Johnston, “Java Agents for Distributed System Management”, LBNL Technical Report, Dec. 1997. B. Tierney, R. Aydt, D. Gunter et al. “A Grid Monitoring Architecture (2002)” , GGF Performance Working Group, http://www-didc.lbl.gov/GGF-PERF/GMA-WG/ papers/GWD-GP-16-1.pdf Fisher.S. “Relational Grid Monitoring Architecture Package”, http://hepunx.rl.ac.uk/grid/wp3/releases.html Li Cha, Zhiwei Xu, Guozhang Lin, “A Grid monitoring system using LDAP”, Computer science and technology department, Beijing Institute of Technology. Journal of Computer Research and Development. August, 2002. Foster, C. Kesselman, J. Nick, S. Tuecke, “The Physiology of the Grid: An Open Grid Services Architecture for Distributed Systems Integration”, Open Grid Service Infrastructure WG, Global Grid Forum, June 22, 2002. Lampson, B. W., “Designing a global name service”, In 4th ACM Symposium on Principles of Distributed Computing (August 1986). Adjie-Winoto, W., Schwartz, E., Balakrishnan, H., and Lilley, J. “The design and implementation of an intentional naming system”, In Proceedings of the 17th ACM Symposium on Operating System Principles (December 199). Vahdat A., Dahlin M., Anderson T. and Aggarwal A. “Active names: flexible location and transport of wide-area resources”, In USENIX Symposium on Internet Technology and Systems (Oct 1999). E.F.Codd, “A relational model of data for large shared data banks”, CACM, 13(6), Jun 1970.

A Parallel Branch–and–Bound Algorithm for Computing Optimal Task Graph Schedules Udo H¨ onig and Wolfram Schiffmann FernUniversit¨ at Hagen, Lehrgebiet Technische Informatik I, 58084 Hagen, Germany {Udo.Hoenig|Wolfram.Schiffmann}@FernUni-Hagen.de http://www.informatik.ti1.fernuni-hagen.de/

Abstract. In order to harness the power of parallel computing we must firstly find appropriate algorithms that consist of a collection of (sub)tasks and secondly schedule these tasks to processing elements that communicate data between each other by means of a network. In this paper, we consider task graphs that take into account both, computation and communication costs. For a homogeneous computing system with a fixed number of processing elements we compute all the schedules with minimum schedule length. Our main contribution consist of parallelizing an informed search algorithm for calculating optimal schedules based on a Branch–and–Bound approach. While most recently proposed heuristics use task duplication, our parallel algorithm finds all optimal solutions under the assumption that each task is only assigned to one processing element. Compared to exhaustive search algorithms this parallel informed search can compute optimal schedules for more complex task graphs. In the paper, the influence of parameters on the efficiency of the parallel implementation will be discussed and optimal schedule lengths for 1700 randomly generated task graphs are compared to the solutions of a widely used heuristic.

1

Introduction

A task graph is a directed acyclic graph (DAG), that describes the dependencies between the parts of a parallel program [8]. In order to execute it on a cluster or grid computer, it’s tasks must be assigned to the available processing elements. Most often, the objective of solving this task graph scheduling problem is to minimize the overall computing time. The time that a task i needs to compute an output by using the results from preceding tasks corresponds to the working load for the processing element to which that task is assigned. It is denoted by a node weight wi of the task node. The cost of communication between two tasks i and j is specified as an edge weight cij . If both tasks are assigned to the same processor, the communication cost is zero. Task graph scheduling comprises two subproblems. One problem is to assign the tasks to the processors, the other problem consists of the optimal sequencing of the tasks. In this paper, we suppose a homogeneous computing environment, e.g. a cluster computer. But, even if we assume identical processing elements, the M. Li et al. (Eds.): GCC 2003, LNCS 3033, pp. 18–25, 2004. c Springer-Verlag Berlin Heidelberg 2004

A Parallel Branch–and–Bound Algorithm

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problem to determine an optimal schedule has been proven to be NP–complete, apart from some restrained cases [8]. Thus, most researchers use heuristic approaches to solve the problem for reasonable sizes of the task graph. Three categories can be distinguished: list–based, clustering–based and duplication–based heuristics. List–based heuristics assign priority levels to the tasks and map the highest priority task to the best fitting processing element [10]. Clustering–based heuristics embrace heavily communicating tasks and assign them on the same processing element, in order to reduce the overall communication overhead[1]. Duplication-based heuristics also decrease the amount of communication while simultaneously the amount of (redundant) computation will be increased. It has been combined with both list-based [2] and cluster-based approaches [9]. In order to evaluate the quality of all those heuristics in a unified manner it would be desirable to compare the resulting schedules lengths of those heuristics to the optimal values. The parallel Branch–and–Bound algorithm proposed in this paper can be used to create a benchmark suite for this purpose. Of course, this is only feasible for task graphs of moderate size (e.g. with lower than 30 tasks). Usually, there are multiple optimal schedules that provide a solution set of the task graph scheduling problem. All these schedules are characterized by the same minimal schedule length. To compute these optimal schedules we have to search for all the possible assignments and sequences of the tasks. The simplest algorithm to compute the set of optimal schedules enumerates all possible solutions and stores only the best ones. But, even for a small number of tasks the number of solutions will be enormous. If we want to get the set of optimal schedules in an acceptable period of time and with maintainable memory requirements we have to devise a more skillful algorithm. The basic idea to shorten the effort of an exhaustive search is to perform a structured or informed search that reduces the state space. Thus, the number of schedules that have to be investigated is much smaller than the number of possible processor assignments multiplied by the number of possible sequences. In this way, an informed search can manage more complex task graphs than exhaustive search strategies. The informed search is often based on a A* algorithm ([3], [5]). In this paper, we will present a Branch–and–Bound approach and its implementation on a parallel virtual machine [4]. The paper is organized as follows. In the next section, the concepts of the Branch–and–Bound algorithm will be explained. The third section is concerned with the parallelization of that algorithm. It describes how the workload is partitioned and how load balancing will be achieved. In the fourth section, we present results and discuss the influence of various parameters on the efficiency of the parallel implementation.

2

Branch–and–Bound Algorithm

If we want to shorten the time to compute the set of optimal solutions, we are not allowed to consider every valid schedule (assignment of the tasks to the processing elements plus determination of the tasks’ starting times). Instead,

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we have to divide the space of possible schedules into subspaces that contain partially similar solutions. These solutions should have in common that a certain number of tasks is already scheduled in the same way. The corresponding subspace contains all the schedules that are descended from the partial schedule but differ in the scheduling of the remaining tasks. Each partial schedule can be represented by a node in a decision-tree. Starting from the root, which represents the empty schedule, all possible schedules will be constructed. At any point of this construction process, we can identify a set of tasks that are ready to run and a set of idle processing elements to which those tasks can be assigned. In this way, each conceivable combination will produce a new node in the decision tree (Branch). Supposed we have an estimate tbest for the total schedule length, we can exclude most of the nodes that are created in the Branch part of the algorithm. This estimate can be initialized by any heuristic. Here, we used the heuristic from Kasahara and Narita [6]. After the creation of a new partial schedule (node of the decision tree), we can estimate a lower bound of it’s runtime tpartial by means of it’s current schedule length and the static b-level values of the remaining (yet unscheduled) tasks. The lower bound tpartial is computed by the sum of the partial schedule length plus the maximum of the static b-level values. If tpartial is greater than tbest , we can exclude the newly created node from further investigation. By this deletion of a node (Bound ) we avoid the evaluation of all the schedules that depend on the corresponding partial schedule (subspace of the search space). In this way, we accelerate the computation of the solution set for the task graph problem. As long as a node’s tpartial is lower or equal to the current tbest we continue to expand this node in a depth-first manner. When all the tasks of the graph are scheduled, a leaf of the decision tree is reached. If tbest = tcomplete we add the corresponding schedule to the set of best schedules. If tbest > tcomplete we clear the set of best schedules, store the new (complete) schedule into the set of best schedules and set tbest = tcomplete . Then we continue with the next partial schedule. The pruning scheme above is further enhanced by a selection heuristic that controls the order of the creation of new nodes. By means of this priority controlled breadth-first search we improve the threshold tbest for pruning the decision tree as early as possible. Likewise to the Bound phase, this procedure reduces further the total number of evaluations. By proceeding as described above, all possible schedules are checked. At the end of the search procedure, the current set of best schedules represents the optimal schedules for the task graph problem.

3

The Parallel Algorithm

The parallelisation of the sequential Branch–and–Bound algorithm requires a further subdivision of the search-space into disjunct subspaces, which can be assigned to the processing units.


21

As already described in Section 2, every inner node of the decision-tree represents a partial schedule and every leaf node corresponds to a complete schedule. The branching rule used by the algorithm, guarantees that the sons of a node will represent different partial schedules. Since the schedules are generated along the timeline, a later reunification of the subtrees, rooting in these sons, is impossible. Therefore two subtrees of the decision-tree always represent disjunct subspaces of the search-space, if none of their roots is an ancestor of the other one. Another result of these thoughts is that every part of the search-space can unambiguously be identified by it’s root-node. In order to achieve a balanced assignment of the computation to the available processing units, the algorithm generates a workpool, containing a certain number of subtree-roots. This workpool is managed by a master-process, which controls the distribution of the tasks to the slave-processes. The workpool is created by means of a breadth-first-search which terminates, when a user defined number of elements is collected in the workpool. The nodes are numbered by the ordinal numbers of the nodes’ permutations. These ordinal numbers allow an unambiguous identification of the nodes. By means of the root’s ordinal number, it is possible to build the corresponding subtree. For that reason, the only information, that need to be stored in the workpool, are the ordinal numbers of the subtrees’ roots. This helps to keep the required memory small. The parallel algorithm can be partitioned into three parts, called Initialisation, Computation and Finalisation. During the Initialisation-Phase, the master launches the slave-processes and splits the whole task into a number of smaller subtasks. The number of subtasks depends on the size of the workpool, which is specified by the user. The Computation-Phase begins as soon as the master assigns a subtask to every slave1 . Then, the master has to wait until it receives a message from one of the slaves which indicates that the slave has completely analysed a given subtree or that it has found an improved schedule. In the last case, the master only stores the broadcasted schedule length and the process-id of the sending slave. In the other case, it sends a new subtask to the slave if the workpool is not empty, otherwise the slave will stay idle. As soon as all subtasks are processed and all slaves are idle, FinalisationPhase will take place. The master informs all slaves about the end of the computation-process. This request is necessary, because the sending of messages appears asynchronously and it is not guaranteed, that every message that indicates a new best solution was already sent and received. Every slave receiving the finalisation message, compares the global best solution to it’s own recent results and possibly deletes it’s own suboptimal results. If the slave recognizes, that it’s own recent temporal solution is better than the global best solution, it sends an appropriate broadcast to the master and to all other slaves. Then, the slave sends the master an acknowledgement to indicate, that it finished the adjustment successfully. The master waits, until it receives an acknowledgement 1

It is required that the workpool contains more tasks than slaves.

22


from every slave. Then it requests the complete schedule from the last slave that reported to have found the best solution so far. Additionally it requests some bookkeeping information of all slaves. The slaves terminate after sending their replies. Finally, the master creates the output-file and terminates as well.

4

Results

To achieve an efficient informed search algorithm, there are some constraints that should be analysed before starting the computation of larger problems. It was found, that some of the most important constraints that influence the search-speed are independent of the given task graph. These aspects belong to the algorithm’s properties such as the size of the workpool and the number of processing elements that are involved in the search-process. Additionally, we demonstrate the suitability of our approach for the evaluation of scheduling heuristics. For this purpose, we analyse the heuristic of Kasahara and Narita [6] using a test bench of approximately 1700 task graphs. 4.1

Size of the Workpool

After it’s creation, the workpool includes the complete search space, subdivided into a user-defined number of subspaces. Apparently, the size of a subspace is determined by the number of schedules that it contains. If the number of subspaces will be increased, the size of every subspace will be reduced. In this way the workpool’s size determines the granularity of the search space and the number of schedules one slave has to analyse. Figure 1 shows how the runtime for different task graph problems depends on the size of the workpool. On the left side, we see how an increase of the workpool size can reduce the runtime on a parallel computing system with 30 processing elements. It is clearly visible that a workpool size between approx. 300 to 1200 elements (partial schedules) will be useful in this case. In contrast, we see on the right side of figure 1, that the situation for light-weight scheduling problems changes to get worse when using a parallel implementation. In this case, the minimal runtime is reached with the sequential implementation and no subtasks at all. The relative slowdown increases with the workpool’s size and might become clearly more than 100 %. Since it is difficult or even impossible to estimate the computational complexity of a schedule, the workpool size has to be chosen carefully in order to minimize the overall runtime. 4.2

Number of Processing Elements

Usually, the maximum speedup of a parallel program will be equal to the number of the available processing elements. In order to evaluate the scaling of the parallel implementation we used three task graphs that had sequential runtimes of approximately one minute each. The workpool size was set to 6000.

A Parallel Branch–and–Bound Algorithm 300

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2 1.8

250 1.6 1.4 200 Runtime (s)

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Fig. 1. Influence of the workpool’s size on the runtime. Sequential runtime on the left side diagram: 50..250s; on the right side diagram: 0,01..1,2s

Figure 2 shows the relation between the speedup-factor and the number of used processing elements. For smaller numbers of processing elements, the speedup-factor increases almost linear (for one example even a slight superlinear speedup can be recognized). If the number of processing elements gets larger than 8 the speedup differs from being linear and it begins to move to a saturation limit of approximately 16.

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Fig. 2. Influence of the number of processing elements on the speedup-factor

4.3

Analysing Scheduling Heuristics

The computation of optimal schedules is a rather time-consuming process which is only possible for small to medium-size task graphs. Although most of the

24


proposed scheduling heuristics aim at large task graphs, this subsection should show, that the efficiency of those heuristics can also be analysed by considering smaller graphs for which the optimal schedule lenghts can be computed by the proposed algorithm. A test with approximately 1700 computed optimal schedules was carried out to evaluate the heuristic’s efficiency. The used graphs were generated randomly in terms of multiple possible settings of the DAGs’ properties, e.g. the connection density between the nodes. Using such a wide spread variation of task graph properties, we can be sure that the results are independent of the chosen task graph set. This way, our approach enables scientists to evaluate and compare their heuristics’ results more objectively. To demonstrate this new opportunity, we use the well-known heuristic of Kasahara and Narita, described in [6].

Table 1. Deviation of the analysed heuristic’s results

Deviation 0% < 5% < 10% < 15% < 20% < 25% ≥ 25% Heuristic [6] 57.67% 71.46% 83.41% 90.96% 94.94% 97.14% 2.86%

Table 1 shows the deviation of this heuristic’s results from the optimal schedule lengths. The heuristic finds a solution with the optimal schedule length for 57.67% of the investigated task graphs. Regarding to the other task graphs, the observed deviation from the optimal schedule length is rather low (< 10%) in 83.41% of the cases. Only 2.86% of all solutions are worse than 25%. The performance of heuristics is usually evaluated by comparing their solutions with the ones of another (well known) heuristic. We argue that it would be more meaningful to use the deviations from the optimal solutions introduced above. For this purpose we will soon release a benchmark suite that provides the optimal solutions for 36.000 randomly created task graph problems which cover a wide range of different graph properties.

5

Conclusion

In this paper we presented a parallel implementation of a Branch–and–Bound algorithm for computing optimal task graph schedules. By means of parallelization the optimization process is accelerated and thus a huge number of test cases can be investigated within a reasonable period of time. The runtime needed for the computation of an optimal schedule is highly dependent of the workpool’s size and the number of processing elements that are available for computation. In order to reduce the runtime, the size of the workpool has to be chosen carefully. A nearly linear speedup can be achieve, provided that an appropriate workpool size is used.


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By means of the parallel Branch–and–Bound algorithm, the optimal schedules for a benchmark suite that comprises 1700 task graphs were computed. This allows for a more objective evaluation of scheduling-heuristics than comparisons between heuristics. We evaluated the solutions of the heuristic of Kasahara and Narita [6] by comparing the corresponding schedule lengths towards the optimal schedule lengths of all the 1700 test cases. The authors’ future work will include the release of a test bench, which will provide a collection of 36000 task graph problems together with their optimal schedule lengths. This benchmark suite will enable researchers to compare the performance of their heuristics with the actually best solutions. Acknowledgement. The authors would like to thank Mrs. Sigrid Preuss who contributed some of the presented results from her diploma thesis.

References 1. Aguilar, J., Gelenbe E.: Task Assignment and Transaction Clustering Heuristics for Distributed Systems, Information Sciences, Vol. 97, No. 1& 2, pp. 199–219, 1997 2. Bansal S., Kumar P., Singh K.: An improved duplication strategy for scheduling precedence constrained graphs in multiprocessor systems, IEEE Transactions on Parallel and Distributed Systems, Vol. 14, No. 6, June 2003 ¨ uner F.: Optimal and Suboptimal reliable scheduling of precedence3. Dogan A., Ozg¨ constrained tasks in heterogeneous distributed computing, International Workshop on Parallel Processing, p. 429, Toronto, August 21-24, 2000 4. Geist A., Beguelin A., Dongarra J., Jiang W., Mancheck R., Sunderam V.: PVM 3 Users Guide and Reference Manual, Oak Ridge National Laboratory, Tennessee 1993 5. Kafil M., Ahmad I.: Optimal Task assignment in heterogeneous distributed computing systems, IEEE Concurrency: Parallel, Distributed and Mobile Computing, pp. 42-51, July 1998 6. Kasahara, H., Narita, S.: Practical Multiprocessor Scheduling Algorithms for Efficient Parallel Processing. IEEE Transactions on Computers, Vol. C-33, No. 11, pp. 1023-1029, Nov. 1984 7. Kohler, W.H., Steiglitz, K.: Enumerative and Iterative Computational Approaches. in: Coffman, E.G. (ed.): Computer and Job-Shop Scheduling Theory. John Wiley & Sons, New York, 1976 8. Kwok, Y.-K., Ahmad, I.: Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Computing Surveys, Vol. 31, No. 4, 1999, pp. 406–471 9. Park C.-I., Choe T.Y.: An optimal scheduling algorithm based on task duplication, IEEE Transactions on Computerss, Vol. 51, No. 4, April 2002 10. Radulescu A., van Gemund A. J.C.: Low-Cost Task Scheduling for DistributedMemory Machines, IEEE Transactions on Parallel and Distributed Systems, Vol. 13, No. 6, June 2002

Selection and Advanced Reservation of Backup Resources for High Availability Service in Computational Grid Chunjiang Li, Nong Xiao, and Xuejun Yang School of Computer, National University of Defense Technology, Changsha, 410073 China, +86 731 4575984 [email protected]

Abstract. Resource redundancy is a primary way to improve availability for applications in computational grid. How to select backup resources for an application during the resource allocation phase and how to make advanced reservation for backup resources are challenging issues for grid environment to provide high availability service. In this paper, we proposed a backup resources selection algorithm base on resources clustering, then devised several policies for advanced reservation of backup resources. With this algorithm and these policies, the backup resource management module in grid middleware can provide resource backup service more efficiently and cost-effectively. These algorithm and policies can be implemented on the GARA system, make the QoS architecture more powerful and practical for computational grid.

1

Introduction

Grid computing [1] is a kind of distributed supercomputing, in which geographically distributed computational and data resources are coordinated for solving large-scale problems. The resources in the grid environment are wide-area distributed, heterogeneous in nature, owned by different individuals or organizations; which makes the grid a more variable and unreliable computing environment. The most common failure include machine faults in which hosts go down, and network faults where links go down. When some resources fail, the applications using such resources have to stall, waiting for the recovery of the failed resources or migration to other resources. Ways to reduce the stall-time for applications are critical for grid middleware to provide high availability service. Resource backup is such a kind of methods, that is, at the resource allocation phase, allocates redundant resources as the backup resources for the application, when some resources fail, the application’s tasks running on the failed resources can migrate to the backup resources,

This work is supported by the National Science Foundation of China under Grant No.60203016 and No.69933030; the National High Technology Development 863 Program of China under Grant No.2002AA131010.


Selection and Advanced Reservation of Backup Resources

27

without asking the global resources manager to reallocate resources which is time-consuming in the grid. The application in computational grid usually uses a large amount of resources, and the resources usually differ from each other on type, hardware/software architecture, and performance. So, how to select backup resources for this application is more complex than ever. The backup resources allocated for the application may not be used at all during the application running process. If making advanced reservation for all those backup resources during this phase, it is more feasible to get the resource wasted, which makes the application less cost-effective. So, it is necessary to design multiple policies for advanced reservation of backup resources. In this paper, firstly, we proposed a backup resources selection algorithm based on resources clustering. This algorithm simplified the selection process by dividing the resources used by the application into subsets, the resources in each subset share somewhat similarity and can share backup resources. Then we devised several policies for advanced reservation of backup resources. These policies can be implemented with the GARA system [2]. This paper is organized as follows. The resource backup process is described in section 2. In section 3, the selection algorithm of backup resources based on resources clustering is introduced. In section 4, the policies for advanced reservation of backup resources are presented, and briefly discussed the architecture of the policy engine for resource backup. Conclusion is drawn in section 5.

2

Resource Backup

Resource backup [3] in a single computer system, like a cluster or a server, is often done by hardware component redundancy. In computational grid, however, as the resources belong to different administrative domains, the grid middleware cannot rearrange the hardware components for redundancy. The only way for resource backup is to allocate redundant resources for the application that need high availability. Here, we call the resources allocated to the application on which the application is running as primary resources, and call the resources for redundancy as backup resources. As we know, the resource allocation process in the grid includes two phases [4]: resource discovery and reservation, for example, when a user submits an application with resources requirement described with RSL [5] to the computational grid, the global scheduler first analyzes the resource requirement, performs resource discovery using the grid information system, then computes a primary resource list; secondly, it try to obtain reservations for these resource. If the application needs high availability service, the scheduler can allocate redundant resources at these phases, keeping advanced reservation of some resources as backup. So, in the running process, when the resources on which some application’s task running are failed, these tasks can migrate to the backup resources and continue to run without reallocation process. This can reduce the stall time of the application, increasing its availability. Usually, the application in the grid does not explicitly declare which resources should be allocated as backup resources when it is submitted. The backup resources management module should select backup resource according to the resources requirement and

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availability requirement of the application. So, backup resources selection algorithm is absolute necessary. Furthermore, the backup resources may not be used during the running process, in order to avoid the waste of resources, flexible advanced reservation policies is also necessary. In this paper, we first present a backup resource selection algorithm based on resource clustering, then designed some policies for the advanced reservation of backup resources. They are very valuable for providing high availability services in computational grid.

3 3.1

Selection Algorithm Definitions

Before process resource clustering, we give the following definitions for the relations between resources. Definition 1, Substitution Relation, if the task running on resource ri can also run on rk , and can satisfy the performance requirement of the tasks, then we call that resource ri is substitutable by rk , denoted as Sub < ri , rk >. Definition 2, Similarity relation, if two resources ri and rk can substitute each other, i.e. Sub < ri , rk > and Sub < rk , ri >, then we call rk and ri as similar resources, denoted as Sim < ri , rk >. Definition 3, Complete resource set, a resource set Rk , if ∀rn , rm ∈ Rk , Sim < rn , rm >, then we call Rk as complete resource set. Definition 4, Similarity relation of resource set, complete resource set Ri and Rk , for ∀rn ∈ Ri , ∀rm ∈ Rk , if Sub < rn , rm > and Sub < rm , rn >, then we call Rk and Ri as similar resource set, denoted as Sim < Ri , Rk >. 3.2

Resource Clustering

It is obvious that the resources in a complete resource set can share backup resources. The primary resources allocated to an application can cluster into several complete resource set based on similarity relation, then we can choose backup resources for each set. The clustering includes two steps: inner domain clustering and inter domain clustering. First we will examine the determination of similarity relation between two resources. Determination of Similarity Relation. The similarity relation can be determined at two levels: physical level and logical level. At the physical level, it is obvious that homogeneous resources allocated to an application are similar. At the logical level, for two resources allocated to an application, ri and rk , if the tasks running on ri can also run on rk , and vice versa, and the application performance can still be satisfied, then ri and rk are similar resources. For example, a grid application GA, the tasks of it are programmed with Java, and there are two workstations can run these two tasks , one is a Linux workstation, and the other is a Windows workstation. If the tasks running on these two resources can be exchanged without degradation of the performance, then these two workstations are similar computing resources for the application, although they are heterogeneous computing resources.


29

Inner Domain Clustering. It is common that quite a few resources in the primary set are belonging to one administrative domain, for example a cluster or a mainframe. Usually, the resources in a domain are homogeneous, we suppose this is true for all administrative domains. So, the inner domain clustering is easy to perform. The primary resource set can be denoted as P = {r1, r2, · · · , rm}, these resources are from k domains, P = {R1 , R2 , · · · , Rk }, and the resources in each domain is a complete resource set. Ri is the resources set from the ith domain. If the resources coming from a domain are heterogeneous, we can divide the domain into sub-domains, and keep the resources in each sub-domain are homogeneous. Inter Domain Clustering. In order to have the primary resources of the application cluster into complete resource sets, the application level clustering must be performed. Inter domain clustering is based on the similarity between domains, which can be determined with following rule: for ∀rj ∈ Ri and ∀rh ∈ Rk , if Sim < rj , rh >, then Sim < Ri , Rk >. We use a matrix Dn×n to describe the similarity relation between n domains. If Sim < Ri , Rk >, then D(i, k) = 1, otherwise D(i, k) = 0. The algorithm for inter domain clustering is given in 1. [Algorithm 1] Input: domain set R = {R1 , R2 , ⋅⋅⋅, Rn }, and the similarity relation matrix Dn×n ; Output: the set of complete resource sets R = {RS1 , RS 2 , ⋅⋅⋅, RS k }; Begin: 1. i = 1, Rm = R1 ; ments 2. Add the domains which corresponding to the non-zero elem in the row and column of Rm to RSi ; 3. Add the domains which corresponding to the non-zero elements inn the row and column of all domains in RSi to RSi ; 4. Repeat step 3; until RSi does not increase; 5. R = R − RSi ; if R = Φ,, exit; otherwise, get ∀Rk ∈ R, Rm = Rk , i = i + 1, go to step 2; End.

Fig. 1. Inter Domain Resources Clustering

3.3

Backup Resource Selection for a Resource Set

Availability Measurement. For selecting backup resources, it is necessary to devise a measurement method for the availability of a resource set. In this paper, for simplicity, we use an availability measurement model based on probabilistic model [6]. Suppose a primary resource set of an application is R = {r1 , r2 , . . . , rm }, only when all the resources in it are available, the application can run smoothly; otherwise, the application will stall. Suppose A(R)is the availability of this resource set, and the failure rate of ri is f (ri ), then,

30


A(R) =

m n=1

(1 − f (rn )).

Suppose the availability demand of this application is Av (R), if A(R) < Av (R),then need to allocate backup resources for it. Suppose we have selected m backup resources for this resource set, and the , the availability of it can be defined as: expanded resource set is R A(R , m) = Pr {at least m resources in R is available } If add a backup resource to R, denoted as rm+1 , R = R ∪ {rm+1 } then m+1 m+1 m+1 A(R , m) = f (ri )(1 − f (rj )) + (1 − f (rk )). i=1 j = 1 k=1 j = i

If add more than one backup resources, the computation of A(R , m) is very difficult, for simplicity, we use an approximate method. In this approximate method, we suppose the failure rate of each resources in R is equal to the highest one, denoted as f (R). Then the availability of resource set R is A(R) = (1 − f (R))m (1) In selecting backup resource for this resource set, the failure rate of the backup resource should not over f (R), suppose we chosen k backup resources, then k m+i (1 − f (R))m+i (f (R))k−i A(R , m) = i=0 m + k k i (1 − f (R))m+k−i (f (R))i = (2) m + k i=0

Backup Resource Selection algorithm. Based on the upper discussions, we proposed the algorithm for selecting backup resources for a resource set R, shown in 2 In this algorithm, each iteration only choose one backup resource for

[Algorithm 2] Input: resource set R, the number of resources in it is m, the availability demand is Av ( R); Ouput: resource set Rb used for backup; Begin: 1. Calculate A( R) using formula (1), if A( R) ≥ Av ( R), then Rb = Φ, exit; 2. Rb = Φ, j = 0; 3. j = j + 1, select a backup resource for R, denoted as rm + j , its failure is not over f ( R); 4. Rb = Rb ∪ {rm + j }, calculate A( R ∪ Rb, m) using formula (2); if A( R ∪ Rb, m) < Av ( R ), goto step 3; 5. Return Rb, together with the failure rate of each resource in Rb; End. Fig. 2. Backup Resources Selection Algorithm for a Complete Resource Set


31

the complete resource set. This can determine the minimum number of backup resources for it. In practice, we can choose more than one resource at each time, improve the efficiency of this algorithm. 3.4

Backup Resource Selection Algorithm Based on Resource Clustering

Now we can describe the whole backup resource selection algorithm based on resource clustering as shown in 3: [Algorithm 3] Input: R(GA), Primary resource set of application GA, and its availability requirement Av (GA);; Output: Backup resource set Rb (GA); Begin: 1. Inner domain clustering of R(GA); 2. Determining the similarity relation between domains; 3. Inter domain clustering using algoorithm 1, get R(GA) = {RS1 , RS 2 , ⋅⋅⋅, RS k }; 4. Determine the availability requirement of each RSi (1 ≤ i ≤ k ) according to the availability of the whole application . 5. For eachh RSi (1 ≤ i ≤ k ) , select backup resources using algorithm 2; End.

Fig. 3. Backup Resource Selection Algorithm based on Resource Clustering

4

Policies for Advanced Reservation of Backup Resources

The allocation of backup resources is fulfilled by advanced reservation, in which the reservation is requested before it is needed. The GARA system based on the famous grid middleware Globus Toolkit [7,8], can process advanced reservation for multiple resources such as CPU slot, network bandwidth and storage capacity. But the GARA system lacks flexible policies for advanced reservation. In order to reduce the waste of resources, we designed several policies for advanced reservation of backup resources, these policies can be implemented on the GARA system, providing flexible resource backup mechanism. Flowing, we will introduce the idea of each policy briefly. Here, we use Lb (GA) to denote the list of backup resource for application GA, the resources in which is selected by the selection algorithm proposed above. 4.1

Policies

Totally Advanced Reserved, TAR. When allocating backup resources, make advanced reservation for all of the resources in Lb (GA) of application GA; that is

32


only when all the backup resources have been advanced reserved, the application can begin to run. Partially Advanced Reserved, PAR. In the advanced reservation stage, only make advanced reservation for some of the resources in Lb (GA), form the backup resource set Rb−rsv (GA). During the running process, if the advanced reserved resources are not enough for failure recovery, then make advanced reservation for other backup resources in Lb (GA). Compensate for Risk, CR. For reducing the number of backup resources in the whole grid environment, different applications can share their backup resources. In this policy, the backup resources which are advanced reserved can assemble into a resource pool, like the insurance mechanism in the society. But for this policy, the time management for advanced reservation is critical, because it is very often that backup resources for different application have different time period for reservation. Delayed Advanced reservation, DAR. In this policy, before the application start, none of the resources in Lb (GA) is advanced reserved. Only when resource failure occurs, the resource reservation agent begins to reserve the backup resources in the backup resource list of this application. The number of resources reserved can be determined according to the failure condition and the availability demand of the application.

Grid Applications

Gather and Analysis of

Policy Engine Policy Selection

the Information of Failed Resources

Implementation of the Policies MLBR

MARR

TMARR

Info. of Failed Resources

GARA System

Grid Middleware and Toolkit Resource Layer

Fig. 4. The Architecture of Policy Engine

No-backup, NB. For completeness, we also call the method that does not prepare any backup resources for the application as a backup policy. That is, never make advanced reservation of resources used for backup. When failure


33

occurs, the job management module in the grid reallocates resources for the failed tasks. 4.2

Policy Engine

It is obvious that each policy has benefits and drawbacks. The backup resource management module in the computational grid will never use only one policy to serve all the applications. It is undoubtedly that a policy engine should be implemented for resource backup service. In 4 we described the architecture of the policy engine for resource backup service of computational grid. There are three critical modules in the policy engine: MLBR is the module that Manage the List of Backup Resources for applications, MARR is the module that Manage the Advanced Reserved Resources in the whole environment, and TMARR is Time Management module for Advanced Reserved Resources. All these modules can call the GARA API to control the advanced reservation of resources. The policy engine can provide flexible resource backup service for the applications.

5

Conclusion

Backup resource management for providing high availability service in computational grid faces two critical issues: backup resource discovery and advanced reservation. In this paper we proposed a backup resource selection algorithm and devised several policies for advanced reservation. These algorithm and policies can provide flexible resource backup service for the applications in computational grid.

References 1. Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers (1999) 2. Simoni, A.: End-to-End Quality of Service for High-End Applications. PhD thesis, the University of Chicago (2001) 3. Hwang, K., Xu, Z.: Scalable Parallel Computing. Mc-Graw-Hill Companies Inc. (1998) 4. K. Czajkowski, I. Foster, N.K.C.S.M.W.S., Tuecke, S.: A resource management architecture for metacomputing systems. In Proceedings of the IPPS/SPDP’s 98 Workshop on Job Scheduling Strategies for Parallel Processing (1998) 5. WWW: The globus resource specification language rsl v1.0. (http://www.globus.org/gram/rsl spec1.htm) 6. Archana Sathaye, S.R., Trivedi, K.: Availability models in practice. In Proceedings of Int. Workshop on Fault-Tolerant Control and Computing (FTCC-1) (2000) 7. Foster, I., Kesselman, C.: Globus: A metacomputing infrastructure toolkit. International Journal of Supercomputing Applications 11 (1997) 115–128 8. WWW: (http://www.globus.org)

An Online Scheduling Algorithm for Grid Computing Systems Hak Du Kim and Jin Suk Kim * 1

1

2

Electronics and Telecomunications Research Institute, Taejon, Korea 2 School of Computer Science, University of Seoul, Seoul, Korea [email protected]

Abstract. Since the problem of scheduling independent jobs in heterogeneous computational resources is known as NP-complete [4], an approximation or heuristic algorithm is highly desirable. Grid is an example of the heterogeneous parallel computer system. Many researchers propose heuristic scheduling algorithm for Grid [1], [8], [9], [10]. In this paper, we propose a new on-line heuristic scheduling algorithm. We show that our scheduling algorithm has better performance than previous scheduling algorithms by extensive simulation.

1 Introduction A Grid computing system is a system which has various machines to execute a set of tasks. We need high performance Grid computing systems in the field of natural science and engineering for large scale simulation. In this paper, we propose a scheduling algorithm which assigns tasks to machines in a heterogeneous Grid computing system. The scheduling algorithm determines the execution order of the tasks which will be assigned to machines. Since the problem of allocating independent jobs in heterogeneous computational resources is known as NP-complete [4], an approximation or heuristic algorithm is highly desirable. In the scheduling algorithms, we consider that the tasks randomly arrive the system. We assume that the scheduling algorithms are nonpreemptive, i.e., tasks must run to completion once they start, and the tasks have no deadlines. All the tasks are independent, i.e., there is no synchronization or communication among the tasks. In the on-line mode, a task is assigned to a machine as soon as it arrives at the scheduling system

2 Related Works The scheduling problem has already been investigated by several researchers [1]. In MET(Minimum Execution Time), the scheduling algorithm is developed to minimize execution time, i.e., the algorithm assigns a task to the machine which has the least *

Corresponding author: Jin Suk Kim


An Online Scheduling Algorithm for Grid Computing Systems

35

amount of execution time. Since this algorithm does not see the ready times of the machines, it has imbalance of load among machines. This algorithm calculates only the minimum one among m machine execution times, and then the algorithm assigns the task to the selected machine. The time to find the machine which has minimum execution time is O(m). The MCT(Minimum Completion Time) assigns a task to the machine which has minimum completion time, i.e., the algorithm gets the completion time for each task by adding begin time and execution time, and calculates the minimum one among m machine completion times. The algorithm assigns the task to the selected machine. Therefore, the time to get the machine which has minimum completion time is O(m). The KPB(K-Percent Best) first finds (k/100)*m best machines based on the execution time for the task, and the algorithm calculates the minimum one among selected machine completion times, and then the algorithm assigns the task to the machine which has the minimum. The time to get the subset of machines is O(mlogm) because the time for sorting m execution times is needed. The time to determine the machine which has minimum completion time is O(m). Overall time complexity of KPB is O(mlogm).

3 A Scheduling Algorithm The execution time eij denotes the amount of time which is taken to execute task Ti on machine Mj [1]. The completion time cij denotes the time at which machine Mj completes task Ti. Let the begin time of Ti on Mj be bij. We can see that cij = bi + eij from the above definition. T = { T1, T2, … Tn } is defined as the set of tasks. n is the number of tasks and m is the number of machines. In this paper, we use a makespan as a performance metric for scheduling algorithms. The makespan is the maximum completion time when all tasks are scheduled. Figure 1 shows a proposed scheduling algorithm MECT(Minimum Execution Completion Time). The inputs of the scheduling algorithm are a task Ti and a set of execution times {ei1, ei2, ... , eim} which is taken to execute Ti on machines {M1, M2, ... , Mm}. In the step I, MECT finds maximum begin time bmax among the begin times of all machines. In the step II, find a subset of machines M’ such that eij’ < bmax for Mj’ in M’. In the step III, if M’ is not empty, MECT determines the machine Mk in M’ which has minimum execution time to execute the task Ti. Otherwise, MECT determines the machine Mk in M which has minimum completion time to execute the task Ti. Finally, MECT returns the index of machine k. Here, we compute the time complexity of MECT. The time that is taken to get a maximum begin time is O(m). The time to find a subset of machines M’ is also O(m). In the step III, we can get k in O(m). Therefore, overall time complexity of MECT is O(m).

36

H.D. Kim and J.S. Kim

Fig. 1. A Scheduling Algorithm MECT

4

Simulation Results

In this section, we made a simulation program with SimJava which is used in discrete model simulations [6]. In this simulation, we assume that the execution time for each task on each machine is known prior to execution. This assumption is used when studying scheduling algorithm for heterogeneous computing systems [11]. We use task-machine matrix which has the execution times. Figure 2 shows an example of task-machine matrix. For example, the 3rd row represents execution times for T3 on each machines, M1, M2, ... , M5 i.e., e31 = 30,480, e32 = 30480, e33 = 7,112, e34 = 45,720, e35 = 26,416 . To simulate the scheduling algorithms on various scheduling situations, many studies use task-machine matrix consistency model [11]. We say a task-machine matrix is consistent if machine Ma executes task Tc faster than machine Mb, then machine Ma executes all tasks faster than machine Mb. We say a task-machine matrix is inconsistent where machine Ma executes some tasks faster than machine Mb and machine Mb executes other tasks faster than machine Ma. A task-machine matrix is said to be semi-consistent if some columns are consistent and other columns are inconsistent. Figure 2 represents inconsistent task-machine matrix which has 10 tasks and 5 machines. In this matrix, we can see that M1 executes T2 faster than M3 but M1 executes T3 slower than M3. In this simulation, we made task-machine matrices which have 1,000 tasks and 20 machines. We use the arrival rate of tasks 100. Figure 3 shows the average makespans for scheduling algorithms in inconsistent model. The machine heterogeneity is varied 10 to 120 and the task heterogeneity is 3000. We have 50 tests for each cases. It can be noted that MECT outperforms previous scheduling algorithms.

An Online Scheduling Algorithm for Grid Computing Systems M1 50,160 46,345 30,480 42,720 53,136 8,520 23,954 61,490 3,168 1,428

T1 T2 T3 T4 T5 T6 T7 T8 T9 T10

M2 50,920 7,130 30,480 39,840 25,272 5,396 56,994 86,086 4,950 4,046

M3 36,480 69,161 7,112 6,240 27,216 14,768 20,650 51,084 1,287 6,664

M4 36,480 50,623 45,720 20,640 12,312 6,816 4,130 17,974 3,762 1,904

M5 41,800 54,901 26,416 5,760 6,480 22,152 43,778 65,274 2,376 5,831

Fig. 2. A 10 × 5 Task-Machine Matrix

8 .E+0 5 7 .E+0 5

makespan

6 .E+0 5

MET

5 .E+0 5

MCT KPB(k=2 0 )

4 .E+0 5

MECT

3 .E+0 5 2 .E+0 5 1 .E+0 5 10

20

30

40

50

60

70

80

90 100 110 120

m a chine hetero g eneity

Fig. 3. The makespans for scheduling algorithms in the inconsistent model

6.E+06

makespan

5.E+06 4.E+06

MET MCT

3.E+06

KPB(k=20) MECT

2.E+06 1.E+06 0.E+00 10

20

30

40

50

60

70

80

90 100 110 120

machine heterogeneity

Fig. 4. The makespans for scheduling algorithms in semi-consistent model

37

H.D. Kim and J.S. Kim

1.E+07 9.E+06 8.E+06 7.E+06 6.E+06 5.E+06 4.E+06 3.E+06 2.E+06 1.E+06 0.E+00

MET MCT KPB

11 0

90

70

50

30

MECT

10

makespan

38

machine heterogeneity

Fig. 5. The makespans for scheduling algorithms in consistent model

Figure 4 and 5 show the simulation results in the semi-consistent model and the consistent model, respectively. In these figures, we can see that MECT competes with MCT. Note that the performance of MET is lower than that of other three algorithms. In the last simulation, the machine heterogeneity is 20 and the task heterogeneity is varied from 500 to 3000. Figure 6 compares the scheduling algorithms based on makespan. We can see that MECT outperforms previous scheduling algorithms when the task heterogeneity is high. 200000 180000 160000 makespan

140000

MET

120000

MCT

100000

KPB(k=20)

80000

MECT

60000 40000 20000 0 500

1000 1500 2000 2500 3000 machine heterogeneity

Fig. 6. The makespans for scheduling algorithms in consistent model

An Online Scheduling Algorithm for Grid Computing Systems

39

5 Conclusion In this paper, we propose a new scheduling algorithm MECT for heterogeneous Grid computing systems. The proposed scheduling algorithm is a kind of on-line scheduling algorithm. We show that MECT has better performance than the traditional scheduling algorithms especially when the heterogeneity is high.

References [1]

M. Maheswaran, S. Ali, H. J. Siegel, D. Hensgen, and R. F. Freund, “Dynamic Matching and Scheduling of a Class of Indenpendent Tasks onto Heterogeneous Computing Systems,” Proc. of the 8th Heterogeneous Computing Workshop, pp. 30-44, April, 1999. [2] I. Foster, C. Kesselman, and S. Tuecke, “The Anatomy of the Grid: Enabling Scalable Virtual Organizations,” Journal of High-Performance Computing Applications, vol. 15, no. 3, pp. 200-222, 2001. [3] T. D. Braun, H. J. Siegel, and Noah Beck, “A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems,” Journal of Parallel and Distributed Computing, vol. 61, pp. 810-837, 2001. [4] O. H. Ibarra and C. E. Kim, “Heuristic Algorithm for Scheduling Independent Tasks on Nonidentical Processors,” Journal of the ACM, vol. 24, no. 2, pp. 280-289, April, 1977. [5] M. Pinedo, Scheduling: Theory, Algorithms, and Systems, Prentice Hall, NJ, 1995. [6] F. Howell and R. McNab, “SimJava: A Discrete Event Simulation Package For Java With Applications In Computer Systems Modelling,” Proc. of the 1st International Conference on Web-based Modelling and Simulation, January, 1998. [7] A. A. Khokhar, V. K. Prasanna, M. E. Shaaban, and C. L. Wang, “Heterogeneous Computing: Challenges and Opportunities,” IEEE Computer, vol. 26, pp. 18-27, June, 1993. [8] R. Buyya, J. Giddy, and D. Abramson, “An Evaluation of Economy-based Resource Trading and Scheduling on Computational Power Grids for Parameter Sweep Applications,” Proc. of the 2nd International Workshop on Active Middleware Services, August, 2000. [9] H. Barada, S. M. Sait, and N. Baig, “Task Matching and Scheduling in Heterogeneous Systems using Simulated Evolution,” Proc. of the 15th Parallel and Distributed Processing Symposium, pp. 875-882, 2001. [10] B. Hamidzadeh, Lau Ying Kit, and D.J. Lilja, “Dynamic Task Scheduling using Online Optimization,” Journal of Parallel and Distributed Systems, vol. 11, pp. 1151-1163, 2000. [11] T. D. Braun, H. J. Siegel, N. Beck, L. L. Boloni, M. Maheswaran, A. I. Reuther, J. P. Robertson, M. D. Theys, B. Yao, D. Hensgen, and R. F. Freund, “A Comparison Study Mapping Heuristics for a Class of Meta-tasks on Heterogeneous Computing Systems,” 8th IEEE Heterogeneous Computing Workshop, pp. 15-29, 1999.

A Dynamic Job Scheduling Algorithm for Computational Grid* Jian Zhang and Xinda Lu Department of Computer Science and Eng., Shanghai Jiaotong Univ., Shanghai 200030, China {zhangjian, lu-xd}@cs.sjtu.edu.cn

Abstract. In this paper, a dynamic job-scheduling algorithm is proposed for a computational grid of autonomous nodes. This algorithm tries to utilize the information of a practical system to allocate the jobs more evenly. In this algorithm, the communication time between nodes and the scheduler is overlapped with the computation time of the nodes. So the communication overhead can be little. The principle of scheduling the job is based on the desirability of each node. The scheduler would not allocate a new job to a node that is already fully utilized. The execution efficiency of system will be increased. An implementation framework of the algorithm is also introduced.

1 Introduction The Grid concept has recently emerged as a vision for future network based computing. A computational grid is a large scale, heterogeneous collection of autonomous systems, geographically distributed and interconnected by low latency and high bandwidth networks. Networks of workstations, NOWs, represent particular forms of grids. Like an electrical power grid, the Grid will aim to provide a steady, reliable source of computing power. The most difficult problems in a computational grid are the management and control of resources, dependability and security. Obviously, until now, researchers have proposed many techniques to allocate jobs dynamically in a parallel system to improve its performance. But most of these algorithms ignore practical issues such as the speed of nodes, the process ability of the node, and the different size of the jobs. These schedulers might not perform satisfactorily in practical. In this paper, a dynamic job-scheduling algorithm is proposed. In this algorithm, the scheduler tries to allocate a job according to the knowledge of the nodes. This algorithm is very general and it can adapt to more situations to improve the job fairness. This algorithm is also a valid method to prevent the saturation of the nodes. This paper is organized as follows. In the second section, an analysis of the general job scheduling policies is presented. Then in the third section, the job-scheduling algorithm is introduced and explained in detail. The mobile agent based implementation framework is giving in the fourth section, followed by a summary of this paper in the fifth section. *This

work was supported by the National Science Foundation of China (No. 60173031)


A Dynamic Job Scheduling Algorithm for Computational Grid

41

2 Analysis of the General Job Scheduling Policies There are three general job-scheduling policies [1]: Static scheduling: In this policy, the job is assigned to the nodes at compile time and will never be reassigned again. Dynamic scheduling: System makes all scheduling decisions at run time. They use a central work-queue where all idle nodes go to find work to execute. Affinity scheduling: In this policy, schedulers create one local work queue for each node. Each node is statically assigned some work, as if static scheduling were used. If load imbalance actually occurs, idle nodes search the work queues of other nodes to find work to do. In the static scheduling policy, because the scheduler need not communicate with the node before the assigning the job, and the job will never be reassigned, the synchronization and communication overhead will be very little. But the drawback is that it may lead to underutilization of nodes. In the dynamic scheduling policy, the right to take initiative is controlled by the node. When a node is in the idle state, it will send message to the scheduler to ask for the job. The job in the head of job queue will be assigned to this node. In this policy, the load imbalance will be reduced to the minimum. However, this policy may also result in an increase of communication overhead. Before the idle node can get the job, it has to spend time in making communication with scheduler. The affinity scheduling policy attempts to strike a balance among the static and dynamic schedulers. On one hand, it can reduce the load imbalance. There will be no idle node if there is a job waiting for executing. On the other hand, the communication overhead between nodes and scheduler is little. Each node has its own job queue. But in this policy, migration may be taken among nodes. The overhead between nodes is increasing. In some condition, the overhead related to migration is higher than the overhead related to load imbalance. Thus the performance of affinity scheduling policy may be worse than static scheduling policy. If no migration in the affinity scheduling policy, the performance of system would be improved notably. With the utilization of some other practical information, we can do this. The size of jobs is useful practical information, but it is hard to get in advance. We still can get some other information from the system. We can estimate the speed of each node. We can know the maximum number of jobs that can be executed concurrently on each node. And we can also know that there are three jobs running in each node right now. Such information is also useful. The algorithm we proposed in this paper is based on the utilization of these information.

3 The Dynamic Job Scheduling Algorithm 3.1 The Architecture of the Algorithm Intuitively, the best load-balancing status occurs when all nodes are at the point of full utilization and each node’s workload is proportional to its capacity. We try to increase the throughput of the system. We may not allocate more jobs to a fully utilized node, because this will cause imbalance without improving the overall throughput [2]. We

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J. Zhang and X. Lu

should know whether we could allocate a new job to a particular node or not. An important characteristic of the algorithm is to estimate the desirability of executing a new job in each node. We use a scheduling function fi to reflect this desirability. When the node is saturated, scheduler will not allocate new job to it. The architecture of this algorithm is shown in Fig.1. P1

P2

Scheduler Message Queue

CentralJobQueue

Long Job

Short Job

Fig. 1. Architecture of the algorithm

The characteristic of this architecture is that there is no job queue for each node. Additionally, there is a message queue for the scheduler. All the messages sent to the scheduler will be appended to this queue. The scheduler will extract the message from the message queue to process. Taking Fig.1 as an example. Assuming a node will be fully utilized when three jobs are running in it, then the scheduler will not dispatch the next job until a running job is completed. Because all short jobs are in P2, a job in P2 will more likely be completed first. Therefore, the next long job has a higher chance of being allocated to P2. Then we can reduce the chances of overloading the nodes and tend to distribute long jobs to the nodes more evenly. 3.2 The Algorithm In order to explain this algorithm in detail, here some notions are explained at first. P pi si bi

— — — —

The set of all nodes A node in the set P, pi ∈P The initial speed of a node The bounding factor of a node

ji s’i fi Qj Qm

— — — — —

The run queue length of a node The effective speed of a node The scheduling function Central job queue Message queue


43

In a computational grid, all the nodes consist the set of nodes P. pi denotes each node in the node set, pi ∈ P. bi is a bounding factor. It limits the number of schedulable jobs in that node. The bounding factor is generally set such that when bi jobs are scheduled to pi, the node is fully utilized. The value of the bounding factor will affect the response time of system. If we set a high bi for a node, the number of jobs that can be executed concurrently on this node will be high, then the response time will be long. So if in a system where response time is important, the bounding factor bi should be set to a low value. ji is the run queue length of a node, reflecting the number of active jobs which are being executed on this node. Moreover, ji should not larger than bi: ji ≤ bi .

(1)

Each node has its speed. When it is in idle state, its speed will be its initial speed. We use si to denote this speed. This speed should be the highest speed for this node. When some jobs are being executed in this node, the speed of this node will decrease. The current speed will be its effective speed. We use s’i to denote the effective speed of node. The effective speed of a node has an inverse proportion with the number of jobs in this node. The more the number of jobs is, the lower the effective speed is. When the node is in idle state, its effective speed should be its initial speed. We may use the formula below to calculate the effective speed of a node: s’i =

si . ji + 1

(2)

fi is the scheduling function reflecting the desirability of executing a new job on this node pi. The larger fi is, the more desirable sending a job to pi is. If fi equals zero, this means the number of jobs in this node has reached bi, this node has already been fully utilized. The scheduler tries to find the node with the highest si from the nodes whose fi is larger than zero. pbest= maxi{si | fi>0} .

(3)

If fi of all the nodes are equal to zero, fmax= maxi { fi } = 0 .

(4)

this means all the nodes in the parallel system are all saturated. The scheduler will not dispatch additional jobs to overload an already saturated system but to wait for some running jobs to complete. The scheduling function may be expressed like this: fi = s’i -

si si s = - i = si  1 − 1  .  j +1 b +1 bi + 1 ji + 1 bi + 1 i  i 

(5)

From this formula, we see that the larger ji is, the smaller fi is. When ji is less than bi, fi will be larger than zero. When ji equals bi, fi will be zero. If fi is less than zero, it means the node is overloaded with local jobs.

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There are three kinds of messages that the scheduler will receive. Job_done: It is sent from the node to the scheduler when a job in this node is completed. New_job: This message is sent to the scheduler when a new job is coming. The scheduler will append this job at the end of the central job queue. Current_load: The current run queue length of a node. The algorithm is shown below: Initialize B={bi},S={si}; Let Qj=ф, Qm=ф; Let s’i=si/(ji+1), fi=s’i–si/(bi+1) for all i; While (True) Extract the first incoming message (t，k) from Qm; If t=“new-job” then Append k at end of Qj; If t=“current_load” then Let s’k=sk/(jk+1), fk=s’k–sk/(bk+1); If t=“job-done” then Let jk=jk-1,s’k=sk/(jk+1),fk=s’k–sk/(bk+1); While Qj≠ ф and pk= maxi{ si |fi>0} ф Extract the 1st job from Qm and execute it on pk; Let jk=jk+1,s’k=sk/(jk+1),fk=s’k–sk/(bk+1) In the initial phase, it will get the bounding factor bi and the initial speed si of each node pi. The ji for each node will be set zero. Then calculates the effective speed s’i and the value of scheduling function fi for each node. The algorithm then processes the incoming message in the main loop. If the message is “New_job”, it will append the new job at the end of central job queue Qj. If the message is “Job_done”, the ji of that node will be minus 1. The s’i and fi will be recalculated. In the next step, the algorithm will find the pi with the highest fi from the set P. If the highest fi is zero, it will not extract the job from the Qj to execute but to process the next message from the Qm. If the highest fi is larger than zero, it will extract the job from the Qj and execute it on pi. The ji of that node will be added 1. The s’i and fi will also be recalculated. Compared with the dynamic scheduling policy, in our algorithm, the node does not need to use a special time to make communication with the scheduler. The time for communication and the time for computation are overlapped. When it completed a job, it will send a “Job_done” message to the scheduler, and it will continue to execute other jobs assigned on it but to wait for new job. This could reduce the overhead between the node and the scheduler. It alleviates the drawback of the dynamic scheduling policy. Compared with the affinity scheduling policy, when the scheduler want to allocated a job, the algorithm does not select the node blindly. It will select among nodes whose scheduling functions are large than zero, which is calculated with the process ability and the effective speed of that node. Therefore, it can evenly distribute the workload among the nodes, and no migration is needed. Another important characteristic of this algorithm is that it would not allocate more jobs to a node when the desirability of that node is zero. This means it would not allocate more jobs to the node that is already fully utilized. Most other scheduling algorithms submit the jobs to the selected node without checking if the node has become


45

saturated after selection and before sending out the job. These algorithms want to increase the throughput of system, but on contrary, it will decrease the execution efficiency of system.

4 Mobile Agent Based Implementation of the Algorithm 4.1 Mobile Agent Mobile agent is an emerging paradigm that is now gaining momentum in several fields of applications [3]. A mobile agent corresponds to a small program that is able to migrate to some remote machines, where it is able to execute some function or collect some relevant data then migrate to other machines in order to accomplish another task. The basic idea of this paradigm is to distribute the processing through the network: that is, sent the code to the data instead of bringing the data to the code. The type of applications that are most appropriate for mobile agent technology would include at least one of the following features: data collection, searching and filtering, distributed monitoring, information dissemination, negotiating and parallel processing [4]. When the application is computationally intensive or requires the access to distributed sources of data then the parallel execution of mobile agents in different machines of the network seems to be an effective solution. At the same time, the dynamic and asynchronous execution of mobile agents fits very well in changing environments where it is necessary to exploit some notion of adaptive parallelism. 4.2 Implementation Framework The implementation of the algorithm is based on Aglet (IBM). Aglet is probably the most famous mobile agent system. It models the mobile agent to closely follow the applet model of Java, with the following characteristics: object-passing, autonomous execution, local interaction, asynchronous, disconnected operation, parallel execution, etc. Aglets are Java objects that can move from one host to another, their fundamental operations including: creation, cloning, dispatching, retraction, deactivation, activation, disposal, and messaging [6]. It is possible for them to halt execution, dispatch to a remote host, and restart executing again by presenting their credentials and obtaining access to local services and data. Aglet provides a uniform paradigm for distributed object computing [7]. Using Aglet can ease the development of distributed computing system. We are doing research on mobile agent based parallel computing. On the basis of that, an implementation framework of the algorithm is proposed. It is composed of two parts: Console and Monitor. They are both aglets with ability of communication through messaging. Console is responsible for the initialization, decision-making and job dispatching. Monitor is responsible for the load monitoring. Console resides on the central node, waiting for the coming of new jobs and messages from monitor in each node. When a new job comes it will find an appropriate node to execute it according to the algorithm. Meantime it will handle messages from

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monitors: update run queue length and scheduling function. Monitors check the run queue length of each node, and send this information to console if these values change 4.3 Experimental Results The experiments were conducted on a network, consisting of a Sun server and five Sun workstations. The tasks are generated on the server and then scheduled to other machines. Two conditions were considered in the tests: without and with background loads. The background loads are computing agents, which are long time consuming. The tasks are generated with interval of 4 and 6 second respectively, until 100 tasks are generated. For comparing with the dynamic scheduling algorithm (DS), the round robin (RR) scheduling algorithm was used to do the same tests. The experimental results are shown in table 1. Table 1. Comparison of DS algorithm and RR algorithm

4s 6s

Without Background Load RR DS 1041 816 1352 1108

With Background Load RR DS 1287 996 1644 1308

From table 1, it is observed that the performance of DS is better than that of RR. The average speedups for the two conditions are 1.24 and 1.27 respectively. As the tasks are generated and scheduled, the run queue length of some machines may be greater than the bounding factor, their scheduling function is less than zero. DS algorithm will not assign new tasks to these nodes until the scheduling function is positive. While RR algorithm continues scheduling new tasks to them and finally get a bad result.

5 Conclusion In this paper, we introduce a dynamic job scheduling algorithm. The basic principle of this algorithm is trying to utilize the information of each node in a computational grid to allocate the jobs among them. A scheduling function is used to determine the desirability of each node to accept a new job. If a node were already fully utilized, the new job would not be allocated to it again. This algorithm tries to improve the throughput, response time, and fairness of the system. Moreover, a mobile agent based implementation framework is proposed.


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References 1. Markatos E P.: How architecture evolution influences the scheduling discipline used in shared-memory multinodes. Joubert G R. Proceedings of Parco 93. Amsterdam: Elsevier, (1993) 524-528 2. Hui C C, Chanson S T.: Improved strategies for dynamic load balancing. IEEE Concurrency, 3 (1999) 58-67 3. V. Pham, A. Karmouch.: Mobile Software Agents: An Overview. IEEE Communications Magazine, 7 (1998) 26-37 4. B. Venners.: Solve Real Problems with Aglets, a Type of Mobile Agent, JavaWorld Magazine, 5 (1997) 5. Perdikeas M.K., Chatzipapadopoulos F.G., Venieris I.S. An Evaluation Study of Mobile Agent Technology: Standardization, Implementation and Evolution. IEEE International Conference on Multimedia Computing and Systems, 2 (1999) 287 -291 6. Lange Danny B., Oshima Mitsuru.: Mobile Agents with Java: The Aglet API. World Wide Web Journal, 3 (1998) 111-121 7. Lange Danny B., Oshima Mitsuru.: Programming and Deploying Mobile Agents with Java, Addison-Wesley, MA, (1998)

An Integrated Management and Scheduling Scheme for Computational Grid* Ran Zheng and Hai Jin Cluster and Grid Computing Lab Huazhong University of Science and Technology, Wuhan, 430074, China {zhraner, hjin}@hust.edu.cn

Abstract. Computational grids have become attractive and promising platforms for solving large-scale high-performance applications of multi-institutional interest. However, the management of resources and computational tasks is a critical and complex undertaking as they are geographically distributed, heterogeneous in nature, owned by different individuals or organizations with their own policies, different access, and dynamically varying loads and availability. In this paper, we propose an integrated management and scheduling scheme for computational grid. It solves some pivotal and important questions such as resources heterogeneous and information dynamic. It affords transparent support for high-level software and grid applications, enhancing the performance, expansibility and usability of computers, and providing incorporate environment and information service. This scheme has universality for computational grid and makes every grid resource work efficiently.

1 Introduction Computational grids [1][2] are becoming more attractive and promising platform for solving large-scale computing intensive problems. In this environment, various geographically distributed resources are logically coupled together and presented as a single integrated resource. The resource management and scheduling is the key technology of a grid. How to manage the resources efficiently is the pivotal issue, which decides whether grid is available or not. At the same time it is a complex undertaking as the resources are distributed geographically, heterogeneous in nature, owned by different individuals or organizations, and they have dynamically varying loads and availability. Some existing resource managing technologies of the parallel and distributed system cannot fit well for the characteristics of the computational grids mentioned above. This paper presents an integrated resource management and scheduling scheme for computational grid. In section 2, we analyze three resource management models and point out the hierarchical structure is suitable for grid. We put forward an integrated * This paper is supported by National Science Foundation under grant 60125208 and 60273076. M. Li et al. (Eds.): GCC 2003, LNCS 3033, pp. 48–56, 2004. © Springer-Verlag Berlin Heidelberg 2004

An Integrated Management and Scheduling Scheme for Computational Grid

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management and scheduling scheme in section 3, and the prototype is also explained. In section 4, task dispatching and selection algorithms for this architecture are introduced. Section 5 focuses on the performance evaluation of this scheme. Finally, we draw the conclusion and give out future work in section 6.

2 Anatomy of Resource Management Architectures Primarily, there are three different scheduling models: • Centralized management model This can be used for managing single or multiple resources, which suits well for cluster (or batch queuing) systems such as Condor [3], [4], LSF [5], and Codine [6]. There are many advantages: simple structure, convenient maintenance, certifiable consistency or integrality. However, it is hard to achieve in distributed system for the scheduling bottleneck. Therefore it is not suitable for capacious grid. • Decentralized management model In this model resources are partitioned into different virtual domains. In each domain here is a domain scheduler. The model appears high scalable, but remote status is not available so the optimal scheduling is questionable! What’s more, the traffic is heavy and the data are located decentralized, which make against data consistency and scheduling in multi-domains. • Hierarchical management model This model looks like a hybrid (central and decentralized) model, which not only avoids the shortcoming of the two models, but also settles some challenging problems: site autonomy, heterogeneous environment and policy extensibility. It has been adopted in Globus [7][8], Legion [9][10], Ninf [11], and NetSolve [12][13]. Super Scheduler

Computational Grid

Local Scheduler

Virtual Domain

Local Scheduler

Virtual Domain

Local Scheduler

Virtual Domain Da ta Servers（Clusters, supercomputer,etc.)

Workgroup

Workgroup

Other Resources Storage Devices Workgroup (scaner,sensor,etc.)

Fig. 1. Hierarchical Architecture Model

Our grid resource management architecture follows this hierarchical model, shown in Figure 1. It is constructed with a super-scheduler, several local schedulers and published resources, just like a tree. Super-scheduler is root, and local schedulers are non-leaves. All leaves are resources, which are divided into several virtual domains.

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3 Integrated Management and Scheduling Scheme 3.1 Integrated Resource Management and Scheduling Scheme Resource management is highly important in grid, which is similar but more complex than distributed system. It should not only support multi-scheduling, but also suit for the complex surrounding and provide necessary QoS. The integrated management and scheduling scheme is shown in Figure 2. The key components are Grid Resource Scheduler, Grid Information Server and Grid Nodes.

Fig. 2. Integrated Resource Management and Scheduling Structure

Grid Resource Scheduler is a decision-making unit, which incepts user requests, adopts optimal scheduling algorithms and handles seamless managing issues. Furthermore, the scheduler must be able to deal exceptional case. For example, after the fail of one resource, the scheduler can reschedule tasks to other idle resources. Grid nodes include devices and software. The node managers handle all issues from upper scheduler and harmonize the actions of devices and active processes. The dispatcher determines inner scheduling based on upper info or resource statuses that are collected by monitor. Examples of local manager include cluster systems such as MOSIX and queuing systems such as Condor, Codine and LSF. Grid Information Server acts as databases for describing items of interest to the resource management systems, such as resources, jobs, schedulers. 3.2 Grid Scheduler Infrastructure Grid scheduler acts as a mediator between users (application) and grid resources. It is responsible for grid management. The representation of grid scheduler is shown in Figure 3, where there are two hierarchies: decomposing level and scheduling level.


51

Job receiver module incepts user requests and returns results. Task decomposing module decomposes job into several parallel, mutually exclusive or synchronous atomic tasks. The rules are saved in database, which are the foundation of rule-based illation. Resource-task matching module finishes the matching between atomic tasks and resources, identifying the exchangeable and compensatory ability of resources. Scheduling module searches information from information server, and saves or modifies some context. Task scheduling module analyses scheduling strategies with different principles. Rule-based illation module arranges resources for atomic tasks. Scheduler optimizing module can optimize scheduling on-line. Job receiver module takes charge of scheduling generation, creation of atomic tasks, and maintenance of job status. Resource matching module decomposes jobs, searches grid information server, and generates allocating schemes. Scheduling illation module analyses generated reasonable schemes and determines the best. Scheduling module allocates tasks to selected resources according with the result.

Fig. 3. Grid Scheduling Structure

Re-scheduler happens with interruption of outer or inner abnormity. Outer abnormity is caused with the arrival of urgent jobs or cancellation. Inner abnormity is caused by factors such as resource failure. When interruption happens, job scheduler sends signal to task scheduling module, then re-dispatches and reschedules.

4 Grid Scheduling Algorithm 4.1 Dynamic Scheduling Mechanism There are two commonly dynamic methods, named event-based and timed-based. In timed-based method the interval of scheduling is periodical. After a fixed time ∆T, a new period is coming: eliminate all finished tasks and insert new tasks to reschedule.

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Event-based method is different, which inspects the rescheduling status constantly and decides the possibility whether to issue a rescheduling event. The structure of dynamic scheduling is shown in Figure 4. Job has its own priority to be scheduled. A new submitted job springs the scheduling of the selected local scheduler at the same time if its emergency is high enough to exceed the threshold value. Otherwise it is scheduled with other tasks when the scheduling slot comes. The failure of resource or the cancellation of tasks will all lead to reschedule.

Fig. 4. The Structure for Dynamic Scheduling

4.2 Integrated Scheduling Algorithms All tasks in grid can be classified as real-time or best-effort class. All tasks will be space-based dispatched widely and time-based selected in virtual domain. The basic rule is real-time tasks are processed earlier than best-effort tasks. 4.2.1 Grid Task Dispatch Algorithm of Grid Scheduler We propose a mixed scheduling algorithm with the goal of the least number of missed real-time deadlines and load balance of grid resources, called LMLB. The algorithm is invoked at every real-time task or best-effort task arrival. For every real-time task, resource-task matching is done first. If no satisfied resource, add 1 to the scheduling counter whose initial value is 0. If the value is out of the scheduling threshold, it is regarded the task is disable, else put it to renewed window to reschedule next time, then repeat the operation in any case. If some resources are suitable for the request, select one from the matching unit with the goal of the least number of missed deadlines. Compute Tturnover as

T turnover = T deadline − T schedule

(1)

where Tdeadline is the deadline specification, Tschedule is the scheduling time. Estimate the processing time T R on each available resource R i ( 0 < i ≤ n ): i

TRi = RTTi +

TransferSi ze Power + pi × Wait i + BWi Vi

(2)

where RTTi denotes the round-trip time of network, TransferSi ze is transmitted data between resource and user, BW i is the bandwidth, Wait i is the waiting time on R i , pi is a


53

parameter of waiting probability, Power is the logical computational “cost” (in some arbitrary units) for the task, and V i is resource performance (in units per second). Select a suitable resource Ri whose TR is not over and nearest to T final : i ∆T = T

final

∆ T is as small as possible

− TRi ≥ 0

(3)

m

And

Load i =

∑ Power j=0

Power total

j

< Threshold

(4)

load

where Powertotal denotes the logical computation of resource R i , the real-time sum, mation of all tasks’ logical computation “costs” in resource Ri is m Power

∑

j=0

j

Threshold load is the boundary whether resource overloads or not. However, if there is no resource satisfying ∆T ≥ 0, which is shown no suitable resource for the task at this moment, add 1 to its scheduling counter for rescheduling. For every best-effort task, the initial operation is similar with real-time task. Select one with the goal of load balance of grid resources from the matching unit. Estimate current load of resource Loadi on each available resource R i ( 0 , where the meanings are as follows: 1. R is a finite and nonempty set of the non-dedicated and heterogeneous GCRs. 2. C =( Ca ,Cn ,Cs ) represents an application requirement model, satisfying Ca ∩Cn =∅ , Ca ∩Cs =∅ , Cs ∩Cn =∅ . Ca is a finite and nonempty set of application information.

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and Cs are a finite set of GUG network and host minimal application requirement respectively, which provide the basic Qos guarantee. 3. T is a finite and nonempty set of arbitrarily divisible grand-challenge tasks 4. P is a finite and nonempty set of performance models determined by types of T . Cn

T × R× S

5. M = { f | f : 2 × 2 → 2 , S denotes the set of the start time of tasks } is nonempty set of mapping strategies. T

R

6. ω : 2 × 2 → 2 , is a function filtering out the resources that do not meet the GUG minimal job requirements and reduce the resources set for the GDSS. R

7.

τ :2

C

R

× T × P × M → 2 , is a function determining the best fit resource (or set of best resources) to submit a job, which is the core process of the GDSS. R

R

3 The GDSS Design Combing the scheduling model of DCG and the general architecture presented in [8], we begin with a framework of a single GDSS node. The framework as shown in Figure 2 will give a broad overview of the work required to build a generic unit. Di s tri buted Computi ng Gri d Portal

XML doc ument

VRML doc ument XML pars er

Res ul t

Retri ev er

cooperating resources

Tas k s Des c ri pti on

J ob Pri ori ty Queue

Di s patc her

Appl i c ati on Requi rement GMSS

Res ourc e Fi l ter

Av ai l abl e Res ourc es

J ob Sc hedul i ng Dec i s i onModule

GIS Predi c abl e Informati on Col l ec tor

Bes t-fi t Res ourc es

Fig. 2. The framework of the Grid Distributed Scheduling Server

There are three main phases when scheduling on the GDSSs. Phase One is resource filtering, which consists of distributed computing grid portal, XML parser, job priority queue and resource filter. In order to proceed to resource filtering, the users must specify task description and some minimal set of job requirements through Web portal which creates the XML document parsed by our DOM parser. Then, job priority queue is with responsibility for determining the priority of the job. Subsequently, resource filter removes unsuitable resources utilizing the information from GIS. At the end of phase one, the list of potential resources is generated. Phase Two involves mapping tasks and selecting the best resource set. Predicable information collector gathers the detailed information from the GIS. Our system adopts information provider based on Globus and NWS to support dynamic information collection. Job scheduling decision module is the key component of GDSSs,

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which determines the best-fit resource (or the resource set) as a meta request. The efficiency of job scheduling decision module is directly determined by best-fit resource selection algorithm. Our resource selection algorithm based on grid clustering will be explained in the next section. Subsequently, the meta request is sent to the GMSSs and the cooperating resource set is feed back after the GMSSs negotiate a compromise about the contention of different GUGs request the same GCRs at the same time. At the end of phase two, a set of cooperating resource is generated. In Phase Three the job is executed, which includes a file dispatcher and a result retriever. We adopt gridftp, GRAM services based on Globus to implement remote job submission and remote compilation. At last, the result is retrieved and displayed on the Web portal using Virtual Reality Modeling Language (VRML).

4 A Resource Selection Algorithm The resource selection algorithm is at the core of the job scheduling decision module and must have the ability to integrate multi-site computation power. Our Clusteringbased Grid Resource Selection (CGRS) algorithm clusters the set of available resources, generates the candidate schedules for all the subsets in each cluster and evaluates the candidate schedules to select a final schedule. Pseudo-code for our multi-site search procedure is given in Figure 3. The first method called by the procedure is GCRClustering ( ); The method clusters the available GCR into disjoint subsets, such that the network delays within each subset are lower than the network delays between subsets. The clustering algorithm of available GCRs clustering is so important as the basis of CGRS algorithm that we design a sophisticated clustering algorithm based on data mining [10] method, which will be expounded in the following section. Another core method is the MapAndPredict ( ). The method adopts performance model and mapping strategy of some specific applications to predict execution time. Because the predicted execution time directly determines the correctness of the best schedule, performance model and mapping strategy plays an important role. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

BestScheduleTime = Infinit BestGUGCluster = NULL clusters = GCRClustering (AvailableGCRPool) for ( each cluster ) for ( GCRSize = 1 ; GCRSize 0 ; In general, x ≤ 20 when there exists multi-storage system, and m is very large, so there exists ω 1 − ω 2 > 0 under the second one. Totally, the makespan adopting our algorithm decreases considerably in general. Let υ 1 denote the average resource (CPU) utilization rate for generic scheduling algorithm, and υ 2 the average resource utilization rate for our algorithm. We give a period of time t = ω :

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m

υ1 =

ν m

ν1

2

+

m

ν

=

ν

(3) 2

ν1 +ν

, 2

2

m

υ2 =

ν 2 (1 − x %)

m /n

ν1

+ max(

m ( m / n ) * ( n − 1) , ) ν 2 (1 − x %) ν1

(4) .

m ( m / n) * (n − 1) , m Because m ≤ and ( m + m ) ≥ [ m / n + max( , )] ν 2 ν 2 (1 − x %) ν1 ν 2 ν1 ν 2 (1 − x%) ν1 υ 2 ≥ υ 1 . If x is little and m is large, υ 2 will increase considerably. If the dataset of one task/subtask cannot be divided, overlapping computing with data transferring can take place between the task/subtask to being executed and the scheduled task/subtask in a STQ. By analyzing, there exists a similar conclusion: Only if x is little and m is large, the makespan will be reduced and the average resource utilization rate will be increased considerably.

5 Experiments We have developed an engineering computation oriented visual grid prototype system, named VGRID, in which tasks are auto-scheduled in a visual fashion, and it permits a selection of task scheduling core algorithms. In this environment, three pairs of experiments have been designed by using the above scheduling approach. The tasks consist of the iterations of two application examples: Monte Carlo integration in high dimensions, including a small dataset transferring; video conversion application, including a large dataset replication and compression. All nodes are PCs with Intel Pentium 4 processors of 2.0G Hz, memory of 512M, Ethernet 100M, and hard disk 80G/7200rpm. The experiments are described as follows, where two approaches are used. Approach A adopts the FCFS algorithm, whereas Approach B adopts the FCFS algorithm with our scheduling approach. Case 1: One task: video conversion. One node. Case 2: Four tasks: Monte Carlo simulation, video conversion, Monte Carlo simulation, and video conversion in sequence. One node. Case 3: Four tasks: Monte Carlo simulation, video conversion, Monte Carlo simulation, and video conversion in sequence. Three nodes. Experimental results are illustrated in Fig.2 and 3. As shown in these figures, different types of tasks, scheduled task sequences and grid resources have different performance scenarios. In all experiments, all average resource utilization rates increase over 15% by adopting our algorithm. But in Case 1, the new makespan decreases very little and the associated average resource utilization rate increases 18%. This means that overlap of computing and transferring data can-

A Scheduling Approach with Respect to Overlap of Computing

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makespan (m)

not bring benefits, but adds a little workload. Therefore our algorithm isn’t very fit for the tasks of this type as performance decrease percentage x is large to these tasks, it occurs under the conditions of competing grid resources.

1000 800 600

generic algorithm

400

new algorithm

200 0 1

2 case number

3

(%)

utilization rate

Fig. 2. Variation of the makespan

100 80 60 40 20 0

generic algorithm new algorithm

1

2 case number

3

Fig. 3. Variation of the average resource utilization rate

6 Conclusions A scheduling model and an associated algorithm were proposed in the present work. This approach tries its best to reduce task expending time to improve performance by overlapping computing with data transferring. We have theoretically analyzed and instantiated this algorithm with three tests based on a FCFS core algorithm in the VGRID under different conditions. Our results show: Firstly, it is obvious to improve system performance. Secondly, the relation of task execution and its dataset, and the size of data have a significant impact on system performance. Though these results are promising, in interpreting their significance we have to bear in mind that they are based on the simplified grid scenarios. The case, that these dependant subtasks move data for exchange, has not yet been studied in detail.

Acknowledgements. The authors wish to thank the National Natural Science Foundation of China for the National Science Fund for Distinguished Young Scholars under grant Number 60225009. We would like to thank the Center for Engineering

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and Scientific Compu-tation, Zhejiang University, for its computational resources, with which the research project has been carried out.

References 1.

2. 3.

4. 5. 6.

7.

8.

9. 10.

11.

12. 13. 14.

15.

I. Foster, C. Kesselman et al.: The Anatomy of the Grid: Enabling Scalable Virtual Organizations. International Journal of High Performance Computing Applications, 2001, 15 (3): 200-222 J. Subhlok and G. Vondran: Optimal Use of Mixed Task and Data Parallelism for Pipelined Computations. Journal of Parallel and Distributed Computing, 2000, 60: 297-319 O. Beaumont, A. Legrand et al.: Scheduling Strategies for Mixed Data and Task Parallelism on Heterogeneous Clusters and Grids. Proc. of the 11th Euromicro Conference on Parallel, Distributed and Network-Based Processing，2003 S. S. Vadhiyar and J. J. Dongarra: A Metascheduler for the Grid. Proc. of the 11th IEEE International Symposium on High Performance Distributed Computing, 2002 F. Berman et al.: Adaptive Computing on the Grid Using AppLeS. IEEE Transactions on Parallel and Distribted Systems, 2003, 14(4): 369-382 W. Smith, I. Foster, and V. Taylor: Predicting Application Run Times Using Historical Information. Proc. of the IPPS/SPDP Workshop on Job Scheduling Strategies for Parallel Processing, 1998 R. Wolski et al.: The Network Weather Service: A Distributed Resource Performance Forecasting Service for Metacomputing. Future Generation Computing Systems, 1999, (56): 757-768 A. Abraham, R. Buyya et al.: Nature’s Heuristics for Scheduling Jobs on Computational Grids. Proc. of 8th International Conference on Advanced Computing and Communications, Cochin, India, 2000 A. Y. Zomaya et al.: Observations on Using Genetic Algorithms for Dynamic LoadBalancing. IEEE Transactions on Parallel and Distributed Systems, 2001, 9: 899-911. O. Beaumont and L. Carter: Bandwidth-Centric Allocation of Independent Tasks on Heterogeneous Platforms. Proc. of the International Parallel and Distributed Processing Symposium, 2002 A. Dogan and F. Özgüner: Scheduling Independent Tasks with QoS Requirements in Grid Computing with Time-Varying Resource Prices. Proc. of Grid Computing-GRID 2002, 2002, 58-69 H. Casanova, G. Obertelli et al.: The AppLeS Parameter Sweep Template: User-Level Middleware for the Grid. Proc. of Supercomputing 2000, Denver, 2000 D. Thain, J. Bent et al.: Gathering at the Well: Creating Communities for Grid I/O. Proc. of Supercomputing 2000, Denver, 2000 K. Ranganathan and I. Foster: Decoupling Computation and Data Scheduling in Distributed Data-Intensive Applications. Proc. of the 11th International Symposium on High Performance Distributed Computing, 2002 G. R. Nudd et al.: PACE – A Toolset for the Performance Prediction of Parallel and Distributed Systems. Journal of High Performance Computing Applications, 2000, 3: 228251

A Deadline and Budget Constrained Cost-Time Optimization Algorithm for Scheduling Dependent Tasks in Grid Computing 1,3

2,3

2,3

Haolin Feng , Guanghua Song , Yao Zheng , and Jun Xia 1 2

2,3

Chu Kechen Honors College, Zhejiang University, Hangzhou, 310027, P. R. China College of Computer Science, Zhejiang University, Hangzhou, 310027, P.R. China 3 Center for Engineering and Scientific Computation, Zhejiang University, Hangzhou, 310027, P. R. China

Abstract. Computational grid has a promising future in large-scale computing, because it enables the sharing of widely distributed computing resources. Good managements with excellent scheduling algorithms are in great demand to take full advantage of it. Many scheduling algorithms in grid computing are for independent tasks. However, communications are very common in scientific computing programs. In this paper, we will propose an easy-implemented algorithm to schedule the tasks with some communications. Our algorithm is suitable for a large proportion of scientific computing programs, and is based on Binary Integer Programming. It is able to meet the users’ quality of service (QoS) requirements, and to minimize the combination of costs and time consumed by the users’ programs. We will give an example of scheduling a typical scientific computing task to show the power of our algorithm. In our experiment, the grid resource consists of an SGI Onyx 3900 supercomputer, four SGI Octane workstations, four Intel P4-2.0GHz PCs and four Intel P4-1.8GHz PCs.

1 Introduction Computational grids [1] become more and more popular in large-scale computing, because they enable the sharing of computing resources that are distributed all over the world. Those computing resources are distributed widely and owned by many different organizations, and thus, good systems for resource management are essential to take full advantage of grids. Published literature provides us with various management systems with different policies and principles ([2, 3, 4]). The most important part of a good management system is an excellent algorithm for scheduling tasks. It is the management systems that do the jobs of resource discovery[8], selecting machines and scheduling the tasks for the users. Nowadays, numerous scheduling algorithms are available [5, 6, 7]. Most of the scheduling algorithms assume the tasks to be independent. Under this assumption, the existing algorithms can still work with many scientific and engineering computing problems. However, the majority of scientific computing problems and computing in engineering require communications among tasks, e.g. computation in areas of solid meM. Li et al. (Eds.): GCC 2003, LNCS 3033, pp. 113–120, 2004. © Springer-Verlag Berlin Heidelberg 2004

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chanics and fluid dynamics. Without considering the communications among tasks, algorithms are of obvious limitation, and cannot take full advantage of the powerful computational grids. However, it is very challenging to schedule general dependent tasks, and so far, there is no existing satisfying solution, due to causes such as the heterogeneous architectures of different machines in the same grid and the limited bandwidths of network transmission. Thus, for the first step, we try to add some constraints on communications in order to achieve an improved scheduling algorithm. In this paper, we present a model that can schedule both independent tasks and dependent tasks with special communications. In this model, we can reach a balance between the cost and the job completion time for different clients. That means, we provide an optimal solution for an objective function, which consists of the cost and the job completion time. Moreover, both the deadline and the budget set by the clients will be met in the algorithm. We select an array of machines for a batch of tasks, either independent or dependent under a constraint condition, and we show that our solution can be achieved by solving a classical problem -- Binary Integer Programming [11].

2 Problem Modeling Suppose a user has submitted a program consisting of N dependent tasks (denoted as X1, X2, … , XN), and in the grid there are M machines (denoted as S1, S2, … , SM) available at the moment (M >= N). The machines are different in terms of architectures, computing power, prices of CPU time, as well as the distances from the management system, which will result in different speeds of data transfer. Our goal is to assign each task to a specific machine (processor) in order to minimize the total “cost” -- the money and the time for completing all these N tasks. Note that the tasks would sometimes communicate with each other, which will make our problem even more complicated. 2.1 Assumptions 1) For each single task, while it is assigned to a specific processor in the grid, the time spent on it can be known. Many techniques are available to achieve this [9, 10]. 2) Tasks are dependent, and communicate with each other. The communication happens whenever a certain percentage amount of work of every task has been completed for all the tasks. For example, the communication happens when 10% of work of every task has been done, and then, communication happens again when another 6% work of every task has been done, and so on. We need not know the value of percentage before its execution, but we should know how many times of communication are to happen as well as the scale of communications. Remark. Although that is a constraint, most problems in scientific computing satisfy this assumption. For example, computations in computational structural analysis, in computational fluid dynamics, and in DNA sequencing, all satisfy this assumption. Also, a program with independent tasks can be considered as the program with tasks

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that communicate after 100% of work has been completed. Therefore, our algorithm works with both independent and dependent tasks. 3) According to most accounting systems, the charge for a unit of time is proportional to the resource used by the user at that moment. Provided that, for a specific processor, if its resource used is very close to zero in a period of time, the cost it charges is very close to zero. Thus, it is reasonable to assume that when waiting for communication, we do not have any monetary consumption. 2.2 Algorithm Description 2.2.1 Definition of the Object Function Now we have to define a proper function to measure the “cost”. Defining the function as a weighed sum of the money and the job completion time is a good idea [5]. People have different views of value towards money and time; thus, it is necessary to give our clients the right to specify the weights for the money and the time, respectively. For example, a certain user considers one unit of time as valuable as one hundred units of money, then we set wm: wt =100:1, and wm + wt=1. Here wm and wt stand for the weights of money and time, respectively. The users can also set the deadline (denoted as T0) and the maximal cost that can be afforded (denoted as M0). That is why we called it “a deadline and budget constrained cost-time optimization algorithm”. Based on this logic, we define the object function in this way: min Z= wm· (

N i=1

M j=1

Cj · Tij · Rij) + wt · T

(1)

Here, Tij is the time spent on task Xi given that Xi is assigned to prcessor Sj. For each pair of i and j, as we have assumed, Tij is known. Cj is the cost of processor Sj per unit CPU time. If we really assign task Xi to Sj, then the value of Rij will be 1, otherwise 0. T stands for the duration from the beginning of the first task to the end of the last task. 2.2.2 Constrained Condition of the Problem Rij=0 or 1, (i=1,2,…,N; j=1,2,…,M);

(2)

T Attribute* Constraint Rank Attribute -> Expression Constraint -> Profile ( || Profile)* Profile -> Property (&& Property)* Property -> AttributeName ComparisonOperator Expression Rank -> AttributeName Fig. 2. Formalized description of Classad

They can both set limits to the other. Rank embodies the definition of QoS from tasks so that users can define different QoS rules under different circumstances. The most outsanding characteristic of Classad is that it allows computing nodes to define

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their own policies. Those computing nodes will reject any tasks that conflict with policies. 2.2 Pre-sorting Tasks The NCP is a distributed system on which several sub-tasks from a particular task may run at different computing nodes. These subtasks are data-dependent. Directed Acyclic Graph (DAG) [6] can depict their relations.Those sub-tasks are executed in the order depicted in DAG. Subsequent sub-tasks can get the execution chance only when earlier subtasks have finished, so earlier subtasks should get more chances to choose proper computing nodes. In NCP, a DAGP is defined which represents the priorities of subtasks in DAG. An exit node set (ens) is created which contains the lowest level nodes in DAG. For those nodes si not at the lowest level, an immediate successor set (iss) is created which contains the nodes immediately below si and have data-dependent on it. So for each si, DAGP is calculated as follows:

DAGP( si ) = 1

if ( si ∈ ens )

DAGP( si ) = 1 + max ( DAGP( sj )) sj∈iss ( si )

if ( si ∉ ens)

(1) (2)

In fact, the DAGP value of a sub-task is the distance from its position in DAG to the exit. A Waiting Time Priority (WTP) is defined in our neural computation platform. If task T failed in a match, it stayed in the queue and waited its next chance and the WTP value of it shifted left one bit.

WTP ( si, t ) = 1

if (t = 0)

WTP ( si, t ) = WTP( si, t − 1) 0)

(4)

The t in expression 3 and 4 above represents the number of matches has been done on task si. The changing of WTP gets rid of the possibility that a task can not acquire proper resources for its low priority. And the other advantage is that WTP is a complement of Classad. Although Classad endows computing nodes with the power rejecting tasks, it may prevent tasks finding a proper node for ever. So in our platform NCP, when WTP exceeds a predefined number, the task will be placed on the node it chooses. 2.3 Mapping Algorithm As a task and a computing node both own its Classad, a first match is done on the compatibility of the two Classad. A task can only run on a node without Classad conflicts. In Classad, self.attribute represents the attribute of itself while other.attribute represents the attribute of the other. For example, In a task's Classad, other.Memory refers to the Memory attribute of the computing node with which the compatibility

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test will be done; self.Datasize refers to the Datasize attribute of the task itself. Two Classad are compatible if and only if both constraints are true. The priority value of task si is calculated by adding DAGP and WTP of it. Tasks with higher priority can choose node earlier.

Priority ( si ) = DAGP( si) + WTP( si, t )

(5)

If p tasks is waiting for resources and q nodes available, Ti (i 30), what we need to do is only to create M receiving buffers in receiving filter and M + 1 mixing processes in mixing filter. Because they are irrelevant to the sum of users now, resources are greatly saved. The bigger the N is, the more resources is saving comparing with the previous strategy. Table in the right side of Figure 6 is the result of the same experiments on the new AGW. We can see the obvious improvement. CPU load is lower than 15%, and memory usage is much smaller than first AGW.

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Fig. 6. Speaker based mixing

4.3

Padding Format

In section 3.3, we have mentioned additional padding data in the mixed packet for presenter identification. Figure 7 below has shown the detailed design of this special part.

Fig. 7. Padding structure

Following the RTP protocol, the last octet of the padding contains a count of how many padding octets should be ignored. The structure in the figure is used to record the SSRC, encoding codec and volume of the speaking nodes which offered by AGW. If there are speaking nodes detected by AGW, it will insert several these structures behind the output packets’ RTP payload. As said before, there are usually few speakers in one multimedia session, it will not introduce much additional bandwidth consumption to transmit this added padding. CAT can recognize these packets by checking the P parameter in their RTP headers, and then it pick out the padding, and refresh the volume bars on the user list according to the info contains in it. Because RATv4 will simply cut them out, only the mixed stream’s volume will be showed while the others keep mute. So CAT users is easier to grasp the meeting focus than those users of RAT.

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Conclusion and Further Work

In this paper an adaptive Audio Gateway is introduced. This AGW greatly extends scope of ADMIRE system and supplies a dynamical serving selection according to network conditions. With the help of NSPD module, users inside NAT can easily communicate with people on the internet. Also with VAD and such acoustic techniques’ help, it serves clients more exactly and effectively. Its functions fulfill our requests, and have been proved to be efficient in experiments. In the next period, we plan to separate mixing task of one meeting into several parts and distribute them to different AGW based on their load factors. Our goal is to serve tens of meetings with hundreds of clients synchronously.

References 1. International Telecommunication Union, ”Packet based multimedia communication systems”, Recommendation H.323, Geneva, Switzerland, Feb. 1998 2. The Access Grid project,http://www.accessgrid.org 3. J. Rosenberg et al., ”SIP: Session Initiation Protocol”,RFC 3261, Internet Engineering Task Force, June 2002, http://www.ietf.org/rfc/rfc3261.txt 4. Ahmet Uyar, Wenjun Wu, Hasan Bulut, Geoffrey Fox, ”An Integrated Videoconferencing System for Heterogeneous Multimedia Collaboration”, 7th IASTED International Conference on INTERNET AND MULTIMEDIA SYSTEMS AND APPLICATIONS ˜IMSA 2003˜ August 13-15, 2003 Honolulu, Hawaii, USA 5. The ADMIRE project, http://www.nlsde.buaa.edu.cn/admire/en 6. Jin Tian, Chen QingJi, Lu Jian, ”Multimedia Multicast Gateway Infrastructure”, proceedings of the 6th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2002), Orlando, Florida, U.S.A, July 2002. 7. E. Amir, S. McCanne, and Z. Hui, ”An application level video gateway”, ACM Multimedia Conference and Exhibition, Sanfrancisco, CA, November 1995, pp. 255-266. 8. Yueting Zhuang, Yong Rui,Thomas S. Huang,and Sharad Mehrotra, ”Adaptive Key Frame Extraction Using Unsupervised Clustering”, Proceedings of IEEE International Conference on Image Processing, Pages 866-870, October, 1998, Chicago, IL 9. The Internet2 project,http://www.internet2.edu 10. The NSFCNET project,http://www.nsfcnet.net 11. Milena Radenkovic, Chris Greenhalgh, Steve Benford ”A Scaleable and Adaptive Audio Service to Support Large Scale Collaborative Work and Entertainment, International Conference Advances in Infrastructure for e-Business, e-Education, e-Science, and e-Medicine on the Internet L’Aquila, Jan 21 - Jan 27 2002

Kernel Content-Aware QoS for Web Clusters Zeng-Kai Du and Jiu-bin Ju College of Computer Science and Technology, Jilin University, Changchun 130012, China [email protected]

Abstract. While content-aware QoS is increasingly desired for clusterbased Web systems, the high processing overhead it caused can easily make the Web switch a system bottleneck. In this paper, we present a more scalable architecture in which content-aware request distribution and service differentiation can be performed on the back-end server nodes. Based on this scalable architecture, a kernel content-aware QoS mechanism is proposed for Web clusters. Simulation results demonstrate that this kernel content-aware QoS mechanism can provide guaranteed performance for preferred clients even when the server is subjected to a client request rate that is several times greater than the server’s maximum processing rate. . . .

1

Introduction

With the explosive growth of WWW, the Internet is becoming a mature and business-oriented media. The need for QoS-enhanced Web architectures is much stronger. In recent years, numerous QoS mechanisms have been proposed for Web systems, either at kernel or application level. Typically, kernel mechanisms classify client requests based on client IP address and TCP port [1], [2], [3]. On the other hand, user space mechanisms can take into account the content or type of services requested in request classification and further scheduling [4], [5],[6]. In cluster-based Web systems, a front-end Web switch is often employed as the point of contact for the server on the Internet and distributes incoming requests to a number of back-end nodes. As the Web switch has a centralized control on the system status, QoS mechanisms are often deployed on this Web switch. Unfortunately, when these QoS mechanisms are implemented contentaware, the corresponding request distribution can easily make the Web switch a system bottleneck. To achieve more scalable performance, we present a scalable architecture in which all hosts participate in request dispatching1 . On the basis of this architecture, a kernel content-aware QoS mechanism is proposed for Web Clusters. By examining the application layer information in the HTTP header, this mechanism can provide content-aware service differentiation. At the same time, the implementation of this mechanism introduces only a little overhead to the basic CODA architecture. 1

This work was supported by the National Natural Science Foundation of China under Grant No. 60073040.


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The rest of the paper is organized as follows. Section 2 investigates popular Web QoS mechanisms and presents the CODA architecture. In section 3, a kernel content-aware QoS mechanism is proposed for the CODA architecture. Section 4 presents a detailed simulation model for CODA and the parameters of the workload model. We discuss our simulation results in Section 5 and Section 6 presents conclusions.

2

Background

In recent years, a significant amount of research on Quality of Service (QoS) has focused on the network infrastructure. However, network QoS alone is not sufficient to support end-to-end QoS. To avoid the situation where high priority traffic reaching a server is dropped at the server side, the system hosting the Web site should be also enhanced with mechanisms for delivering end-to-end QoS to some classes of users and services. In traditional Web servers, client request are served in a first-come-firstserved way. To support differentiated services, numerous QoS mechanisms have been proposed at application or kernel level aiming to accommodate priority based scheduling schemes. At the kernel level, priority-based scheduling is often located at the system resource that forms the bottleneck in the request path [1], [2], [3]. With these priority-based scheduling, incoming connections can be reordered based on their priorities. As kernel mechanisms can discard incoming connections at early stage of the protocol stack processing, they are quite efficient in protecting Web systems from overload. At application level, Web servers (e.g., Apache) are typically modified in the request accepting procedure to accommodate new priority-based scheduling disciplines [4], [5], [6]. Owing to the easy access to application layer information, user space mechanisms are often content-aware. However, when a request has to be discarded eventually, it has consumed a lot of system resource. So user space mechanisms are always expensive. In cluster-based Web systems, QoS mechanisms are often deployed on the front-end Web switch. To provide differentiated services, the main task is how to assign the server resources to different service classes. In commercial contentaware Web switches, server nodes are statically partitioned into multiple subsets [7], [8]. Client requests of different classes will be assigned to these subsets for service differentiation. Dynamic server partition algorithms have also been proposed in recent literatures [9], [10], [11]. Unfortunately, when content-aware QoS mechanisms are implemented on these Web switches, the corresponding request distribution can easily make them a system bottleneck. To overcome this drawback, we have proposed a completely distributed architecture (CODA for short), as showed in Fig. 1. In this architecture, we employed both layer-4 and layer-7 Web switching techniques. The typical scenario of CODA is as followed: (1) a client sends a connect request to the front-end (layer-4) Web switch, (2) the Web switch selects a server node (called initial server node) with some simple dispatching algorithm, (3) the client connects to

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the initial server node and sends it a request, (4) the initial server node obtains the content of this request, selects a destination server node on the basis of the content from the request and hands this connection off to the destination server node [12], (5) the server at the destination server node sends replies directly to the client.

Fig. 1. Completely Distributed Architecture

From the scenario of CODA, we can see that there is no performance bottleneck in this architecture. Increasing the size of the cluster should result in a proportional improvement in performance. At the same time, all the server nodes in CODA are the same. So the cluster cannot be wholly disabled by the failure of a single node - as is possible under centralized approaches. However, in order to deploy this architecture, some decentralized dispatching algorithm must be implemented on the back-end server nodes. Various issues about designing these algorithms have been addressed in [13].

3

Kernel Content-Aware QoS Mechanism for CODA

From the scenario of CODA presented in Section 2, we can see that the client request has been parsed in the kernel to enable content-aware request distribution. Based on this information, we have designed an adaptive architecture for enhancing CODA with content-aware Quality of Service, as shown in Fig. 2. When a connection is established between a client and the server, a HTTP request from the client will be sent in one or more TCP packets. The Request parser is responsible for parsing the HTTP request from these packets. Then the Time Stamper makes a start timestamp on it. Based on the content from the client request, the Content-aware dispatcher performs request distribution. If this client request is to be served locally, a specific priority will be assigned to this request by the Request classifier according to the application layer information in the HTTP header, such as the URL name or type and other application-specific information available in cookies. Then the client request will be placed into a Class-based accept queue. By using a separate queue for each service class, this Class-based accept queue may support multiple service classes. If this client request is to be served by another server node, the Connection Handoffer on both server nodes will move the established connection to the des-

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Fig. 2. Adaptive QoS architecture for CODA

tination node. Then the client request will be classified by the Request classifier and be placed into according accept queue. As soon as the Web server is ready to receive client requests, the Prioritybase scheduler is responsible for selecting the next request to be served. Many scheduling policies can be used in this scheduler. In section 5, we will use a work-conserving weighted fair queuing accept queue (WFQAQ) scheduler. When the response of the request is sent to the client, the Time Stamper will make an end timestamp on this request. From the start and the end timestamp, the QoS verifier calculates out the delay of this request on the server and compares it with a given SLA. If the SLA is invalidated, it will invoke the Resource adaptor to adjust the server resources assigned to different service classes. Form the scenario above we can see that our QoS mechanism is content-aware and implemented at the kernel level. By examining the application layer information in the HTTP header, this mechanism can provide content-aware service differentiation. At the same time, the implementation of this mechanism introduces only a little overhead to the basic CODA architecture. As this mechanism is wholly implemented in the kernel, the context switch to user space is avoided. So we expect that it work more efficiently than user space mechanisms.

4

Simulation Model

In this section, we describe a detailed simulation model of our completely distributed architecture. With this model, we will show how the kernel QoS mechanism provides guaranteed performance for preferred clients even during high workloads. 4.1

System Model

Our Web cluster consists of multiple back-end server nodes and a dedicated layer4 front-end switch. The Web switch and server nodes are interconnected with


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a local fast Ethernet with 100 Mbps bandwidth. The Web cluster is connected to the Internet through one or more large bandwidth links that do not use the same Web switch connection to the Internet. Being the focus of this paper on server QoS, we did not model the details of the external network that connects the clients to the Web cluster. Due to the efficiency of Layer-4 routing mechanism, we model the front-end switch as a CSIM process [14] that distributes client requests without delay. On the other hand, each server in the cluster is modeled as a separate component with its CPU, main memory, locally attached disk, and network interface. In the simulation, we use system parameters adopted in [12]. Connection establishment and teardown costs are set at 0.145ms of CPU time each, while transmission from memory cache incurs 0.04ms per 512 bytes. If disk access is required, reading a file from disk has a latency of 28 ms (2 seeks + rotational latency). The disk transfer time is 0.410ms per 4 KBytes (resulting in approximately 10 MBytes/set peak transfer rate). For files larger than 44 KBytes, an additional 14 ms (seek plus rotational latency) is charged for every 44 Kbytes of file length in excess of 44 KBytes.The Web server software is modeled as an Apache-like server, where an HTTP daemon waits for requests of client connections. Each client is a CSIM process that, after activation, attempts to setup a connection to the Web cluster. The front-end layer-4 switch selects a server node (called initial server node) with some simple dispatching algorithm. When the initial server node obtains the content of the request, a destination server node will be selected for this request based on the request content. If this client request is to be served locally, the handoff overhead will be ignored due to the simplicity of local TCP handoff. If this client request is to be served by another server node, an extra handoff overhead of 0.3ms will be cost on the initial server node. When the Web server picks a request out of priority-based accept queue, it dedicates a new HTTP process for that connection. 4.2

Workload Model

Special attention has been devoted to the workload model that incorporates all most recent results on the characteristics of real Web load. The high variability and self-similar nature of Web access load is modeled through heavy tail distributions such as Pareto, lognormal and Weibull distributions. Random variables generated by these distributions can assume extremely large values with non-negligible probability. In our experiment, the client arrivals are modeled through a lognormal distribution with a standard deviation set as 25% of its mean parameter. Two types of client requests, static requests and dynamic requests, are considered in our experiment. Their respective percentages in the workload mix are obtained from the Standard Performance Evaluation Corporation. The static workload consists of files in four classes. The frequency of distribution is shown in the Table 1. A dynamic request includes all overheads of a static request and overheads due to computation to generate the dynamic objects. In [15], the dynamic requests are divided into three classes: light, middle-intensive and intensive re-

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Z.-K. Du and J.-b. Ju Table 1. Relative hits per file within each class Workload Class File size Class 0 less than 1K Class 1 less than 10K Class 2 less than 100K Class 3 less than 1000K

Target Mix 35% 50% 14% 1%

quests. Their service times are modeled through exponential distribution with mean equal to 16, 46 and 150 ms, respectively. According to the logfile traces from real e-commerce sites, their percentages are set as 10%, 85%, and 5%. To balance out different types of workloads among the server nodes, we use a distributed CAP algorithm in our simulations.

5

Performance Analysis

As described in Section 3, our QoS mechanism can support multiple service classes with different SLAs. Without loss of generality, we consider two classes of requests denoted as high and low classes in our simulation experiments. Our main goal is to show how the performance of high-class requests can be guaranteed in different architectures. In this section, we consider two architectures: 1. CODA without QoS mechanism. In this architecture, the high-class and low-class requests are treated in the same manner. 2. CODA enhanced with our kernel content-aware QoS mechanism. In this architecture, we use a WFQAQ scheduler in selecting the next request to be served. The SLA in terms of performance is typically measured as the K-percentile of the page delay that must be less than Y seconds. Although the clients may accept 7-8 seconds of response time (including the time for the address lookup phase and network transmission delay), typical measures are 90- or 95-percentile of the requests that must have a delay at the server less than 2-4 seconds. In our simulations, we use 95-percentile of page delay of high-class requests as the main metric to analyze the performance of the two different architectures. Figure 3 shows the 95-percentile of page delay of high-priority requests for the two architectures we compared. As the request arrival rate increases, the page delay of high-priority requests is bound to increase. In CODA without QoS mechanism, all client requests are treated in the same manner. When the rate of incoming requests is below the capacity of the Web cluster, most requests can be processed without introducing large delays. However, when the request arrival rate increases to 500 R/S (requests per second), bare CODA can no longer fulfill the specified SLA. So we consider 500 R/S as the maximum workload that can be handled by this cluster. In CODA enhanced with our kernel content-aware QoS mechanism, the WFQAQ scheduler allows a weight to be assigned to each class, the rate of requests accepted from a class is proportional to its weight. Thus, the weight


309

Fig. 3. 95-percentile of page delay of high-priority requests

setting of a class allows us to control its delay in the accept queue. When the Web cluster is overloaded, high-priority requests may get preferential treatment while the low-priority requests receive degraded or no service. As a result, the given SLA can still be guaranteed at about 2000 R/S in our QoS enhanced CODA. In our simulations, the percentages of high and low class are set as 20% and 80%. When the request arrival rate is at 2000 R/S , the arrival rate of highpriority requests is about 400 R/S . So at this point, about 80% (400/500) of the cluster’s resource is used in processing the high-priority requests. At the same time, most low-priority requests are held in the accept queue and discarded eventually. The left 20 percent of cluster capacity must be spent on the interaction with these eventually discarded requests, including the connection establishment, request parsing and connection teardown. When the rate of incoming requests is above 2000 R/S , more cluster performance will be lost in processing these eventually discarded requests. In the extreme case, the overload condition will lead to server livelock, where the server is busily handling TCP connections in the kernel and is not doing any useful work. At this time, more efficient QoS mechanisms (e.g., TCP SYN policing) are needed to protect Web systems from overload. In our architecture, these mechanisms can be employed on the front-end switch as a complementary.

6

Conclusions

In this paper, we present a more scalable architecture in which content-aware request distribution and service differentiation can be performed on the back-end server nodes. Based on this scalable architecture, a kernel content-aware QoS mechanism is designed for Web clusters. Unlike existing content-blind kernel mechanisms, this mechanism can provide content-aware service differentiation by examining the application layer information in the HTTP header.In addition,

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it only imposes little overhead to the basic CODA architecture. We demonstrate that this kernel content-aware QoS mechanism can provide guaranteed performance for preferred clients even when the server is subjected to a client request rate that is several times greater than the server’s maximum processing rate.

References 1. Thiemo Voigt, Renu Tewari, Douglas Freimuth and Ashish Mehra. Kernel Mechanisms for Service Differentiation in Overloaded Web Servers. 2001 Usenix Annual Technical Conference, Boston, MA, USA, June 2001 2. T. Abdelzaher, K. G. Shin, and N. Bhatti. Performance Guarantees for Web Server End-Systems: A Control-Theoretical Approach. IEEE Transactions on Parallel and Distributed Systems, 13(1), January 2002. 3. P. Pradhan, R. Tewari, S. Sahu, A. Chandra, and P. Shenoy. An Observationbased Approach Towards Self-Managing Web Servers. In Proceedings of the Tenth International Workshop on Quality of Service (IWQoS 2002), May 2002. 4. N. Bhatti and R. Friedrich. Web server support for tiered services. IEEE Network, 13(5), September 1999. 5. R. Pandey and R. Barnes, J. F. Olsson. Supporting quality of service in HTTP servers. In Proc. ACM Symp. on Principles of Distributed Computing, Puerto Vallarta, Mexico, June 1998. 6. N. Vasiliou and H. L. Lutfiyya. Providing a differentiated quality of service in a World Wide Web server. In Proc. Performance and Architecture of Web Servers Workshop, Santa Clara, CA, June 2000. 7. ”Resonate Products–Central Dispatch”, http://www.resonate.com. 8. F5 Networks, http://www.f5labs.com/. 9. V. Cardellini, E. Casalicchio, M. Colajanni, and M. Mambelli. Web switch support for differentiated services. ACM Performance Evaluation Review, 29, 2001. 10. H. Zhu, H. Tang, and T. Yang. Demand-driven service differentiation in clusterbased network servers. In Proc. of IEEE Infocom 2001, Anchorage, Alaska, Apr. 2001. 11. Valeria Cardellini, Emiliano Casalicchio, Michele Colajanni, and Marco Mambelli. Enhancing a Web-server Cluster with Quality of Service Mechanisms. In Proceedings of the IEEE International Performance, Computing, and Communications Conference, Phoenix, AZ USA , April 2002. 12. ”Locality-Aware Request Distribution in Cluster-based Network Servers”, Vivek S. Pai, Mohit Aron, Gaurav Banga, Michael Svendsen, Peter Druschel, Willy Zwaenepoel and Erich Nahum. In Proceedings of the 8th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS-VIII) , San Jose, CA, October 1998. 13. Du Zeng-Kai and Ju Jiu-Bin. Distributed Content-aware Request Distribution in Cluster-based Web Servers. In Proceedings of the Fourth International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT’03), August 27-29, 2003, Chengdu, China. 14. Mesquite Software, Inc., CSIM18 user guide, http://www.mesquite.com. 15. V. Cardellini, E. Casalicchio, M. Colajanni, A performance study of distributed architectures for the quality of Web services. In Proc. of Hawaii Int’l Conf. on System Sciences (HICSS-34), Maui, Hawaii, pp. 3551-3560, Jan. 2001. IEEE Computer Society.

A Collaborative Multimedia Authoring System Mee Young Sung and Do Hyung Lee Department of Computer Science & Engineering, University of Incheon, 177 Dowhadong, Namgu, 402-749 Incheon, South Korea {mysung, oldkill}@incheon.ac.kr

Abstract. The existing authoring tools usually provide an authoring environment where the spatial information and temporal information are edited independently in two different Interfaces, which can inconvenience the users. We created a collaborative authoring system for multimedia presentation, which overcomes this inconvenience. The 3D spatio-temporal editor of our system allows users in different places to author together a multimedia presentation simultaneously in a single unified spatio-temporal space. Conceptually, every temporal relationship can be described using one of seven relations (‘before’, ‘meets’, ‘overlaps’, ‘during’, ‘starts’, ‘finishes’, and ‘equals’). This conceptual representation provides an efficient means for designing an overview of a multimedia presentation. Our authoring system internally represents the edited multimedia presentation using a TRN. A TRN is composed of media objects and a set of temporal relationships among objects, and it corresponds exactly to the conceptual temporal structure of the multimedia presentation. The TRN editor of our system presents graphically the internal TRN and provides users with an intuitive mechanism for representing the conceptual flow of a presentation. The internal TRN and the graphical TRN can be generated automatically from the 3D graphical representation specified by the author of the presentation. The results obtained through some our experiments explain that our system is more advantageous than traditional multimedia authoring systems in terms of authoring time and ease of interaction.

1 Introduction A multimedia authoring system must provide an environment where the temporal relationships and spatial relationships among objects can be edited simultaneously [1, 2, 3, 4]. An interactive multimedia authoring system also needs to support user interactions. Some media (such as video, sound, and animation) require users to specify temporal characteristics, and other media (such as video, animation, images, and text) require users to specify the spatial relationship between objects. The key to authoring a presentation lies in the composition of spatial relationships and temporal relationships between objects. Existing authoring tools usually provide an authoring environment where the spatial information and temporal information are edited independently in two different 2D GUIs (Graphical User Interfaces), which can inconvenience the users. Concerning the temporal editing interface, the traditional scaled timeline M. Li et al. (Eds.): GCC 2003, LNCS 3033, pp. 311–318, 2004. © Springer-Verlag Berlin Heidelberg 2004

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approach allows users to directly view and control the structure of the content; however, the representation is fixed, and the operations are manual. The goal of this study is to develop an easy and efficient multimedia authoring environment where users can create a multimedia presentation in a simple and intuitive manner. Toward this goal, we created a 3D spatio-temporal space which integrates the 2D spatial editing environment and the 1D temporal editing environment into a single, unified editing environment. In addition, we provide users with the capability to edit temporal relationships between media objects at the conceptual level: for example, presenting object A before B, presenting object A during B, etc. In this paper, we propose a collaborative authoring system that is efficient through providing various editing facilities. Figure 1 illustrates the overall structure of our system. We will briefly present the concept of TRN in the following section. In section 3, our main editors; the 3D spatio-temporal editor and the TRN (Temporal Relation Network) editor will be described. We will discuss the collaboration scheme in section 4. Then we will examine some experiments on our system and a comparison of it in section 5. Finally, the last section will provide conclusions. E d it in g S y s t e m 3 D S p a t io - T e m p o ra l E d it o r

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2 TRN (Temporal Relation Network) Our system’s internal representation of presentation is based on Allen’s temporal intervals [5]. Conceptually, every temporal relationship can be described using one of seven relations (‘before’, ‘meets’, ‘overlaps’, ‘during’, ‘starts’, ‘finishes’, and ‘equals’). We proposed to use TRN (Temporal Relation Network) for representing these temporal relationships of multimedia presentations [6]. TRN representations of these seven temporal relations are summarized in Figure 2. The TRN representations in Figure 2 correspond exactly with the internal representation of each corresponding temporal relationship. TRN is a directed and weighted graph. Note that all five parallel relations (such as overlaps, during, starts, finishes, and equals) can be generalized

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as the ‘equal’ relation by adding some dummy delay objects (represented as small black squares) as shown in Figure 2. This conceptual representation provides an efficient means for designing multimedia presentations by sketching the presentation overview at the conceptual level.

Fig. 2. Representation of Temporal Relations

3 Editors Figure 3 (a) illustrates a perspective view of a multimedia presentation in our system. This presentation consists of five media objects, an audio clip, a video clip, two images, and a text object. Authors can create media objects, place objects at the desired positions, and enlarge or shorten the temporal length of objects by dragging and dropping. Authors can change the perspective from which the objects are viewed in 3D space using the arrow keys. Also, authors can quickly change to these default views by selecting a corresponding icon. Details of our 3D spatio-temporal editor are described in the references [6]. Our authoring system is based on SMIL (Synchronized Multimedia Integration Language). It means that our system generates the SMIL codes. A structural view of SMIL tags of the presentation corresponds exactly to the DOM (Document Object Model) structure of the presentation. A view of a SMIL

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object’s attributes are presented on the left of the screen shots in Figure 3 and Figure 4. The bottom right panel of Figure 3 (a) illustrates an example graphical representation of TRN that is created as a user authors the presentation. The internal TRN and the graphical TRN can be generated automatically from the 3D graphical representation specified by the author of the presentation. The algorithm for this automatic conversion to TRN is discussed in the reference [7]. After the authoring is finished, a DOM structure associated with the presentation can be generated from the graphical representation. Our system generates SMIL codes through the interaction between the graphical representation and the DOM structure. Figure 3 (b) presents the spatial projection view of the example in Figure 3 (a). The traditional timeline view of the example is also presented at the bottom of Figure 3 (b). Figure 4 (a) demonstrates the temporal projection view of the example in Figure 3 as well as the textual view of it. Figure 4 (b) shows a screenshot of playback using our built-in player. The author can choose to view between the TRN view, the timeline view, or the textual view using the tabs at the bottom of the panel in the figure.

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Fig. 3. An Example of 3D Representation of a Multimedia Presentation: (a) Perspective View, (b) Spatial View

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4 Collaboration Structure Our authoring system allows a group of users working at different machines to work on the same multimedia presentation and to communicate in real time. In any collaborative computing environment, multiple users or processes can access a shared object concurrently. In this situation, an inconsistency of shared data might occur therefore a concurrency control is required. We implemented some ideas for efficient concurrency control in our system. They are mainly based on user awareness, multiple versions, and access permissions of shared objects. Details of our concurrency control mechanism are described in the reference [7]. The collaboration manager of our system takes charge of the communications of all events generated by users. Each authoring system at different places can be a server as well as a client of a collaboration group at the same time. A server generates itself as the first client of the collaboration group. Any client can connect to the server using TCP (Transmission Control Protocol) and generates packets corresponding to the content that is created as users edit the presentation. It also receives packets from the server, analyses the packets, and invokes appropriate events or modules. Once a client connects to a server, the server updates the list of groups and initializes the new client by sending a group of objects that have been authored up until that time to the new client. After then, the server multicasts any messages passed to it and the client processes and visualizes any received messages. This mechanism is a variation of a clientserver mechanism which can provide better network performance and better portability of the system. C

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5 System Analysis For the purpose of validating our system, we performed some experiments for confirming the usability of our system. We also compared the functionalities of our system to the existing SMIL editors.

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5.1 Usability Analysis We undertook an experiment that compares our system with an existing commercial system: the TagFree SMIL editor. The TagFree SMIL Editor provides two authoring interfaces: one is a 2D temporal authoring interface (a traditional scaled timeline interface) and the other is a 2D spatial authoring interface. However, our system provides only one authoring interface: that is a 3D spatio-temporal authoring interface as shown in Figure 3 and Figure 4. The experiment was conducted with 10 university students who are good at computing. First, we explained the usage of two editors to the students. Then let the students manipulate both editors. And then, we proposed the students to author multimedia content comprised of several media objects. We let five students use the 3D interface (our system) first, then the 2D interface (TagFree SMIL 2000 Editor) second. We let another five students use the 2D interface first, then the 3D interface second. We gave the students 6 different scenarios of presentations whose number of objects increases by 2 (such as 1, 3, 5, 7, 9, and 11) and let them author those presentations. This authoring experiment was performed 2 times with the same students by changing the sequence to use the 2D interface and 3D interface. Table1 presents the average editing time of this experiment. The analysis of the results of this experiment is illustrated in Table 2. Table 1. Editing Time for 2D and 3D Interface 3D Interface 6 min 06 sec 5 min 35 sec

3D Interface First 2D Interface First

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Table 2. Average Authoring Time Proportional to the Complexity (number) of Media Objects 600

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As shown in Table 2, we found that the 3D authoring interface allows users to author faster than the two 2D authoring interfaces. We also found that the difference between the average authoring time for 2D and that of 3D increases as the complexity of media objects increases. There are three reasons why 2D authoring takes more time. The first reason for latency comes from the time consuming adjustment of media objects on the scaled timeline for representing the temporal relationships as ‘parallel’ or ‘sequential’. The second reason originates from the change of the authoring environment from the temporal interface to the spatial interface, or vice-versa. The last reason is caused by the separate editing of layout regions in the spatial interface


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followed by the manual linkages of the layout objects to the objects in the temporal interface. It is inconvenient that the temporal characteristics and the spatial characteristics of an object cannot be recognized at a glance in a 2D authoring environment. In comparison, a 3D authoring interface allows users to recognize the spatio-temporal characteristics at a glance. Moreover, it is obsolete to link the temporal objects to the spatial objects in the 3D authoring environment. The students who participated in this experiment concluded that our 3D authoring interface is an intuitive and faster authoring tool. Generally, our system was favored above the others. However, the students highlighted one inconvenience of our system. That is the difficulty for editing the overlapped objects in 3D, since the grasp of the sense of distance and pointing out the appropriate 3D distance with the mouse in the 3D perspective view is not always evident. Table 3. Comparison of SMIL Editors

5.2 Comparison of SMIL Editors Table 3 summarizes the comparison of commercial SMIL Editors and our authoring system. The commercial products are the SMIL Composer (Sausage Software), the GriNS (Oratrix), the SMIL Editor ver.1.0 (Rikei, Jpan), the TagFree 2000 SMIL Editor (Dasan Technology, Republic of Korea). As shown in Table 3, our system provides several advantages. They are the provision of diverse editing interfaces, allowing users to author in a 3D spatio-temporal environment, the possibility of logical and conceptual editing using the Temporal Relation Network editor, offering realtime feedback of every edit, providing the intuitiveness and efficiency necessary for

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authoring multimedia presentations, providing the collaborative authoring functionality, etc.

6 Conclusions We developed a collaborative multimedia authoring system which is composed of several editors such as a 3D spatio-temporal editor, a TRN (Temporal Relation Network) editor, a timeline editor, a tag editor, an attribute editor, and a text editor. The 3D spatio-temporal editor of our system allows users in different places to author multimedia presentations in unified spatio-temporal space while freely traversing the spatial domain and the temporal domain without changing the context of authoring. Our authoring system automatically converts the authored multimedia presentation to a Temporal Relation Network (TRN) for its internal representation. A TRN corresponds exactly to the conceptual temporal structure of the multimedia presentation. The internal TRN is visualized in the TRN editor. We performed some experiments to validate the usability of our authoring system. The experiments lead us to conclude that the 3D authoring interface allows users to author faster than the two 2D authoring interfaces, and the difference between the average authoring time for 2D and that of 3D increases as the complexity of media objects increases. Acknowledgement. This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Multimedia Research Center at the University of Incheon.

References 1. 2.

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5. 6.

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M.Y. Kim, J. Song, "Multimedia Documents with Elastic Time," Proceedings of ACM Multimedia '95, November 5-9, 1995, San Francisco, California, USA, pp143-154, 1995. J. Song, G. Ramalingam, R. Miller, B. K. Yi, "Interactive authoring of multimedia documents in a constraint-based authoring system," Multimedia Systems, Vol. 7, pp424-437, Springer-Verlag, 1999. M. Vazirgiannis, I. Kostalas, T. Sellis, "Specifying and Authoring Multimedia Scenarios," IEEE Multimedia, Vol. 6, No. 3, pp24-37, July-September 1999. T.D.C. Little, A. Ghafoor, "Spatio-Temporal Composition of Distributed Multimedia Objects for Value-Added Networks," IEEE Computer, Vol. 24, No. 10, pp42-50, October 1991. J.F. Allen, "Maintaining Knowledge about Temporal Intervals," Communications of the ACM, Vol. 26, No. 11, pp832-843, November 1983. M.Y. Sung, S.J. Rho, J.H. Jang, "A SMIL-based Multimedia Presentation Authoring System and Some Remarks on Future Extension of SMIL", Proceedings of Packet Video 2002, Pittsburgh, Pennsylvania, USA, 11 pages, April 24-26, 2002, http://www.pv02.org M.Y. Sung, D.H. Lee, S.J. Rho, S. Y. Rhee "Authoring Together in a 3D Spatio-Temporal Space," Proceedings of ACM Multimedia 2002 Workshop ITP (International Conference on Immersive Telepresence), December 6, 2002, Juan-les-Pins, France, 4 pages, 2002.

Research of Satisfying Atomic and Anonymous Electronic Commerce Protocol Jie Tang, Juan-Zi Li, Ke-Hong Wang, and Yue-Ru Cai Knowledge Engineering Group, Department of Computer, Tsinghua University, P.R.China,100084 [email protected], [email protected]

Abstract. Atomicity and anonymity are two important attributes for electronic commerce, especially in payment systems. Atomicity guarantees justice and coincidence for each participant. However traditional atomicity either bias to SELLER or CUSTOMER. Anonymity is also intractable, unanonymous or anonymity both leads to dissatisfying ending. Therefore, it is important to design a system in which satisfying atomicity and revocable anonymity are both enabled. In this paper, based on AFAP(Atomic and Fair Anonymous Protocol), we propose an approach to realize satisfying atomicity. In this method, not only SELLER’s satisfying atomicity but also CUSTOMER’s satisfying atomicity is supported. At the same time, based on Brands’ fair signature model, this method satisfies both anonymity and owner-trace or money-trace.

1 Introduction Recent years, e-commerce (electronic commerce) is one of the most important and exciting area of research and development in information technology. Many ecommerce protocols corresponding to traditional credit card payment, cheque payment and cash payment have been put forward, such as, SET[1], NETBILL[2], DigiCash[3], etc. However, application of e-commerce doesn’t grow up as expect, the main reasons can be classed into two aspects. (1) Lack of fair transaction. A fair transaction is a transaction satisfying atomicity, which means that both sides agree with the goods and money before the transaction, and satisfy with the goods (or money) after the transaction. Otherwise transaction terminates with reconversion to both sides. Unfortunately, most existing e-transaction protocols don’t provide atomicity. None of them supports satisfying atomicity. (2) Lack of privacy protection. Most of the current systems can’t provide protection to users’ privacy. In these systems, all clients’ activities on the web are logged involuntary which lead to potential possibility of misusing the clients’ privacy. Another is anonymous system [3], in which client could execute payment anonymously, but new problems emerge, e.g. cheating, money laundering, etc. Therefore, these two categories both have some defects. We proposed an electronic transaction protocol to realize both atomicity and fair anonymity in [4]. In this paper, we further the research and propose Satisfying Atomicity and Fair Anonymous Protocol to realize both satisfying atomicity and fair anonymity. This paper is structured as follow. Chapter two introduces related work. M. Li et al. (Eds.): GCC 2003, LNCS 3033, pp. 319–326, 2004. © Springer-Verlag Berlin Heidelberg 2004

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Chapter three reviews AFAP. Chapter four presents a new electronic transaction protocol (SAFAP). Chapter five analyzes the satisfying atomicity and fair anonymity of the protocol. Finally, we conclude the paper with a discussion.

2 Related Work 2.1 Satisfying Atomicity Tygar brought forward that atomicity should be guaranteed in e-transaction [5,6]. He divided atomicity into three levels: Money Atomicity, Goods Atomicity, Certified Delivery. These three definitions can refer to [4] and [13]. Jean Camp presented a protocol realizing atomicity and anonymity [7]. But in it, fair anonymity is unavailable. Additional, goods are limited to electronic ones. Based on the atomic classify by Tygar, we propose an extended atomicity classification: satisfying atomicity, namely strong fair transaction. In this scene, atomicity can be fallen into two classes: satisfied atomicity and unsatisfied atomicity. Satisfied transaction: All participants agree with the transaction before the submission of the transaction and satisfy the ending after submission. Obviously, satisfied transaction is a subset of transaction. On the other side, satisfying transaction has to be transaction of successful submission. Transaction fulfilling goods atomicity does not have to be satisfying one, which might due to the shopper's dissatisfaction with payment or customer’s dissatisfaction with goods. Unsatisfied transaction: the submitted transaction is not a satisfied transaction. Submission of satisfied transaction is named satisfied submission. In e-transaction, customer always couldn’t validate the goods supply before she receives goods. At the same time seller couldn’t validate the payment before the payment really take place. Therefore, when payment and goods supply are both assumed correctly, the transaction is called satisfying transaction. 2.2 Fair Anonymity David Chaum put forth the concept of the anonymity in electronic commerce, and he realized the privacy in his Digicash[3]. But absolutely anonymity provides feasibility to criminal by untraceable e-crime, such as corruption, unlawful purchase, etc. In 1992, van Solms and Naccache[8] discovered a serious attack on Chaum’s payment system. Therefore, Chaum and Bronds proposed some protocols to implement controllable anonymity[9,10]. Afterward, Stadler brought forward fair anonymity[11]. Some other methods are proposed in the later years[12]. With fair anonymity, the anonymity of one participator could be revoked by the authorized organization when illegal transactions are found. Unfortunately, in most existing systems, certificates are usually used to authenticate merchants and customers, and the credit card is employed as payment method, consequently, the transaction detail is opened to the bank.

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3 Atomic and Fair Anonymous Electronic Transaction Protocol We proposed a protocol, in which atomicity and fair anonymity are both satisfied. We define the protocol as AFAP(Atomic and Fair Anonymous Protocol). The system model is a 5-tuple

AFAPM := (TTP, BANK, POSTER, SELLER, CUSTOMER)

Where SELLER is electronic merchant; CUSTOMER is an online buyer; TTP is third trusted partner; BANK is the bank network; POSTER is a delivery system. AFAP comprises five sub-protocols: TTP Setup, BANK Initialization, Registration, Payment and Trace. 3.1 TTP Setup and BANK Initial Sub-protocol Based on the PKI trusted tree, TTP can be authenticated level by level, which is similar to the CA authentication. TTP selects systemic parameters G q , g , g1 , g 2 , q, and chooses private key publishes

xT ∈ Z q randomly, then computes yT = g 0xT , finally

G q , g , g1 , g 2 , q, yT in signature certificate.

Two main steps are included in BANK Initial sub-protocol, viz. obtainment of the certificate and installation of local basic database. BANK selects private key

x B ∈ Z q randomly, and computes y B = g xB . Then TTP generates certificate for BANK. At last, BANK builds up account database and deposit audit database. 3.2 CUSTOMER Register Sub-protocol CUSTOMER should register at TTP to get private signature certificate firstly, and then she apply for an account number at BANK. To get private certificate is similar to the process when BANK applies for the certificate. To apply for the account number at BANK, CUSTOMER selects

u1 ∈ G q randomly, and computes I = g 1u1 where

Ig 2 ≠ 1 , at the same time CUSTOMER selects a random binary string m to compute the knowledge signature DPODL(m, g 1 , I ) . BANK verifies DPODL(m, g 1 , I ) and saves I as the account number of CUSTOMER, where I is global unique in AFAP. Then BANK computes

z = ( I g 2 ) x , sends z back to CUSTOMER.

3.3 Payment Sub-protocol Payment sub-protocol is the most important sub-protocol. Detail of the sub-protocol can refer to [4] and [13]. The process of the payment sub-protocol in AFAP is elaborated as Fig.2.

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(7)Trans_guarantee (8)Pickup_fashion (9)Pickup_guarantee Estimate the whole transaction

(10)Trans_commit if estimate is OK Trans_rollback otherwise

Fig. 1. The process of the payment sub-protocol in AFAP

3.4 Trace Sub-protocol When illegal transaction occurred, such as repayment, payment forgery and electronic commit, etc. trace sub-protocol is activated, which include three aspects: (1) Owner Trace. It is aimed to revoke the owner anonymity and discover the account based on the known illegal payment. Firstly, BANK queries the payment token and sends t c || t o to TTP, TTP executes the following computation

I = (t o(1 / xT ) ) / t c to traces the owner of the illegal payment. (2) Payment Trace. To discover payment token based on known drawn token. Firstly, BANK sends t c to TTP. TTP computes t c = t o T to get payment token. (3) Drawn Trace. Drawn trace is the inverse action to payment trace. It is aimed to discover drawn token according to the known payment token. BANK searches and '

sends

'(1 / x )

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4 Satisfying Atomic and Fair Anonymous E-transaction Protocol AFAP implements atomicity, but it doesn’t realize satisfying atomic, in which SELLER agreed with the payment, but CUSTOMER might receive wrong goods viz. correspond to the description on Trans_detail. In this scene, CUSTOMER doesn’t have any guarantee. She can only make a request to TTP to audit the transaction, withdraw the anonymity, open the Trans_detail and validate it with POSTER. To implement the satisfying atomicity for CUSTOMER it is necessary to assume her that the goods she will receive is a satisfying one, namely equal to Trans_detail. This section presents a new protocol SAFAP (Satisfying Atomic and Fair Anonymous Electronic Transaction Protocol) to achieve it with a little anonymous loss. In this method, POSTER takes responsible for CUSTOMER’s satisfying atomicity. When receive the goods, POSTER validates the goods and generates a new description for the goods and send it to CUSTOMER. CUSTOMER confirms it and then send the confirm message to TTP, TTP commits the whole transaction. The flow and relation of participators in the payment sub-protocol can refer to Fig.3. 1 Customer

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The process of the payment sub-protocol in SAFAP is elaborated as Fig.4. In step 3, CUSTOMER computes the drawn token Token_w and payment token Token_p. With these tokens CUSTOMER can execute payment. Then she sends blinded Token_p to BANK. In step 4, BANK executes a blind signature protocol with CUSTOMER. At the end of the protocol, BANK sends a signature Sign SK _ BANK ( Act , Aot , Bt ) to CUSTOMER, which is the signature of blinded Token_p by BANK. At the same time, BANK subtracts corresponding value from the CUSTOMER’s account. To keep the coherence, BANK maintains a database to save the pair of signature and value. In this way, the signature is regarded as coin worthy of value in later transaction. In step 5, CUSTOMER selects the Pickup_fashion, sets Pickup_Response, which is kept secreted by CUSTOMER and POSTER. CUSTOMER then chooses and fills the good and quantity to generate Trans_detail. In the end, CUSTOMER sends the signature Sign SK _ BANK ( Act , Aot , Bt ) and Trans_detail to SELLER.

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In step 6, SELLER logs TID+ and startup a local transaction clock. As well SELLER validates Sign SK _ BANK ( Act , Aot , Bt ) with y B . CUSTOMER has to prove that payment token Token_p is signed in the signature. Afterward SELLER sends Sign SK _ BANK ( Act , Aot , Bt ) and value to BANK. TTP

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Fig. 3. The process of the payment sub-protocol in SAFAP

In step 7, BANK validates the blind signature

Sign SK _ BANK ( Act , Aot , Bt ) .

Given failure, BANK summits RollbackReq to TTP to rollback the whole transaction. And if validation passed, BANK queries the signature-value database, compares the received signature-value with the record in the database, then BANK validates dual payment with the signature. Passed all these steps, BANK adds payment signature on the SELLER’s account, and generates Trans _ guarantee for SELLLER. In step 8, received Trans _ guarantee from BANK, SELLER startup the process of dispatch, she transfers Pickup_fashion to POSTER, and notifies POSTER to ready for delivering goods.


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In step 9, POSTER picks up TID+||Pickup_fashion. Afterward, POSTER estimates whether Pickup_fashion is permitted, if passed, POSTER inspects the goods and generates a Goods_Description. Finally POSTER generates Pickup_guarantee and sends Goods_Description||Pickup_guarantee to CUSTOMER. In step 10, CUSTOMER compares the Goods_Description with what she selected, if validation passes, she sends Pickup_guarantee||Agreement to TTP. In step 11, given received Pickup_guarantee before Expiration_Date, TTP checks whether there includes Rollback request, if exist, TTP sends Rollback command to all participators, or TTP sends Trans_Commit. On the other hand, if TTP do not receive Pickup_guarantee before Expiration_Date, she sends Rollback to all participators to rollback the transaction. Finally, received Trans_Commit, BANK begins really transfer process, viz. transfer value corresponding to the signature from the account of CUSTOMER to that of SELLER. SELLER dispatches goods to POSTER; POSTER delivers goods according to the Pickup_fashion. To pick up the goods, CUSTOMER is required to provide correct Response to avoid cheating.

5 Analysis of Satisfying Atomicity and Fair Anonymity In this section, we validate atomicity and anonymity informally. At the begin of the transaction, CUSTOMER applies for TID and Expiration_Date, then obtains drawn token and blind signature of payment token from BANK, as well she signs Trans_detail. SELLER sends the dispatch message to POSTER only when she received Trans _ guarantee from BANK. So, we can conclude that SELLER and CUSTOMER are both satisfied with Trans_detail. Meanwhile, CUSTOMER selects Pickup_fashion, and POSTER estimates it and provides Pickup_guarantee, therefore they are both satisfied with Pickup_fashion. After received payment token, SELLER validates it with BANK and receives Trans _ guarantee , which means that BANK and SELLER are satisfied with Trans _ guarantee . Additional, if, for the artificial reasons or network failure, TTP can’t receive Pickup_guarantee in Expiration_Date, then TTP sends Rollback command to rollback the whole transaction. SELLER sends cancel message to POSTER. BANK deletes payment token saved on account of SELLER, and returns it to CUSTOMER. In general, CUSTOMER initiates purchase request, and SELLER could receive correct transfer guaranteed by BANK, CUSTOMER could receive correct goods guaranteed by POSTER. Logs in the local databases are available for audit. Sum up, money atomicity, goods atomicity and certified delivery are enabled in this protocol. To enable satisfying atomicity, another way is to embed Trans_detail in the message to POSTER in step 8. POSTER inspects goods according to the Trans_detail. Only if the goods are coincident to the Trans_detail, POSTER submits to TTP, and then TTP commits the whole transaction. Anonymity in this protocol covers three aspects, CUSTOMER to SELLER, CUSTOMER to POSTER and CUSTOMER to BANK. The detail of the verification can be referred to [13]. As well as, when illegal transaction occurred, BANK retrieves

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payment token to TTP, TTP executes computation to realize owner trace, payment trace and origin trace. Accordingly, fair anonymity is available in this protocol.

6 Conclusion Online businesses are becoming prosperous. One of the most important things to prosper e-commerce is to provide fair, secure, privacy unsealed transaction. For existing application, either atomicity is not provided, or fair anonymity is unavailable. In this paper, we analyze satisfying atomicity and fair anonymity and propose a new protocol to enable them. For further work, we will improve in these aspects: (1) Electronic payment based on group blind signature. (2) More efficient atomicity without loss of anonymity. To improve the atomicity always means a loss of anonymity, therefore how to balance them is a practical problem.

References 1. 2. 3. 4.

5. 6. 7. 8. 9. 10. 11. 12. 13.

Larry Loeb. Secure Electronic Transactions Introduction and Technical Reference. ARTECH HOUSE,INC. 1998 B. Cox, J. D. Tygar, M. Sirbu, Netbill security and transaction protocol. Proceedings of the st 1 USENIX Workshop on Electronic Commerce. 1995:77-88 D.Chaum, A.Fiat, M.Naor. Untraceable electronic cash. Advances in cryptology:Crypto’88 Proceedings, Springer Verlay, 1990:200-212 Tang Jie, Li Juan-Zi, Wang Ke-Hong, Cai Yue-Ru. Research of Atomic and Anonymous Electronic Commerce Protocol. 9th RSFDGrC (International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing). Springer-Verlag, LNCS/LNAI. 2003, 5. 2639:711-714 J. D. Tygar. Atomicity versus Anonymity: Distributed Transactions for Electronic Commerce. Proceedings of the 24th VLDB Conference, New York, USA, 1998:1-12 J. D. Tygar. Atomicity in Electronic Commerce. Proceedings of the Fifteenth Annual ACM Symposium on Principles of Distributed Computing,1996,5:8-26 L. Camp, M.Harkavy, J. D. Tygar, B.Yee. Anonymous Atomic Transactions. In Proceedings of 2nd Usenix Workshop on Electronic Commerce. 1996,11:123~133 S. von Solms and D. Naccache. On blind signatures and perfect crimes. Computers and Security. October, 1992, 11(6): 581-583 D. Chaum, J.H.Evertse, J.van de Graff, R.Peralta. Demonstrating Possession of a Discrete Logarithm without Revealing It. Advances in Cryptology-CRYPTO ’86 Proceedings. Springer Verlag. 1987: 200-212 S. Brands. Untraceable Off-line Cash in Wallets with Observers. In Advances in Cryptology-Proceedings of CRYPTO’93, Lecture Notes in Computer Science. Springer Verlag. 1993, 773:302-318 M. Stadler, M. M. Piveteau, J. Camenisch. Fair blind signatures. In L. C. Guillou and J.J. Quisquater, editors, Advances in Cryptology—EUROCRYPT’95, Lecture Notes in Computer Science, Springer Verlag. 1995, 921:209-219 T. Sander, A. Ta-Shma. Auditable, Anonymous Electronic Cash. Lecture Notes in Computer Science, Spring Verlag. 1999, 1666:0555-571 Tang Jie. The Research of Atomic and Fair Anonymous Electronic Transaction Protocol. YanShan University. 2002:35–59

Network Self-Organizing Information Exploitation Model Based on GCA Yujun Liu, Dianxun Shuai, and Weili Han East China University of Science and Technology, Shanghai 200237, P.R.China. [email protected],[email protected]

Abstract. At present, the exploitation mode for network information has some serious drawbacks such as non-aftereffect, independence of each other and point-to-point in the massive, stochastic, parallel and distributed networks. Thus, this paper is devoted to a novel self-organizing exploitation mode for network information which is proposed in [1], and a new generalized cellular automaton (GCA) is used to discover the selforganizing information configuration, and some performance analysis was made under complex environment including metabolism, intermittent congestion and stochastic fault. The theory and experiment results have shown its advantage.

1

Introduction

With the rapid development of Internet, network construction and environment are becoming more and more complicated. To satisfy the performance demand of network, many methods have been put forward. Grid computing realizes the integration of the wide area scope and the cooperation computing, and users can access pellucidly the data through grid gate of network where information resources are distributed at the each grid nodes [2]. HeYong [3] applied the programming methods to the selection and distribution of information resources which was the bottle-neck problem of LAN. Yongxin Feng [4] provided a network reorganization algorithm in the network management. In the research of network dependability and network behaviors, various network behaviors have been observed([5]-[7]). Claudio & Michela presented a methodology to model the behavior of TCP flow in [5], which stems from a Markovian model of a single TCP source, and eventually considers the superposition and interactions of several sources using standard queueing analysis techniques. Mathis et al.[6] focus on stochastic behavior of the congestion avoidance mechanism, deriving an expression for the throughput. Internet exhibits more and more complicated behaviors, such as large-scale non-linear dynamics, social interactions and metabolic phenomenon among massive network entities. In the matter of fact, both of the information exploitation

Supported by the National Key Foundational R&D Project(973) under Grant No. G1999032707, the National Natural Science Foundation of China under Grant No. 60135010 and No. 60073008, and the State Key Laboratory Foundation of Intelligence Technology and System, Tsinghua University.


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and the network behaviors are deeply affected by each other. Up to now, however, the study on the network behavior has been almost restricted itself to such issues as traffic modelling, congestion avoidance, transmission delay, network throughput, packet-losing rate etc., and the resolutions are all static, close, serial, non-self-organizing and based on the simple environment. It can also be seen that the attention is mainly concentrated on how to alleviate various negative influences of the network behavior. The novel exploitation mode for network information is focused on making use of some positive influence of the network behavior on information exploitation. This paper is devoted to it which is based on the self-organizing network behavior and makes it possible to let the network information behavior become related, multi-to-multi, after-effected and dynamically adjust the distribution of network information. To this purpose, we present a new conception about the information exploitation events in the networks, and a new generalized cellular automaton (GCA) approach to the self-organizing information exploitation mode.

2

2.1

The Self-Organizing Information Exploitation Model under Complex Environment Conceptual Model

An information exploitation event is always regarded as a proliferating process of some information contents. The information proliferation can lead to a self-organizing configuration of information contents by which the networks can respond much more quickly and reliably to the users’ requests. And it can dynamically change with the occurrence of new exploitation events, and can be easily combined with other complicated mechanism involved in the performance phase-transition, social interactions, metabolic evolution, intermittent congestion and nodes stochastic faulty and so on. The ideal network environment does not exist congestion, network nodes crash and network information metabolism. But in reality, the networks are complex and full of these phenomenon. For the convenience of theory study and performance research under the real networks, we introduce the ideal network and complex network to the paper. The network congestion has two forms: intermittent congestion and persistent congestion, and the paper is confined to the discussion of the network intermittent congestion. Considering the lifetime of the network nodes, the random exception also should be included that its no-exception-time obeys the distribution of negative exponent. And the network information have the same character, which we call metabolism phenomena. The complicated network environment that was discussed in the paper contains primarily three aspect: the metabolism of the network entity, the intermittent congestion of the network and the random fault of the network. The metabolism phenomena reflects the adaptation of the network entity, survival of the fittest. The network random fault consider the life cycle of the network entity that

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the no-fault-time obeys the distribution of negative exponent. In the network information self-organizing exploitation mode, we describe the network entity metabolism, intermittent congestion, random fault to the adaptation factor, the congestion factor and the life factor, which respectively represent the past, current and the future state. The architecture of the model, as shown in Fig. 1, consists of three parts which are the source selection strategies, information proliferating strategies and the network self-organizing configuration of network information besides the network users. It manages the magnanimity random concurrence distributed information exploitation behaviors and their after-effect to form a self-organizing configuration of the information distribution in the networks. The configuration of network self-organization for network information

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Fig. 1. The architecture of the network information exploitation mode. 1. The two point lineation shows that networks provide the user an needed information which was completely specified by the information type and source address in the traditional network information exploitation mode. 2. The dashed frame shows the mechanism and framework of the network self-organizing information exploitation. The network self-organize the configuration of network information according to the information contents, and decide information source addresses, provide the users an needed information, dynamically update the network self-organizing information configuration by the information proliferation strategies.

2.2

Mathematic Model

We will consider the question of complex network environment which includes intermittent congestion, nodes stochastic fault and information contents metabolism in the mathematic model. In the following, its formalization is described. Definition 1. The i-th exploitation event εi (t) for network information at time t is a subset of the quadruple set defined over Ω×Φ×Ψ ×Π, namely εi (t) = {(ωi (t), ϕi (t), ψi (t), πi (t)) | ωi (t) ∈ Ω, ϕi (t) ∈ Φ, ψi (t) ∈ Ψ, πi (t) ∈ Π} (1)

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where Ω represents the network information contents; Φ is a set of information source addresses; Ψ is a set of the objective addresses for information exploitation; Π is a set of network performance factors needed for information self-organizing, which is a subset of the triple set defined over ∆×Θ×Γ , where ∆ is a set of adaptation factors of network entity metabolism; Θ is a set of delay factors of intermission congestion; Γ is a set of life factors of random fault. At the time t, the state at the location ϕi (t) can be represented as πi (ϕi (t)) = (δi (t), θi (t), τi (t)) ∈ Π, and the state function can be defined as S(ϕi (t)) −→ [0, 1], where δi (t), θi (t), τi (t) denote respectively the adaptation factor, the congestion factor and the life factor. Definition 2. Denotation ω ≺ ϕ means that there is information content ω ∈ Ω in the information address ϕ ∈ Φ . So we have an address set A(ω) = {ϕ| ω ≺ ϕ, ϕ ∈ Φ} ⊆ Φ and an information set (ϕ) = {ω| ω ≺ ϕ, ω ∈ Ω} ⊆ Ω. Definition 3. Source Selecting Process(SSP). For a network information exploitation εi (t), the source selecting process ℵ can be thought as the mathematics programming problem that is as follows: under considering the network entity performance factors of set A(ωi (t)), it ensures the cost function µi (t) minimum when source address ϕij (t) are selected according to the probability pij (t), j = 1, ..., ki (t), µi (t) = Σj pij (t)dij (t)qij (t), Σj pij (t) = 1, S(ϕij (t)) ≥ S0 ; (2) dij = ϕij (t) − ψij (t) , ϕij (t) ∈ A(ωi (t)) where ki (t) is a set of source address number in the set A(ωi (t)); dij (t) is the distance between source address ϕij (t) in and information destination address ψij (t); qij (t) is the data package number transported. S0 is the threshold of the state function. Definition 4. Information Proliferating Process(IIP). IIP æ will consider the event εi (t) and the network state, then recombine the distribution of the information content ωi (t), that is to say, to expand and modify the set of A(ωi (t)). The destination address of information content proliferated to must nearer to the source address. IIP æ will adopt different strategies to satisfied the following formulas according to the set of proliferating address B(εi (t)). ρij (t) − ψi (t) ≤ ϕ(t) − ψi (t) , ρij (t) ∈ B(εi (t), ϕ(t) ∈ A(ωi (t))); (3) S(ρij (t)) ≥ S0 2.3

Strategies of SSP and IIP

For the sake of research, as for the model of network self-organization information exploitation, there have three strategies to be proposed respectively to SSP and IIP. For source selecting strategy(SSS), we present three methods which are the closest source selecting strategy(C), the maximal processing capability source


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selecting strategy(MPC) and the first fitted source selecting strategy(FF). According to the definition 2 and the Strategy C, the node which is the nearest to the source address and the state function will take larger value has the bigger probability selected. it can be described as the following formula. −1 −1 pij =

1 if ϕij (t) − ψi (t) [S(ϕij (t))]

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ϕ − ψi (t) [S(ϕ)] min ϕ ∈ A(ωi (t))

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we also can find that the node which have maximal processing capability and have larger value state function will easy be selected in the strategy MPC. 1 if b(ϕij (t))S(ϕij (t)) = b(ϕ)S(ϕ); max ϕ ∈ A(ωi (t)) pij = (5) 0 otherwise. where b(ϕ) is the capability of the information service of source address ϕ(t). With regard to the strategy FF, the following formula is valid. 1 if ϕij (t) selected randomly f rom A(ωi (t)), and S(ϕij (t) ≥ S0 ); pij = 0 otherwise. (6) Three strategies of information proliferating(IPS) are also brought forward which include direct proliferating strategy(D), random proliferating strategy(R) and compromise strategy(CS). The strategy D can proliferate information directly to the visiting node. The strategy R will randomly proliferate in the range of the set of proliferation address set. The strategy CS proliferates the information to the node located at the middle of the source and the destination. 2.4

Network Complex Environment

Concerning the network environment, we discuss in three sides which are the metabolism of the network information, the intermittent congestion of the network traffic and the random fault of the network entity. The metabolism phenomenon of the network information obey the principle of ”using to grow and unused to destroy” to adjust the distribution of the information content. A node’s adaptability of network information can be measured by the adaptation factor δ which is direct proportion to the frequency of information used, 0 < δi (t) < 1, when δ is less than the threshold δ0 , the information content will be deleted. It reflects the past of the network environment. The intermittent congestion of the network traffic have the character of outburst, intermission and ability to restore. The congestion factor θ describe the current state of the network environment, and it can be measured by many parameters such as the package lost rate, the fractal number, the delay time and the traffic flux. When the θ is larger than its threshold θ0 , 0 < θi (t) < 1,the network enter into the stage of the congestion, and control strategy must be carried out, when the proliferation address is far away the area.

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The network entities often crashed suddenly when running. We denote the no fault lifetime as a random variable, which distribution density function obey the negative exponent distribution which is f (t) = λe−λt where λ is the attenuation intensity, t is the time variable. We note that the mode of the traditional information exploitation has the single path rather than the multiple path of the network self-organization mode. The dependability of the information exploitation can be described by the lifetime factor τi (t). Moreover, the fault rate and the average lifetime of the single path respectively are resumed as λ(t) = λ, µ(t) = µ. When there exist n paths between the source and destination address, the lifetime of the multiple paths is the maximal lifetime of the n paths, Z = max Xi , which distribution 1 0

(1)

(2) (3)

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maximize :W (α ) = ∑ α i − i =1

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2.2 Multi-class SVM and DDAG Learning Architecture SVM was originally designed for binary classification. How to effectively extend it for multi-class classification is still an ongoing research issue. Several methods have been proposed where typically the binary SVMs are combined to construct the multiclass SVMs. There are three main methods: one-against-one, one-against-all and DAGSVM. It has been pointed out in [10] that DAGSVM is very suitable for practical use. Previous experiments have proved that DAGSVM yields comparable accuracy and memory usage to the other two algorithms, but yield substantial improvements in both training and evaluation time [10][11]. 2.3 Challenges of Large-Scale Hypertext Categorization Tasks So far, all issues we have discussed are under the assumption that the whole computation process is executed in one computer. Nevertheless, does DAGSVM work well while handing real large-scale hypertext categorization tasks? How can we handle thousands of HTML files even millions of them? How can we handle too many categories? How can we update the decision rules efficiently? Unfortunately, most practical categorization systems are isolated and unable to deal with these problems. Driven by the idea of implementation of hypertext categorization in the cooperative computing environment, we propose the distributed system (DHCS) as an interesting and practical case of knowledge share in the cooperative computing context. 2.4 Distributed Hypertext Categorization System We find that binary SVM nodes in DAGSVM are very similar to the real nodes in computer networks. Thus we may divide the training workload of the whole DAGSVM into several separated groups (each group contains one or more binary SVM nodes). Fig. 1 describes the basic idea:

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Fig. 1. Allocate the DAGSVM nodes to physical computer nodes

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In the distributed hypertext categorization system, there are several key problems to be solved including: How to divide the DAGSVM nodes and allocate them to computer nodes? How do the computer nodes communicate with each other? How to share categorization knowledge among the computer nodes? We will discuss these problems in the later sections.

3 Knowledge Share and Cooperative Learning 3.1 Information Knowledge in DHCS According to the structure and the learning algorithm of DAGSVM, we can explain the concepts of information and knowledge in DHCS. First, divided DAGSVM node groups need their training samples. When one computer node has labeled some samples that do not belong to its categories, it should send them to other computers. We may notice that the exchange of the labeled samples among the computer nodes is very similar to the regular information exchange in a traditional peer-to-peer computer networks. But it is more meaningful that we can implement knowledge share in DHCS. Since each computer node has known some “knowledge” after it finish the training of its own DAGSVM nodes, other computer nodes can share its learning results. Once all computer nodes get enough “knowledge” from others in the cooperative environment, they can assemble the whole DAGSVM respectively. That is difficult in other cooperative learning systems but is considerate easy in DHCS for the special structure and features of DAGSVM. We will discuss this in detail in the following sections. 3.2 Knowledge Share in DHCS Before we go any further, it is necessary to take a look at the decision rules in SVM. After we solve the optimization problems in (1) and (4), we get the optimal α * = (α1* ,α 1* ,…α n* ) , then we have: n

w* = ∑ α i* yi xi

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b * = y i − ( w * ⋅ xi )

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i = 1,2, …, n

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In (4), xi is a support vector if the corresponding α i* > 0 . And the decision rule is: f ( x ) = w* ⋅ x + b*

(10)

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Combining (10) with (7) and (8), we see only those support vectors can affect the decision function. Researchers have found that the proportion of support vectors in the training set can be very small (usually 2%~5% in text categorization tasks [8][12]) via proper choice of SVM hyper-parameters. In fact, other computer nodes can restore the categorization rules only through the support vectors and their coefficients. Thus, for a computer node, it can transfer its categorization knowledge to other computer nodes in the form of support vectors and the corresponding coefficients. And fig. 2 shows the knowledge share in the context of DAGSVM:

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In VSM style hypertext categorization, sparse matrix technique can be applied so that the communication cost is not an important issue any longer. For example, Joachims [12] has pointed out that an average document in the WebKB collection is 277 words long and contains 130 distinct terms. And the whole collection leads to 38,359 features. Apparently combining VSM and sparse matrix technique can benefit DHCS greatly. Thus the local knowledge of the categorization can easily be transferred to other computer nodes so that all nodes can obtain the global knowledge of the hypertext categorization.

4 Some Implementation Issues of DHCS 4.1 Allocation of Computation Load to Computer Nodes DHCS is implemented in the LAN environment so that we can ignore the cost of communications. And all computers in DHCS communicate with each other via simple broadcast. We have found this simple strategy works very well in DHCS. The next step is mapping DAGSVM nodes to the computer nodes. We focus on classify web pages in CERNET (China Education & Research Net). We define twelve top

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categories, which needs to construct 12(12-1)/2=66 DAGSVM nodes. In our DHCS, the four computers are assigned 15,17,17 and 17 DAGSVM nodes respectively. (Obviously, DHCS can be easily extends to the P2P computing environment.) 4.2 Information Exchange in DHCS To implement dynamic and incremental learning in DHCS, the four computers need to exchange information periodically. Here, we refer to the information but not the knowledge yet, because the computers broadcast the labeled samples periodically. Users can label the hypertext files independently while they surfing the Internet. In our demonstrative DHCS, the system is running on four personal computers, and the users belong to one research group, that is, they have the same research background and trust others information and knowledge. 4.3 SVM Training Algorithm One key issue of SVM is the training algorithm. Iterative training algorithms are very suitable for solving the SVM optimization problem. We implement the modified SMO algorithm [9] in DHCS, which is proved efficient. Meanwhile, the optimal hyper-parameters can be achieved via minimizing the generalization error. Based on the leave-one-out cross validation estimation, one can derive the performance estimators for finding the best hyper-parameters. In DHCS, we develop an efficient algorithm for tuning the hyper-parameters of DAGSVM [13].

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5 Experimental Results To evaluate the performance of DHCS, we run the system on four daily-using computers in our laboratory. Considering the vast dynamic web, we stop a computer node if it reaches a set point (we name it “satisfying point”), that is, the accuracy is over 50%. (In fact, with enough time and enough effort, higher accuracy is definitely reachable.) And every user’s judgment of the navigated web pages is the expert validation to other computers’ decision rules. Fig.8 shows how our DHCS runs in 12 days. Although this is a simple experiment, we can still see DHCS is stable and effective.

6 Conclusions and Future Work Hypertext categorization plays an increasingly important role in web information acquisition systems. It provides fundamental application interfaces for web information retrieval and web mining, and also benefits other research fields such as e-mail filtering and web users’ relevance feedback, etc. To avoid expensive manual labeling, cooperative learning method is a must for distributed web page categorization systems. In this paper, we have introduced a distributed hypertext classification system, which implements DAGSVM in the cooperative learning environment. With little communication cost, knowledge share is achieved at the same time. Experimental result has shown the proposed DHCS works well in a laboratory LAN. Nevertheless, there are still some problems to be explored. For example, how about running DHCS in P2P networks? In the hetergeneous P2P environment, is it still ac-ceptable to ignore the communication cost in DHCS? How does DHCS handle the hierachical categorization tasks? We will explore these aspects in the future work.

Acknowledgements. This work was supported by the Science & Technology Committee of Shanghai Municipality Key Research Project Grant 02DJ14045 and Key Technologies R&D Project Grant 03DZ15027.

References 1. 2. 3.

O’Day, V., R. Jeffries: Orienteering in an Information Landscape: How Information Seekers Get From Here to There. In: Proc. INTERCHI 93, 1993, pp. 438-445. Twidale, M. B., D. M. Nichols, G. Smith, J. Trevor: Supporting Collaborative Learning During Information Searching. In: Proceedings of CSCL95, 1995, pp. 367-374. Hertaum, M., and Pejtersen, A.M. The Information-seeking Practices of Engineers: Searching for Documents as well as for People. In: Information Processing & Management, 36, 2000, 761-778.

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Fidel, R. Bruce, H. Pejtersen, A. Dumias, S. Grudin, J. and Poltrock, S. Collaborative Information Retrieval. In: L. Höglund, ed. The New Review of Information Behavior Research: Studies of Information Seeking in Context. London & Los Angeles: Graham Taylor. Jochims, T. Text Categorization With Support Vector Machines: Learning With Many Relevant Features. In: Proceedings of ECML-98. Berlin: Springer. 1998. 137-142. Jochims, T. Transductive Inference For Text Classification Using Support Vector Machines. In: Proceedings of ICML-99. US: Morgan Kaufmann Publishers. 1999. 200-209. S. T. Dumais, J. Platt, D. Heckerman and M. Sahami. Inductive Learning Algorithms and Representations for Text Categorization. In: Proceedings of CIKM98, 1998, pp. 148-155. J. Platt. Fast Training of Support Vector Machines Using Sequential Minimal Optimization. In: Advances in Kernel Methods – Support Vector Learning. Cambridge, MA: MIT Press. 1998. 185-208. S.S. Keerthi, S.K. Shevade, C. Bhattacharyya and K.R.K. Murthy. Improvements to Platt's SMO Algorithm for SVM Classifier Design. In: Neural Computation, 2001,Vol. 13, pp. 637-649. Chih-Wei Hsu, Chih-Jen Lin. A Comparison of Methods for Multicalsss Support Vector Machines. In: IEEE Transactions on Neural Networks. 2002, Vol. 13, No. 2, 415-425. J. C. Platt, N. Cristianini, and J. Shawe-Taylor. Large Margin DAGs for Multiclass Classification. In: NIPS2000. Cambridge, MA: MIT Press. 2000. 547-553. Thorsten Joachims. Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms. Norwell [M], MA, USA: Kluwer Academic Publishers. 2002. Shui Yu, Liang Zhang, Fanyuan Ma. Design and Implementation of a Large-scale Multiclass Text Classifier. Submitted to Journal of Harbin Institute of Technology, 2003.

Improving the Performance of Equalization in Communication Systems Wanlei Zhou1, Hua Ye1, and Lin Ye2 1

School of Information Technology Deakin University 221 Burwood HWY, Burwood. VIC. 3125. Australia. {wanlei, hye}@deakin.edu.au 2 School of Adults Education Harbin Institute of Technology Harbin City, P.R.China [email protected]

Abstract. In this paper, research on exploring the potential of several popular equalization techniques while overcoming their disadvantages has been conducted. First, extensive literature survey on equalization is conducted. The focus has been placed on several popular linear equalization algorithm such as the conventional least-mean-square (LMS) algorithm, the recursive leastsquares (RLS) algorithm, the filtered-X LMS algorithm and their development. The approach in analysing the performance of the filtered-X LMS Algorithm, a heuristic method based on linear time-invariant operator theory is provided to analyse the robust performance of the filtered-X structure. It indicates that the extra filter could enhance the stability margin of the corresponding non filteredX structure. To overcome the slow convergence problem while keeping the simplicity of the LMS based algorithms, an H2 optimal initialization is proposed.

1

Introduction

The least-mean-square (LMS) based adaptive algorithm have been successfully applied in many communication equalization practices. The importance of the LMS algorithm is largely due to two unique attributes[1]: • Simplicity of implementation • Model-independent and therefore robust performance The main limitation of the LMS algorithm is its relatively slow rate of convergence. Two principal factors affect the convergence behaviour of the LMS algorithm: the step-size parameter µ , and the eigenvalues of the correlation matrix R of the tapinput vector. The recursive least-square (RLS) algorithm is derived as a natural extension of the method of least square algorithm. The derivation was based on a lemma in matrix algebra known as the matrix inversion lemma. [2]. [3].


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The fundamental difference between the RLS algorithm and the LMS algorithm can be stated as follows: The step-size parameter µ in the LMS algorithm is replaced in RLS algorithm by

φ −1 (n) , that is, the inverse of the correlation matrix of the input

vector U(n). This modification has a profound impact on the convergence behavior of the RLS algorithm in a stationary environment, as summarized here [4]-[10]: 1. The rate of convergence of the RLS algorithm is typically an order of magnitude faster than that of the LMS algorithm. 2. The rate of convergence of the RLS algorithm is invariant to the eigenvalue spread ( i.e., condition number ) of the ensemble-averaged correlation matrix R of the input vector U(n). 3. The excess mean-squared error J ex (x) of the RLS algorithm converges to zero as the number of iterations, n, approaches infinity. The computational load of the conventional RLS algorithm is prohibited in real time applications. The recursive least-squares ( RLS ) algorithm is characterized by a fast rate of convergence that is relatively insensitive to the eigenvalue spread of the underlying correlation matrix of the input data, and a negligible misadjustment, although its computational complexity is increased[11]-[14].

2 Experiment and Results We present the experiment results of three adaptive equalization algorithms: leastmean-square (LMS) algorithm, discrete cosine transform-least mean square (DCTLMS ) algorithm, and recursive least square ( RLS ) algorithm. Based on the experiments, we obtained that the convergence rate of LMS is slow; the convergence rate of RLS is great faster while the computational price is expensive; the performance of that two parameters of DCT-LMS are between the previous two algorithms, but still not good enough. Therefore we will propose an algorithm based on H2 in a coming paper to solve the problems. It is well known that high data rate transmission through dispersive communication channels is limited by the inter-symbol interference (ISI). Equalization is an effective way to reduce the effects of ISI by cancelling the channel distortion. However, dynamic, random and time-varying characteristics of communication channels make this task very challenging. High speed of data transmission demands a low computational burden. Hence, simplicity and robust performance play a crucial role in equalizer design. Due to its good robust performance and computational simplicity, least-mean-square (LMS) based algorithms have received a wide attention and been adopted in most applications [2], but one major disadvantage of the LMS algorithm is its very slow convergence rate, especially in high condition number case . To solve this problem, a variety of improved algorithm have been proposed in the literature. Although their actual implementations and properties may be different but the underlying principle remains the same: trying to orthogonalize as much as possible the input autocorrelation matrix and to follow a steepest-descent path on the transformed error function. Therefore, we extend the least square algorithm to a recursive algorithm for the design of adaptive transversal filter. An important feature of the RLS algorithm is that

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it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. The resulting rate of convergence is therefore typically an order of magnitude faster than the simple LMS algorithm. This improvement in performance, however, is achieved at the expense of a large increasing in computational complexity. The RLS algorithm implements recursively an exact least squares solution [10]. At each time, RLS estimates the autocorrelation matrix of the inputs and cross correlation between inputs and desired outputs based on all past data, and updates the weight vector using the so-called matrix inversion lemma. The DFT/LMS and DCT/LMS algorithms are composed of three simple stages [5]. First, the tap-delayed inputs are preprocessed by a discrete Fourier or cosine transform. The transformed signals are then normalized by the square root of their power. The resulting equal power signals are input to an adaptive linear combiner whose weights are adjusted using the LMS algorithm. With these two algorithms, the orthogonalizing step is data independent; only the power normalization step is data dependent. Because of the simplicity of their components, these algorithms retain the robustness and computational low cost while improving its convergence speed. Although the structure of the filtered-X LMS adaptive equalization scheme is a little bit different from that of the basic LMS adaptive equalization scheme, the control adjustment process is the same: adjusting the FIR model of the equalizer to minimize the least mean square error e k . Therefore, the H 2 optimal solution is actually the limit of the best solution for the filtered-X LMS adaptive equalization algorithm. Similar to the case of the basic LMS adaptive equalization scheme, the filtered-X LMS adaptive equalization scheme can not, in general, achieve this limit. However, its optimal solution is still expected to be close to that point if the adaptive size is small. Therefore, the optimal H 2 initialization method proposed still applies here. Why not simply use the H 2 optimal model matching filter as the final equalizer? This is because of the presence of the model uncertainty and other unexpected disturbances. The optimal solution obtained in offline computation may not be optimal when the filter is implemented in the real world system because of model uncertainty and other unexpected disturbance. For the optimal initialization, a poorly identified system model may give rise to a low quality model matching solution. However, due to robustness of filtered-X LMS adaptive equalization scheme, this solution may still be well within the convergence region. By extensive simulations and experiments, it is observed that method proposed here can also cope with wide eigenvalue spread of the input without having to use Discrete Cosine Transformation (DCT) that was conventionally required. This is an advantage in real-time operation environment where computation burden is a critical factor. The focus of this work is on improving the equalization performance of the powerful LMS and RLS adaptive algorithms while minimizing the increase of the related computational complexity. Since these algorithms are very popular in real world applications, the attemption is significant. The channel equalization is an effective signal processing technique that compensates for channel-induced signal impairment and the resultant inter-symbol interference (ISI) in communications system. Many sophisticated techniques have


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been proposed for equalization, most of successful real world applications are still dominated by techniques that are related to several popular algorithms, such as the adaptive LMS algorithm, the filtered-X LMS algorithm and the RLS algorithm. For high-speed commercial communication systems, simplicity, robust and fast convergence rate are critical criteria for the design of a good equalizer. The adaptive LMS algorithm, the filtered-X LMS algorithm, and the RLS algorithm meet some of these criteria. Unfortunately, none of them, alone, satisfies all these criteria. Therefore, research on exploring the potential of these techniques while overcoming their disadvantages is important and necessary, which is exactly what has been conducted in this paper.

3 A Fast Start-Up Technique Though the LMS algorithm does not actually converge to the least--mean--square solution that optimal H 2 model matching solution achieves, they are very close if the adaptive step size µ is small enough. Interestingly, not much effort is needed to ^

P (z) as the filtered-X LMS algorithm still converges so long as the 0 estimate of the channel P (z) has less than ± 90 phase shift and unlimited amplitude distortion. The H ∞ robust performance analysis of the LMS algorithm

find the filter

conducted by Hassibi, et al. reveals that sum of the squared errors is always upper ^

bounded by the combined effects of the initial weight uncertainty (

W (0) – W

0 )

and the noise υ ( i ). This evidence strongly supports that the H 2 optimal initialisation presented in this thesis can confine the error to a low level right from the beginning and hence improve the convergence rate dramatically. A major benefit of this approach is that it makes the adaptive process a virtual finetuning process if a reasonable initialization is obtained, which avoids experiencing a possibly long adaptation process in transit to the fine-tuning period. The advantage will be more clearly illustrated by a high eigenvalue spread case. Extensive simulation experiment has shown that, in many cases, the adaptive process starts from an acceptable performance, and it does not need any remedy like Discrete Cosine Transform ( DCT ) or Discrete Fourier Transform ( DFT ) even in the case with a very high input signal eigenvalue spread where the conventional LMS algorithm may fail and traditionally a remedy like DCT and DFT technique is required. The conventional filtered-X LMS is modified and introduced for the purpose of equalization. Generic integration of the filtered-X structure, LMS algorithm, RLS algorithm and optimal H 2 initialization is conducted to meet all paramount criteria of simplicity, robust and fast convergence for equalization of high-speed, distorted communication channels. Finally, various techniques proposed in this thesis are tested using a popular communication channel example, under both slight non-stationary and sever nonstationary conditions. Comparisons are made with other conventional methods.

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Significant performance improvement has been observed by Mont Carlo. The effectiveness of the methods proposed in this thesis has been verified.

Fig. 1. Learning curves of the various adaptive algorithms experiencing a abrupt increase of impulse response of the channel by 35%

This experiment has verified a well known fact that the conventional adaptive LMS algorithm can track slight non-stationary environments such as slowly varying parameters. Now, a more severe non-stationary situation is tested by abruptly increasing the channel impulse response coefficients by 35% of its nominal value. Fig. 1 shows the simulation result. The conventional adaptive LMS algorithm begins to diverge while the filtered-X LMS algorithm with or without optimal initialization still maintains a good robust performance. The conventional RLS algorithm still has an acceptable performance, which matches the observation that when the time variation of the channel is not small, the RLS algorithm will have a tracking advantage over the LMS algorithm . The filtered-X RLS algorithm has a better robust performance. The robust performance enhancement by the introduction of the filtered-X structure is obvious and significant. From a computational point of view, optimal initialization needs an additional effort to solve an H 2 optimal model matching or H 2 filtering problem. Since this procedure is a non-iterative solution and can be done off-line, it does not increase the computational burden in online operation. The only extra online computational burden concerned comes from the extra filter that is involved in every adaptive step. However, that structure increases only a computation of one simple algebraic convolution. This poses no serious problem in computation at all.


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4 Conclusions (1). The practical importance of LMS algorithm is largely due to simplicity of implementation and its robust performance and its main limitation is relatively slow rate of convergence. The RLS algorithm is characterized by a fast rate of convergence that is relatively insensitive to the eigenvalue spread of the underlying correlation matrix of the input data, and a negligible misadjustment. Although it is computational complexity. (2). The conventional filtered-X LMS is modified and introduced for the purpose of equalization. The famous filtered-X LMS algorithm has found very successful applications in the field of active noise and vibration control. It has inherited the elegant simplicity of the conventional LMS algorithm, and is very robust. For approach in analyzing the performance of the filtered-X LMS algorithm, a heuristic method based on linear time-invariant operator theory has been provided to analyze the robust performance of the filtered-X structure. It indicates that the extra filter could enhance the stability margin of the corresponding non filtered-X structure. In this thesis, a generic integration of the filtered-X structure, LMS algorithm, RLS algorithm and optimal H 2 initialization has been conducted to meet all paramount criteria of simplicity, robust and fast convergence for equalization of high-speed communication channels. (3). To overcome the slow convergence problem while keeping the simplicity of the LMS based algorithms, an H 2 optimal initialization is proposed. Though the LMS algorithm does not actually converge to the least-mean-square solution that optimal H 2 model matching solution achieves, they are very close if the adaptive step size

()

µ

is small enough. Interestingly, not much effort is needed to find the filter Pˆ z as the filtered-X LMS algorithm still converges so long as the estimate of the channel P(z) has less than ± 90 phase shift and unlimited amplitude distortion [21]. The H ∞ robust performance analysis of the LMS algorithm conducted by Hassibi, et al. reveals that the sum of the squared errors is always upper bounded by the combined 0

effects of the initial weight uncertainty

(Wˆ (0) − W 0 ) and the noise v(i). This

evidence strongly supports that the H 2 optimal initialization presented in this thesis can confine the error to a low level right from the beginning and hence improved the convergence rate dramatically. A big benefit of this approach is that it makes the adaptive process a virtual finetuning process if a reasonable initialization is obtained, which avoids experiencing a possibly long adaptation process in transit to fine-tuning period. The advantage will be more clearly illustrated by a high eigenvalue spread case. As it is well known that the conventional LMS converges very slowly or even fails to converge with a no matter how small adaptive step size due to high input signal eigenvalue spread. H 2 optimal model matching solution is independent of this input signal eigenvalue spread, and hence could avoid this trouble. Moreover, this idea can be combined with other speed-up techniques such as Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT) as well as various adaptive algorithms. Extensive

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simulation experiment has shown that, in many cases, the adaptive process starts from an acceptable performance, elated ven in the case with a very high input signal eigenvalue spread. Another approach proposed here is that it generally does not require detailed knowledge of the external signal which is a great advantage in practice. Since there exist many powerful tools solving H 2 filtering problem, including explicit solution, the method proposed in this thesis is very promising. (4). A popular communication channel example is used to test the proposed techniques, under both slight non-stationary and severe non-stationary conditions. The level of channel distortion is deliberately raised to a level that is much higher than any published result with system condition number as high as nearly 390. Furthermore, it is assumed that each tap weight of the channel undergoes an independent stationary stochastic process with each parameter fluctuating around its nominal value with a uniform probability distribution over the interval hk − 8%hk , hk + 8%hk , in addition to the white noise disturbance of variance 0.001 at the channel output. Mont Carlo simulation experiment of 1000 independent trials is conducted to obtain an ensemble-averaged learning curve. All adaptive algorithms have shown a good robust performance against the time varying, random Gaussian impulse response coefficient fluctuations specified above. The filtered-X LMS with the optimal H 2 initialization has been shown to have the fastest convergence rate and best performance. (5). A more severe non-stationary situation was tested by abruptly increasing the channel impulse response coefficient by 35% of its nominal value. The conventional adaptive LMS algorithm begins to diverge while the filtered-X LMS algorithm with or without optimal initialization still maintains a good robust performance. The conventional RLS algorithm has an acceptable performance, which matches the observation that when the time variation of the channel is not small, the RLS algorithm will have a tracking advantage over the LMS algorithm [1]. The filtered-X RLS algorithm has a better robust performance. The performance improvement by using the proposed techniques is significant and hence, the effectiveness of the new method has been verified. The contributions of this paper are: we are compared the LMS with DCT-LMS and RLS for adaptive equalizer first, then we will be conducted on how to speed up the convergence rate of LMS based algorithm while keeping the increased in-line computational burden as low as passible, we will overcome the slow convergence problem while keeping the simplicity of the LMS based algorithm, and the H2 Optimal initialization has been applied in adaptive equalizer for communication systems. There still exists many open problems. For instance, the analysis of the stability margin of the filtered-X LMS was conducted in a heuristic manner. Can we extend this to a general case such as a discrete time MIMO case? What about the filtered-X RLS algorithm? Can we apply the ideas to other adaptive equalization techniques such as decision-feedback equalization, etc.? What happens if we use the H ∞

(

)

optimal initialization instead of the H 2 optimal initialization? Another very active area of equalization is wireless communication where the phenomenon of fast multiple-path fading (Rayleigh fading) is very challenging. As indicated in the


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simulation, rapid and not so small channel variations can cause the conventional LMS algorithm to diverge. It will be interesting and challenging, therefore, to apply the new techniques presented here to those areas in the future.

References 1. 2. 3. 4.

5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

R.D. Gitlin, J.F. Hayes, and S.B. Weinstein, Data communication principles, Plenum Press, New York, 1992. E.A. Lee and D.G. Messerschmitt, Digital communication, Second Edition, Kluwer Academic Publishers, 1994. S. Haykin, Adaptive filter theory, Third Edition Edition, Prentice Hall Information and System Sciences Series, 1996. David S. Bayard, "LTI representation of adaptive systems with tap delay-line regressors under sinusoidal excitation," "Necessary and sufficient conditions for LTI representations of adaptive systems with sinusoidal regressors," Proceedings of the American Control Conference, Albuquer, New Mexico, June 1997, pp. 1647-1651, pp. 1642-1646. Steven L. Gay, "A fast converging, low complexity adaptive filtering algorithm," Proceedings of 1993 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, USA, 1993, pp.4-7. S. Elliott and P. Nelson. "Active noise control," IEEE Signal Processing Magazine, Oct. 1993. Markus Rupp and Ali H. Sayed, "Robust FXLMS algorithms with improved convergence performance," IEEE Trans. on Speech, Audio Processing, vol.6, no.1, Jan.1998, pp.78-85. E.A. Wan, Adjoint LMS: an efficient alternative to the Filtered-X LMS and multiple error LMS algorithms. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1996, ICASSP-96, vol.3, pp.1842-1845. Markus Rupp, "Saving complexity of modified filtered-X LMS and delayed update LMS algorithm," IEEE Trans. on Circuits and System-II: Analog and Digital Signal Processing, vol.44, no.1, Jan. 1997, pp.57-60. Steven L. Gay, "A fast converging, low complexity adaptive filtering algorithm," Proceedings of 1993 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, USA, 1993, pp.4-7. J.M. Cioffi and T. Kailath, "Fast recursive least-squares transversal filters for adaptive filtering," IEEE Trans. on Acoustics, Speech, and Signal Processing, vol. ASSP-32, April 1984. pp. 304-338. F.T.M. Slock, "Reconciling fast RLS lattice and QR algorithms," 1990 International Conference on Acoustics, Speech and Signal Processing, vol.3, New York, USA. 1990. pp.1591-1594. M. Bouchard, S. Quednau, "Multichannel RLS Algorithms and Fast-Transversal- Filter Algorithms for Active Noise Control and Sound Reduction System," IEEE Trans. Speech and Audio Processing, vol.8, no.5, Sep. 2000 Bouchard, M. and Quednau, S. "Multichannel Recursive-Least-Squares Algorithms and Fast-Transversal-Filter Algorithms for Active Noise Control and Sound Reproduction Systems," IEEE Transactions on Speech and Audio Processing, vol. 8. No. 5, September 2000.

Moving Communicational Supervisor Control System Based on Component Technology Song Yu and Yan-Rong Jie School of Computer, North China Electric Power University, Baoding 071003,China [email protected] Abstract. Based on XYZ/E language, the moving communicational supervisor control system(MCSCS) based on component technology is introduced in the paper. The authors presented the system architecture and gave its XYZ/E description briefly, discussed separately that central supervision center is case of server and case of both client and server, gave the data transmission program of the XYZ/E description briefly, presented the implement of the system combination state environment. Keywords: Component; supervisor control system; XYZ/E language; client; server

1 Introduction A component is a program body that works alone or in cooperation with other components. Once is defined, it hasn’t relation to its concrete implementation language [1].Components existence relies on architecture techniques to a certain extent [2], only in suitable architecture, a software may be abstracted, isolated, and ultimately turns into components. Components are minimum units of the software resue, ideally, the whole system is composed of several components which connected each other through interface definitions. CBD (Component-Based Development) is looked on software architecture as packaging blueprint[3], took resue software components as packaging prefabricated blocks, supports packaging software resue, is one of the effective ways which enhance software productivity and quality, reduce the side effect of developers leave, shorten the product delivery date. As software industry and software engineering techniques develop, software resue [4] is paid more and more attention to. During the developing course, the first thing we should do is to define the specification (static semantics) of the component according to the requirement. Then select the right architecture style; create the subcomponent and connector; write out the specification of every subcomponent. After constructing a whole component’s structure, we can create the correspondent procedure in XYZ/E (dynamic semantics), and decompose the abstract component in the next level till all static semantics having been converted into dynamic semantics and executable program. The CBD techniques has come a noticeable issue. The paper describes the component dynamic semantics of the MCSCS development course in XYZ/E, this method has evidently enhanced the software productivity and reliability.


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2 XYZ System and Temporal Logic Language XYZ/E XYZ system [5] is the software engineering tool system that is foundation of temporal logic language XYZ/E, it combines temporal logic with software engineering organically, its goal is to enhance the software productivity and reliability. XYZ/E corresponds to a wide spectrum language which each sublanguage represents different program mode or program paradigm. There are three forms of the control structure in XYZ: basic XYZ/E which represents status transformation directly, structured XYZ/E and production rule form XYZ/E. There are two temporal logic operators in XYZ/E, the future temporal logic operators: $O, , ◊, $U and $W; the past temporal logic operators: $⊙, $［·］, $〈·〉, $S and $B. The basic commands are called conditional elements(CE) in XYZ/E, they take the following form: LB=y∧R=>$O(v1,…,vk)=(e1,…,ek) ∧$OLB=z LB=y∧R=>@(Q∧LB=z) The basic component of a XYZ/E program is called unit, it takes form: [A1；…；An] WHERE B1∧…∧Bm The structured CEs (statements) includes conditional statement, loop statement, case statement, wait statement, continue statement, select statement and parallel statement. All the features of XYZ/E indicate that it will benefit the abstract description, stepwise refinement, and procedure synchronization [6,7], it can express the real world flexibly, and figure out the implementation of the procedure, it is very natural and meaningful to apply XYZ/E to formalizing architecture.

3 System Design MCSCS includes 3 layers structure: CSC (Central Supervision Center) part, LSC (Local Supervision Center) part and SU (Supervision Unit) part. CSC and LSC is composed of software, SU mainly hardware. It is mainly running background software in CSC which includes processing parameter setting with data, configuration module, and basic stand module management, and it also includes image terminal. LSC includes data transmission program, protocol transformation service program, and historical data processor etc.. MCSCS can be looked as client/server (C/S) mode entirely. Being looked from TCP/IP process procedure, LSC acts as server, terminals and its down supervision units act as clients. CSC echoes client requests and supervises their actions, they send communicational requests each other through TCP/IP protocol and transfer data; looked from system logical structure, LSC acts as server, supervision terminals (ST) as clients, ST pose request, LSC works according to concrete request, SU is server and LSC is client between relationship of them, LSC issue request to SU or send out command parameters request, SU respond to it.

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As LSC is server case, using LscServer represents server role. There are two client roles, TerminalClient represents terminal client role, SuClient represents down level client roles, the C/S mode between LscServer and TerminalClient is: CS1 == || [ TerminalClient；LscServer] TerminalClient==[ LB = y =>Request ∧ $O LB = y1 ; LB = y1 => Disposal1 ∧ $O LB = z ] LscServer == [ LB = w => Provide ∧ $O LB= v ] TerminalClient communicates with LscServer according to TCP/IP protocol through network. As LSC is both client and server case, LSC is double level roles, using LscDouble represents it. Terminal clients are represented by TerminalClient, the server role of SU is represented by SuServer. The C/S mode between LscDouble and TerminalClient is: CS1 == || [ TerminalClient；LscDouble] TerminalClient==[ LB = y =>Request ∧ $O LB = y1 ; LB = y1 => Disposal1 ∧ $O LB = z ] LscDouble == [ LB = w => Provide ∧ $O LB= v ] TerminalClient communicates with LscDouble according to TCP/IP protocol through network by means of message. Data transmission program is situated in foreground LSC server, the upper is connected with service program, the lower is connected with serial interfaces (NPORTi, i=1…n), achieving double communication. Because of data transmission through network by means of message mode, it is hard to avoid bringing about errors in data transmission process for outer interference. MCSCS is demanded data to be reliable and real time, so command parameters from terminal reception and data from lower send to upper must be right, there for a data transmission program is needed, which fulfils the process of data analysis, check, and repacked. The data transmission program act as filter, which similar to a instance of pipe-filter style[8]. The filter object is two messages which corresponds to upgoing and down data (the upgoing data is that from SU send to LSC, the down data is that from LSC send to SU). The input data of the filter is get from buffer, after the data read from buffer, they are divided package, verified and packed, at last the data is put to queue waiting to be transmitted. Configuration environment is a auxiliary tool that was attached to supervisor control software, user needn’t professional programming to fulfill special function. Because the system requires displaying configuration environment for figure mode, it is used some configuration tools of editing figure interface. After configuration environment was built, we find suitable components to insert in the architecture based on definite requirements. If components just satisfy the requirement, we can only program link code; if the components has sum distance to requirements, we can properly modify the components and make them satisfy the requirements; we program corresponding code on the requirements, satisfy the interfaces demands of function requirements and architecture.

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4 Conclusion This paper applies the based on components/architecture development idea to real supervisor control system. In course of development, we combine the black with white box reuse way to build the system. If the component can directly be used, we only developed interface program, or if it is not directly, we make suitable modification to fulfill white box reuse. The program of MCSCS in XYZ/E has been transformed C++ program through corresponding transformation tools, and has run correctly.

References 1. Pat Hall, Educational Case Study –What is the model of an ideal component? Must it be an object? Third International Workshop on Component-Based Software Engineering: Reflection on Practice. Papers: 2000 International Workshop on Component-Based Software Engineering 59-62 2. Ralph E. Johnson , Components, Frameworks, Patterns, ACM Software Engineering notes 1997,22(3)10-18 3. MEI Hong, Software Component Composition based on ADL and Middleware, SCIENCE IN CHINA(Series F), 2001,44(2)136-151 4. Premkumar T. Deranbu, Next Generation Software Reuse, IEEE Transactions on Software Engineering, 2000,26(5)423-424 5. Tang Zhi Song, Temporal logic programming and software engineering, Beijing: Scientific publishing house, 2002(5)40-66 6. Tang Zhi Song, Object, meaning and application of XYZ system, journal of software,1999,10(4)337-341 7. Zhang Guang Quan, Software architecture concepts, styles and its descriptive language, Journal of Chong Qing Teacher-training Institute, 2000, 17(3)1-5 8. Mary Shaw, Software Architecture Perspectives on An Emerging Discipline, Prentice Hall,(1996)

A Procedure Search Mechanism in OGSA-Based GridRPC Systems Yue-zhuo Zhang, Yong-zhong Huang, and Xin Chen Department of Computer Science & Technology, Information Engineering University of PLA, Zhengzhou Henan 450002,China [email protected]

Abstract. This paper presents a way of searching remote procedures in OGSAbased GridRPC systems. GGF recommends a grid-enabled, remote procedure call mechanism (GridRPC) to provide a low barrier to acceptance for grid by providing a well-known and established programming model that allows the full use of grid resources while hiding the tremendous amount of infrastructure necessary to make grids work. In this paper, by defining a kind of Grid service called Procedure Search Service in OGSA, we present a procedure search mechanism for discovering the remote procedures in grid computing implementations based on OGSA and GridRPC.

1 Introduction Although Grid computing is regarded as a viable next-generation computing infrastructure, the widespread adoption of grid is still hindered by several factors [1]. One of the factors is that for an application programmer, it is very difficult to program directly on Globus I/O. In order to provide a low barrier to acceptance for grid use, GGF will produce a recommendation for a grid-enabled, remote procedure call mechanism (GridRPC) [1]. This GGF proposed recommendation will primarily consists of an Application Programming Interface (API), and associated programming model, that will enable simple, RPC-based use of grid computing resources. GridRPC will provide a well-known and established programming model that allows the full use of grid resources while hiding the tremendous amount of infrastructure necessary to make grids work. A draft programming model and API already exist [1].The current GridRPC model and API presented by GGF is a first-step towards a general GridRPC capability, there are certainly a number of outstanding issues regarding widespread deployment and use, one of which is simply discovery. Currently a remote procedure is discovered by explicitly asking a well-known server for a well-known function through a name string lookup. Establishing this function-to-server mapping is all that the user cares about and, hence, the GridRPC model does not define how discovery is done. This paper will discuss how to find the remote procedures for an application running in a grid computing environment which is based on OGSA [2] and uses GridRPC mechanism.


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2 Related Works Currently you can implement GridRPC by Netsolve or Ninf-G[1]. The current GridRPC model has four prototype implementations and has not built on OGSA. As we know, OGSA is called the next generation grid architecture; it will be widely used in building grid computing environments. In grid computing systems that based on OGSA and GridRPC, the remote procedures can be seen as a kind of Grid service, and they can be registered and searched by the way that OGSA provides. In the following part of this paper, we will define the remote procedure as a kind of Grid service called Remote Procedure Service. The discovery of remote procedure becomes the discovery of such a Grid service instance by querying the registers. We will define a kind of Grid service called Procedure Search Service; this kind of service is used specially for searching Remote Procedure Service. A client can find the procedures it wants simply by sending its requirements of the procedures to the Procedure Search Service; and then the Procedure Search Service will return a number of procedures, each of them accords with the client’s requirement. The client can select a valid procedure from these procedures without query the registry service for many times if the procedure that it found is not valid.

3 Remote Procedures Search Mechanism 3.1 Remote Procedure Service Definition We will discuss how to define the remote procedures as a kind of Grid service at first, and we call this kind of Grid service Remote Procedure Service. The Remote Procedure Service will be consisting of Grid service data and Grid service interfaces. The part of Grid service data including all the information required specifying a remote procedure, such as the name, the parameters and functions of the procedure. The Grid service interfaces include at least three portTypes [2]: GridService, Registration and Factory; the other portTypes can also be included in the definition of the service if required.

3.2 Procedure Search Service Definition We define the Procedure Search Service to search remote procedures in OGSA-based GridRPC systems. A group of remote procedures’ GSHs will be returned to an application, which wants to find a procedure via sending a query to a Procedure Search Service. The Procedure Search Service’s interface includes five core portTypes in OGSA: GridService, Factory, Registry, NotificationSource, NotificationSink and a userdefined portType: Compare.

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Procedure Search Service implements a user-defined portType called Compare; and this portType will help the service to compare the WS-Inspection document [3] returned by a register with the specification document comes from a client which specified what kind of procedure it wants to find, if the former is the match of the later the Procedure Search service will return the GSH to the client as an answer.

3.3 Procedure Search Course in Details There are three roles in the discovery course: an application which we call it a client, a Procedure Search Service and the registers. The main idea of the discovery mechanism is that a client sends it requirements of the remote procedure to an Procedure Search Service at first, then the service subscribe information of remote procedures registered in a register, if the requirement accord with the information returned by the register, it means that the procedure with such information is the answer. The discovery course can be described as the flowing steps: 1. A client sends its procedure requirement to a Procedure Search Service. 2. The Procedure Search Service describes the information registered in a local register as notification sink; and the local register sends the information registered in it to the Procedure Search Service periodically. 3. The Procedure Search Service compare the information returned by the register with the requirement comes from the client, if the former is the match of the later it will register the corresponding Grid service’s GSH in itself. The information returned by the local register maybe refer to a register; the Procedure Search Service will also register the GSH of such a register. With the register of other registers, the Procedure

Client

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getRegisteredInformation Ack(GSHs,WS-Inspection) Compare registerAcceptedGSHs getRegisteredInformation Ack(GSHs,WS-Inspection) Compare registerAcceptedGSHs

Ack(GSHs)

Fig. 1. Sequence diagram of procedure search course

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Search Service can search in such registers if it can’t find valid information from the local register, in this case we call the local register the first register and the other registers the second, the third, etc. 4. Init the registers that registered in the Procedure Search Service as a set, take the second register out and search it by following step2 and step3, then take the third, four, etc. After each search, some new registers will be added in the set, search each of the registers iteratively and delete the register from the set if it has been searched. If the set is null, stop the searching. 5. The Procedure Search Service can search the registers in the order of depth-first or breadth-first. 6. The search will not stop until a number of procedures have been found, or it will stop when the search time reaches a predefined threshold value. The Procedure Search Service unregistry all the registers that has been registered in it. Now, what registered in the Procedure Search Service is GSHs of the Remote Procedure Services that can meet the client’s need. 7. The Procedure Search Service returns the GSHs registered in it to the client. If it can not find a remote procedure that meet the client’s need, it will return a fault message and ask if the client want to search again, if the client want to search again, the steps mentioned above will be repeated. Figure 1 illuminates the procedure search course.

4 Discussion and Conclusions We discuss a procedure discovery mechanism in OGSA-based GridRPC systems. We define the remote procedures as a kind of Grid service called Remote Procedure Service and design a Procedure Search Service as an agent for discovering remote procedures instead of discovery by directly querying the registry service. The discovery mechanism presented above can obtain a group of procedures that accord with the client’s requirement. The client can find a valid procedure from them without querying the registry service for many times.

References 1. Hedemoto Nakada, Satoshi Matsuoka, Keith Seymour, Jack Dongarra. GridRPC: A Remote Procedure Call API for Grid Computing (2002). http://graal.ens-lyon.fr/GridRPC/pub/APM_GridRPC_0702.pdf 2. S. Tuecke, K. Czajkowski, I. Foster, J. Frey, S. Graham, C. Kesselman, T. Maguire, T. Sandholm, P. Vanderbilt, D. Snelling, S. Open Grid Services Infrastructure (OGSI) Version 1.0. Global Grid Forum Draft Recommendation (6/27/2003). http://www.globus.org/research/papers/Final_OGSI_Specification_V1.0.pdf

An Improved Network Broadcasting Method Based on Gnutella Network* 1,2

2,3

1

Zupeng Li , Xiubin Zhao , Daoyin Huang , and Jianhua Huang 1

1

National Digital Switching System Engineering & Technological R&D Center, [email protected] 2 Telecommunication Engineering Institute, Airforce Engineering University 3 Northwestern Polytechnical University No. 783 P.O.Box 1001, Zhengzhou, 450002, P.R.China Tele: 86-371-3532770; Fax: 86-371-3941700

Abstract. Peer-to-peer networking is a hot buzzword that has been sweeping through the computing industry over the past year or so. Gnutella, as one of the first operational pure P2P systems, is considered as an important case study for P2P networking. By analyzing the Gnutella network topology data, we can discover both the small diameter and the clustering properties characteristic of "small-world" networks. Based on this, the paper aims at analyzing and predicting the performance of current broadcasting algorithm and proposing an improved broadcasting method on Gnutella. By avoiding the unnecessary message forwarding in the network, flow of network communication is remarkably reduced in the new algorithm.

1 Introduction Peer-to-peer network (P2P) technology is a currently emerging technology in the network research domain [1,2].In the P2P network research domain, Gnutella [3] is considered to be the first completely decentralized peer-to-peer protocol which has been created. The first client was written largely as an experiment by developers at Nullsoft, a subsidiary of AOL. Upon launch, Gnutella was swiftly labeled an "unauthorized freelance project" by AOL and removed from the Nullsoft website. The Open Source community soon continued its development and there now exist billions of clients operating the protocol. In this paper, the "small-world" property of Gnutella network is firstly discussed. In the following section, we present an analysis of the problems of Gnutella network. Armed with the "small-world" property of the underlying network topology, an improved network broadcasting method - Intelligent Network Broadcasting Method (INB) is proposed to avoid the unnecessary message forwarding in the Gnutella network. Finally the conclusion is given in the last part. _____________________________ * This research is supported by the National High Technology Development 863 Program of China under Grant Nos. 2001-AA-11-1-141.


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2 "Small-World" Properties The term "small-world" originated with a famous social experiment conducted by Stanley Milgram in the late 1960s. By analyzing the Gnutella network topology data obtained by, we can discover both the small diameter and the clustering properties characteristic of "small-world" networks. Table 1. Clustering coefficients for the Gnutella, Barabási-Albert, Watts-Strogatz, random graph, and the 2D torus topologies (l=2) Gnutella BA WS G(n,p) 2D torus 11/13 0.0224 0.015 0.0373 0.004 0.0606 11/16 0.0089 0.0096 0.0372 0.0025 0.0606 12/20 0.0301 0.0179 0.0537 0.0062 0.0606 12/27 0.0206 0.0185 0.0539 0.0062 0.0606 12/28 0.0207 0.0174 0.0536 0.0056 0.0606 Table 2. Characteristic path length for the Gnutella, Barabási-Albert, Watts-Strogatz, random graph, and the 2D mesh topologies Dd Gnutella BA WS G(n,p) 2D 11/13 3.7230 3.4749 4.5971 4.4873 20.6667 11/16 4.4259 4.0754 4.6116 5.5372 21.3333 12/20 3.3065 3.1902 4.2249 3.6649 22.0000 12/27 3.3036 3.1805 4.1917 3.7100 21.3333 12/28 3.3282 3.2075 4.2520 3.7688 22.6667

The values for the Gnutella topology graphs are benchmarked against two widely used "small-world" models, the Watts- Strogatz [4] and the Barabási-Albert model [5], the random graph and the 2-D torus in tables 1 and 2. As can be seen, all of the Gnutella topology snapshots demonstrate the "small-world" phenomenon: characteristic path length is comparable to that of a random graph, while the clustering coefficient is an order of magnitude higher. These results clearly indicate strong "smallworld" properties of the Gnutella network topology.

3 Intelligent Network Broadcasting Method 3.1 Problems of Gnutella Network Gnutella, as one of the first operational pure P2P systems, is considered an important case study for P2P networking. It consists of many peers, all of which are similar in functionality. There are no specialized directory servers. Peers must use the network of which they are part to locate other peers. As outlined in [6], the main problem with Gnutella is its use of broadcasts for searching (and discovering) the network. As the network grows in size, not only does the rate of messaging increase, but the traffic potentially generated by each message increases too.

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3.2 Description of INB Algorithm According to the "small-world" property, the system will tend to form more highly connected clusters of nearby machines in use. The important fact is that if nodes A and B are connected, along with B and C, then A is more likely to be connected to C than if the network was purely randomly connected. With more clustering, this effect will be more apparent. We can exploit this to remove much of the redundancy in broadcasting. Firstly, the algorithm assumes each node knows who each of its immediate neighbors is connected to. This knowledge can easily be passed to neighboring nodes, either with regular refreshes, or notifications of changes. Armed with this information, some unnecessary message forwarding can be avoided, which is shown in (a) of Fig. 1:

B

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Fig. 1. Example I of intelligent network broadcasting

In this small example, node A receives a broadcast message and forwards it to B and C. With Gnutella, B and C would then forward the message to each other (and both promptly ignore the repeat), resulting in two unnecessary messages. However, with this scheme B knows that C is connected to A, so will have already received the broadcast. Hence, B does not forward to C, and similarly C does not forward to B. We can also do slightly better, if we make the algorithm a little more complex, as we can see in (b) of Fig. 1.Implemented Gnutella-style (assuming nodes do not forward the message back to where it was received from), this would again result in two wasted messages. In this scheme, C uses the knowledge that A is connected to B and is connected B to D to imply that D will have already received the broadcast from A. Of course, B could imply the same given the knowledge that C is connected to both A and D, in which case B would not forward the message to D. To prevent this, some ordering of nodes is needed, so that in these situations the nodes know which one needs to forward the message. Using standard alphabetical ordering above, B must forward the message as B 2. We give two definitions as important as the D4 to explore and describe the internal dynamic characterization of the network traffic as follows. Dq = lim

r→0

Definition 2. Let Dq be the multifractal spectrum of the network traffic, the fractal spectrum width α is defined as follows: α = Dmax − Dmin

(3)

where Dmax = max{Dq |q ∈ I}, Dmin = min{Dq |q ∈ I}. Definition 3. Let Dq be the multifractal spectrum of the network traffic, denote ∆i = Dqi − Dqi−1 , i ∈ I, thus ∆ = max{∆i |i ∈ A} is called the sensitive dimension difference.

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The experiment environment where we performed our monitor and analysis is an Ethernet made up of over 10 hosts. We choose the Ethernet because it is a typical small-sized LAN, whose traffic is moderate and has a relatively concentrative working hour. We recorded the packets/s and octets/s values everyday and stored them into sample pool according to their own types. Then samples of packets/s at different hours or types are chosen as experiment objects. It has been proved that the two distinct signal datas may have the same fractal dimension[6]. On the other word, it is, however, not enough to characterize the internal dynamic structure of the traffic burst from those macro nonlinear quantities, such as the Fractal dimension, Kolmogorov entropy and the largest Lyapunov exponent.

(a)

(b)

Fig. 1. (a)The four kinds of network traffic models (b).The multifractal spectrum curves D1 ∼ q of the four kinds of network traffic models

Table 1. The Multifractal Parameters of the four kinds of traffic model model model model

A B C D

α 3.05 3.36 5.665 6.379

D0 1.3 2 0.31 1.6

D2 0.45 0.76 0.1 0.68

∆ 1.2 2.3 3.37 3.68

So, in this section, we will study the characteristics of the network traffic by the multifractal theory. Firstly we sort out four kinds of network flow models approximately according to their different burst characters. They are: smooth flow model, continuous burst flow model, intermittent burst flow model and single burst flow model with highest peak value. Fig.(1-a) shows example of such four kinds of traffic model in real network. Then we calculate the multifractal spectrum Dq of these traffic models using the improved GP algorithm, where q=-8,-6,-0,-6,8, as q=2. As shown in Fig. (1-b), their incidence dimensions D2 are quite similar, but the multifractal spectrum curves of the four traffic models are substantially different. So it is

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proved that the multifractal spectrum curves are more effective characters to describe the network traffic dynamics than those scalar quantities, especially for the outburst traffic. In addition, as the iteration value q increases, the multifractal spectrum Dq of the model A gradually decreases, which is resemble to the sigmoid curve of the fractal function, but the Dq of others changes abruptly on some iteration value q. In other word, the Dq ∼ q curve of the smooth traffic model exhibits better steady than the other three burst traffics. Hence it verifies that the network traffic dynamics will change seriously when emergent traffic is coming. In order to further analyze the traffic characters of the different burst traffic, we get some multifractal parameters of the four kinds of traffic, such as the fractal spectrum width, the sensitive dimension difference and the capability dimension, where the fractal spectrum width and the sensitive dimension difference could be calculated through definition 2 and definition 3, respectively. As shown in Table.1, these quantities of the smooth traffic model are all less than those of others traffic models, and their value will be increases abruptly with the emergent traffic coming. Moreover the more outstanding the traffic burst is, the larger these multifractal parameters become. Thus in the model D, these multifractal parameters values of are the largest ones because there are the highest peak value, whereas the parameters values of the model B are close to those of model A, because the outburst flow in model B is standing and average. So we can draw the similar conclusions: these multifractal parameters are the valid characteristic quantities to the distinct dynamic characterization of the different burst traffic models.

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In this paper, we analyze network flow time sequence that we collected in real traffic, by applying chaos and fractal theory. By studying the multifractal spectrum curves Dq ∼ q, we obtained micro quantities of the four kinds of network traffic models. The results verify that these multifractal parameters are effective characteristic quantities to depict the network internal dynamics, especially for the outburst network traffic. Theoretically, these parameters can reflect macronature of network flow behavior from different aspects.

References 1. Feldmann A., Gilbert A. C. et al.: Data networks as cascades: Inverstigating the multifractal nature of Internet. WAN traffic [C]. Vancouver, Canada: Proc. of the ACM Sigcomm’98 (1998) 25–38 2. Gilbert A. C. et al.: Scaling Analisis of random cascade, with applications to network traffic [J]. IEEE Trans. Inform, Theory. 3 (1999) 971–991 3. Takens F.: Detecting strange attractors in turbulence[J]. Lecture Notes in Math, 1981, (898):366-381. 4. Grassberger Procaccia I.: Dimension and Entropy of Strange Attractors from a Fluctuaing Dynamics Approach[J]. Physica. 1984,13D:34.

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5. V. Paxson, S. Floyd.: Wide area traffic – The failure of Poisson modeling, IEEE/ACM Trans. Networking. 3 (1995) 226-244 6. Arduini F., Fioravanti S., Giusto D. D.: A multifractal-based approach to natural scene analysis[C]. NY, USA: International Conference on Acoustics Speech and Signal Processing. IEEE. 4 (1991) 2681-2684

Network Behavior Analysis Based on a Computer Network Model* Weili Han, Dianxun Shuai, and Yujun Liu East China University of Science and Technology, 200237, Shanghai, P.R.China [email protected], [email protected], [email protected]

Abstract. This paper applies a new traffic model, iterated function systems (IFS) for network traffic modelling, to explore computer network behaviour and analyse network performance. IFS model can generate various self-similar data flows according to a certain character. In the light of the experiments, we have discovered interesting phenomena. It is found that increasing the routers’ processing capacity will have quite different influence on the power spectrum from that for enlarging the routers’ buffer size. Furthermore, phase transition and 1 / f n type power law is found under mixed traffic model.

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IFS for Fractal Traffic Modeling of Computer Networks

In recent years the fractal nature of Internet traffic has been observed [1, 2], and many self-similar models for network traffic (such as on/off models, FARIMA models, etc.) have been presented so far [3]. Here we introduce a new approach, IFS, to produce the fractal network traffic. Definition 1. The two dimensional affine transform Ω : R 2 → R 2 is defined as

 x   a b   x1   e     +   Ω :  2  =   y 2   c d   y1   f 

(1)

When b=0, c=0, 0 Hosting environment requests revoking a Grid Service, because time expires. < I4, R>→< O4, S> Revoking a Grid Service, not because time expires. User-defined Loading: Based on the above two definition of Σ and f, we only define the additional elements. Σ3 ={ I3} I3: consumer-defined GSL. < I3,I2,L>→< O0,I> Loading a consumer-owned Grid Service which its Loader has made proper reconfiguration on it

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→< I3,I4,R> Hosting environment request revoking consumer-defined GSL < I3,I4,R>→ Revoking a consumer-defined GSL Based on the above definition of K, Σ and f, we should additionally provision a Systematic GSL and a mechanism for dynamical installation and initialization of Grid Services and management of consumer-owned Grid Services, including loading and revoking, on existed hosting environments.

3 Grid Service Naming In the foregoing section, we represent Grid Service using , and N denotes the name of a Grid Service. Before a Grid Service is indeed loaded to the target hosting environment, the Grid Service Naming (GSN) convention and the definition of equivalence of Grid Services proposed in this paper can distinguish the Grid Service. Definition 4. GSN := P + SV. P: A set of the Qname of portType, all operations in the portType, and all type defines employed by the operations. All of three parts are described in Grid Service WSDL. In practice, we use hash codes to represent P. For example we use MD5 to hash every parts of the portType file. SV: Semantic Version is a set of semantic strings. Each semantic string represents semantic activates of one operation of a Grid Service PortType. In CORBA and COM, the version and IDL file identify a component. But version and IDL are not semantic. To extend this model, Grid Service provider can defines a Semantic Version for the service. One semantic string for an operation not only defines a version of the operation but also represents its semantic. Suppose that a provider provides two different Grid Services with an identical WSDL description, but function of two Grid Services is different; thus, the provider should give two different SV to the two Services. And we don’t limit the content of SV. Furthermore, provider must define a mechanism to apprehend SV and how to distinguish two Grid Services by their SV. Definition 5. iff GS1 = GS2↔ GSN1 = GSN2↔P1= P2, SV1= SV2, where GS1, GS2 are two Grid Services. Definition 6. if P1≠ P2 →GSN1≠ GSN2, i.e. If portTypes of two Grid Services are different, the Grid Services are different. Definition 7. If two Grid Service (GS1, GS2) have identical WSDL descriptions but one or more operation(s) of them have different semantic strings and the rest operations have identical semantic strings, we call that GS1 is partly equal to GS2, represented as GS1≌GS2

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4 Security Considerations and Implementation We adopt the security mechanism of Grid Security Infrastructure (GSI). Grid Service Loader (GSL) is implemented as a secure Grid Service with the authorization policy predefined by the target hosting environment, and consumers must use a valid certificate to access the secure GSL. The GSL will authenticate consumers and authorize them according the authorization policy. Besides, hosting environment must limit what the consumer-defined GSL can do and what information it can get. Furthermore, the Grid Service loading should not affect other on-serving Grid Services in the same hosting environment. So when we implement the Grid Service management scheme, our strategy is giving a sub hosting environment, an abridged version of the hosting environment, which runs separately from the hosting environment, runs as a special system user probably differing from the system user who startups the hosting environment, and supports run-time environment for Grid Services. Based on this design, we let consumer-defined GSL run in sub hosting environment, and after Grid Service successful loading, the consumer-defined GSL will be revoked and the sub host environment will be shutdown. Fig 1 gives us the implementation mode of this scheme and Fig 2 gives us the detail framework of Grid Service Lifecycle Management.

Grid Ftp

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Fig. 1. Architecture of hosting environment (left) and sub hosting environment (right)

Installation

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Fig. 2. Components in Grid Service Lifecycle Management

5 Related Works We have referred to a range of related works in the body of the paper. Here we make additional comments concerning the use of globus toolkit, other works on component technologies and JVM. We use globus toolkit 3 as our infrastructure, especially OGSI implementation and of it. But globus toolkit doesn’t support management of whole

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Grid Service lifecycle. We refer lifecycle of a Class specified in JVM to define Grid Service lifecycle and the model of GSN comes from illumination of version definition and IDL in component technologies.

6 Conclusions and Future Work Definition of Grid Service lifecycle in this paper, complementary to the definition of lifecycle of Grid Service instance specified in OGSI specification, covers the whole life cycle of a Grid Service, which comprises 5 phases of loading, installation, initialization, Serving, and revoking. Based on the definition, a powerful mechanism for dynamically loading Grid Services on the hosting environment has been proposed and the notion of Grid Service Loaders presented in this paper has changed the general pattern of Grid Service usage. In the past, a grid Service must be well deployed before consumers access it and consumers must follow the fixed procedures prescribed by service provides, which have limited to most extent the interaction between consumers and providers. In future, computing should be consumer-centered other than now popularly provider-centered. Under such circumstance, consumers can upload own services to fulfill their whole business by employing other services provided by Grid Service providers. But this facility will always violate security of hosting environments. So we must take further consideration in secure violation of this mechanism and system protection of hosting environments. The Grid Service naming convention proposed in this paper is an experimental solution to the key question in the field of grid computing: how to identify a Grid Service. In this paper, we have suggested a definition of equivalency of two Grid Services. And we have first proposed semantic string as a part of identification of a Grid Service. But its implementation mechanism need further research.

References 1. S. Tuecke, K. Czajkowski, I.Foster, J. Frey, S. Graham, C. Kesselman, T. Maquire, T. Sandholm, D. Snelling, P.Vanderbilt: Open Grid Services Infrastructure (OGSI) Version 1.0. Global Grid Forum 2. I. Foster, C. Kesselman, J. Nick, S. Tuecke: The Physiology of the Grid: An Open Grid Services Architecture for Distributed Systems Integration. Globus Project, 2002. 3. Yih-Shin Tan, Brad Topol, Vivekanand Vellanki, Jie Xing: Business Service Grid. IBM developerWorks(2003). 4. Czajkowski, S. Fitzgerald, I. Foster, C. Kesselman: Grid Information Services for Distributed Resource Sharing. Proceedings of the Tenth IEEE International Symposium on HighPerformance Distributed Computing (HPDC-10), IEEE Press, August 2001. 5. R.Raman, M.Livny, and M.Solomon: Matchmaking: Distributed Resource Management for th High Throughput Computing, In proc. 7 IEEE Symp. On High Performance Distributed Computing. IEEE Computer Society Press, 1998.

An OGSA-Based Quality of Service Framework Rashid Al-Ali1,2 , Kaizar Amin1,3 , Gregor von Laszewski1 , Omer Rana2 , and David Walker2 1

Argonne National Laboratory, Argonne, IL, U.S.A 2 Cardiff University, UK. 3 University of North Texas, U.S.A.

Abstract. Grid computing provides a robust paradigm to aggregate disparate resources in a secure and controlled environment. Grid architectures require an underpinning Quality of Service (QoS) support in order to manage complex data and computation intensive applications. However, QoS guarantees in the Grid context have not been given the attention they merit. In order to enhance the functionality offered by computational Grids, we overlay the Grid framework with an advanced QoS architecture, called G-QoSM. The G-QoSM framework provides a new service-oriented QoS management model that leverages the Open Grid Service Architecture (OGSA) and has a number of interesting features: (1) Grid service discovery based on QoS attributes, (2) policy-based admission control for advance reservation support, and (3) Grid service execution with QoS constraints. This paper discusses the different components of the G-QoSM framework, in the context of OGSA architectures.

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Introduction

Grid computing [1,2] has traditionally focused on large-scale sharing of distributed resources, sophisticated applications, and the achievement of high performance. The Grid architecture integrates diverse network environments with widely varying resource and security characteristics into virtual organizations (VO). Computational Grids offer a high end environment that can be exploited by advanced scientific and commercial applications. Soft Quality of Service (QoS) assurances are made by Grid environments by the virtue of their establishment. Grid services are hosted on specialized “high-end” resources including scientific instruments, clusters, and data storage systems. High connectivity is maintained between resources via dedicated high-speed networks. A well-established resource administration facilitates constant resource connectivity, resource monitoring, and fault tolerance. Hence, some preliminary level of QoS is provided by the committed members of the VO based on their pre-agreed Grid policy and their dedication in the overall collaboration. Nevertheless, the complexities involved in several critical Grid applications make it imperative to provide hard and guaranteed QoS assurances beyond that provided by the basic Grid infrastructure. Considering the increasing sophistication of Grid applications and new hardware under development [3] such provisions become an inherent requirement within the Grid architecture. This implies a need for a QoS management entity that facilitates a negotiation mechanism, where the clients can select the appropriate resources with QoS constraints that suit client needs. M. Li et al. (Eds.): GCC 2003, LNCS 3033, pp. 529–540, 2004. c Springer-Verlag Berlin Heidelberg 2004

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Motivated by this need, to overlay an advanced QoS framework on existing Grid architectures allowing them to support complex QoS requirements, we propose a QoS management framework, called as G-QoSM. Supporting the recent standardization efforts of the Global Grid Forum [4], the G-QoSM framework is based compatible with the latest Open Grid Services Architecture (OGSA) specification. The G-QoSM framework presented in this paper has a number of important features: (1) a ‘QoS brokering service, 2) a ‘policy service and 3) a generic resource ‘reservation manager that includes: – support for advance and immediate reservation, – support for single and collective resource reservations (co-reservation), – accommodation of arbitrary resource types, for example, compute, network and disk, and – scalability and and flexibility through an object-oriented that is uses underlying resource characteristics at run-time. The paper is structured as follows. In Section 2 we provide an overview of related research in the area of resource reservation to support QoS needs. In Section 3.1 we outline the general requirements of the Grid QoS model, and present the OGSA-based G-QoSM framework with reservation support. In Section 7.1 we define the reservation, and we present a reservation admission control mechanism and reservation features. We conclude the paper with a summary of conclusions.

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Related Work

Immediate and advance reservation is considered in a wide variety of systems mostly in networking, communication, and distributed applications including distributed multi media applications (DMM). Hence it is of considerable interest to the Grid community. – In the context of Grid computing, GARA [5] is a QoS framework that provides programmers a convenient access to end-to-end QoS. It provides advance reservations with uniform treatment to various types of resources such as network, compute, and disk. GARA’s reservation is a promise that the client/application who initiated the reservation will receive a specific level of service quality from the resource manager. GARA also provides reservation application program interface (API) to manipulate reservation requests, such as, create, modify, bind and cancel. – NAFUR [6] describes the design and implementation of a QoS negotiation system with advance reservation support in the context of DMM applications. NAFUR aims to compute the QoS that can be supported at the time the service request is made, and at certain carefully-chosen, later times. For example, if the requested multimedia service with the desired QoS cannot be supported at the time the service request is made, the proposed approach allows the computation of the earliest time the user can start the multimedia service with the desired QoS. – In [7] a resource broker (RB) model in the context of middleware for DMM application is proposed. The proposed RB has the following design goals: 1) advance and immediate reservation, 2) a new admission control scheme based on using a timely adaptive state tree (TAST) and 3) the RB processes brokerage requests for reservation, modifications, allocation and release.

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– In [8] advance reservation is formalized in the context of networking systems and the fundamental problem of admission control associated with resource reservation is introduced. Based on the authors literature review it is concluded that none of the previous approaches is sufficiently flexible to cover all potential needs of all users. The proposed solution to this fundamental problem is to separate the issue into a technical and a policy part supported by a specifying a generic reservation service description and a corresponding policy layer. This combination improves the flexibility of resource advance reservation compared to the other approaches. None of the research efforts address advance reservation in the context of service-oriented architecture, as in our approach. In general, resource reservation is not widely explored in service-oriented Grids. Nevertheless, the GGF Grid Resource Agreement and Allocation Protocol (GRAAP) Working Group, has produced a ‘state of the art’ document, which lays down properties for resource reservation in Grids [9]. We envision that our reservation model can be used to support the reservation properties outlined by the GRAAP-WG. The features that distinguish our work from existing QoS management approaches are that the – generic QoS management service is not coupled to any specific resource type, or even limited to resource quantity; – the object-oriented design and the abstraction approach gives the proposed service the ability to integrate with any brokerage system that supports web service interaction; – dynamic information gathering and management, such as, resource characteristics and policy information improves scalability; and – usage policy frameworks for resource providers/administrators and users to enable a fine-grained request specification. In addition to the projects mentioned above, a general negotiation model called Service Negotiation and Acquisition Protocol (SNAP) is introduced in [10], which proposes a resource management model for negotiating resources in distributed systems. SNAP defines three types of SLAs that co-ordinate management across a desired resource set, and can, together, be used to describe a complex service requirement in a distributed system environment: task SLA (TSLA), resource SLA (RSLA) and bind SLA (BSLA). The TSLA describes the task and the RSLA describes the resources needed to accomplish the task in the TSLA. The BSLA associates the resources from the RSLA and the application ‘task’ in the TSLA. The SNAP protocol necessitates the existence of resource management entity that can provide promises on resource capability; for example, RSLA. Therefore, our reservation model can encapsulate such a requirement and implement the RSLA negotiation.

3 The Proposed QoS Framework In this section we introduce the proposed Grid QoS Management framework. We outline general requirements for the framework, and then we provide discussion on QoS management and the proposed system.

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Requirements

The proposed framework must adhere to certain important requirements: Service Discovery. The system should be able to discover services based on QoS attributes. These attributes are a) quantitative and b) qualitative. For example, quantitative attributes include computation, networking and storage requirements, while qualitative attributes include the degree of service reputation and service licensing cost. To support service discovery based on these attributes, a discovery mechanism needs to be employed within the proposed framework. Resource Advance Reservation. The system should support mechanisms for advance, immediate, or ‘on demand’ resource reservation. Advance reservation is particularly important when dealing with scarce resources, as is often the case with high performance and high end scientific applications in Grids. Reservation Policy. The system should support a mechanism which facilitates Grid resource owners enforcing their policies governing when, how, and who can use their resource, while decoupling reservation and policy entities, in order to improve reservation flexibility. [8]. Agreement Protocol. The system should assure the clients of their advance reservation status, and the resource quality they expect during the service session. Such assurance can be contained in an agreement protocol, such as Service Level Agreements (SLAs). Security. The system should prevent malicious users penetrating, or altering the data repositories that holds information about reservations, policies and agreement protocols. A proper security infrastructure is required, such as Public Key Infrastructure (PKI). Simple. The system should have a simple design that requires minimal overheads in terms of computation, infrastructure, storage, and message complexity. Scalability. The system should be scalable to large numbers of entities, as the Grid is a global scale infrastructure. 3.2

Grid Quality of Service Management

Grid Quality of Service Management (G-QoSM) is a new approach to supporting Quality of Service (QoS) management in computational Grids, in the context of Open Grid Service Architecture (OGSA). QoS management includes a range of activities, from resource selection, allocation, and resource release; activities applied in the course of a QoS session. A QoS session includes three main phases: i) the establishment phase, ii) the active phase, and iii) the clearing phase [11]. In QoS-oriented architectures,during the ‘establishment phase’, a client’s application states the desired service and QoS specification. The QoS broker then undertakes a service discovery, based on the specified QoS properties, and negotiates an agreement offer for the client’s application. During the ‘active phase’, additional activities, including QoS monitoring, adaptation, accounting and possibly re-negotiation, may take place. The ‘clearing phase’ is responsible to terminate QoS session, either through resource reservation expiration, agreement violation or service completion, and resources are freed for use by other clients.


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Quality of service management has been explored in a number of contexts, particularly for computer networks [12], multimedia applications [13] and Grid computing [5]. Regardless of the context, a QoS management system should address the following needs: – – – – – – – –

Specifying QoS requirements. Mapping QoS requirements to resource capabilities. Negotiating QoS with resource owners - where a requirement cannot be exactly met. Establishing service level agreements (SLAs) with clients. Reserving and allocating resources. Monitoring parameters associated with a QoS session. Adapting to varying resource quality characteristics. Terminating QoS sessions.

The G-QoSM [14] framework aims to operate in service-oriented architectures. It provides three main functions: (1) support for resource and service discovery based on QoS properties, (2) support for providing QoS guarantees at middleware and network levels, and establishing Service Level Agreements (SLAs) to enforce these guarantees, and (3) providing QoS adaptation for the allocated resources. The G-QoSM delivers three types of QoS levels: Guaranteed, Controlled Load and Best Effort QoS. At the ‘guaranteed level’, constraints, related to the QoS parameters of the client, need to exactly match the service provision. ‘Controlled load’ is similar to the ‘guaranteed’ level, with the exception that less stringent parameter constraints are defined, and the notion of range-based QoS attributes is used along with range-based SLAs. At the ‘best effort’ QoS level the resource manager has full control in choosing the QoS level without constraints, corresponding to the default case when no QoS requirements are specified. The G-QoSM is an ongoing project, previously investigated and implemented in the context of Globus toolkit (GT) 2.0, [14] [15] using the GARA framework to provide QoS support for ‘compute’ resources. However, with the emergence of service-oriented Grids, and Open Grid Service Architecture (OGSA) [16] it is necessary to introduce new features to the G-QoSM, to make it OGSA-enabled and GT3 compliant. In this new GQoSM architecture GARA is not utilized, and is replaced by a new reservation manager, policy service, allocation manager and a newly-developed Java API for a Dynamic Soft Real Time (DSRT) scheduler [17]. The new features in the OGSA-enabled G-QoSM are as follows: – – – –

QoS brokering service as a Grid service. generic resource reservation manager. policy service as a Grid service. A framework that is OGSA-enabled and can be instantiated in the context of GT3. Figure 1 shows the new G-QoSM OGSA-enabled architecture.

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QoS Grid Service

QoS Grid Service (QGS) is the focal point of this architecture and exists in every Grid node. The QGS interacts with the client’s application, the QoS selection Service, the reservation manager, and the policy Grid service to support:

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Fig. 1. Framework Architecture.

Interaction with Client’s Application. To primarily capture the service request with QoS constraints, and to negotiate a QoS agreement SLA interaction with client’s application is needed. This negotiation can be summarized as attempting to find the ‘best match’ service, based on given properties and priority levels, for example, one might request that cost has a higher priority than service reliability, and the matching process should comply with such a requirement. Once the best service match is found, and corresponding resources are reserved, an agreement offer is proposed to the client’s application. If the proposed agreement is approved, it becomes a commitment, and the QGS regards this agreement as a fixed guarantee. Otherwise resources are released and no agreement takes place. Interaction with the QoS Selection Service. In order to support basic concept queries, a QoS selection service is provided with QoS constraints similar to the one supplied by the client’s application. It’s main function is to provide information for selecting the best service. Normally, the selection service replies with a list of service matches, which necessitates the QGS selecting one of the returned services. To enable the best selection, we adapted a selection algorithm based on a Weighted Average (WA) concept, taking into account the proportional value of each QoS attribute, using the importance level supplied by the user in the ‘service request’, rather than each attribute being treated equally. The ‘importance level’ associates a level of importance or priority, such as High (H), Medium (M) and Low (L), to each QoS attribute, with this importance level mapped


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to a numerical value (real number). The algorithm computes the WA for every returned service and selects the service with the highest WA. Interaction with Reservation Manager. After selecting a Grid service the functional requirements, required in support of the reservation, are extracted and formulated as resource specifications. These resource specifications are then submitted to the reservation manager for resource reservation, and a reservation ‘handle’ is returned in the case of a successful reservation. This reservation handle can be later used to claim, or manipulate, the reservation. Interaction with Policy Grid Service. Interaction with the policy grid service enables the QGS to capture policy information necessary to validate the service request. For example, to discover if there is any limitation on resource utilization per service, or the class of service requested. The QGS validates the service request by applying the rules obtained from the Policy Grid Service.

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The Allocation Manager’s primary role is to interact with underlying resource managers for resource allocation and de-allocation, and to inquire about the status of the resources. It has interfaces with various resource managers employed in this framework, namely, the Dynamic Soft Real Time Scheduler (DSRT) [17] and a Network Resource Manager (NRM). It associates the execution of Grid services with a previously-negotiated SLA agreement, which process, of associating Grid services with SLAs, is beyond the scope of this paper. The Allocation Manager further interacts with adaptive services to enforce adaptation strategies, with more details on adaptation to be found in [15]. The DSRT [17] is a user-level soft real-time scheduler, based on the changing priority mechanism supported by Unix and Linux operating systems. The highest fixed priority is reserved for the DSRT and the real-time process admitted by the DSRT can then run under the DSRT scheduling mechanism. The real-time process can thus be scheduled to utilize a specific CPU percentage. Therefore, the compute QoS supported by the DSRT can be specified in terms of CPU percentage; for example, a real-time process might request the allocation of 40% of the CPU. The Network Resource Manager (NRM) is conceptually a Differentiated Services (Diffserv) Bandwidth Broker (BB) (a concept described in [18]), and manages network QoS parameters within a given domain, based on agreed SLAs. The NRM is also responsible for managing inter-domain communication, with NRMs in neighboring domains, to coordinate SLAs across domain boundaries. The NRM may communicate with local monitoring tools to determine the state of the network and its current configuration.

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QoS Policy Service

Policy Service is a Grid service aiming to provide dynamic information about the domainspecific resources’ characteristics and the domain’s policy concerning when, what and who is authorized to use resources. This policy service relies heavily on the existence of a policy repository, such as, the ‘policy controller’ in our framework. Resource owners

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include in the policy repository domain-specific rules; for example, resource capacity allowed to be utilized with user authentication, time of the day and class of service. These rules are utilized by the policy service manager to provide information on resource characteristics and domain policies. Having a separate policy manager as a Grid service allows the following advantages: – The ability for resource owners to update their policy repository without interfering with other broker services. – The resource owner may delegate a remote ‘super’ policy service to act as the policy controller of their resources. Similarly, a policy service might control more than a single administrative domain. – Decoupling the policy service from other broker services, allows the ability to dynamically change resource usage policy and system scalability.

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QoS Reservation Manager

Reservation support plays a major role in QoS-oriented architecture. In a shared resource environment, such as Grids, QoS brokers can provide promises on delivering certain resource quality to their clients, if, and only if, a reservation mechanism exists. A reservation can be viewed as a promise from the resource broker to clients on expected quality. Advance resource reservation is defined as: a possibly limited or restricted delegation of a particular resource capability over a defined time interval, obtained by the requester from the resource owner through a negotiation process [9]. As pointed out earlier, resource reservation can be categorized into: (a) Advance reservation and (b) Immediate or ‘on demand’ reservation, and can be for a specified duration, or indefinite. In the proposed reservation manager, we support advance/immediate reservation for a specified duration. Indefinite reservation is undesirable as it introduces blockages, which may result in a waste of unused resources. An important feature of this reservation approach is support for the co-reservation of various resources in service Grids. In this section we further discuss the formal definition of reservation, admission control and outline reservation features. 7.1

Reservation Definition

We define a reservation model for collective Grid resources, with as few restrictions as possible, to increase the flexibility of the admission control. The fundamental problem with advance reservation, as discussed in literature [8], is that when an advance reservation is granted, the time from when the reservation is submitted until the start time, is called ‘hold-back time’, and to utilize, or grant, reservations during hold-back time is a complex problem. The problem arises when clients request immediate reservation for an indefinite period, which may, obviously, overrun a previously-granted advance reservation. A number of solutions are proposed to solve this problem; for example, all reservations, including immediate reservation, must be specified within a time frame (i.e. indefinite reservation is not supported); another solution proposes to partition resources for immediate reservation, and advance reservation with specified durations. In


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this model we opt for the first proposal; that all reservations must be accompanied by duration specifications. We consider this a valid assumption as we deal with high performance resources, and application domains, like scientific experiments or simulations, means there is prior knowledge of the need for such resources, and no ad-hoc requests for simple resources. We formally define reservation R in terms of the following (5) parameters: ts : reservation start time te : reservation end time cl : reservation class of service ri : each resource i has a resource type. Such types can be “compute”, “network”, and “disk”, ... . – c(r, t) : is a function that returns the capacity of resource r at time t.

– – – –

With these notation one can express reservation request as a co-reservation for n resources, with start time ts and end time te , using QoS reservation class cl on ri with the associated capacities c(ri ), as follows: R(ts , te , cl, {(r1 , c(r1 )), .., (rn , c(rn ))}) We also introduce in this definition the concept of pre-emption priority, which has been explored in the context of networking and communication service [8]. The preemption priority is that when the reservation is not in effect, either before or after the reservation period, the job, or service that makes use of the reserved resource is not turned down or eliminated, but is rather assigned a low priority value, which means switching its status from ‘guaranteed’ to a ‘best effort’ type of service. In practice to support this concept the underlying resource manager should be a priority-based system, such as the Dynamic Soft Real Time (DSRT) scheduler [17]. This feature is very useful in protecting applications when reservations expired. 7.2 Admission Control Admission control is the process of granting/denying reservation requests based on a number of factors, such as, the actual load of the specified resource, the policy that governs who, how and when reservation for resource usage should be granted. To perform an admission control process an admission control mechanism must be employed. We formally describe our admission control mechanism as a ‘Boolean’ function that returns true or f alse for a reservation request R at time t. true means the reservation can be granted for the given time t with the resource specifications, and f alse means otherwise. To further define the admission control function algorithm, we first define the notion of resource load L at time t: g(t) c(rj , t) L(rj , t) = i=1

where g(t) is the number of granted reservations for time t and c(rj , t) is the amount of capacity reserved on the resource type j at time t.

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We also need to define resource total capacity as the maximum capacity the underlying resource can provide; formally max(ri ) is the maximum capacity that the resource i can provide. With the above basic primitives, we can now define the algorithm for the admission control function.

Algorithm 1 Admission Control Function

7.3

Reservation Features

As the reservation manager presented in this work operates in a Open Grid Service Infrastructure (OGSI), the service has a number of ‘operations’ can be used by other components. These operations are implemented as an API with a set of primitives, briefly described as: – reserve: is invoked by sending a reservation tuple R, this replies with a ‘reject reservation’, if the reservation cannot be granted. Otherwise it returns a reservation ‘handle’, a reference for the newly-made reservation request. – isAvailable: is used for checking the status of some resource prior to placing the actual reservation; this operation returns a Boolean result accordingly. – nextAvailable: is used for ‘counter-proposals’ brokering service if the user’s request for reservation cannot be granted. Rather than replying with a yes/no answer, as is the case with most reservation systems, the operation can reply with a ‘no’ and a counter-proposal for the next availability. – extend: can modify a reservation by extending it for a specified duration. – find: finds a reservation, and replies with all details about the reservation. – cancel: cancels a reservation. With this set of reservation operations on the reservation manager a higher level brokering service, or agent, can make use of this manager to provide immediate reservations, and reservations in advance, and also manipulate these reservations.


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Conclusion

In this paper, we propose a QoS service model in service-oriented Grids comprising a brokering service and a number of supporting modules, including policy service, reservation manager, allocation manager, and QoS selection service. Throughout this paper we describe the individual components of our framework and outline their patterns of interaction. We also discuss an OGSA compliant prototype implementation for our G-QoSM architecture. The important features of our approach are: the QoS manager is a Grid service and dynamically interacts with a reservation and policy service modules, which makes it possible for resource owners to update/modify their policies during run-time; and the reservation is abstracted as a generic service for co-reservation support, which makes it very suitable for distributed computing, such as Grids. This abstraction allows the reservation service to operate with any underlying resources, without previous knowledge of the resource characteristics, with the association of resource characteristics taking place during run-time by querying the policy service. This novel feature demonstrates scalability - highly desirable in Grid infrastructure. Acknowledgment. This work was supported by the Mathematical, Information, and Computational Science Division subprogram of the Office of Advanced Scientific Computing Research, Office of Science, U.S. Department of Energy, under Contract W-31109-Eng-38. DARPA, DOE, and NSF support Globus Project research and development. The Java CoG Kit Project is supported by DOE SciDAC and NSF Alliance.

References 1. G. von Laszewski and P. Wagstrom, “Gestalt of the Grid,” in Performance Evaluation and Characterization of Parallel and Distributed Computing Tools, ser. Series on Parallel and Distributed Computing. Wiley, 2003, (to be published). http://www.mcs.anl.gov/ ∼gregor/papers/vonLaszewski--gestalt.pdf 2. I. Foster, C. Kesselman, and S. Tuecke, “The Anatomy of the Grid: Enabling Scalable Virtual Organizations,” International Journal of Supercomputing Applications, vol. 15, no. 3, 2002. http://www.globus.org/research/papers/anatomy.pdf 3. “TeraGrid,” Web Page, 2001. http://www.teragrid.org/ 4. “The Global Grid Forum Web Page,” Web Page. http://www.gridforum.org 5. I. Foster, C. Kesselman, C. Lee, R. Lindell, K. Nahrstedt, and A. Roy, “A distributed resource management architecture that supports advance reservation and co-allocation,” in Proceedings of the International Workshop on Quality of Service, vol. 13, no. 5, 1999, pp. 27–36. 6. A. Hafid, G. Bochmann, and R. Dssouli, “A quality of service negotiation approach with future reservation (nafur): A detailed study,” Computer Networks and ISDN, vol. 30, no. 8, 1998. 7. K. Kim and K. Nahrstedt, “A resource broker model with integrated reservation scheme,” in IEEE International Conference on Multimedia and Expo (ICME2000), 2000. 8. M. Karsten, N. Berier, L. Wolf, and R. Steinmetz, “A policy-based service specification for resource reservation in advance,” in International Conference on Computer Communications (ICCC’99), 1999.

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9. J. MacLaren, “Advance reservations: State of the Art,” GGF GRAAP-WG, See Web Site at: http://www.fz-juelich.de/zam/RD/coop/ggf/graap/graap-wg.html, Last visited: August 2003. 10. K. Czajkowski, I. Foster, C. Kesselman, V. Sander, and S. Tuecke, “SNAP: A Protocol for Negotiating Service Level Agreements and Coordinating Resource Management in Distributed Systems,” in Proceedings of the 8th Workshop on Job Scheduling Strategies for Parallel Processing, 2002. 11. A. Hafid and G.Bochmann, “Quality of service adaptation in distributed multimedia applications,” ACM Springer-Verlag Multimedia Systems Journal, vol. 6, no. 5, pp. 299–315, 1998. 12. A. Oguz et al., “The mobiware toolkit: Programmable support for adaptive mobile networking,” IEEE Pesronal Communications Magazine, Special Issue on Adapting to Network and Client Variability, vol. 5, no. 4, 1998. 13. G. Bochmann and A. Hafid, “Some principles for quality of service management,” Universite de Montreal, Tech. Rep., 1996. 14. R. Al-Ali, O. Rana, D. Walker, S. Jha, and S. Sohail, “G-QoSM: Grid Service Discovery using QoS Properties,” Computing and Informatics Journal, Special Issue on Grid Computing, vol. 21, no. 4, pp. 363–382, 2002. 15. R.Al-Ali,A. Hafid, O. Rana, and D. Walker, “Qos adaptation in service-oriented grids,” in Proceedings of the 1st International Workshop on Middleware for Grid Computing (MGC2003) at ACM/IFIP/USENIX Middleware 2003, Rio de Janeiro, Brazil, 2003. 16. I. Foster, C. Kesselman, et al., “The physiology of the grid:an open grid services architecture for distributed systems integration,” Argonne National Laboratory, Chicago, Tech. Rep., January 2002. 17. H. Chu and K. Nahrstedt, “A cpu service classes for multimedia applications,” in IEEE Multimedia Systems ’99, 1999. 18. B. Teitelbaum, S. Hares, L. Dunn, R. Neilson, R. Narayan, and F. Reichmeyer, “Internet2 qbone: Building a testbed for differentiated services,” IEEE Networks, 1999.

A Service Management Scheme for Grid Systems Wei Li, Zhiwei Xu, Li Cha, Haiyan Yu, Jie Qiu, and Yanzhe Zhang Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China {liwei, zxu, char, yuhaiyan, zhangyanzhe}@ict.ac.cn, [email protected]

Abstract. In this paper, we propose a service management scheme named Grid Service Space (GSS) model, which provides application developers a highlevel logical view of grid services and a set of primitives to control the full lifecycle of a grid service. The GSS model provides a novel approach to meet the desired management requirements for large-scale service-oriented grids, including location-independent service naming, transparent service access, fault tolerance, and controllable service lifecycle.

1 Introduction The physical resource independent property, such as location transparency and access transparency, is a general design principle for resource management in distributed systems. With the emergence of grid computing, the distributed resources are abstracted as Grid Services [3], which aims at the hidden of the heterogeneity of various resources and focuses on the standardization of interface descriptions, access semantics and information representations. Strictly speaking, the current definition of grid service does not endow distributed resources with fully virtual properties due to the use of location-dependent naming mechanism (e.g. The OGSA [3] framework leverages a URL-based naming scheme to indicate a service instance’s physical location) and the lack of transparent service access mechanisms. Under such circumstances, developers have to take extra efforts on much general-purpose resource management work, such as service discovery, scheduling, error recovery, etc. Another problem is that a developer has to modify his applications when the URL-based name of a service changes. How to achieve complete physical resource independency remains a challenge for grid resource management. From the knowledge of traditional operating system design, we know that the virtualization technologies, such as virtual memory [1] and virtual file system [6], are common ways to obtain physical resource independent properties. The virtual memory technology can fulfill the requirements of dynamic storage allocation, i.e. desires for program modularity, machine independence, dynamic data structures, elimination of manual overlays, etc. The virtual file system technology enables the accommodation of multiple file system implementations within an individual operating system kernel, which may encompass local, remote, or even non-UNIX file systems.


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To obtain the full physical resource independent properties, we adopt a service management scheme called Grid Service Space (GSS) model, which is similar to the experiences of virtual memory and virtual file system. With this model, a programmer can refer to a service by a location-transparent name without knowing the service location, status, capabilities, etc. Hence, the runtime system can obtain several benefits such as load balancing (by choosing lightly loaded services), fault tolerance (by switching to a new service in response to service failure), locality of service access (by locating a nearer service), etc. The paper is organized as follows. Section 2 analyzes the requirements for grid service management. Section 3 presents the detail description of the GSS model. Section 4 introduces the implementation and section 5 concludes this paper.

2 Requirements for Service Management In current grid research, main efforts have been put on standardizing physical resources as grid services. Analogous to the traditional operating system harnessing the use of hardware, a grid operating system (GOS) becomes a natural solution to manage the use of grid resources. More precisely speaking, a GOS is a runtime system that can manage the heterogeneous, distributed, and dynamical resources efficiently. To realize such a GOS, it is necessary to analyze the lifecycle of a grid application carefully, which can be divided into programming phase and runtime phase. At the Programming phase, a programmer needs to integrate various services together to solve a problem. In most cases, programmers do not care about the location of services (i.e. where the task to be executed). From the view of programmers, the services should be physical resource independent, and a programmer can refer to a service just by a unique name and desired attribute descriptions. At the Runtime phase, when a program is running, it often encounters problems such as resource scheduling, error recovery, task migration, etc. A GOS should provide transparent service access mechanisms including service discovery, error recovery, lifecycle control, etc. to reduce burden of attacking the above issues. From the above analysis, we can summarize the main requirements of a service management system as physical resource independent naming, transparent service discovery and scheduling, service lifecycle control and fault-tolerance. In addition, the GOS should also consider the implementation issues such as resource topology, programming language support, performance, reliabilities, etc.

3 The GSS Model The GSS model is proposed to abstract and define the key concepts for a service management system. In this model, the basic elements are virtual services and physical services, which also construct the Virtual Service Space (VSS) and Physical Service Space (PSS). The difference is that a virtual service is a logical representation for

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a computational resource while a physical service is a computational entity with network access interface. A functional equivalence of multiple services is indicated by a coessential identifier, which means that these services have same processing functions (though they may have different capabilities and attributes). The mapping between two coessential services is called coessential mapping. The coessential mapping from a virtual service to a physical service is called scheduling mapping, and the coessential mapping from a virtual service to a virtual service is called translating mapping. For a given virtual service with a certain coessential identifier, all physical services, which have same coessential identifiers, are called discoverable set of this virtual service. All physical services in a discoverable set are candidates for this virtual service to bind to. In addition to the above basic definitions, we introduce the Virtual Service Block (VSB), which is a subset of VSS and groups related services together within a VSS. we also provide a set of primitives for service lifecycle control. Several service states are defined to indicate different phases of a service lifecycle. A service can switch to different states via the service lifecycle control primitives. 3.1 Formal Definitions Definition 1. A Service Space is a set denoted by S = {s1, s2, …, sh}, where sh is the name of a service. Definition 2. For all services in a service space S, they can be divided into two types, which are denoted by a set L = {vs, ps}. vs represents a virtual service, whose name is location independent, and ps represents a physical service, which has a locatable address. We denote the type of a service si as A (si), where i ≤ h. A service space is called a Virtual Service Space of S and denoted by V, if V⊆S and for each service si∈V, there is A (si) = vs. A service space is called a Physical Service Space of S and denoted by P, if P⊆S and for each service si∈P, there is A (si) = ps. Definition 3. If s1, s2, …, si∈S have a same function, we say s1, s2, …, si are coessential services. The function is expressed by a coessential identifier e. All coessential identifiers construct a set ES = {e1, e2, …, em}, m ≤ h, which is called coessential identifier set for service space S. For each si∈S, it has one and only one coessential identifier. That is, for each si∈S, there is a mapping χ: S → ES. If two service si and sj are coessential services, there is an equation χ (si)= em = χ (sj). Definition 4. The set C(Sx, em) = {sj | sj∈Sx and χ (sj) = em and Sx ⊆ S} is called the coessential service set of em for service space Sx. For every service space Sx⊆S, there is C(Sx, e0)∪C(Sx, e1) ∪…∪C(Sx, en) = Sx, and for random two coessential service sets C(Sx, ex) and C(Sx, ey), ex≠ey, there is C(Sx, ex)∩C(Sx, ey) = ∅. Definition 5. The mapping ω: C(Sx, em) → C(Sy, em) is called the coessential mapping of em from Sx to Sy, where C(Sx, em) = { sj | sj∈Sx and χ (sj)= em and Sx ⊆S } and C(Sy,

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em) = { sj | sj∈Sy and χ (sj)= em and Sy ⊆S }. Especially, the coessential mapping of em from a VSS V to a PSS P is called scheduling mapping. For each virtual service s∈V, P is called the discoverable set of s. In addition, the coessential mapping of em from one VSS V to another VSS V’ is called translating mapping. For each virtual service s∈V, V’ is called the translatable set of s. 3.2 Semantics of GSS Management Service naming mechanisms. The definitions of virtual service and physical service do not give the semantic of a service name explicitly. For a virtual service, the only prerequisite to its name is to differentiate it from other virtual services in one VSS. The location-independent means this name contains no physical resource information and should be translated to a locatable resource before a program can access this virtual service. In our model, the virtual service can use a code-based name or stringbased name, which can be user-friendly or even semantic-based. With the location independent naming mechanism of VSS, programmers can develop applications at a virtual resource layer. For a physical service, the GSS model does not restrict its naming mechanism only if the service name can indicate a locatable address. For example, we can use an IP address and a TCP port to indicate a service instead. The URL-based naming mechanism in the OGSA framework guarantees the global uniqueness of grid services and gives each resource a locatable address. Virtual Service Block. Normally, application developers require the ability to organize a group of related services together. In addition, a programmer needs the ability to refer to this group of services by a name. Similar to the virtual memory design, a service management scheme should fulfill the objectives such as program modularity, module protection and code sharing. The Virtual Service Block (VSB) can achieve the above objectives. In the GSS model, a VSS is composed by a set of named VSB. Each block is an array of service names. The service name in a blocked VSS is comprised by two components (b, s), where b is the name of a VSB and s is a unique name within b. The first objective can be achieved by allocating each module to one VSB. Therefore, other programs can easily share this module just by changing the block name, and the name within this block can remain unchanged. The second objective can be gained by adding extra information and checking mechanism. Each VSB can set the information such as block owner, access rights, etc., to implement the space protection. By mapping one VSB into multiple VSS and using different block names, multiple programs can share the code of modules in other programs. Virtual-Physical Service Space Mapping. Different from the memory mapping technology, the virtual-physical service space mapping in the GSS model is more complex. Although a VSS is similar to a virtual memory space, the PSS is much different from the memory space due to the feature of autonomous control and huge


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size. These two limitations brought several difficulties for efficient service space mapping. The first one makes it hard to deploy a physical service to a specific address (except for service owners). The second one may cause the performance of service locating even worse because of the huge search space of PSS. In the GSS model, we use coessential mapping mechanism formally described in Definition 5, parallel pre-mapping technology and discoverable set to address the above problems. When mapping a virtual service to a physical service, we should consider two important issues: correctness and performance. The correctness can be guaranteed by following the definitions of GSS model. The performance can be improved by better organization of physical services, efficient service locating, and scheduling policies. Several research work [2] [4] [5] have concentrated their efforts on the above issues. To improve the performance of service space mapping, we exploits the parallel pre-mapping technology together with VSB to improve the overall service space mapping performance by hiding the service locating time. The idea is to keep locating and mapping multiple physical services for a group of given virtual services (such as all virtual services in a VSB) in parallel before a running program refers to these virtual services actually. In addition, for each coessential identifier, we use the discoverable set defined in Definition 5 to build a small search space for service mapping, which also can reduce the searching time. Execute Here

PSS

VSS Block 0 Block ... Block n

(0, 1) (0, ...) (0, i) (..., 1) (..., ...) (..., j) (n, 1) (n, ...) (n, k)

Parallel Pre-Mapping

Fig. 1. Using Parallel Pre-mapping technology together with VSB to hide the searching time. PSS VSS (0,1) (0,...)

Discoverable Set

(...,1) (...,...) (...,j) (n,1)

Fig. 2. Using discoverable set to reduce search space.

Figure 1 illustrates the parallel pre-mapping technology used in service space mapping. When loading a program, the GOS will map several virtual spaces in parallel at first. When a program starts to run, it can directly access the mapped physical

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services. At the same time, the GOS will continually map virtual services of subsequent VSB in parallel. Figure 2 illustrates using of a discoverable set to reduce the search space for a virtual service. The GOS will build a discoverable set for each coessential identifier before loading programs. When a program is loaded, the search operations can be performed within a relative small discoverable set. The parallel pre-mapping and discoverable set technologies can be utilized together to improve the overall performance of service space mapping. 3.3 Service Lifecycle Control Compared to physical memory access, the lifecycle of service access is more complex. When a user accesses a service, there may have single send/receive operation or multiple send/receive operations. While in virtual memory systems, accessing a memory cell is in fixed time and the access mode is determined. To perform correctly and determinedly, lifecycle control of services is needed. In our GSS model, the different capabilities and properties between virtual services and physical services imply that they have different lifecycle patterns. Different control primitives are needed to manage the status transition of virtual services and physical services respectively. Here we mainly introduce the lifecycle control of virtual services. When a programmer refers to a virtual service, he not only want know a location independent name but also the full process of service access. In this section, we provide a set of primitives to control the activities of a virtual service. In order to describe the lifecycle control of a virtual service properly, we use a more concrete entity called mService to represent a virtual service. The mService can be defined as a tuple m = (n, e, i, ω, V, p, st), where n is a unique service name in a VSS, e is the coessential identifier, i is the session identifier, ω is the coessential mapping, V is a VSS, p is a physical service name and st is a service state indicator, which is an element of a set ST = {Created, Binded, Running, Waiting, Terminated}. The lifecycle of a virtual service includes several relevant operations, such as the virtual service creation, service discovery and scheduling, session control, etc. The lifecycle control primitives for virtual services are summarized as follows: – create (n), performed when we create a new virtual service and start up a new session with it. After this operation, the state of mService st = Created and a session identifier i is returned. – open (n), performed when we reopen an existing virtual service that is out of session. After this operation, the state of the virtual service remains unchanged and a session identifier i is returned. – delete (n), performed when we remove a virtual service from a VSS. After this operation, the virtual service with name n is deleted from this VSS. – bind (i, e, ω), performed when we map a physical service to a virtual service. After this operation, there is ω (n) = p and st = Binded. In addition, the virtual service n is added to VSS V, the coessential identifier e is added to EV.


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– invoke (i), performed when we call a method of a virtual service. After this operation, st = Running. – sleep (i), performed by program or GOS kernel. After the operation, st = Waiting. – interrupt (n, i), performed when an external event occurs. After this operation, st = Running. – close (i), performed when we cut off the current session with a virtual service. After this operation, the virtual service is out of session and the users cannot interact with this virtual service until using open primitive to create a new session.

4 Implementations The GSS model is a key feature of the Vega GOS in Vega Grid project [8] [9], which aims at learning fundamental properties of grid computing, and developing key techniques that are essential for building grid systems and applications. The Vega GOS is also used to build a testbed called China National Grid (CNGrid), which is sponsored by the 863 program and aims at integrating high performance computers of China together to provide a virtual super computing environment. Grid Applications

Application Layer

Virtual Services Vega Grid Operating System Grid Services

GOS Layer

Web Services

Globus Toolkit 3

Tomcat, WebSphere

Resources

Resources

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Fig. 3. The layered architecture of Vega Grid.

The architecture of Vega Grid is conformed to the OGSA framework and Figure 3 shows the layered architecture of Vega Grid. At the resource layer, resources are encapsulated as grid services or web services. The GOS layer will aggregate these services together and provides a virtual view for developers, who can use the APIs, utilities, and developing environments provided by Vega GOS to build VSS-based applications. At this layer, the most import work is deploying and publishing a physical service to upper layers. In our implementation, each physical service should have a unique coessential identifier. After generating the coessential identifier for a physical service, we register this service to a resource router with the coessential identifier and other information needed. According the algorithm in [7], this physical service will be published to all resource routers and every GOS can know the existence of this physical service. At the GOS layer, the resource router plays an important role to locating resources. The current implementation of Vega GOS is developed as grid services specified in [3]. In addition, Vega GOS implements the virtual service lifecycle management defined in Section 3.3. As a full functional integrated system, the Vega GOS also

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considers the implementation issues such as security, user management, communication, etc., which are not covered in this paper. At the application layer, programmers can use the GOS APIs to build a custom application. We also provide a GUI tool called GOS Client based on GOS APIs to help users to utilize the services in Vega Grid.

5 Conclusions and Future Work We have discussed the issues on grid service management. In order to overcome the obstacles in grid application development and system management, the GSS model is proposed to provide the location independent naming, transparent service access and service lifecycle control abilities to developers. As a fundamental component of our service management scheme, the GSS model also helps other research work such as grid-based programming model. We are currently implementing the Vega GOS and the GSS model on the CNGrid testbed. We hope the practical running of Vega GOS and its applications can verify the basic concepts and technologies in the GSS model.

References [1] [2]

[3] [4] [5] [6] [7]

[8] [9]

P. J. Denning, “Virtual Memory”, ACM Computing Surveys, vol. 2:3, pp. 153-189, 1970. S. Fitzgerald et al., “A Directory Service for Configuring High-Performance Distributed Computations”, Proc. 6th IEEE Symposium on High Performance Distributed Computing, pp. 365-375, 1997. I. Foster et al., “Grid Services for Distributed Systems Integration”, Computer, pp. 37-46, 2002. A. Grimshaw et al., “Wide-Area Computing: Resource Sharing on a Large Scale”, Computer, pp. 29-37, 1999. A. Iamnitchi et al., “On Fully Decentralized Resource Discovery in Grid Environments”, International Workshop on Grid Computing, 2001. S. R. Kleiman, “Vnodes: An architecture for multiple file system types in Sun UNIX”, In USENIX Association Summer Conference Proceedings, pp. 238-247, 1986. W. Li et al., “Grid Resource Discovery Based on a Routing-Transferring Model”, 3rd International Workshop on Grid Computing (Grid 2002), LNCS 2536, pp. 145-156, 2002. Z. Xu et al, “Mathematics Education over Internet Based on Vega Grid Technology”, Journal of Distance Education Technologies, vol. 1:3, pp. 1-13, 2003. Z. Xu et al, “A Model of Grid Address Space with Applications”, Journal of Computer Research and Development, 2003.

A QoS Model for Grid Computing Based on DiffServ Protocol * 1

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Wandan Zeng , Guiran Chang , Xingwei Wang , Shoubin Wang , Guangjie Han , and 3 Xubo Zhou 1

School of Information Science and Engineering, Northeastern University Shenyang, Liaoning 110004, China [email protected],[email protected] 2 Beijing Institute of Remote Sensing, Beijing 100085, P.R.China 3 China National Computer Software & Technology Service Corporation, Beijing 100083, P.R.China

Abstract. Because Grid comprises various kinds of dynamic and heterogeneous resources and provides the users with transparent services, how to achieve the quality of services for Grid Computing will be in face of more challenges. A QoS Model for Grid Computing based on DiffServ protocol is proposed in this paper. The implementation method is introduced. A mathematical model for scheduling on the Grid nodes has been established. This model is evaluated with a simulated Grid environment. The simulation results show that the Grid QoS model based on DiffServ proposed in this paper can improve the performance of Grid services for a variety of Grid applications. Making full use of it and optimizing it further are of great significance for the use of Grid resources and gaining high quality of Grid services.

1 Introduction Grid Computing comprises different kinds of resources such as super computers, large-scale storage systems, personal computers, and other equipment into a unified framework. When the resources needed by a computing project surpass the local processing capability, the Grid permits this project uses the CPU and storage resources of distant machines [1]. Traditional Internet achieved the connection of computer hardware, Web achieved the connection of web pages [2], and Grid integrates Internet resources to realize the sharing of all kinds of resources. Distributed, heterogeneous, and dynamic resources are used by Grids to provide users with transparent, unified, and standard services [3]. The characteristics of Grid challenge the qualities of service of Grid. Many Grid applications have the high qualities of service needs, such as tele-immersion, distributed *

This work was supported by the National Natural Science Foundation of China under Grant No.60003006 (jointly supported by Bell labs) and No.70101006; the National High-Tech Research and Development Plan of China under Grant No.2001AA121064.


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real-time computing, multi-media applications, and so on. OGSA (Open Grid Services Architecture) proposed the seamless quality of service that spanned integrated Grid resources. The Chinese Academy of Science proposed a set of consumer satisfaction and quality of service evaluation standards like service level agreement [4]. How to efficiently use the distributed and dynamic Grid resources to provide high quality of service for Grid applications has become an indispensable part of Grid Computing research.

2 QoS for Grid Computing 2.1 The Basic QoS Framework for Grid Computing IP QoS provides us a set of mature protocols for QoS. They can provide high beneficial services, increase bandwidth, and improve the quality of end-to-end services. The current leading Grid Computing operating systems such as Globus are based on TCP/IP, so IP QoS protocols should be the important technologies that can be used in Grid Computing QoS. The fundamental framework for Grid Computing IP QoS is as follows. Grid Application task Grid Operating System

control and manage the flow

task and signaling based on QoS Protocols Grid Node Grid Node Grid Node implementation of QoS on node Broadband Network

Fig. 1. Fundamental Framework for Grid Computing IP QoS

2.2 The Grid Computing QoS Strategy Current Grid Computing QoS pays more attention to resource management but take little notice to task scheduling [5]. RSVP is the most commonly used protocol in Grid Computing QoS [6]. RSVP is an in-band signaling protocol and transmits the soft state signaling through channels out of data streams [7]. RSVP gains high performance but at the same time makes the transmission more complicated, thus it is suitable for small-type access network [8]. But the amount of services Grid Computing provided is very large and properties of Grid resources are complicated and dynamic. The out-of-band signaling will bring out huge additional burden for network transmission and will be very hard to control.

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DiffServ protocol is used in the Grid Computing QoS model in this paper. DiffServ protocol uses in-band signaling. The signaling in band is transmitted with the data in data-stream together. It supports eight kinds of priority and obeys to DSCP protocol. The core of DiffServ is “classifying at the edge and transmitting inside”. It will not add the transmission burden to the networks and it has good expansibility. This will be more suitable for the characteristics of Grids.

3 The Implementation Method of Grid Computing QoS The techniques used for the implementation of Grid Computing QoS include service classification, speed limitation, queuing scheduling, congestion control, traffic engineering, and so on [9]. DiffServ protocol is used in this paper. The tasks are classified by Grid operating system. The Grid node schedules tasks by queues and is responsible for the congestion control of tasks. 3.1 The Implementation of Grid Computing DiffServ Grid Computing applications send task requirements including the answering time and some requirement information to the Grid operating system. After that the Grid operating system uses the DiffServ protocol to classify the tasks and then sign them. The Grid operating system converges the tasks as different assemble groups with different priorities. D iffS e r v D o m a in S u p p ly in g D iffe r e n tia te d S e r v ic e s R o u te r

G r id N o d e

C la s s ify in g ,S ig n in g M o n ito r in g T a s k s ittin g T ra n s m r R o u te

G r id O p e r a tin g G r id A p p lic a tio n S y s te m

c o mp u t e r

Fig. 2. DiffServ QoS Model for Grid Computing

In this model, tasks are classified by their answering time. Some tasks are in high demand of quick answering, such as multi-media display, video conference, and so on. These tasks ask for short time delay answering. The priorities of these kinds of

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services are high. Some are not sensitive to the answering time, such as some common transaction process, E-Mail, and so on. Their priorities are comparatively low. In order to be compatible with the sub-field IP Precedence of IPv4 ToS, there are at most eight kinds of service priorities. Here we define eight types of services differentiated by their request for answering time. And each priority represents one type of the emergency degree of the task. The DS field is defined as follows: 0

1

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setting 0

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Fig. 3. The Assignment of DS Field in Grid Computing DiffServ

Bit 0-2 are set to 000, 001, 010, 011, 100, 101, 110, or 111, and represent the priority from lowest to highest. Bit 3-5 are all set to 0. The sixth and seventh are reserved. The Grid operating system sends the task data packets with DS sign to Grid nodes through the network. When a Grid node receives the packet, it parses the content in DS and then assigns the tasks to different queue with different priority. In the same queue every task is processed circularly by the time slice. 3.2 The Implementation Model of Grid Computing DiffServ on Grid Node The arrival of every class of tasks on Grid node obeys to Poisson distribution [10], λ i is the average reaching ratio of tasks with priority i. The service time for every class of task obeys to the negative exponential distribution and the average service time is -1 µ . Wqi ,Wsi represent the average waiting and lingering time of the task of priority i. Now: -1 2 ρ 1= λ 1 µ ， λ = λ 1，ES = 2 2 ，we can get: µ 2 λ1 1 1 1 λ ES = ,W s1 = Wq1 + = . W q1 = 2 µ µ −λ 1 2 1 − ρ1 µ − µλ 1 If Ws,1-2 represents the lingering leaving time of tasks with priority 1 and 2 when there are both priority 1 and 2 tasks in the system. Then: ( λ 1+ λ 2) Ws,1−2 = λ 1Ws1 + λ 2 Ws2 ，and so

W s 2 = (1 +

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λ )Ws,1−2 − 1 Ws1 . λ2 λ2

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priority from low to high

queue 2 queue 3

packages with DS sign parse DS arrange tasks in different queues

grid node processor

queue 4

. . . .

queue 5 queue 6

grid node scheduler

queue 7 queue 8

Fig. 4. Task Scheduling on Grid Nodes

W s,1− 2 can be worked out by the M/M/1 model queue with the arrival ratio of

λ = λ1 + λ 2 . W s ,1− 2 =

W s 2 = (1 +

λ1

1 µ − (λ 1 + λ 2 )

, applying it to (1), we have

λ 1 , And, − 1. λ 2 µ − (λ 1 + λ 2 ) λ 2 µ − λ 1 )

1

1 W q2 = Ws2 − µ

Similarly, we can get W s,1−3 =

1 µ − (λ 1 + λ 2 + λ 3 )

λ λ λ λ 1 W s 3 = (1 + 1 + 2 ) − 1 Ws1 − 2 Ws2 λ 3 λ 3 µ − (λ 1 + λ 2 + λ 3 ) λ 2 λ3 W q 3 =W s3 -

1 µ

We can conclude that, W s,1−k =

1 k µ − ∑λ i i=1

k k −1 ∑λ i ∑ λ i Wsi W sk = i =1 Ws,1−k − i =1 , λk λk

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Fig. 5. The Comparison of Grid Packets Processing Time

WRR uses circular inquiring strategy and every queue has the same length of time slice. When congestion occurs, the tasks with lower priority will be dropped. From the table we can see that the losing ratio of the packet increases gradually. The processing time of the same packet in different queues is different and it becomes longer as the priority becomes lower. This model can provide different service quality for Grid applications. It can make full use of Grid resources and improve the performance of Grid Computing services for a variety of Grid applications.

5 Conclusion The characteristics of Grid services are the most important factors for the complexity of the quality of Grid Computing services. The Grid Computing QoS Model discussed in this article is effective according to the results of simulation. It can improve the performance for a variety of Grid Computing applications. But it is still not an ideal model for its own limitation. Because the lack of end to end signaling, DiffServ QoS cannot ensure end to end Grid QoS. And it has only 8 grades. Further research on Grid QoS, for example, further optimizing the model and more economic concepts taken into account, is the main aim and task in the future.

References 1.

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3.

Xiao, N., Ren, H., Gong, S.S.: Design and Implementation of Resource Directory in National High Performance Computing Environment. Journal of Computer Research and Development, 2002, 902 (8) Foster, I., Kesselman, C.: Globus: A metacomputing infrastructure toolkit. International Journal of Supercomputer Applications and High Performance Computing, 1997. 11(2): 115-128 Foster, I., Kesselman, C.: The Grid, Blueprint for a New Computing Infrastructure. San Francisco: Morgan Kaufmann Publishers Inc., 1998. 279-309

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Xu, Z.W., Li, W.,: Architectural study of the vega Grid. Journal of Computer Research and Development (in Chinese), 2002, 39 (8): 923-929 5. He, X.S., Sun, X.H., von Gregor, L.: QoS Guided Min-Min Heuristic for Grid Task Scheduling. Workshop on Grid and Cooperative Computing. Comput. Sci. & Technol, 2003, 18 (4): 442 451 6. Foster, I., Roy, A., Winkler, L.:A quality of service architecture that combines resource reservation and application adaptation. In: Proceedings of the 8th International Workshop on Quality of Service (IWQOS 2000). 2000. 181-188 7. Xiao, X.P.: Internet QoS: a big picture. IEEE Networks, March/April 1999: 8 18 8. Grenville, A.,: Quality of Service in IP Networks: Foundations for a Multi-Service Internet, Beijing: China Machine Press, 2001.1 9. Subrat, K.: Issues and architectures for better quality of service (QoS). Proceedings of the 6th National Conference on Communications (NCC2000), New Delhi, India: 181-187 10. Lu, Z.Y., Wang, S.M.: The Information Content Theory of Communication, Beijing: Publishing House of Electronics Industry, 1997. 42-45

Design and Implementaion of a Single Sign-On Library Supporting SAML (Security Assertion Markup Language) for Grid and Web Services Security Dongkyoo Shin, Jongil Jeong, and Dongil Shin Department of Computer Science and Engineering, Sejong University 98 Kunja-Dong, Kwangjin-Ku, Seoul 143-747, Korea {shindk, jijeong, dshin}@gce.sejong.ac.kr

Abstract. In recent years, the Grid development focus is transitioning from resources to services. A Grid Service is defined as a Web Service that provides a set of well-defined interfaces and follows specific conventions. SAML is an XML based Single sign-on (SSO) standard for Web Services, which enables the exchange of authentication, authorization, and profile information between different entities. This provides interoperability between different security services in distributed environments. In this paper, we designed and implemented Java-based SAML APIs to achieve an SSO library.

1 Introduction Grid computing denotes a distributed computing infrastructure for advanced science and engineering. It supports coordinated resource sharing and problem solving across dynamic and geographically dispersed organizations. Moreover, the sharing concerns not only file exchange but also direct access to computers, software, data, and other resources [1]. In recent years, the Grid development focus is transitioning from resources to services. A Grid Service is defined as a Web Service that provides a set of welldefined interfaces and follows specific conventions [2]. The interfaces address discovery, dynamic service creation, lifetime management, notification and manageability. Web Services [3], proposed by World Wide Web Consortium (W3C), provide a standatd which is designed to support interoperable machine-to-machine interaction over a network. It has an interface described in a machine-processable format (specifically WSDL - Web Services Description Language). Other systems interact with the Web services in a manner prescribed by its description using SOAP(Simple Object Access Protocol) messages, typically conveyed using HTTP with an XML serialization. Single sign-on (SSO) is a security feature, which allows a user to log into many different services offered by the distributed systems such as Grid [4] and Web Services, while the user only needs to authenticate once, or at least always in the same way [5, 6]. Various SSO solutions have been proposed that depend on public key M. Li et al. (Eds.): GCC 2003, LNCS 3033, pp. 557–564, 2004. © Springer-Verlag Berlin Heidelberg 2004

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infrastructure (PKI), Kerberos, or password-stores, which require an additional infrastructure on the client’s side and new administrative steps [7]. Recently a new standard for exchange of security-related information in XML called Security Assertions Markup Language (SAML) [8,9] is recommended by the Organization for Advancement of Structured Information Standards (OASIS). SAML enables the exchange of authentication, authorization, and profile information between different entities to provide interoperability between different security services in distribution environments such as Grid and Web Services. The security information described by SAML is expressed in XML format for assertions, which can be either: authentication assertions, attribute assertions, or authorization decision assertions. SAML authorities issue assertions, which can be security service providers or business organizations. Assertions provide a means of avoiding redundant authentication and access control checks, thereby providing single sign-on functionality across multiple target environments. SAML also defines the protocol by which the service consumer issues the SAML request and SAML authority returns the SAML response with assertions. While SAML is an authentication standard for Web Services, it is also proposed as a message format for requesting and expressing authorization assertions and decisions from an OGSA(Open Grid Services Architecture) authorization service [10]. In this paper, we designed and implemented a Java-based SSO library made up of SAML Application Programming Interfaces (APIs).

2 Background The basic idea of single sign-on (SSO) is to shift the complexity of the security architecture to the SSO service and release other parts of the system from certain security obligations. In the SSO architecture, all security algorithms are found in the single SSO service, which acts as the single and only authentication point for a defined domain. The SSO service acts as the wrapper around the existing security infrastructure that exports various security features like authentication and authorization [5,6]. For SSO implementation, classical three-party authentication protocols that exploit key-exchanges such as Kerberos and Needham-Schroeder are used. Since these protocols start with a key-exchange or key-confirmation phase, the client application uses the new or confirmed key for encryption and authentication [11]. For a different approach for SSO implementation, token-based protocols such as cookies or SAML are used [11]. Being different to key-exchange protocols, an authentication token is sent over an independently established secure channel. In other words, a secure channel is established without an authenticated client key, in which the Secure Socket Layer (SSL) [12] is usually used with browsers, and then an authentication token is sent in this channel without conveying a key. The main advantage of token-based protocols is that a majority of service providers already have SSL server certificates and a suitable cryptographic implementation is available on all client machines via the browsers. In addition, one can use several unrelated authenti-

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cation tokens to provide information about the user in the same secure channel with the service provider [11]. SAML is a standard suitable for facilitating site access among trusted security domains after single authentication. Artifacts, which have a role of tokens, are created within a security domain and sent to other security domains for user authentication. Since the artifacts sent to the other domains are returned to the original security domain and removed after user authentication, this resolves the problems of session keys being revealed and stolen tokens in the browser. In addition, artifact destination control is fully achieved since artifact identification is attached to the Uniform Resource Locator (URL) and redirects the message sent to the destination [8]. 2.1 SAML (Security Assertion Markup Language) Recently, OASIS has completed SAML, a standard for exchanging authentication and authorization information between domains. SAML is designed to offer single signon for both automatic and manual interactions between systems. It will let users log into another domain and define all of their permissions, or it will manage automated message exchanges between two parties. SAML is a set of specification documents that define its components: y Assertions and request/response protocols y Bindings (the SOAP-over-HTTP method of transporting SAML requests and responses) y Profiles (for embedding and extracting SAML assertions in a framework or protocol) y Security considerations while using SAML y Conformance guidelines and a test suite y Use cases and requirements

Fig. 1. Structure of Assertion Schema

Fig. 2. Assertion with Authentication Statement

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SAML enables the exchange of authentication and authorization information about users, devices or any identifiable entity called subjects. Using a subset of XML, SAML defines the request-response protocol by which systems accept or reject subjects based on assertions [8,9]. An assertion is a declaration of a certain fact about a subject. SAML defines three types of assertions: y Authentication: indicating that a subject was authenticated previously by some means (such as a password, hardware token or X.509 public key). y Authorization: indicating that a subject should be granted or denied resource access. y Attribution: indicating that the subject is associated with attributes. Figure 1 shows an assertion schema and Figure 2 shows the assertion statement, which includes authentication assertion issued by SAML authority. SAML does not specify how much confidence should be placed in an assertion. Local systems decide if security levels and policies of a given application are sufficient to protect an organization if damage results from an authorization decision based on an inaccurate assertion. This characteristic of SAML is likely to spur trust relationships and operational agreements among Web-based businesses in which each agrees to adhere to a baseline level of verification before accepting an assertion. SAML can be bound with multiple communication and transport protocols. It can be linked with Simple Object Access Protocol (SOAP) over HTTP [8,9].

Fig. 3. Browser/Artifact

Fig. 4. Browser/Post

SAML operates without cookies in one of two profiles: browser/artifact and browser/post. Using browser/artifact, a SAML artifact is carried as part of a URL query string as shown in Figure 3, where a SAML artifact is a pointer to an assertion. The steps in Figure 3 are explained as follows. (1) User of an authenticated browser on Server A requests access to a database on Server B. Server A generates a URL redirect, which contains a SAML artifact, to Server B. (2) Browser redirects user to Server B, which receives an artifact pointing to the assertion on Server A. (3) Server B sends artifact to Server A and gets a full assertion. (4) Server B checks the assertion and either validates or rejects the user’s request for access to the database. With browser/post, SAML assertions are uploaded to the browser within an HTML form and conveyed to the destination site as part of an HTTP post payload, as show in Figure 4. The steps in Figure 4 are explained as follows.


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Fig. 5. Java Packages of SAML APIs

(1) User of authenticated browser on Server A requests access to database on Server B. (2) Server A generates an HTML form with SAML assertion and returns it to user. (3) Browser posts the form to Server B. (4) Server B checks assertion and either allows or denies user’s request for access to database.

3 Design and Implementation of Java-Based SAML APIs We designed and implemented SAML APIs as Java packages as shown in Figure 5. The classification of packages is based on the specification “Assertions and Protocol for the OASIS Security Assertion Markup Language (SAML)” [8]. We designed three basic packages named assertion, protocol and messaging packages. To support the messaging function, we also designed generator, uitilities and security packages. The implemented SAML APIs are grouped into these packages. The function of each package is as follows. y Assertion package: dealing with authentication, authorization and attribution information. y Protocol package: dealing with SAML request/response message pairs to process assertions. y Messaging package: including messaging frameworks which transmit assertions. y Security package: applying digital signature and encryption on the assertions y Utilities package: generating UUID, UTC Data format and artifacts, and so on. y Generator package: generating SAML request/response messages. The Structure of major packages will be shown as continuous figures. The structure of assertion package is shown in Figure 6. The structure of protocol package is shown in Figure 7. A protocol defines an agreed way of asking for and receiving an assertion [13]. The structure of the messaging package is shown in Figure 8. The messaging package transforms a document into a SOAP message and defines how the SAML messages are communicated over standard transport and messaging protocols [13]. We verified the message according to the SAML specifications. When we generated SAML request messages as shown in Figure 9, we used RequestGenerator class in generator package (refer to step 1 of Figure 3 and 4).

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Fig. 6. Structure of ac.sejong.saml.assertion package

Fig. 7. Structure of ac.sejong.saml.protocol package

Fig. 8. Structure of ac.sejong.saml.messaging urn:oasis:names:tc:SAML:1.0:am:password …j&5D#Siks233$...

Fig. 9. Generation of SAML Request Message

This SAML request message is signatured as shown in Figure 10, using signature class in security.sign package. The signature process of signature class follows XMLsignature standards in the enveloped form. Figure 11 shows the generation of SAML response messages, in which ResponseGenerator class in generator package is used (refer to step 4 of Figure 3 and 4). This SAML response message is also signatured using signature class in security.sign package.


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….7znAenuew=….. …zQdWDKC… …gdADMHy0=… AQAB … 1045440891 … …Xs+69s=… ……………

Fig. 10. SAML Request Message signatured in Enveloped Form this is a message about status …… jeongjongil

Fig. 11. Generation of SAML Response Message

4 Conclusion We designed and implemented an SSO library supporting the SAML standard. The implemented SAML APIs have following features. y Since SAML messages are transmitted through SOAP, XML based message structures are fully preserved. This enables valid bindings. y Integrity and non-repudiation are guaranteed by using signatures on transmitted messages. y Confidentiality is guaranteed by encryption of transmitted messages. Since XML encryption is applied, each element can be efficiently encrypted.

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Even though digital signatures on a SAML message using RSA is default and using an XML signature is optional, we fully implemented both APIs in security package. Specific encryption methods for SAML messaging are not mentioned in the SAML specification and XML encryptions are a suitable candidate for encryption of SAML messages. We also implemented APIs for XML encryption [14]. Recently, Grid systems have adopted Web Services standards that were proposed by W3C and SAML is an XML-based SSO standard for Web Services. SAML will become widely used in Grid Architecture, as distributed applications based on Web Services become popular. We will further apply this SAML library to real word systems such as the Electronic Document Manage Systems (EDMS) and the groupware systems and continue research on authorization for users.

References 1. 2.

3. 4.

5. 6. 7.

8. 9. 10. 11. 12. 13. 14.

Foster, I., Kesselman C.: The Globus Project: A Status Report. Future Generation Computer Systems, Volume: 15, (1999) 607-621 Foster I., Kesselman C., Nick J.M., Tuecke S.: The Physiology of the Grid - : An Open Grid Services Architecture for Distributed Systems Integration, http://www.globus.org/research /papers/ogsa.pdf Web Services Activity, http://www.w3.org/2002/ws Foster I., Kesselman C., Tsudik G., Tuecke S.: A Security Architecure for Computational Grids. Proc. 5th ACM Conference on Computer and Communications Security Conference, (1998) 83-92 Volchkov, A.: Revisiting Single Sign-On: A Pragmatic Approach in a New Context. IT Professional, Volume: 3 Issue: 1 , Jan/Feb (2001) 39 -45 Parker, T.A.: Single Sign-On Systems-The Technologies and The Products. European Convention on Security and Detection, 16-18 May (1995) 151-155 Pfitzmann, B.: Privacy in Enterprise Identity Federation - Policies for Liberty Single Signon. 3rd Workshop on Privacy Enhancing Technologies (PET 2003), Dresden, March (2003) Assertions and Protocol for the OASIS Security Assertion Markup Language(SAML) V1.0: http://www.oasis-open.org/committees/security Bindings and Profiles for the OASIS Security Assertion Markup Language(SAML) V1.1: http://www.oasis-open.org/committees/security Global Grid Forum OGSA Security Working Group: Use of SAML for OGSA Authorization, http://www.globus.org/ogsa/Security Pfitzmann, B., Waidner, B.: Token-based Web Single Signon with Enabled Clients. IBM Research Report RZ 3458 (#93844), November (2002) Frier A., Karlton P., and Kocher P.: The SSL 3.0 Protocol. Net Scape Communications Corporation, Nov 18, (1996) Galbraith, B., Trivedi, R., Whitney, D., Prasad D. V., Janakiraman, M, Hiotis, A., Hankison, W.: Professional Web services Security, Wrox Press, (2002) XML Encryption WG, http://www.w3.org/Encryption/2001/

Performance Improvement of Information Service Using Priority Driven Method Minji Lee1, Wonil Kim2*, and Jai-Hoon Kim1 1Ajou

University, Suwon 442-749, Republic of Korea {mm223j,jaikim}@ajou.ac.kr 2.Sejong University, Gwangjin-Gu, Seoul, 143-747 Republic of Korea [email protected]

Abstract. Grid is developed to accomplish large and complex computation by gathering distributed computing resources. Grid employs information service to manage and provide these collected resources. The accurate information on resource provider is essential to stable service. In Grid information service, GRIS and GIIS are used to gather and maintain the resource information. These two directory servers store resource information in a cache only for a fixed period to improve fast response and accurate access. Since resource information search is performed in the cache, the system performance depends on the search time in a cache. In this paper, we propose a novel cache management system based on the resource priority. Priority of resource is determined by the frequency of resource usage, and the number of available resources in GRIS. The simulated priority driven schemes provide more accurate information and faster response to a client than the existing cache update scheme of information service.

1 Introduction In Grid, directory service [1] is provided to keep and maintain resource information components. Variable types of connections are opened in directory service to update and search the resource status. Most of connections between the directory server and the resource producer are used in modifying the status of resources. The information transmitted by a resource provider is stored in a cache of directory server for fixed time interval. Current directory servers perform query processing on cache. They rummage their caches to search requested information after receiving a query from a client. If there is no proper resource information in the cache, the query is transmitted to other directory servers. If there are not many resource providers cache size is not an important problem. However, as the number of resource provider increase, cache size becomes a critical issue because it can be a major factor for fast response and accurate access. If there is too much information in a cache, it takes long time to respond to a query whereas the information correctness increases. *

Author for Correspondence +82-2-3408-3795


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Cache update is usually performed as a sequence of FIFO (First In First Out). In order to provide the accurate information, old information, which stays in a cache more than the fixed time, is also deleted. As the number of resource providers and requests increase, resources status changes fast. Consequently, it needs effective cache update mechanism that guarantees the accuracy of information. The best strategy for efficient cache management is to predict the next information being requested by a client and stores the information in a cache in advance. However it is impossible to predict every user’s requests and store the information. Instead of predicting every user’s request, this paper proposes two schemes to decide the importance of given information. One method gives priority on the basis of resource access frequency. In order to know the access frequency, GRIS logs the number of requests whenever it receives a resource request message from a client. Another method gives priority to the resource provider having more available resources than the others.

2 Information Service in Grid Grid is an important field in the distributed system. Grid can provide huge amount of resources to the requestor. In order to manage those resources, Grid maintains systematically organized architecture. 2.1 Grid Information Service Protocol In order to provide proper resources to a requestor, it is important to manage the resources efficiently and effectively. It is also important to provide correct information about resource status. Grid Information Service (GIS) supports such a service [2]. GIS provides the initial discovery and ongoing monitoring of the existence and characteristics of resources, services, computations, and other entities that are part of Grid system [2]. In order to support the functions listed above, GIS assumes that resources are subject to fail and the total number of resource provider is large. Furthermore, the type of resource is various. GIS architecture comprises highly distributed resource providers and specified aggregate directory services. Resource provider furnishes dynamic information according to the prototype defined by VO-neutral infrastructure. GIS uses two basic protocols; Grid Resource Information Protocol (GRIP) and Grid Resource Relationship Protocol (GRRP). GRIP is used to access information and GRRP is used to notify aggregate directory services of the availability of the information. For example, GRIP adopts standard Light Weighted Directory Access Protocol (LDAP) [5] as a protocol and it defines a data model, query language, and wire protocol. 2.2 Current Information Service in Grid Metadata Directory Service (MDS) is an implementation of information service in a Grid [4]. The service is provided with GRIS and GIIS. GRIS gathers the status infor-

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mation of resources and reports the status information to GIIS [4]. GIIS is hierarchical structure organized with several GIIS and GRIS [4]. GRIS is located at the bottom of the hierarchy and it is connected to several GIIS. GIIS receives data from many GRIS and stores the data in a cache. Fig 1 shows a prototype of query processing generated by a client. Initially, a query accesses the highest-level GIIS. If the data requested by a client are in the cache, the data are returned to a client. If not, query is sent to other GIIS to find data. This process is repeated until the query finds requested data. In other words, if the requested information is not in the cache, the query has to be transmitted to many GIIS and the requestor has to wait until the search sequence finishes. Therefore, the performance of a query to a GIIS is dependent upon the performance of the resource information servers that the query accesses. Query Return Information to Registrar

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Fig. 1. The Query processing in MDS. A query comes to the highest-level GIIS. GIIS searches its cache. If there is requested information, the information is transmitted to a requestor. If not, the query is sent to other GIIS or GRIS until requested information is found.

Warren Smith [3] showed the different searching time between two cases; first case is that data is at top-level cache and second is that data is at the bottom-level cache. It takes 10 seconds for the first case and 60 seconds for second case to receive data. This result shows the importance of containing data in the higher-level cache.

3 The Proposed Information Service System 3.1 Proposed Directory Service Since current directory service does not consider the importance of resources all the resource information has the same priority. Thus cache update is influenced only by its resource information registration time. However, such a current scheme cannot provide fast and accurate resource information for clients. If each resource has various priorities on the basis of some rules and the priority is applied to cache update method, its cache update result will be different from current cache update result in some ways. Two methods are proposed to decide the priority among resources. One method determines the resource priority with the frequency of resource usage. This scheme

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implies that frequently used resources have higher possibility of being accessed by a client than the resource that is used occasionally. Another method determines resource priority with the number of available resources that GRIS has. Proposed methods Method 1. Considers Method 2. Considers the number of the Resource available resources access frequency

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Fig. 2 shows how the resource access frequency is recorded and used in a simulated directory service. Each GRIS records the type of a requested resource whenever it receives a resource request message from a client. At this time, the GRIS records the requested resource type without considering the availability of resource. 3.2 Data Processing of Proposed Methods Two cache update schemes of the proposed methods are shown in Fig. 3. Both of the methods start data processing when a message arrives. Then they check if there is empty space in their caches to store new data. If there is empty space, new data is inserted into the cache. If there is not, each method decides which data is discarded


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from the cache considering the data priority. One method decides the priority by the number of available resources and another decides the priority by the resource access frequency. After deciding data to be discarded, the data is compared with data in a message. If data in a message priority is higher than the data priority in a cache, the message is inserted into the cache or the message is discarded. Fig.3 explains an example of the proposed method. In case of method 1, access frequency of a resource update message for resource X is 6 per day. Access frequency of resource G in a cache is 1 per day. Thus resource G is discard from a cache and resource X is inserted into a cache. In case of method 2, there are 6 resources in the resource update message and its resource type is X. In the cache, resource D has only 1 resource. Thus resource D is discarded from the cache and resource X is inserted into a cache.

4 Simulation Simulation is performed with three types of directory service distinguished by the cache update method. First simulation is performed with the information service applying general cache update scheme. Second simulation is performed with the directory service applying new cache update scheme considering the resource access frequency and ttl. Third method is completed after applying new scheme considering the number of available resources and ttl. # of nodes

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4.1 Construction of Directory Service There are 160 GRISs in the simulation. Each GRIS controls 20 resources and the type of each resource is the same. 5 types of resource are used for the simulation. The component ratio of each resource is 50%, 20%, 10%, 10% and 10% respectively. Resource request ratio is identical to the component ratio Each GIIS controls two GIIS except the level 5. In level 5, each of 16 GIIS controls 10 GRISs. Table 1 shows the cache size at each level. Cache size increases according to GIIS levels. Simulations are performed changing the cache size of each level.

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(c) When Priority Driven Scheme by the Number of Available Resource is performed. Fig. 5. Cache Hit Ratio at each Level GIIS When Cache Size of the Highest Level GIIS is 5. In order to show the performance improvement of new cache update schemes, cache size is fixed as simulation 1 shown in table 1.

4.2 Cache Hit Ratio of Each Algorithm Fig. 5 shows cache-hit ratio at GRIS. High cache hit ratio at the GRIS level means poor performance, because the GRIS is the last directory where information search is performed. When the highest-level cache size is 10, cache-hit ratio of the previous scheme is 25%. On the other hand, cache hit ratio of proposed schemes is 10%. In case of the cache size is 15, cache-hit ratio of all schemes is about 10%. An increase


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of cache-hit ratio at GRIS means an increase of response time for a query. Performance of the previous algorithm decreases as cache size reduces as shown in Fig 5. Performance of priority driven schemes shows almost the same performance when cache size changes. The proposed schemes show better performance than the previous scheme.

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Fig. 7. Accuracy of cache information by measuring the success rate of resource request

Fig 5 and 6 show cache hit ratio when cache size changes. If cache size is large enough to store information transmitted by lower level GIIS or GRIS, three algorithms find information from cache at the ever rate. As the cache size decreases their cache hit ratio is discriminated. Cache hit ratio at the highest-level GIIS of new cache update schemes decreased of 50% compared with previous cache update scheme. Furthermore, the cache hit exactness of proposed schemes increases up to 92%. 4.3 Accuracy of Cache Information Fig. 7 shows the accuracy of returned information. When previous update scheme is applied to information service, accuracy of information is decreased as cache size decreases. In contrast to the previous scheme, accuracy of proposed two schemes are unchanged when cache size changes. Furthermore the proposed schemes show higher accuracy than the previous cache update scheme. For example, the Usage-Priority scheme shows almost 90% of accuracy. This rate is stable though the cache size changes.

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4.4 Performance Evaluation Proposed algorithms are fast and accurate as shown in Fig. 5, 6 and 7. When previous cache update algorithm is applied to information system, cache hit ratio of the highest-level directory server also decreases as cache size of directory server decreases. It is natural that small size cache has small number of information than the large size cache and the cache makes less responds to a query. In order to increase the cache-hit ratio, new cache update scheme is needed. Basic concept of proposed algorithms is priority driven information. When a cache is full of data, one of the information in a cache is discarded. The previous algorithm considers only staying time in a cache of the information. On the other hand, the proposed algorithms do not only consider staying time but also priority of information.

5 Conclusion Current cache update method is FIFO on the basis of sequence that information enters cache. In this paper we proposed a novel method applying priority to resource information on the basis of resource usage frequency and the number of available resources. The proposed cache update method increased exactitude of cache information. Even though the cache size decreases cache-hit ratio and exactitude of a query response are unchanged. As the resources and users increase, the performance of previous information service may depreciate. Information accuracy of the proposed schemes is increased up to 90% when the previous scheme shows only 70%. Furthermore, the cache-hit ratio at the highest-level directory server doubles comparing with the previous scheme. The simulation shows that the proposed cache update schemes provide fast and accurate information.

References 1. Steven Fitgerald, Ian Foster, Carl Kesselman and Gregor VOn Laszewski, “A Directory Service for ConFiguring high-Performance Distributed computation,” Posted on “http://www.globus.org.” 2. Ian Foster, Carl Kesselman and Steven Tuecke, “The Anatomy of Grid,” “http://www.globus.org.” 3. Warren Smith, Abdul Waheed, David Meyers and Jerry Yan, “An Evaluation of Alternative Designs for Grid Information Service,” 9th IEEE international Symposium on High Performance Distributed Computing 4. “MDS 2.2: Creating a Hierarchical GIIS”, ”MDS 2.2: GRIS specification Document: Creating New Resource providers”, http://www.globus.org/mds/NewFeatures.html, 2002 5. Gregor von Laszewski and Ian Foster, “usage of LDAP in globus,” http://www.globus.org/mds/NewFeatures.html, 2002

HH-MDS: A QoS-Aware Domain Divided Information Service* Deqing Zou, Hai Jin, Xingchang Dong, Weizhong Qiang, and Xuanhua Shi Huazhong University of Science and Technology, Wuhan, 430074, China [email protected]

Abstract. Grid computing emerges as effective technologies to couple geographically distributed resources and solve large-scale problems in wide area networks. Resource Monitoring and Information Service (RMIS) is a significant and complex issue in grid platforms. A QoS-aware domain divided information service, HH-MDS, is introduced in this paper. It is an important component of our service grid platform. HH-MDS solves system scalability issue effectively. In additional, several effective QoS strategies are provided to improve the efficiency of resource monitoring and information searching. Based on these strategies, service-oriented definitions and SLA specification are proposed to describe serving capability and relating QoS issues.

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Introduction

Grid technologies [1][13] enable large-scale sharing of resources within all kinds of consortia of individuals and/or institutions. In these environments, the discovery, characterization, and monitoring of resources and computations is a challenging issue. The RMIS need record the identity and essential characteristics of services available to community members, and maintain service validity. Notification framework is proposed as a basic means for determining the existence and properties of an entity in wide area network. Each framework message is propagated with timestamps. Based on soft-state model, a robust notification mechanism coupled with a graceful degradation of stale information is provided. Inter-operation between different systems is also a challenging issue in grid platforms. Web services [2] are Internet based applications that communicate with other applications to offer service data or functional services automatically. A service level agreement is an agreement regarding the guarantees of a web service. A service provider contracts with a client to provide some measurable capability or to perform a task by a service [3]. As resources in a particular grid system have apparently geographical characteristic, it is suitable to divide the whole grid into several parts and manage them separately. The rest of the paper is organized as follows: Section 2, we discuss related works about the information services. Section 3, we propose HH-MDS framework. A domain divided architecture is adopted and system scalability is discussed within this framework. Sections 4, some strategies about QoS guarantees are described. Finally we analyze HH-MDS performance and conclude this paper. *

This paper is supported by National Science Foundation under grant 60125208 and 60273076.


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Peer-to-peer [4] paradigm dictates a fully distributed, cooperative network design, where nodes collectively form a system without any supervision. Its advantages include robustness in failures, extensive resource sharing, self-organization, load balancing, data persistence, anonymity, etc. Current search algorithms for unstructured P2P networks [5] can be categorized as either blind or informed. In a blind search, nodes hold no information that relates to document locations. While in informed methods, there exists a centralized or distributed directory service that assists in the search for the requested objects. Informed method is more suitable for global information searching and performance prediction than blind method. Globus [14] platform can be viewed as a representative peer-to-peer system. There does not exist a centralized supervision over all kinds of grid sources, and each task is submitted and controlled by a grid source. Globus adopts an informed method, the Monitoring and Discovery Service (MDS2) [6][7], for resource searching. MDS2 provides an effective managing strategy for both static and dynamic information of resources. It adopts a hierarchical structure to collect and aggregate service information. MDS2 is one of most popular RMISs at present. If the information service of a grid system is constructed in tree architecture, system load in high-level information servers will increase violently, and information searching efficiency could not be guaranteed. Index service in Globus Toolkit 3 is developed based on web services, but there is not an effective name space to organize services in the whole grid. It depends on domain name or IP address to locate the corresponding services. The Relational Grid Monitoring Architecture (RGMA) [8][12] monitoring system is developed based on the relational data model and Java Servlet technologies. Hawkeye is a tool developed by the Condor group [9]. The main use is to offer monitoring information to anyone interested and to execute actions in response to conditions. They could not extend to a large scale. Based on domain divided principle, we propose a peer-to-peer architecture for high-level information servers to manage domain services and provide global service information. One domain includes many organizations, and one organization includes one or several service providers. A domain information server is responsible for the validity management and general capability provision of domain services. Besides the validity management, an organizational information server is also responsible for current capability provision of services within an organization. Effective strategies, such as performance statistic, prediction, and dynamic notification cycle, are provided to evaluate current service capability of the service provider.

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Fully studied the above popular RMISs, a perfect RMIS oriented service should conform to five principles: 1) Service usability; 2) Information consistency; 3) Query performance; 4) System scalability, and 5) Distributed architecture. HH-MDS framework is designed based on the above principles. As depicted in Figure 1, the whole framework is divided into several domains with peer-to-peer relationship. In each domain, HH-MDS information services are classified into three

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levels: (1) Domain Information Server (DIS), (2) Information Server (IS), and (3) Service Provider (SP). In order to make a DIS from becoming a single point of failure, two DISs are constructed in a domain.

Fig. 1. The HH-MDS Architecture

A SP registers to an IS with local services within an organization. It monitors local services, evaluates and predicts their performance, and reports their current serving capability to the IS. An IS registers to a DIS with services within a domain. Registered services in the DIS include service’s functions and general serving capability. The descriptions of registered services in the IS differ from those in the DIS. Besides those service descriptions in the DIS, the IS also includes current serving capability of services, described by SLA specification. In HH-MDS framework, we propose four types of protocols to achieve global resource sharing in the whole grid: (1) inter-domain GRid Registration Protocol (Inter-domain GRRP), which is available when a new domain is added to current grid system, at first, a new legal certificate signed by Domain CA is required for a new DIS; (2) Inter-domain GRid Information Protocol (Inter-domain GRIP), which is used by a DIS to query service information from the other DISs; (3) Intra-domain GRid Registration Protocol (Intra-domain GRRP), which is available to a SP or a IS when it registers to a higher-level IS or DIS with services; and (4) Intra-domain GRid Information Protocol (Intra-domain GRIP), which is used by users when they query service information from a DIS or a IS. A hierarchical naming method described in XML schema is proposed to organize registered elements. Elements “HHMds-Domain-name”, “HHMds-Organizationname = XXX”, and “HHMds-Host-name” are used to describe a host, and such above elements and element “HHMds-Service-name” are used to describe a service.

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Fig. 2. QoS Framework of HH-MDS

3.1 HH-MDS QoS Criteria As depicted in Figure 2, HH-MDS QoS framework [10][15] is divided into three levels: DIS level QoS, IS level QoS, and SP level QoS. DIS level QoS is used to guarantee service usability in a domain and provide global service information. Notification mechanism is adopted by the DIS to send service information to users subscribing the corresponding services when notification event occurs. IS level QoS is used to guarantee service validity and provide current serving capability of services in an organization. SP level QoS is used to monitor local services. Based on user request rate, available bandwidth, and local available resources, SP level QoS is used to determine current serving capability of a service and predict its future serving capability. 3.1.1 DIS Level QoS DIS level QoS includes three parts: service SLA specification, subscription and notification, and service validity management. Based on subscription and notification mechanism, a user can subscribe interesting services and get notification message in time once anyone registers to the DIS, or is invalid. A grid service is a WSDL-defined service that conforms to a set of conventions relating to its interface definitions and behaviors. It includes many kinds of service data to describe service functions. Service SLA specification provides general serving capability and policy description. The definition part consists of many SLA parameters, which are assigned a metric defining how its value is measured or computed. These parameters include total response time, total throughout ratio, performance curve, and usage policy. Service SLA specification provides service lifetime, and service obligations, which define QoS guarantees that service provider offers to service consumer. These guarantees represent promises with respect to the state of SLA parameters and promises to perform an action. There are three kinds of service status: normal, failure, and revised. When the status is revised, status report couples with the revised service description.


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3.1.2 IS Level QoS IS level QoS embodies at three aspects: dynamic SLA management (such as current response time, current throughout ratio, etc) of services, cache management, and subscription and notification. Subscription and notification mechanism is realized at two levels in the IS: service level, and node level. At service level, a user interacts with a service directly and subscribes information about its current capability. Once its current capability meets the requirement of user request, the service will notify him. The IS container sets up a queue for each service, which records users who subscribe this service. At node level, the IS container sets up a common queue for user subscription. A user can subscribe interesting services and get notification. Dynamic service information is obtained from the SP, and cached in the IS memory. Cache management is introduced to fasten information searching speed, but it reduces information veracity. It is unadvisable to set a fixed notification interval for a service when it registers to the IS, because its current serving capability is dynamic and undetermined. When current serving capability of a service changes slowly, the notification interval is big, vice versa. Alterable notification improves information veracity and lighten system load. 3.1.3 SP Level QoS SP level QoS includes two parts: statistic decision-making, and changing detection. Statistic decision-making is used for performance evaluation and alterable notification. Changing detection is used to detect immediate change of local services. As different service type has its special resource requirement, we take data intensive computing service type as an example to describe statistic decision-making process. z P donates available node resource, including CPU, memory, disk, and P(t) donates available resource changing rate, both including available amount and current access speed. z N donates current available bandwidth, and N(t) donates available network bandwidth changing rate. z F donates serving capability of a service, and F(t) donates the changing rate of serving capability. If memory and disk space are large enough, F is related to CPU and network bandwidth. We donate F with F1(CPU, N). If network bandwidth is equal to the constant n, we conclude: F1(CPU , n) = {F ( P, N ) | N = n, MEM idle = MEM enough , DISK idle = DISK enough }

The statistic decision-making flow is described as follows: For each n in {N i | 0 ≤ i ≤ I , N i +1 − N i = MaxBand / I , N 0 = 0, N I = MaxBand} : {F1(CPU i , n) | 0 ≤ i ≤ K , CPU i +1 − CPU i = MaxCPU / K , CPU 0 = 0, CPU K = MaxCPU }

(1)

I donates bandwidth interval, and K donates CPU available rate interval. According to Eq.1 and linear interpolation formula, we have:

F1(CPU , n) =

CPU − CPU i +1 CPU − CPU i F1(CPU i , n) + F1(CPU i +1 , n) CPU i − CPU i +1 CPU i +1 − CPU i

0 ≤ i ≤ K −1

(2)

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We get the following performance evaluation aggregation:

{F1(CPU , N i ) | 0 ≤ i ≤ I , N i +1 − N i = MaxBand / I , N 0 = 0, N I = MaxBand}

(3)

Based on Eq.3, we get current serving capability of a service as:

F1( P (0), N (0)) =

N (0) − N i +1 N ( 0) − N i F1( P (0), N i ) + F1( P(0), N i +1 ) N i − N i +1 N i +1 − N i

(4)

P(0) and N(0) are obtained by operating system and network detector separately. If

| F ( P (0), N (0)) − F ( P (t past ), N (t past )) |> BasicValue , (tpast donates the

last notification time), the SP notify F1( P (0), N (0)) to the IS. BasicValue is a threshold specified as a performance parameter of a service. There exist some gusty events in the SP. Those events will cause service performance change violently. Four situations exist: (1) user request number changes violently, (2) system load changes violently, (3) resource reservation, and (4) resource release. For these situations, F should be calculated again and notified the IS at once.

4 Performance Analysis A user obtains global service information from a DIS. There exists information inconsistency when a service fails at status report intervals. If the interval is large, information inconsistency is apparent. The interval should be adjustable according to network status. A user queries current serving capability of services from the IS directly. As our service grid system is developed based on Globus Toolkit 3, information server scalability with both users and service providers, and query performance have been studied in [11]. In this section, we focus on the information service in an organization. Three parameters, including serving capability, looking-up error rate and communication load will be studied. The experiments were run on two sites: the server-sided services are provided at Internet and Cluster Computing Center (ICCC) at Huazhong University of Science and Technology (HUST) with a 16 nodes cluster, each node of Xeon 1GHz, 40GB HD, 512MB memory. The IS is on a node, and the other nodes are used as the SPs. The client is a personal computer with Power604E 200MHz, 2 GB HD, 128MB Memory at High Performance Computing Center (HPCC), HUST. The bandwidth between ICCC and HPCC is around 50Mb/s. A 100 Mbps bandwidth is provided within a cluster. A service for data conversion is provided in one of the SPs. The service has four parameters: 1) size of input data: 1.2Gb, 2) size of output data: 800Mb, 3) size of application code: 2MB, and 4) execution time on a Base Machine (P4 1GHZ CPU, 512MB Memory): 40 sec. Response time of the above service is depicted in Figure 3. When output bandwidth in edge router is limited to a fixed value, response time decreases as CPU available rate increases. But when CPU available rate increases to certain value, response time is basically stable. During the serving process of the service, CPU available rate and output bandwidth are detected timely. Based on response time in Fig.3, current response time of the service is achieved.


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We send queries for the service to the IS from the client side timely. By comparing service information with the same time at two sites, looking-up error rate is obtained. BasicValue is used to determine the time the SP reports service information to the IS, and its value is set to certain percent of average response time. Looking-up error rate related to the service under different BasicValue is depicted in Figure 4. The corresponding communication load related to the service is depicted in Figure 5. The SP computes response time per second and determines whether to send information to the IS or not. Communication load statistic is got at one-minute interval. Generally, looking-up error rate and communication load are related to service type closely.

0

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Fig. 4. Looking-up Error Rate

60 55 50 45 40 35 30 25 20 15 10 5 0

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Fig. 5. Communication Load

5 Conclusions and Future Works In this paper, we propose a QoS-aware domain divided information service, HHMDS, as an important component of our service grid platform. We have proposed four types of protocols to construct HH-MDS. Distributed management and distributed information searching are proposed in HH-MDS. Three-level QoS framework of HH-MDS is discussed in this paper to guarantee service usability and information consistency, and improve query performance. In the future works, we will fully study the characteristics of all kinds of services, and propose a more perfect statistic model for serving capability of grid services.

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References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

[11] [12] [13] [14] [15]

I. Foster, C. Kesselman, and S. Tuecke, “The Anatomy of the Grid”, Intl. Journal of Supercomputer Applications, 2001. The Web Services Industry Portal, http://www.webservices.org/. H. Ludwig, A. Keller, A. Dan, and R. King, “A Service Level Agreement Language for Dynamic Electronic Services”, Proceedings of the 4th IEEE Int’l Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems, IEEE, 2002. R. Schollmeier, “A Definition of Peer-to-Peer Networking for the Classification of Peerto-Peer Architectures and Applications”, Proceedings of the First International Conference on Peer-to-Peer Computing (P2P’01), IEEE, 2002. M. Kelaskar, V. Matossian, P. Mehra, D. Paul, and M. Prashar, “A Study of Discovery Mechanisms for Peer-to-Peer Applications”, Proceedings of CCGrid’02, pp.414-415. G. Aloisio, M. Cafaro, I. Epicoco, and S. Fiore, “Analysis of the Globus Toolkit Grid Information Service”, Technical report GridLab-10-D.1-0001-GIS_Analysis, GridLab project, http://www.gridlab.org/Resources/Deliverables/D10.1.pdf. K. Czajkowski, S. Fitzgerald, I. Foster, and C. Kesselman, “Grid Information Services for Distributed Resource Sharing”, Proceedings of 10th IEEE International Symposium on High-Performance Distributed Computing (HPDC-10), 2001. S. Fisher, “Relational Model for Information and Monitoring”, Technical Report GWDPerf-7-1, GGF, 2001. M. Litzkow, M. Livny, and M. Mutka, “Condor – A Hunter of Idle Workstations”, Proceedings of the 8th International Conference of Distributed Computing Systems, pp.104-111, June 1988. J. Al-Ali, F. Rana, W. Walker, S. Jha, and S. Sohail, “G-QoSM: Grid Service Discovery Using QoS Properties”, Computing and Informatics Journal, Special Issue on Grid Computing, Institute of Informatics, Slovak Academy of Sciences, Slovakia, 21(4), pp.363-382, 2002. X. Zhang, L. Freschl, and M. Schopf, “A Performance Study of Monitoring and Information Services for Distributed Systems”, Proceedings of HPDC’03, 2003. DataGrid, DataGrid Information and Monitoring Services Architecture: Design, Requirements and Evaluation Criteria, Technical Report, 2002. W. E. Johnston, D. Gannon, and B. Nitzberg, “Grids as Production Computing Environments: The Engineering Aspects of NASA's Information Power Grid”, Proceedings of 8th IEEE Symposium on High Performance Distributed Computing, 1999. I. Foster and C. Kesselman, “Globus: A Metacomputing Infrastructure Toolkit”, International Journal of Supercomputer Applications, Vol.11, No.2, pp.115-128, 1997. C. Li, G. Peng, K. Gopalan, and T. Chiueh, “Performance Guarantees for Cluster-Based Internet Services”, Proceedings of CCGrid’03, IEEE, 2003.

Grid Service Semigroup and Its Workflow Model 1

2

3

1

Yu Tang , Haifang Zhou , Kaitao He , Luo Chen , and Ning Jing

1

1

School of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, P.R.China [email protected], {luochen, ningjing}@nudt.edu.cn 2 School of Computer, National University of Defense Technology, Changsha, Hunan, P.R.China [email protected] 3 China Geological Survey, Beijing, P.R.China [email protected]

Abstract. Grid service is defined by OGSA as a web service that provides a set of well-defined interfaces and that follows specific conventions. To classify different Grid services and describe their relations, we present Grid Service SemiGroup (GSSG) based on group theory. Moreover, a novel concept, i.e. meta-service, is proposed based on the definition of generating element in cyclic monoid. To meet integration and collaboration demands of distributed and heterogeneous Grid services, some special elements, such as time, resource taxonomy and etc, are introduced to extend basic Petri net for workflow modeling. A new workflow model for GSSG named Grid Service/Resource Net (GSRN) is proposed and presented as well. And some new analysis methods based on graph theory, which complement traditional analysis methods of basic Petri net, are introduced to analyze and evaluate GSRN. The practicability and effectivity of GSRN are demonstrated in an application project.

1 Introduction As a novel technology defined by the Open Grid Services Architecture (OGSA) to implement resource sharing and cooperation, Grid service has become the focus of research and web-based applications. Grid service is a web service that provides a set of well-defined interfaces and that follows specific conventions. The interfaces address discovery, dynamic service creation, lifetime management, notification, and manageability; the conventions address naming and upgradeability [1, 2]. According to different purposes, developers and service types, Grid services belong to different organizations and should be classified into different Grid service sets. Since some relations exist among Grid service sets, it is necessary to provide a mechanism to describe these relations. According to our best knowledge, the related research has not been attracted appropriate attention. Then, we propose a novel concept and framework called Grid service semigroup (GSSG) to classify different Grid service sets and describe their relations. And based on the definition of generating element in cyclic monoid[3], meta-service is presented for the first time.


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On the other hand, for most web-based applications and works, integration and cooperation of different Grid services are indispensable. The course of Grid services cooperation is a service chain and can be described by workflow model. There exist many ways to define and describe the workflow model, such as WFMC define language [4], RAD graph, EPCM model, and etc, in which Petri net has been the focus of research and application. But Basic Petri net can’t model dynamic, timed and conditional workflow [5-7]. So we introduce some additional elements, such as time, condition, resource taxonomy, and etc, to extend basic Petri net. Then we propose a new workflow model named Grid Service/Resource Net (GSRN). Because GSRN is an extended Petri-net-based workflow model for GSSGs, we introduce some new algorithms and methods based on graph theory [3, 8] to complement traditional Petri net methods for analyzing and evaluating GSRN. The remainder of this paper is organized as follows: the definitions and concepts of GSSG are introduced in Section 2. Section 3 is devoted to definitions and related rules of the extended Petri-net-based workflow model for GSSGs (GSRN). In section 4, some new methods for analyzing and evaluating GSRN are discussed in detail. GSRN are illustrated and demonstrated by means of an application example in Section 5. Finally, Section 6 provides some concluding remarks.

2 Grid Service Semigroup A Grid service can connect and invoke other Grid services through standard interfaces to implement sharing and integration. The connecting relation and invoking relation among Grid services can be regarded as an operation (we define it as Join). Based on Grid service sets and Join operation, we find that Grid service sets are similar to semigroup [3]. So we propose Grid service SemiGroup (GSSG) and induce some useful definitions and theorems to determine structural similarity of GSSGs. Definition1: A semigroup (S ,*) is a nonempty set S which has a binary operation * such that ∀a , b, c ∈ S , ( a * b) * c = a * (b * c ) . Generally, the symbol * can be omitted, i.e. ( a * b) * c = ( ab)c . Definition2: A semigroup (S ,*) is a monoid if ∃e ∈ S , ∀a ∈ S , ae = ea = a . A monoid is denoted by ( S ,*, e) and such e is called identity. Definition3: A monoid is a cyclic monoid if ∃h ∈ S , ∀ a ∈ S , a = h m , ( m ∈ Z , m ≥ 0) and such h is called generating element. Definition4: Join denoted by ＋ is a binary operation which describes the connecting and invoking relations between any two Grid services. Definition5: Grid service semigroup (GSSG) is a semigroup (GS ,+ ) in which GS is a set of Grid services, i.e. ∀x, y, z ∈ GS , ( x + y ) + z = x + ( y + z ) . Definition6: Empty service (∅) is a service which has no operation and function. Definition7:Grid service monoid (GS ,+, ∅ ) is a GSSG such that ∀x ∈ GS , x ∅ = ∅x = x .

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Definition8: A meta-service (ms) is a basic Grid service such that and (GS ,+, ∅ ) is a Grid service monoid. Definition9: T is a subset of a GSSG (GS ,+ ) , if (T ,+ ) is a GSSG, then T is a subGSSG of (GS ,+ ) . Definition10: Given two GSSGs (GS1,+ ) and (GS 2,+ ) , map f : G → H is a homomorphism if ∀x, y ∈ GS1 ⇒ f ( x + y ) = f ( x ) + f ( y ) . Monomorphism, epimorphism and isomorphism are defined as homomorphism between GSSGs. Different GSSGs may have same elements or similar structure, so homomorphism is necessary and useful in determining structural similarity of GSSGs. The related theorems are built on definition 10 and will be discussed in our subsequent papers. ∃ GS , s.t ., GS = { x i | x i = gs n , ( n ∈ Z , n ≥ 0 )}

3 Grid Service Semigroup Workflow Model: GSRN We extend basic Petri net to describe and model workflow of GSSGs. The basic definitions and concepts of Petri net are in [5-7]. Definition11: Grid Service/Resource Net (GSRN) is an extended Petri net, i.e. a tuple SRN = (P, T , F , K , CLR, CLS , AC , CN , TM ,W , M 0 ) where: − P is a finite set of places, P＝ {PR , PS }, | P |= n , PR = { p R1 , p R 2 , " , p Rn } is a set of resource(data, information, and etc), PS = { p S1 , p S 2 , " , p Sn } is a set of Grid services. − T is a finite set of transitions representing the activities, T = {t1 , t 2 , ", t m }, m =| T | . − F is a set of flow relation, F ⊆ P × T ∪ T × P , P ∩ T = ∅, P ∪ T ≠ ∅ ; dom ( F ) ∪ cod ( F ) = P ∪ T , dom( F ) = {x | y : ( x, y ) ∈ F } , cod ( F ) = { x | y : ( y , x ) ∈ F } − K is a places capacity function, generally K = ∞ , − CLR is a resource taxonomy function, CLR ( PR ) → {1,2,3, "} , − CLS is a services taxonomy function, CLS( PS ) → {GS1 , GS 2 , GS 3 , "}, GSi is a GSSG, − AC is a flow relation markup function, AC ( F ) → {CLR( PR ), CLS ( PS )} , − CN is a condition restriction function on F, CN ( F ) → {true, false} , − TM is a time function on T. GSRN can be classified as fixed time-delay net and unfixed time-delay net. In fixed time-delay net, there is a fixed time value for each transition. And in unfixed time-delay net, a value area is endued to each transition, i.e. TM (t ) = [t i , t j ], (0 ≤ t i ≤ t j ), t ∈ T ; each transition has a scheduled executive time which is decided by practical flow. If a transition is scheduled to execute at time b, then its actual execution time (t ) satisfies b + t i ≤ t ≤ b + t j . − W is a weight function, W ( x, y ) = ( w(cl1 ), w(cl 2 ), " , w(cl l )) , w( cl i ) → {0,1,2,3,"} , CL = CLR ∪ CLS , cl ∈ CL , ( x, y ) ∈ F , l =| CL |

− M is a marking function, M = ( m( p1 ), m( p 2 ), ", m( p n )) denotes the system marking of whole net, m( p i ) = (| cl1 |, | cl 2 |," , | cl l |), p i ∈ P , M 0 is the initial marking.

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Definition12: The pre-set of GSRN place/transition is a set denoted by • x such that ∀ x ∈ P ∪ T , • x = { y | ( y ∈ P ∪ T ) ∧ (( y , x ) ∈ F )} , the post-set of GSRN place/transition is a set denoted by x • such that x • = { y | ( y ∈ P ∪ T ) ∧ (( x , y ) ∈ F )} . GSRN is a directed graph which is composed of places, transitions and arc lines. In GSRN, we use token (black spot) to mark resource distribution (tokens exist in places) and arc lines to express the flow relation between places and transitions. As for extended elements, time elements are marked on transitions, and conditions are kept on the corresponding transitions or places. The running of GSRN is implemented by firing transitions. A transition can fire only if its input places have corresponding tokens (markings). After the transition being triggered and fired, the number of tokens in pre-set decreases and the number of tokens in post-set increases accordingly. Definition13: The transition t i ∈ T , i = 1, " , m is enabled under M, in symbols M [t i > , iff ∀ p j ∈ P ( j = 1, " , n ), p j ∈• t i ⇒ M ( p j ) ≥ W ( p j , t i ) , M ( p j ) = (| cl1 |, | cl 2 |, " , | cl l |) , ∀h ≤ l , | cl h |≥ w( cl h ) , and CN ( p j , ti ) = true . If M [t i > holds, the transition ti may occur

(or fire), resulting in a new marking M ' , in symbol M [ti > M ' , with:  M(p j ) - W(p j , t i )   M(p j ) + W(t i , p j ) M '(pj) =   M(p j ) - W(p j , t i ) + W(t i , p j )  M(p j ) 

p j ∈• t i - t i

•

• •

p j ∈ t i - t i ∀p ∈ P ( j = 1, " , n ) j p j ∈• t i ∪ t i • p j ∉• t i ∪ t i •

The transition structures describe the dependent relation among different resource and services. The basic transition structures of GSRN are concluded as six types (figure 1 shows) and workflow models can be composed of these six basic structures (basic place structures are similar to basic transition structures). p2 ... tn

t1

p1 (1)

pn

t

p1

p2 ... pn

(4)

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p2 t

(5)

P

p1

... and pn

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tn ...

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pn ... (2)

t

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p1

branch structure ii p1 p1 t P P ... or ... xor pn pn k ii assembling structure t1 p2 pn ...

t

P

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Fig. 1. Basic transition structures of GSRN

4 Analysis and Evaluating Methods of GSRN Characteristics of GSRN are very important in analyzing and evaluating GSRN. Main characteristics of GSRN include boundedness, reachability, liveness, and etc. Their


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definitions are as same as those of basic Petri net (see [5-7]). Because GSRN is an extended Petri-net-based workflow model, traditional methods of Petri net should be combined with new algorithms based on graph theory to form a new analysis and evaluating system of GSRN. Follows are two new analysis and evaluating methods. 4.1 Resource Matching Algorithms of GSRN In GSRN, different sub-flows may request same resource and Grid services, but the resource can’t meet all demands at the same time. Then, confliction problem between resource and requests is induced as the resource matching problem [3, 8]. Based on graph theory, we put emphasis on algorithm of bigraph maximal matching for GSRN. Algorithms of bigraph optimal matching [3, 9] will be discussed in another paper. Definition14: Given a graph G and its edge subset M, if any two edges in M have no intersectant vertexes, then M is a matching of G. The vertexes related to edges of M are called saturated points; otherwise, the vertexes are called non-saturated points. Definition15: Given a matching M of graph G = (V , E ) , if for any matching M ' of G, ∃ | M |≥| M ' | (|M| is the number of edges in M), then M is a maximal matching of G. Definition16: Given a matching M of graph G = (V , E ) , interleaved path is a path that is composed of edges belong to M and not belong to M alternately. Definition17: Given an interleaved path of matching M of G, i.e. P, if the two vertexes of P are non-saturated points, then P is called augment path. Theorem1：M is the maximal matching of G iff there is no augment paths in M. Proof: See [3]. Theorem 1 is the foundation of algorithms of bigraph maximal matching, and we use Hungary algorithm [3] to get maximal matching of GSRN. 4.2 Linear Temporal Inference Rules As defined in section 3, time element is an important element to evaluate GSRN, so we deduce some linear temporal inference rules as an evaluating method [10, 11]. Before giving rules, we define some symbols: T1 , T2 , T are transitions in GSRN, b1 , b2 , b denote scheduled time of T1 , T2 , T , t1• , t 2 • , t • are actual executive time of T1 , T2 , T . According to transforming structures shown in figure 2 and definition of

time element, linear temporal inference rules, i.e. (T1 , T2 ) → T [ti , t j ] , are proposed as follows [11]. 1. Rule1 (sequence): Based on sequence structure, we get follow equations. •

•

(1)

t1 = b 2

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b1 + t 1i ≤ t 1 ≤ b1 + t 1 j ; b 2 + t 2 i ≤ t 2 ≤ b 2 + t 2 j ; b + t i ≤ t • ≤ b + t j

•

b = b1 ; t • = t 2

•

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T1 = [t 1i , t1 j ] T2 = [t 2 i , t 2 j ] i o

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o (2)

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T 2 = [t 2i , t 2 j ]

i T 1 = [ t 1i , t 1 j ]

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Fig. 2. Transforming structures of linear temporal inference

By (1), (2) and (3), we get b1 •

•

b + t 1i + t 2 i ≤ t = t 2 ≤ b + t 1 j + t 2 j

+ b 2 + t 1i + t 2 i ≤ t 1 • + t 2 • ≤ b1 + b 2 + t 1 j + t 2 j ,

then

, so t i = t1i + t 2i , t j = t1 j + t 2 j .

Rule1: (T1 , T2 ) → T [t i , t j ] = T [t1i + t 2i , t1 j + t 2 j ] The inducing courses of other rules are similar to rule1 and omitted in this paper. 2. Rule2 (paralleling): (T1 , T2 ) → T [t i , t j ] = T [max(t1i , t 2i ), max(t1 j , t 2 j )] . 3. Rule3 (free choice): (T1 , T2 ) → T [ti , t j ] = T [min( t1i , t 2 i ), min( t1 j , t 2 j )] . 4. Rule4 (conditional choice): (T1 , T2 ) → T [ti , t j ] = T [min(t1i , t 2i ), max(t1 j , t 2 j )](T1 ∩ T2 ≠ ∅) , (T1 , T2 ) → T [t i , t j ] = [t1i , t 2 i ] ∪ [t1 j , t 2 j ](T1 ∩ T2 = ∅ )

5. Rule5 (circle): (T1 , k ) → T [ti , t j ] = T [kt1i , kt1 j ] (k is the circle times). These rules above can’t be used for all GSRN models, and their applicable conditions are as same as the conditions discussed in [11].

5 A GSRN Example In our research project, we use GSRN to model the workflow of layout planning for the area nearby a bridge. Application courses are listed as follows: 1. Urban planning bureau proposes application request and the workflow begins. 2. Mapping bureau provides area map. 3. Traffic bureau provides related traffic data, Geological bureau provides related data, and business enterprises provide related business data. 4. Corresponding services process and integrate map and various data. 5. Eventually, the results return to urban planning bureau and the workflow finishes. The corresponding GSRN model is shown in figure3. And the meaning of resource and service taxonomy elements in this GSRN model is explained in following tables. In accordance with GSRN modelin figure 3, figure 4 demonstrates the application flow.


p1

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Meaning

1 ○ 2 ○

map request

3 ○ 4 ○

Map

data request traffic data

Resource markup

taxonomy

Meaning

5 ○ 6 ○

geological data

7 ○ 8 ○

Integrated map and data

business data plan

Table 2. Resource taxonomy mapping Service taxonomy markup

Meaning

Service markup

taxonomy

Meaning

GS1

mapping service

GS3

geological data service

GS2

traffic data service

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business data service

As experiment result shows, GSRN are effective and practical in modeling Grid services workflow. Based on GSRN, we can combine and aggregate distributed Grid services which belong to different GSSGs to fulfill large tasks.

6 Conclusion According to application demands, we propose GSSG and its related theorems based on group theory. And a new concept, i.e. meta-service, is presented. To describe and model workflow of grid services in different GSSGs, a novel extended Petri-netbased workflow model (GSRN) is proposed and discussed in detail. Moreover, some new algorithms and methods based on graph theory are introduced to analyze and evaluate GSRN. And the practicability of GSRN is verified in an application example. GSSG and GSRN are novel concepts and technologies. We will perfect and extend the definitions, theorems, and algorithms in the future. And we will put research emphasis on more key technologies, such as additional theorems of GSSG, rules for

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(a)

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GSRN model predigestion, new theories and methods for analyzing and evaluating GSRN, optimal resource matching algorithm for common graph, and etc. Acknowledgements. This work is supported in part by the National High Technology Research and Development 863 Program of China (Grant Nos.2002AA104220, 2002AA131010, 2002AA134010).

References 1.

2. 3. 4. 5. 6. 7. 8.

Foster, C.Kesselman et al. The Physiology of the Grid:An Open Grid Services Architecture for Distributed Systems Integration. June, 2002. See http://www.gridforum.org/ogsiwg/drafts/ogsa_draft2.9_2002-06-22.pdf. S.Tuecke, K.Czajkowski et al. Grid Service Specification. Open Grid Service Infrastructure WG, Global Grid Forum, Draft 2. July 2002. See http://www.globus.org. Y.Q.Dai, G.Z.Hu, and W.Chen. Graph Theory and Algebra Structure (in Chinese). Tsinghua University Press, Beijing, China, 1999. D.Hollingsworth. Workflow Management Coalition: The Workflow Reference Model. Document Number WFMC-TC00-1003, Brussels, 1994. T.Murata. Petri Nets: Properties, Analysis and Applications. In Proceedings of the IEEE, 77(4), pages 541-580, April 1989. J.Peterson. Petri Net Theory and the Modeling of Systems. Prentice Hall, Englewood Cliffs, New Jersey, 1981. C.Y.Yuan. Petri Net Theory(in Chinese). Publishing House of Electronics Industry, Beijing, China, 1998. R.Johnsonbaugh. Discrete Mathematics, 4th Edition. Prentice Hall, Englewood Cliffs, New Jersey, 1997.


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9. J. Edmonds. Path, trees, and flowers. Canadian J. Math., 17:449-467, 1965. 10. M. Silva, E. Teruel, and J. M. Colom. Linear algebraic and linear programming techniques for the analysis of place/transition net systems. In Lectures on Petri Nets I: Basic Models, W. Reisig and G. Rozenberg, Eds. Vol. 1491, Lecture Notes in Computer Science, pages 309–373, Springer-Verlag, 1998. 11. T.Liu, C.Lin, and W.D. Liu. Linear Temporal Inference of Workflow Management System Based on Timed Petri Net Models (in Chinese), ACTA ELECTRONICA SINICA, 30(2):245-248, Feb 2002.

A Design of Distributed Simulation Based on GT3 Core Tong Zhang, Chuanfu Zhang, Yunsheng Liu, and Yabing Zha College of Mechaeronics Engineering and Automation, National University of Defense Technology, Changsha 410073 [email protected]

Abstract. Aimed at coordinated resource sharing in distributed, heterogeneous dynamic environment, OGSA supports distributed simulation effectively in resource management. GT3 Core provides a structure of service container, based on which a new mode of distributed simulation system has been designed. The new mode realized the separation of simulation resource and simulation applications, and supplied a simulation server responsible for the organization of simulation resource. The server provides service index for higher simulation applications and enable the interaction among them. Under this new simulation mode, a combat simulation application has been developed as a prototype. It achieved well reusability, portability of simulation resource, and supported heterogeneous, cross-platform application development.

1 Introduction Building on technologies from the Grid [1, 2] and Web services [3], OGSA[4] has appeared as the most important Grid architecture. It defines a uniform exposed service semantics-Grid service, and provides well-defined interfaces for the components in Globus Toolkit (GT) [5]. GT3 is based on a new core infrastructure complied with OGSA, and is an open source implementation of OGSI [6]. GT3 Core [7] offers a runtime environment hosting Grid services, and mediates between the application and the underlying network, and the transport protocol engines. Distributed simulation is geographically distributed simulators interconnected via LAN or WAN, and aims to gain interoperability and reusability. In current systems based on High Level Architecture (HLA)[8], the reuse and cooperation is conditional and lack of wide applicability, which can hardly satisfy increasing simulation requirements. They care more about the operations in applications than the resource management. OGSA provides a new method for building and managing distributed system. Based on its service-oriented mechanism, resources can be encapsulated in a more standard and effective way. Therefore, OGSA serves as a middleware between the simulation resources and applications, and supports the system with great power.


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2 Backgrounds 2.1 The Framework of Web Service Web Service is one of the bases that support OGSA architecture. It describes a collection of operations which are network-accessible through standard XML messaging. Web Service is intended to facilitate the communication between computer programs, and builds on such standards as HTTP, XML, SOAP, WSDL and UDDI. It defines techniques for describing software components, methods accessing them, and discovery methods that enable the identification of service providers. OGSA takes great advantage of Web Service. First, dynamic discovery and composition of services in heterogeneous environment necessitates mechanisms for registering and discovering interface definitions and endpoint implementation descriptions, and for dynamically generating proxies based on bindings for specific interfaces. WSDL supports this requirement by providing a standard mechanism for defining interface separately from their embodiment within a particular binding. Second, the widespread adoption of Web services mechanism means that a framework based on Web services can exploit numerous tools and extant services. [4] 2.2 GT3 Core – A Grid Service Container [7] The model of GT3 Core is based on the notion of a container that hosts various logic components. These components can be deployed into the container with varying quality of service (QoS) and behaviors. The container must be flexible enough to be deployed into a wide range of heterogeneous hosting environments. Compared to conventional Web services toolkits, it provides three major functions. First, it supports light-weight service introspection and discovery, where information flows in a both pull and push way. Second, it provides dynamic deployment and soft-state management of stateful service instances that can be globally referenced using an extensible resolution scheme. Third, it has a transport independent Grid Security Infrastructure (GSI) supporting credential delegation, message signing, encryption, and authorization.

3 Key Points in GT3 Core 3.1 Service Data Service data refers to descriptive information about Grid service instances, which can support service discovery, introspection, and monitoring. It is a structured collection of information. Each instance has a set of service data elements (SDEs) with different types. OGSI defines extensible operations for querying, updates, and subscribing to notification of changes in SDEs. The application of the GridService interface’s findServiceData operation is service discovery. The essence of service discovery is to obtain the GSH (Grid Service Handle) of a desired service. A Grid service that supports service discovery is called

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registry. A registry service is defined by two things: the Registry interface, which provides operations by which GSHs can be registered with the registry service, and an associated SDEs used to save information about registered GSHs. Thus, the Registry interface is used to register a GSH and the findServiceData operation is used to retrieve information about registered GSHs. 3.2 Notification The purpose of notification is to deliver interesting messages from a notification source to a notification sink. A notification source is the sender of notification messages, who can send notification messages to any number of sinks. A notification sink receives notification messages from any number of sources. The notification model has such a close integration with service data that subscription operation is a request for subsequent “push” delivery of service data. Whenever it happens to the source that something changed, there will be a notification delivered. It is different from “pull” model that has to do the periodical query. [6]The detailed realization of notification includes the following steps, as showed in figure 1. ①addListener

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3.3 Registry A service should be exposed to the outer client. Registry service provides information for the client to invoke services. Based on registry service, a registry server which contains index information of services in a virtual organization can be realized. Having finished local deployment, service providers register service GSHs to the registry server. Registry server is a remote and virtual server. Remote registration is the basis for remote service identification and utilization. Registry server supports the publication and lookup of a remote service. It logically contains all the services, while actually isolates from their implementations. Every service has its own hosting environment, and can be exploited in heterogeneous platforms.


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4 Distributed Simulation Based on GT3 Core 4.1 Design of the Framework We supposed a scene of combat simulation in a two-dimension world, where a missile is launched from the ground to fire at a plane. When the missile hit the plane, the combat is over. In this simulation, three members have been designed: plane member, missile member and manager member. The manager has to control the process of the whole combat. They are distributed in different computers. Based on members’ requirements, the resource of entity model and manager model is abstracted as services. Entity model is responsible for calculating the state of an entity in the combat and holding all the necessary information about them. And manager model take charge of simulation time to support manager member..

Fig. 2. Logical Structure of the Application

Figure 2 shows the logical structure of our application, which is based on a clientserver model. The server is realized as a registry server holding simulation services, namely simulation service container. The model resources in simulation are encapsulated as grid services, including entity model and manager model. As for the client, three members involves: plane, missile and manager. They complete their tasks using underlying services and interact with others through GT3 Core. The separation of simulation resource and applications is the key point here, which shows the superiority of grid-based system. It will largely reduce the coupling between systems and resource, and facilitate the reusability of resource. Further, OGSA specifies interactions between services in a manner independent of any hosting environment, so the services are portable to heterogeneous platforms. 4.2 Design of the Server – Simulation Service Container The server provides an index of all the services related to the simulation application for the clients. Based on registry service, it puts all simulation services together logically, in which a client can look up for desired one, while physically the services are distributed and implemented in various local containers.

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Simulation services registered in the server are factory services, including entity factory and manager factory. The client has to create its own service instance from the proper factory, and make the instance serve as one member in the simulation and finish its supposed task. Here, the concept of instance is similar to that of federate in HLA’s federation [8]. The relationship among the service container, the service provider, and the client is shown in figure 3. The structure shows the way how the server provides these registered services to the clients. The content of registry list can be defined as service data in the registry service, which could be subscribed as notification. All these operations are supported by GT3 Core.

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4.3 Design of the Whole Process Based on the structure of the above framework, the development of simulation application can be summarized as the following steps: Step1: Define the concept model of simulation application, and specify distributed tasks. Then abstract desired simulation services from the requirement.. Step2: Design the server. Base on the common structure of simulation server, different simulation services are developed and deployed in their local container, and required to register to the simulation service container. The service interface and its service data should be designed and implemented under the mechanism of GT3 Core. Step3: Design the client. The client programs enable the utilization and interoperation of the simulation services. The simulation members will execute these client programs to finish the whole simulation task.


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5 Realization of the Application 5.1 Simulation Services in the Server The manager service takes charge of the management of distributed simulation, especially the advancement and management of simulation time. It is defined as the service data. The service interface provides related operations as setting/getting the value of the time, and advancing the time with the process of simulation. This service must have the ability to send notification, which can assure the synchronization in the simulation. The entity service describes the model of entity participating the simulation. Its service data is the state information, including the entity’s position, time, and entity ID. The position is a two-dimension coordinate, and the entity ID appears as a GSH, which is a globally unique name. The interface defines operations to calculate the entity’s next moment position on the base of its current position and calculation formula. In order to enable interoperation between different combat members, the ability of notification is also required. 5.2 Simulation Client The realization of three members in combat simulation shows the execution of distributed task under the utilization and interaction among service instances. The manager client serves as a command center and orders to start simulation and advance the simulation time. It collects the information from all the members in the combat. When it makes sure that all the members have finished their own task at this moment, it will advance the time to next moment and send notifications. The manager subscribed notification messages from more than one member. The time when those messages will be sent is unsure, and they invoke the same callback, so how to identify the source and guarantee receiving every message once for all is quite important. So, the entity ID was set to identify notification source. And a boolean variable flag was set to each member, when the message has been sent set it true, else set it false. Only when all the flags turn true, simulation time could be advanced. As for the other two clients, they are quite similar in function. They need set their initial position, velocity, and subscribe to simulation time first. When the notification comes, they will calculate the entity position of next moment and cause position data changes notification to manager.

6 The Conclusion This paper uses grid technology to build a distributed simulation environment and develop a simple combat simulation application. This new system architecture supports reuse and standardization of simulation resource more than before, and achieves well heterogeneity, portability in various platforms. With the development of grid technology, the research of the combination of grid and distributed simulation will have a leap and the system will become more powerful.

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References [1] [2] [3] [4] [5] [6] [7] [8]

I. Foster, C. Kesselman.: The Grid: Blueprint for a Future Computing Infrastructure. Morgan Kaufmann Publisher. San Francisco (1999) I. Foster, C. Kesselman, S. Tuecke.: The Anatomy of the grid: Enabling scalable virtual organizations. International Journal of Supercomputer Applications, Vol. 15. (2001) 200~222 S. Graham et al.: Building Web Services with Java: Making Sense of XML, SOAP, WSDL, and UDDI. Sams Technical Publishing. Indianapolis, Ind. (2001) I. Foster, C. Kesselman, J. Nick, S. Tuecke.: The Physiology of the Grid: An Open Grid Services Architecture for Distributed Systems Integration. Globus Project, http://www.Globus.org/research/papers/ogsa.pdf, (2002) I. Foster, C. Kesselman.: Globus: A Metacomputing Infrastructure Toolkit. International Journal of Supercomputer Applications, Vol. 11. (1997) 115-128 S. Tuecke, K. Czajkowski, I. Foster, J. Frey, S. Graham, C. Kesselman, T. Maquire, T. Sandholm, D. Snelling, P. Vanderbilt.: Open Grid Service Infrastructure (OGSI) Version 1.0. http://www.ggf.org/ogsi-wg (2003) Thomas Sandholm, Jarek Gawor. Globus Toolkit 3 Core – A Grid Service Container Framework. http://www-unix.globus.org/toolkit/3.0/ogsa/docs/gt3_core.pdf (2003) IEEE Standard for Modeling and simulation (M&S) High Level Architecture (HLA) －Frame and Rules. IEEE Std 1516-2000. (2000)

A Policy-Based Service-Oriented Grid Architecture* Xiangli Qu, Xuejun Yang, Chunmei Gui, and Weiwei Fan School of Computer Science, National University of Defence Technology, Changsha, China,410073 [email protected]

Abstract. Recently, a promising trend towards powerful and flexible Grid executing circumstances is the adoption of a service-oriented infrastructure. Meanwhile, for such requirements as QoS, load balance, security, scalability etc., network paradigm is being shifted from the current hardware-based, manually configured infrastructure to a programmable, automated, policy-based one. Based on the above two observations, in this paper we propose a policybased service-oriented grid architecture, outline its basic model, primary components and corresponding functionalities. Keywords: Grid, policy-based, service-oriented, small world

1 Introduction The Grid concept was first introduced as enabling resource sharing within desperate faraway scientific collaborations [4],[5],[6]. In [3], Grid technologies and infrastructures are defined as supporting the sharing and coordinated use of diverse resources in dynamic, distributed “virtual organizations”(VOs). With the booming of Web Services, component-based programming and middleware technologies, recently, trends show that Grid is more viewed as an extensible set of Grid services. Both in ecommerce and in e-science, integrating services from distributed, heterogeneous, dynamic VO is needed [2]. Therefore, many service-oriented Grid infrastructures and solutions have been presented, among which OGSA is a typical instance. OGSA “defines standard mechanisms for creating, naming, and discovering transient Grid service instances; provide location transparency and multiple protocol bindings for service instances; and supports integration with underlying native platform facilities”[2]. And some specific implementation of this architecture has already come into being, such as ICENI (Imperial College e-Science Networked Infrastructure) [9]. In terms of service orientation, such network-enabled entities as computational resources, storage resources, networks, programs, databases, mediums, etc., can all be classified as a kind of Grid services. For the diverse natures of these entities, for the various requirements from users, for the dynamics of the environment, and for the *

This paper is sponsored by Chinese 863 OS New Technologies Research 2003AA1Z2060.


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end guarantee of QoS, finding the “best” service capable of meeting the needs of a user, or a community of users, is inherently complex and challenging. Wherein, no single policy can satisfy the whole situation. Therefore, to enable a transparent, organic and efficient composition of services, to realize the blueprint of a Semantic Service Grid, to change a loosely coupled system to a tightly coupled one, with security, scalability, fault-tolerance, interoperability etc. in mind, it is of great importance to introduce dynamic adaptive multi-policies into the whole infrastructure. Meanwhile, today’s network is moving beyond simple, insecure, best-effort data communications, heading for policy-based infrastructures to enable advanced features such as dynamic traffic engineering, guaranteed bandwidth, secure traffic tunneling and so on [1]. Driven by the two changes, this policy-based service-oriented grid architecture is suggested. The rest of the paper is organized as follows: the targets of this architecture are listed in section 2; section 3 outlines the basic model; section 4 details the components and corresponding functionalities; and a brief summary and an outlook to future work conclude this paper in section 5 and section 6, respectively.

2 Targets The targets of such a policy-based service-oriented grid architecture outlined in this paper are: • High performance: It should be of efficient, flexible and succinct organization. • Context sensibility: policies will make dynamic adjustment according to network status, workload distribution and requirement variations. • QoS capability: to satisfy needs both from service requesters and service providers, to provide differentiated services. • Multi-protocol interoperability: to enable seamless cooperation between incompatible domains running different protocols. • Scalability: to allow the infrastructure to scale flexibly. • Security • Fault tolerance • Mechanism, not policy

3 Basic Model According to analytical results of network behaviorism, network interactive patterns take on a feature of “small world” [10],[11], exhibiting the following two characteristics: • high clustering and • a small average shortest path between two random nodes (sometimes called the diameter of the network), scaling logarithmically with the number of nodes.

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Taking this into account, we adopt a two-level hierarchical structure in this architecture: an inter-domain policy manager, a backup policy manager and an edge policy manager per domain. The whole Grid infrastructure can be divided into a number of relatively independent domains, which will be in the charge of a corresponding edge policy manager. And the inter-domain policy manager will take the responsibilities of coordinating different domains, making system-wide policies, managing edge policy managers. The backup policy manager, as its name implies, mainly serves to be a backup for the inter-domain policy manager. The whole infrastructure is depicted in Fig. 1:

Fig. 1. Basic Model

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Components and Corresponding Functionalities

After outlining the basic model, next we will focus on illustrating the components and corresponding functionalities. 4.1 Inter-domain Policy Manager Generally speaking, a policy will take the form of “If < condition (s) >, then < action (s) >. Considering the two parts consisting a policy-based system: a set of conditions under which the policy applies, including application types, protocol bindings, QoS priorities, workload distributions etc.; and a set of actions that apply as a consequence of satisfying (or dissatisfying) the conditions, including service matching, protocol

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selection, channel allocation, data migration and so on, a bundle of active and passive components consist this architecture: a policy maker, a policy base, a service repository, a multi-protocol interactor, a monitoring server, an auditing server and an event logger, as is illustrated in Fig. 2.

Fig. 2. Inter-domain Policy Manager

The corresponding functionalities of each component are: Auditing Server is responsible for such security problems as accessing control, user identification and so on. A service request will first enter this component, and will not get through unless qualified. Service Repository, as its name indicates, is a service collector, which is in charge of service registry, service discovering, service aggregation, service caching and service labeling. As an inter-domain service repository, it mainly interacts with those edge service repositories for service information. Policy Base is filled with all kinds of policies, such as security assuring policies, load balancing policies, protocol selecting policies, service matching policies and so on to cope with different situations. Administrators can input policies in Policy Description Language [7]. It also accepts the feedback of a service transaction to dynamically adjust some policies, embodying some self-learning capabilities.


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Policy Maker, in a way, is a critical component here. The final decisions, involving service matching, protocol selection, channel allocation, data migration are made here, according to some specific policies in policy base, while taking the external conditions, such as workload distribution, network traffic, network topology, QoS requirements etc. into consideration. And the final result will be logged to event logger for fault-tolerance and service feedback. Multi-protocol Interactor: this component mainly functions to bridge domains running different protocols, which can be implemented with a Proteus Multiprotocol Message Library [8]. Monitoring Server: this is an observer to external conditions, including workload distributions, network traffics, network topologies, service availableness. The information collected will be saved in info base to enable workload balancing, dynamic network topology depicting, and traffic shaping, so as to provide more powerful aids for real-time policing. Event Logger is responsible for servicing transaction logging. Each time a service intercourse is initiated, each participator will be logged. As soon as the transaction succeeds, a “success” signal will be sent here. In this way, servicing information can also be offered to service repository for service labeling. On the condition that the signal is timed out or a “failure” signal is received, this servicing transaction will be rolled back and the policy maker will be notified to make another policy again. By this means, fault is tolerated to a certain degree. Synchronizing Server is responsible for synchronizations with backup policy manager in service repository, event log, info base and policy base, and with edge policy managers for dynamic service refreshment. For efficiency, synchronizing data can be transmitted in wormhole manners. 4.2 Edge Policy Manager An edge policy manager is responsible for local domain policing, whose components are quite similar to the inter-domain policy manager. Since each domain, in a way, constitutes a small world running almost the same protocol, multi-protocol interactor can be removed. Under this circumstance, service transactions occur much more frequently, therefore a channel selector is configured for proper assignment of channels. Meanwhile, for services cannot be fulfilled within the same domain, an outgoing interactor will relay these service requests to the inter-domain policy manager. And the final policy from the inter-domain policy manager is passed down by way of this component. At the same time, it is also responsible for periodically sending an “alive” signal to the backup policy manager. This infrastructure is shown in Fig. 3. 4.3 Backup Policy Manager The backup policy manager acts as a backup for the inter-domain policy manager. It is configured almost of the same components, wherein the monitoring server plays an

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Fig. 3. Edge Policy Manager

important part monitoring the aliveness of all the other policy managers instead of other status, as long as the inter-domain policy manager is alive. Otherwise, it will take the place of the inter-domain policy manager. If some edge policy manager is observed to be offline, the backup policy manager will choose another node from the in-problem domain to act as the edge policy manager.

5 Summary Our policy-based service-oriented grid architecture is put forward based on two observations: grid is evolving towards a service orientation, while policy-based network is springing up. From the illustration of its primary components and corresponding functionalities, it can be concluded that: this architecture is dynamic, context sensible, QoS capable, workload balanceable, secure, scalable, multi-protocol interoperable and fault-tolerant.

6 Future Work • So far, this architecture remains just a blueprint; we will try to implement a prototype in the future.


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• For the limited fault-tolerant capabilities of this architecture and frequent volatilities in network, strong and efficient fault-tolerant measures such as service dependence analysis will be taken later. • Since the policy maker plays a critical role in this architecture, parallelism will be exploited in avoidance of bottleneck.

References 1.

David Durham: A New Paradigm for Policy-Based Network Control. Intel Developer Update Magazine, November 2001 2. Ian Foster, Carl Kesselman, Jeffrey Nick, and Steve Tuecks: The Physiology of the Grid: An Open Grid Service Architecture for Distributed Systems Integration. http://www.globus.org/ogsa/ 3. Foster, I., Kesselman, C. and Tuecke, S.: The Anatomy of the Grid: Enabling Scalable Virtual Organizations. International Journal of High Performance Computing Applications, 15 (3), (2001) 200-222 4. Catlett, C. : In Search of Gigabit Applications. IEEE Communications Magazine (April). 42-51. 1992 5. Catlett, C. and Smarr, L. : Metacomputing. Communications of the ACM, 35 (6), (1992) 44-52 6. Foster, I. The Grid: A New Infrastructure for 21st Century Science. Physics Today, 55 (2), (2002) 42-47 7. Jorge Lobo, Randeep Bhatia, Shamim Naqvi: A Policy Description Language. proceedings AIII, (1999) 291-298 8. Kenneth Chiu, Madhusudhan Govindaraju, Dennis Gannon: The Proteus Multiprotocol Message Library. Proceedings of the IEEE/ACM SC2002 Conference November 16 - 22, 2002 Baltimore,Maryland. p. 30 9. Nathalie Furmento, William Lee, Anthony Mayer, Steven Newhouse, and John Darlington: ICENI: An Open Grid Service Architecture Implemented with Jini. Proceedings of the IEEE/ACM SC2002 Conference November 16 - 22, 2002 Baltimore, Maryland. p. 37 10. Jörn Davidsen, Holger Ebel, and Stefan Bornholdt: Emergence of a Small World from Local Interactions. Modeling Acquaintance Networks Physical Review Letters, 2002 11. D.J. Watts: Small worlds: The Dynamics of Networks between Order and Randomness. Princeton University Press ,1998

Adaptable QOS Management in OSGi-Based Cooperative Gateway Middleware Wei Liu1 , Zhang-long Chen1 , Shi-liang Tu1 , and Wei Du2 1

Department of Computer Science and Engineering, Fudan University, Shanghai 200433 {wliu, chenzl, sltu}@fudan.edu.cn 2 College of Management, University of Shanghai for Science and Technology, Shanghai 200093 [email protected]

Abstract. The Open Services Gateway Initiative (OSGi) Specification defines a service-oriented cooperative framework between home and outer home. It uses the OSGi-gateways to deliver products and services to endusers, such as home security control and intelligent home equipments. The paper studies the QOS problem of OSGi technology, and puts forward the QOS problems and other limitations. And it uses Real-Time Specification for Java (RTSJ) and dynamic adaptable QOS management integrating the OSGi framework to solve the QOS problem.

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Internet connections for private users are becoming much cheaper and faster. While the embedded and telecommunication equipments are getting smaller and more powerful, it needs an embedded server that is inserted into the network to connect the external internet to internal clients. The Open Services Gateway Initiative (OSGi) is making developers and enterprises realize the potential of the consumer equipments market such as virtual intelligent home and intelligent home health care etc. But how to provide reliable quality of service management in OSGi-based open middleware is a stringent problem. The central component of the OSGi specification is the service gateway that acts as the platform for many communication-based services. The service gateway can enable, consolidate and manage voice, data, internet and multimedia communications from the home, office and other locations.

2 2.1

Adaptable QOS Management of OSGi-Based Cooperative Middleware Limitation of QOS in OSGi Framework

Nowadays the specification of OSGi is 3.0. It does not provide rational QOS solution in middleware layer and framework. But the OSGi-based applications M. Li et al. (Eds.): GCC 2003, LNCS 3033, pp. 604–607, 2004. c Springer-Verlag Berlin Heidelberg 2004

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maybe have requirements for real-time ability and predictability such as virtual intelligent home and intelligent home health care. Increasingly, applications in the domains are to perform more demanding functions over highly networked environments, which in turn places more stringent requirements on the underlying computing and network systems. Therefore the OSGi-based middleware are requiring a broad range of features, such as service guarantees and adaptive resource management, to support a widening performance, secure operation and predictability. 2.2

Software Solution: Adaptable QOS Management

To meet these research challenges, it is necessary to preserve and extend the benefits of existing middleware, while defining new middleware services, protocols and interfaces in OSGi-based middleware. The paper puts forward to integrate the OSGi-based specification using Real-time Java specification such as RTSJ. OSGi-based Middleware by RTSJ. For developing the standard for realtime Java, IBM, Sun and other organizations from industry and academia formed a team called the Real-Time for Java Expert Group, and proposed the RealTime Specification for Java (RTSJ). RTSJ is the definitive reference for the semantics, extensions and modifications to the Java programming language that enable the Java platform to meet the requirements and constraints of real-time system performance, predictability and capabilities. This specification provides programmers with the ability to model applications and program logic that require predictable execution, which meets hard and soft real-time constraints. However, the development of the RTSJ-compliant Java Virtual Machine has been slow for most vendors of real-time operating systems. It decided to design a real-time extension library that can satisfy the basic requirement of developing real-time programs. Dynamic Adaptable QOS Management. It needs to use QOS monitoring etc some kinds of method to provide reliable QOS in OSGi-based middleware using RTSJ specification. QOS violations are reported to diagnosis functions that identify the causes of poor QOS. Allocation analysis identifies possible reallocation actions to improve the QOS, and selects the best node of these possible actions. This section illustrates the use of the system model for QOS monitoring, for QOS forecasting, allocation analysis. QOS Forecasting. Monitoring of real-time QOS involves the collection of timestamped events sent from applications, and synthesis of the events into path-level QOS metrics. Forecasting of the real-time QOS allows early prediction of QOS overload or underload violations. Such conditions may occur when an unanticipated increase in tactical data causes the resource utilization to exceed the appropriate threshold levels. For forecasting QOS violations, the system model must be flexible enough to adapt to dynamic changes in resource utilization.

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QOS Adaptation and Allocation Mechanisms Analysis. However, to perform monitoring, the QOS requirements specified in application level terminology need to be translated into transport level terminology, that is, for example, from video frame or audio packet to transport protocol data units. A further level of translation is needed. This rescaling of QOS parameters is called QOS parameters mapping. A mapping between the type of services the transport protocol offers and the traffic classes the network offers is also needed. In this section we illustrate the use of the system model and the load indexes for selection of the best node for allocation purposes. In describing a best-node selection algorithm, we use the notation Li(hi,t) and Li(Li,t) to denote the load index of a host hi and LAN Li at time t, respectively, since a variety of different load index functions may be used. The best-node algorithm determines the best node on which to restart or scale a candidate application. The best host is determined using a fitness function that simultaneously considers both host and LAN load indices. The algorithm first computes the trend values of load indices of hosts and LANs over a moving set of samples. The trend values are determined as the slope of a simple linear regression line that plots the load index values as a function of time. QOS Levels. The application set has four applications, each having four and nine levels with associated benefit and CPU usage numbers. While these applications and levels do not correspond exactly to some applications, the ranges of CPU usages and benefit values used test the QOS level model and vary at least as much as one would find in most actual applications. For the next set of experiment, application period is fixed at 1/10 of a second for all QOS levels of all applications.

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Experiment and Related Analysis

The test results reported in this section were obtained on an Intel Pentium 1.7GHz with 256 MB DDR RAM, running Linux Red Hat 7.3 with the TimeSys Linux/RT 3.0 GPL5 kernel . The Java platforms used to test the RTSJ features are described below:TimeSys RTSJ Reference Implementation(RI). TimeSys has developed the official RTSJ Reference Implementation (RI), which is a fully compliant implementation of Java that implements all the mandatory features in the RTSJ. The RI is based on a Java 2 Micro Edition (J2ME) Java Virtual Machine (JVM) and supports an interpreted execution mode i.e., there is no just-in-time (JIT) compilation. Run-time performance was intentionally not optimized since the main goal of the RI was predictable real-time behavior and RTSJ-compliance. The result shows the QOS levels at which the four applications run with a skip value of 0. The QOS levels change fast at the beginning, because it is starting the system in a state of CPU overload, i.e., the combined QOS requirement for the complete set of applications running at the highest level(level 1) is about 200% of the CPU. By the 10 th sample, the applications have stabilized at levels that can operate within the available CPU resources. There is an additional

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level adjustment of application 3 at the 38th sample due to an additional missed deadline probably resulting from transient CPU load generated by some nonQOS applications. The test result shows the requested CPU allocation for the applications in the same experiments. Here it shows that the total requested CPU allocation starts out at approximately twice the available CPU, and then drops down to about 100% as the applications are adjusted to stable levels. Note also the adjustment at sample 38, lowering the total requested CPU allocation to approximately 80%. As depicted in above, when the CPU running queue length, or the load average - which is mainly based on the CPU running queue length - are used as load indices, the path latencies are the best. This indicates that unlike other load indices considered, the resource manager component of the middleware made the best allocation decisions using Li and Xt.The most important content of the project is the design and development of open OSGi-based Middleware. It will allow services to be remotely deployed and administered onto home network gateways such as set-top boxes and DSL modems.

4

Conclusions and Future Work

In the paper, it brings forward to integrate the RTSJ and adaptable QOS management in OSGi-based cooperative middleware to solve the QOS problem in OSGi. On the other hand, for highly dynamic systems, adaptive QOS-driven resource management is necessary to utilize system resources efficiently and to provide the appropriate end-to-end application-level QOS support. For future work, it needs to adapt the transport to wireless environments, design a feedback scheme for multicast that is scalable and derive equations for exact QOS mapping.

References 1. Open Services Gateway Initiative, “OSGI Service Platform,” Release 3, 2003. 2. G. Bollella, J. Gosling, B. Brosgol et al: The Real-time Specification for Java. Addison Wesley, 2000. http://www.rtj.org . 3. K. Chen. Programming Open Service Gateways with Java Embedded Server Technology. Addison-Wesley,2001. 4. www.timesys.com 2003 5. D. Jordan: “Java in the Home: OSGi Residential Gateways”, Java Report, September, 2000, pp 38-42, 104. 6. E. S. Gardner, Jr.: “Exponential Smoothing: The State of the Art,” Journal of Forecasting. 7. Campbell and G. Coulson: “QOS Adaptive Transports: Delivering Scalable Media to the Desk Top,” IEEE Network ,1997. 8. R. Rajkumar, C. Lee, J. Lehoczky, and D. Siewiorek: “A Resource Allocation Model for QOS Management,” 18th IEEE Real-Time System Symposium , 1997. 9. D. Hardin: “The real-time specification for Java,” Dr. Dobb’s Journal, Vol. 25.

Design of an Artificial-Neural-Network-Based Extended Metacomputing Directory Service* Haopeng Chen and Baowen Zhang Distributed Computing Technique Centre, Shanghai Jiao Tong University, 200030 Shanghai, P.R.China {Chen-hp, Zhang-bw}@cs.sjtu.edu.cn http://www.cs.sjtu.edu.cn

Abstract. This paper analyzes a serious limitation of existing metacomputing directory service of Globus project that the existing metacomputing directory service doesn’t support application-oriented queries, and then designs an artificial-neural-network-based GRC (grid resources classifier) to eliminate this limitation. This classifier extends the metacomputing directory service by classifying grid resources into application-oriented categories. The classification precision of this GRC can be continuously improved by self-learning. This kind of new metacomputing directory service will be compatible with the old ones. Thus, the practicability of metacomputing directory service will be improved.

1 Introduction Globus is the most influential one of the current grid computing projects. In globus, MDS (metacomputing directory service) provides the functions for users to discover, register, query, and modify the information about grid computing environment. It reflects the real-time state of grid computing environment. [1] Users can locate grid resources and get their attributes by invoking MDS. [2] However, the functions provided by the existing MDS are incomplete because existing MDS doesn’t support the application-oriented query. For example, the existing MDS doesn’t support the query about which resource is suitable for massive data analyzing. However, for the most users, the application-oriented queries are more useful, so the functions of MDS need to be extended. This paper aims to the limitation of existing MDS, puts forward an ANN (artificial neural network) based solution to extend the existing MDS to be able to support application-oriented queries. Thus, the practicality of MDS will be improved.

* This paper is supported by the Shanghai Science and Technology Development Foundation under Grant No. 03DZ15027. M. Li et al. (Eds.): GCC 2003, LNCS 3033, pp. 608–611, 2004. © Springer-Verlag Berlin Heidelberg 2004

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2 The ANN Topology of the GRC We designed a GRC(grid resources classifier) which can execute application-oriented classification by the information about grid resources to extend the existing MDS. We have chosen the ANN to design the application-oriented GRC because the input attributes of the instances of grid resources are their information which is stored in the LDAP server in the form of attribute-value pairs, and the result of classification is represented by a vector that each element of it represents the probability that the instance is suitable to be classified as the category that this element represents. So ANN learning is suitable for GRC [3] We decide to employ the sigmoid units as the basic units of the ANN of GRC. The ANN of GRC should be a two layers network which has a hidden unit layer inside, and there should be three sigmoid units in the hidden unit layer. The main reason for such design is that according to the practice, such design can be able to solve the most functions, and if we add more layers or more sigmoid units in the hidden layer, we’ll find that the precision couldn’t be improved markedly, but the training time would be prolonged greatly. The input vector of the ANN of GRC should include all static and dynamic information of the specified grid resource. We can use a linear function to scale-up and/or –down the numerical information into a suitable range. The number of the sigmoid units included in the output layer of the ANN of GRC is as many as the number of the application-oriented categories of grid resources. We use the probability that the instance is suitable to be classified as the category that this element represents to represent each element. According to the above analysis, we can obtain the topology of ANN of GRC. It has been illustrated in Figure 1. output

t1

t2

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tk

… …

layer

hidden layer input values

x1

x2

x3

…

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Fig. 1. The topology of ANN of GRC

3 The Employed ANN Learning Algorithm BP Algorithm, (Back Propagation Algorithm) is the most common ANN learning algorithm. [4] In order to prevent get into the local minima in the error surface, and gradually increase the step size of the search in regions where the gradient is un-

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changing, we employ the BP algorithm which has a momentum term. This algorithm is described as the followings: Backpropag ation _ for _ GRC (training _ examples , η , n in , n out , n hidden )

Statements of symbols: • training _ examples represent the training instances of grid resources. Each of them is a pair of the form < x , t > , where JxG is the vector of network input values, and Gt is the vector of target network output values. • η is the learning rate. It is a constant with very small value. We can specify its value according to the proper characteristics of grid, for example, η = 0.05 . • n in is the dimension of the network input vector. • n out is the dimension of the network output vector. It is equal to the number of units in the output layer. • n hidden is the number of units in the hidden layer, we evaluate it as 3. • The input from unit i to unit j is denoted x ji . • The weight from unit i to unit j is denoted w ji . • α is a momentum constant. The value of α is very small, for example, α = 0.1 . The process of this algorithm is described as followings: • Create a feed-forward network with n in inputs, n hidden hidden units, and n out output units. • Initialize all network weights to small random numbers. • Until the termination condition is met, Do: • For each < x , t > in training _ examples , Do: 1. Input the instance x to the network and compute the output

ou of each unit

u in the network. 2. For each network output unit k , calculate its error term δ k δk ←  o k (1 − o k )(t k − o k )

(1)

3. For each hidden unit h , calculate its error term δ h δh ←  o h (1 − o h )

∑w

k∈outputs

kh

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(2)

4. Update each network weight w ji w ji ←  w ji + ∆w ji

Where

∆ w ji = ηδ j x ji + α ∆ w ji ( n − 1)

(3) (4)

The description of this algorithm is over. In the above algorithm, the GRC always uses the current learned function to classify some grid resource in real time. According to the feedback of users, the GRC will modify the current function to obtain the new one. So this algorithm would not be stopped for ever, we just continuously use the newest learned function. The values of η , α , nin and nout should be specified according to the own characteristics of differ

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Fig. 2. The architecture of the extended MDS which has a GRC

ent grids. It is unnecessary and impossible to give a set of values which can be applied to any grid.

4 The Architecture of the Extended MDS Which Has a GRC The architecture of the extended MDS which has a GRC is shown in Figure 2. User B and user C access MDS by the primary ways. User A access MDS by accessing GRC, GRC gets the static and dynamic information of the current available resources, filters information by the learned function, and returns the information of suitable resources to the user A. User A will send a feedback to GRC according to his final choice. GRC will modify its classification function by learning this feedback to improve the precision of classification.

5 Conclusion This paper designs an artificial-neural-network-based GRC to extend the metacomputing directory service by classifying grid resources into application-oriented categories. However, several aspects of the GRC given in this paper still need to research, such as the time complexity of training process, the space complexity of instance space, the training algorithm, and simulation.

References 1. Dou Zhi-hui, Chen Yu, & Liu Peng: Grid Computing. Interior materials. (2002) 87-96 2. The Globus Toolkit 2.2 MDS Technology Brief Draft 4 – January 30, 2003 http://www.globus.org/mds/ mdstechnologybrief_draft4.pdf 3. Tom M. Mitchell: Machine learning. McGraw-Hill Companies, Inc. (1997) 70-74 4. Martin T. Hagan., Howard B. Demuth., & Mark H. Beale: Neural network Design. PWS Publishing Company. (1996) 197-207

Gridmarket: A Practical, Efficient Market Balancing Resource for Grid and P2P Computing Ming Chen, Guangwen Yang, and Xuezheng Liu Dept. of Computer Science and Technology, Tsinghua University {cm01,ygw,liuxuezheng00}@mails.tsinghua.edu.cn

Abstract. The emergency of computational Grid and Peer-to-Peer (P2P) computing system is promising to us. It challenges us to build a system to maximize collective utilities through presumed participants’ rational behavior. Although economic theories sound reasonable, many existent or proposed solutions based on that face problem of feasibility in practice. This paper proposes Gridmarket: an infrastructure relying on resource standardization, continuous double auction, and straightforward pricing algorithms which are based on price elasticity inherent in consumers and suppliers. Gridmarket efficiently equates resource’s demand with supply through continuous double auction and price tracing mechanism in the required price ranges. Software agent employing Gridmarket’s schedule is easy to write. To demonstrate its efficacy and efficiency, we have designed, built a simulation prototype and found the experiments promising.

1

Introduction

Computational Grid and P2P computing system’s emergence provides promising solutions for cooperatively solving large-scale computing problems. Such systems consist of organically and economically independent entities. The rationality of human beings derives from their individuals’ selfishness. The contribution of resources only depends on the fickle concept of goodwill. Lacking mechanism to temper supplier and demander, the system tends to be unbalanced and eventually to collapse. A good incentive mechanism can allocate resource efficiently and boost the system’s prosperity. Mechanism built on economic models are better than schedule resolution only concerning system-parameters. In such geographically distributed systems spanning multiple independently organisms and entities, it provides a clear and familiar model for users. Several approaches[2..11] in this direction have been proposed to bring balance between demand and supply into these systems. But they are not practical in realms for lacking feasibility, or price formation mechanism[2], or complete support for required price ranges set by consumers and

Supported by National Natural Science Foundation of China (60373004,60373005)


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suppliers, or schedule scope[3][4][5], or transaction efficiency[10][11]. The above all are necessary preconditions for a productive market. In this paper we present Gridmarket, an practical infrastructure aiming to balance demand and supply in grid and P2P systems. Gridmarket is composed of following components: traded resource standardization, continuous double auction, intuitive and straightforward pricing algorithms based on price elasticity set by consumers and suppliers. The pricing algorithms greatly lift burden on consumers and suppliers: once consumers and suppliers set three simple parameters, software agents of consumers automatically bid resources to execute tasks while software agents of suppliers sell idle resources of themselves in the continues double resource market. Gridmarket features maneuverability, simplicity, efficacy, and efficiency.

2

Market Model

Enlightened by the economic phenomenons of realms, we reason that a fully competitive resource market where consumers and suppliers trade standard resources is necessary for Grid and P2P computing. Resource market provides the basic exchange function for resource consumers and suppliers. Resources traded in resource market are immediately consumable right after transactions. Because it is a perfectly competitive market, resource suppliers can’t manipulate prices to maintain high prices and fleece consumers unless they conspire together. Considering possible prohibitive expensive legal penalties and the difficulties to collude among large number of independent suppliers, the possibility of ostensible collusion is very low. Resource suppliers have to sell their resources at the prices of market to get returns on and of their sunk investments. This is their only available choice. In such a completely competitive market, supplier’s margin revenue == market resource price. A supplier can maximize its profit by providing as many as resources at the cost less or equal to market price. On the other side, consumers also can’t manipulatively depress market price to extort suppliers. Lower price stimulates demand and restrains supply while higher price chokes off demand and fuels supply. The market tend to be equivalent. The invisible hand [1] of market guarantees the full employment of resources. To increase the liquidities of the resource market, all items traded in it must be standardized, say resources are classified into different predefined standardized categories with unique identifiers. Consumers and suppliers can and only can bid/be asked for standard resources. Although this design may limit the flexibility of expression for resource, it provides standardization and reduces the complexity of communication and match process both for programmes and human beings. Backed by human standardization history, we envision that with the evolution of P2P and Grid, traded resources will gradually be standardized,too. Every transaction price in the resource market is published to market participants. The publication makes the market transparent, fair and efficient. Con-

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sumers and suppliers can make orders according to transaction price and their own pricing strategies. Orders are directly sent to the resource market for match.

3

Market Components

In this section, we describe match process, and pricing algorithms in detail in order. 3.1

Match Process

Resource market periodically uses price-driven continuous double auction process to match consumer’s bidding orders and supplier’s asked orders. Double Auction is one of the most common exchange institutions in the marketplace. Most stock markets (e.g.: NASDAQ, Shanghai Stock Exchange, and Shenzhen Stock Exchange) use double auction to exchange equities, bonds, and derivatives. In the double auction model, bid orders (buy orders) and asked orders (sell orders) can be submitted at anytime during the trading period. At the end of a match period, if there are open bids and asks that match or are compatible in terms of price and requirements (e.g., quantity of goods or shares), a trade is executed. Bids are ranked from highest to lowest in term of bid price while asks are ranked from lowest to highest in term of ask price. The match process starts from the beginning of ranked bids and asks. Some complex algorithms[12][13] have been developed to automate bidding in double auction process for stock trading. If prices are equal, match priorities are based on the principle of time first and quantity first: previous orders superiors later orders and orders with larger quantities arriving at the same time precede those with less quantities. 3.2

Pricing Algorithms

We propose two pricing algorithms: consumer pricing algorithm (Figure 1) and supplier pricing algorithm (Figure 2). The consumer pricing function is: Pbid (t) = α + β∆t where α denoting base price and β expressing price elasticity are consumer-specific coefficients and t is the time parameter. This function is intuitive and straightforward. With time elapsing, a consumer usually may bid with a higher and higher price if he can’t successfully buy needed resource. The supplier function is: Pasked (t) = α − β∆t where α denoting base price and β expressing price elasticity are supplier-specific coefficients and t is the time parameter. This function is also easy to understand. With time elapsing, a supplier usually may tend to offer his idle resource with a lower and lower price if he can’t successfully sell his resource. These two functions automatically make temporal differences between bid price and ask price to converge to clear the market. Consumers can set their ceiling prices and suppliers can set their floor prices. Increase in ceiling price in bid improves the demand power for consumers and decrease in floor price in ask buildup the supply competitiveness for suppliers.

Gridmarket: A Practical, Efficient Market Balancing Resource

Fig. 1. Consumer Pricing Algorithm

4 4.1

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Fig. 2. Supplier Pricing Algorithm

Analysis and Experiments Analysis

The system are modelled as a M/M/N queuing network[17]. Task streams of all consumers are bound into a single task streams as system input stream. We employs below equations[17] to theoretically analyze the resource utilization rate and responsive time of our system: ρ=

λNconsumers µNsuppliers

(1)

where Nconsumers and Nsuppliers are the number of consumers and suppliers separatively and ρ is the system resource usage rate. N −1

K=

i=0 N i=0

(N ρ)i i! (N ρ)i

C=

1−K 1 − ρK

i!

System responsive time is: Tresponsive 4.2

time

=

C µ + 1/µ N (1 − ρ)

(2)

Experiments

We use a event-driven prototype to measure our algorithms. The prototype samples before the 3000th task arrives.

M. Chen, G. Yang, and X. Liu

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floor=4 floor=8

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λ Movies > Databases: IMDB, AllMovie and Rotten Tomatoes. We manually match three ontologies to each other to measure the matching accuracy that can be defined as the percentage of the manual mappings that machine predicted correctly. We found about 150 movies in each website. Then we exchange the keywords and found 300 movies again. So each ontology holds about 400 movies data instances except repetition. We use a three-fold cross-matching methodology to evaluate our algorithms. We conduct three runs in which we performed two experiments that map ontologies to each other. In each experiment, we train classifiers using data instances of target ontology and classify data instances of source ontology to find the matching pairs from source ontology to target ontology. Table 1. Results matrixs of statistic classifier and the First-Order classifier

Table 1 shows the classification result matrixes of partial categories in AllmovieIMDB experiment, respectively for the statistic classifier and the First-Order classifier (The numbers in the parentheses are the results of First-Order classifier). Each column of the matrix represents one category of source ontology Allmovie and shows how the instances of this category are classified to categories of target ontology IMDB. Boldface indicates the leading candidate on each column. These matrixes illustrate several interesting results. First, note that for most classes, the coverage of champion is high enough for matching judgment. For example, 63% of the Movie column in statistic classifier and 56% of the Player column in FirstOrder classifier are correctly classified. And second, there are notable exceptions to this trend: the Player and Director in statistic classifier; the Movie and the Person in First-Order classifier. There will be a wrong matching decision according to results of Player column in statistic classifier, where Player in AllMovie is not matched to Actor but Director in IMDB. In other columns, the first and the second are so close that we can’t absolutely believe the matching results according to these classification results. The low level of classification coverage of champion for the Player and Director is explained by the characteristic of categories: two categories lack of feature properties.

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For this reason, many of the instances of two categories are classified to many other categories. However, our First-Order classifier can repair the shortcoming. By mining the information of neighboring instances-awards and nominations, we can learn the rules for two categories and classify most instances to the proper categories. Because the Player often wins the best actor awards and vice versa. The neighboring instances don’t always provide correct evidence for classification. The Movie column and the Person column in table 6 belong to this situation. Because many data instances between these two categories link to each other, the effectiveness of the learned rules descends. Fortunately, in statistic classifier, the classification results of two categories are ideal. By using our matching committee rule, we can easily integrate the preferable classification results of both classifiers. After calculating and comparing the degree of outstandingness, we more trust the matching results for Movie and Person in statistic classifier and for Player and Director in First-Order classifier.

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Fig. 3. Ontology matching accuracy

Figure.3 shows three runs and six groups of experimental results. We match two ontologies to each other in each run, where there is a little difference between two experimental results. The three bars in each experimental represent the matching accuracy produced by: (1) the statistic learner alone, (2) the First-Order learner alone, and (3) the matching committee using the previous two learners.

5 Related Works From perspective of ontology matching using data instance, some works are related to our system. In [2] some strategies classify the data instances and another strategy Relaxation Labeler searches for the mapping configuration that best satisfies the given domain constraints and heuristic knowledge. However, automated text classification is the core of our system. We focus on the full mining of data instances for automated classification and ontology matching. By constructing the classification samples according to the feature property set and exploiting the classification features in or among data instances, we can furthest utilize the text classification methods.

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Furthermore, as regards the combination multiple learning strategies, [2] uses microcommittees and manually evaluate the classifier weights. But in our system, we adopt the degree of outstandingness as the weights of classifiers that can be computed from classification result. Not using any domain and heuristic knowledge, our system can automatically achieve the similar matching accuracy as in [2]. [5] also compare ontologies using similarity measures, whereas they compute the similarity between lexical entries. [6] describes the use of FOIL algorithm in classification and extraction for constructing knowledge bases from the web.

6 Conclusions The completely distributed nature and the high degree of autonomy of individual peers in a P2P system come with new challenges for the use of semantic descriptions. We propose a multi-strategy learning approach for resolving ontology heterogeneity in P2P systems. In the paper, we introduce the SIMON system and describe the key techniques. We take movie as our experiment domain and extract the ontologies and the data instances from three different movie database websites. We use the general statistic classification method to discover category features in data instances and use the first-order learning algorithm FOIL to exploit the semantic relations among data instances. The system combines their outcomes using our matching committee rule called the Best Outstanding Champion. A series of experiment results show that our approach can achieves higher accuracy on a real-world domain.

References 1. J. Broekstra, M. Ehrig, P. Haase. A Metadata Model for Semantics-Based Peer-to-Peer Systems. Proceedings of SemPGRID ’03, 1st Workshop on Semantics in Peer-to-Peer and Grid Computing 2. A. Doan, J. Madhavan, P. Domingos, and A. Halevy. Learning to Map between Ontologies on the Semantic Web. In Proceedings of the World Wide Web Conference (WWW-2002). 3. I. H. Witten, T. C. Bell. The zero-frequency problem: Estimating the probabilities of novel events in text compression. IEEE Transactions on Information Theory, 37(4), July 1991. 4. J. R. Quinlan, R. M. Cameron-Jones. FOIL: A midterm report. In Proceedings of the European Conference on Machine Learning, pages 3-20, Vienna, Austria, 1993. 5. A. Maedche, S. Staab. Comparing Ontologies- Similarity Measures and a Comparison Study. Internal Report No. 408, Institute AIFB, University of Karlsruhe, March 2001. 6. M.Craven, D. DiPasquo, D. Freitag, A. McCalluma, T. Mitchell. Learning to Construct Knowledge Bases from the World Wide Web. Artificial Intelligence, Elsevier, 1999. 7. F. Sebastiani. Machine Learning in Automated Text Categorization. ACM Computing Surveys, Vol. 34, No. 1, March 2002.

SkyEyes: A Semantic Browser for the KB-Grid Yuxin Mao, Zhaohui Wu, and Huajun Chen Grid Computing Lab, College of Computer Science, Zhejiang University, Hangzhou 310027, China {maoyx, wzh, huajunsir}@zju.edu.cn

Abstract. KB-Grid was introduced for publishing, sharing and utilizing an enormous amount of knowledge base resources on Semantic Web. This paper proposes a generic architecture of Semantic Browser for KB-Grid. Semantic Browser is a widely adaptable and expandable client to Semantic Web and provide users with a series of functions, including Semantic Browse, Semantic Query and so on. We introduce the key techniques to implement a prototype Semantic Browser, called SkyEyes. Also, an application of SkyEyes on Traditional Chinese Medicine (TCM) is described in detail.

1 Introduction The emergence of Semantic Web [1] will result in an enormous of knowledge base (KB) resources distributed across the web. In such a setting, we must face the challenges of sharing, utilizing and managing huge scale of knowledge. Traditional web architecture seems to be quite insufficient to meet these requirements. Since Grid technologies have the ability to integrate and coordinate resources among users without conflict and insecurity, we propose a generic model of Semantic Browser based on the basic ideas of Grid and Semantic Web and implement a prototype Semantic Browser, called SkyEyes. In the following two subsections, we will introduce the background knowledge as well as some related work. And we will propose a generic architecture of Semantic Browser for KB-Grid. We will also discuss the key techniques to implement a prototype Semantic Browser, SkyEyes. Besides, an application of SkyEyes on Traditional Chinese Medicine (TCM) will be described in detail. In the end, we will take a brief summary on our job and look forward to our future work. 1.1 Background The scale of the Internet has grown at a startling rate and provided us large amount of information. We have to face the problems of publishing, sharing and utilizing the web information, so Knowledge base Grid (KB-Grid) [2] was introduced to meet the requirements. KB-Grid is a project being developed by the Grid Computing Lab of Zhejiang University. KB-Grid suggests a paradigm that emphasizes how to organize, publish, discover, utilize, and manage web KB resources. In KB-Grid, distributed knowledge is presented by lightweight ontology languages such as RDF(S) [3]. M. Li et al. (Eds.): GCC 2003, LNCS 3033, pp. 752–759, 2004. © Springer-Verlag Berlin Heidelberg 2004

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In such a setting, traditional web browsers would be useless, and some particular browser for KB-Grid should be promoted and developed. SkyEyes is just such a new type of Semantic Browser, aimed at browsing, querying, managing and updating knowledge from distributed KBs for KB-Grid. 1.2 Related Work There have been a variety of researches and applications related to our work: z Ideagraph [4] is a personal knowledge management tool for creating, managing, editing and browsing personal KB, which is an easy-to-use software. However, it’s just a local tool for personal use rather than distributed application. z IsaViz [5] is a visual working environment for creating and browsing RDF model. It makes use of GraphViz library to display an RDF model as a bitmap, which is lack of proper layout, so its interaction and effect are not satisfying. z OntoRama [6] is a prototype ontology browser, which takes RDF/XML as input format and models ontology as a configurable graph. However, the function of OntoRama is confined to browse and display and it hasn’t supported query yet. There is still no Semantic Browser in real sense. Most of these related researches and applications still aim at visualizing or browsing ontology or knowledge, lack of more intelligent functions such as Semantic Query and reasoning. Besides, many applications are only for local use rather than distributed environment. The application background of our work is TCM Information Grid and our goal is to build Open Knowledge Service Architecture to provide a wide series of knowledge services. The idea of SkyEyes is to build a widely adaptable and expandable intelligent client to Semantic Web, which will provide more intelligent functions. The immediate application of SkyEyes is browse and query of TCM ontology.

2 Overview Semantic Browser works as an intelligent client to Semantic Web and is an interface between KBGrid and KB-Grid users. Any user of KB-Grid can publish, browse, query, manage and utilize knowledge via this browser. Here Fig. 1. A generic architecture of Semantic Browser we propose a generic architecture of Semantic Browser for KB-Grid as figure 1.

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2.1 Knowledge Server KB-Grid consists of many decentralized KB-Grid nodes. Each node may include several KBs. KB-Grid nodes exchange knowledge and deliver knowledge services through Grid Service interface. These nodes work collectively as a super knowledge server and the inner structure of KB-Grid is transparent to clients. Clients interact with knowledge server through Grid Service. A meta-information register center is set to coordinate KB resources. A shared ontology described by RDF(S) is stored in the register center, which performs as an index for distributed KBs. 2.2 Semantic Browser Plugins Semantic Browser remotely accesses knowledge services through Grid Service. For each type of knowledge services, we accordingly develop a plugin, which is an independent module in Semantic Browser. z Service Discovery Plugin. Knowledge server will dynamically deliver various knowledge services and Service Discovery plugin accesses Service Discovery services to get meta-information about services. z Semantic Browse Plugin. Semantic Browse is to visualize concepts and their instances that are explicitly described and the relationships among them as semantic graphs and assist users to browse semantic information with semantic links. Semantic Browse plugin accesses Semantic Browse services to carry out Semantic Browse. z Semantic Query Plugin. When KB-Grid becomes very huge or they are not very familiar with the structure of the knowledge, users would better query the knowledge instead of browse. Semantic Query is to query semantic information or knowledge with semantic links. Semantic Query plugin accesses Semantic Query services to query semantic information from distributed KBs and optimizes query results. z Knowledge Management Plugin. Knowledge Management plugin accesses Knowledge Management services to manage knowledge both local and remote. z Reasoning Plugin. Reasoning plugin accesses reasoning services to perform reasoning based on domain ontology to solve practical problems. Users can dynamically choose rule set and case base according to specific problems. Besides, SkyEyes still reserves slots for extended plugins to access possible knowledge services that may be delivered by knowledge server in the future. 2.3 Intelligent Controller Since each plugin just implements a single function separately, Semantic Browser needs an intelligent controller to combine them as a whole. The intelligent controller is the kernel of Semantic Browser, which coordinates and schedules various plugins, making proper plugins access proper services of KB-Grid.


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2.4 SGL-Parser and SG-Factory For different formats of semantic information such as RDF(S), XML, OWL and so on, Semantic Browser should display a uniform semantic graph. Besides, if we want to make semantic graphs look clear without loss of semantics, we have to take more into account. Since it’s hard to draw semantic graphs just based on RDF or other existed languages, we develop Semantic Graph Language (SGL) for displaying semantic graphs. And the semantic information acquired from server will be translated uniformly into SGL by Semantic Browser plugins. The SGL Parser will read and parse SGL and the SG-Factory will produce uniform and standard semantic graph, despite the formats of semantic information.

3 Implementation and Key Techniques According to the generic architecture of Semantic Browser, we develop a prototype Semantic Browser, called SkyEyes and have implemented two major functions of Semantic Browser, Semantic Browse and Semantic Query. The user interface is similar with traditional web browsers as figure 2 displays; so common users can operate it easily and well. However, to solve some problems with SkyEyes, users should have enough domain knowledge. SkyEyes was implemented with JAVA, so it’s portable and can be used in different environments. There are several key techniques to Fig. 2. SkyEyes: a Semantic Browser implement SkyEyes. 3.1 Expandable Plugin Mechanism SkyEyes is a lightweight client, which calls special plugins to access remote knowledge services to solve problems. As the scale of knowledge increases or users’ requirements change, knowledge services of KB-Grid may be dynamically changed and delivered. The expandable plugin mechanism allows SkyEyes to expand its function easily just by adding and updating new plugins, without the code and structure modified. In this way, users can even custom their own browsers by subscribing or unsubscribing services as they wish.

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3.2 Operatable Vectographic Components Each user operation in SkyEyes will result in a semantic graph, which is composed of vectographic components. Vectographic components can be scaled and dragged freely without the quality of the graph reduced, so the display effect of semantic graph is much better and more acceptable to users. A vectographic component itself doesn’t contain or store any data and it is used as proxy or view for semantic information. In a semantic graph, each vectographic component provides users not only a view of semantic information but also a series of functions. If a great deal of semantic information is returned from server, the structure of corresponding semantic graph will become so complex that a lot of nodes and arrows will overlap with each other in one graph. Many visualization tools do suffer this problem that is quite inconvenient to users and sometimes intolerable. In order to solve this problem, SkyEyes adopts radial layout algorithm [6] to arrange the global layout of a semantic graph., so as to avoid overlaping. 3.3 SGL: Semantic Graph Language Not like general graph languages, SGL takes semantics in and treat semantics as part of graph elements. Graph elements described in SGL is related with each other by semantic link not hyper link or graphic link. We can use SGL to describe both the semantics and the appearance of a semantic graph. SGL is an XML-based language and here is part of brief BNF definition of SGL. SGL ::= ‘’ graph* ‘’ graph ::= ‘’ subgraph* ‘’ idAttr ::= ( an unique string begins with a letter ) depthAttr ::= 1 | integer /* the level of semantic information to be displayed */ subgraph ::= ‘’ root, (edge, node | subgraph)* ‘’ typeAttr ::= ‘instance-property’ | GraphType GraphType ::= ‘class-class’ | ‘class-instance’ | ‘instance-property’ | ‘correlative’ | ‘extended’ /* types of semantic graph supported by SkyEyes */ root ::= ‘’ ‘’ edge ::= ‘’ ‘’ node ::= ‘’ ‘’ resourceAttr ::= URI /* the URI of a RDF Resource */ radiusAttr ::= 0 | integer /* the radius of subgraph */ sonAttr ::= 0 | integer /* the number of son nodes owned by root*/ angleAttr ::= 0 | integer /* the position of root */ spaceAttr ::= 0 | integer /* the relevant space of subgraph */ directionAttr ::= ‘out’ | DirectionType DirectionType ::= ‘in’ | ‘out’ /* the direction of edge */ literalAttr ::= (any well-formed XML)

The structure of a semantic graph is clear in such a SGL document, and therefore SkyEyes can draw out a standard semantic graph. There is a simple example.


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Fig. 3. A simple example of SGL

4 Application: Semantic Browse on Traditional Chinese Medicine A practical domain of SkyEyes is Traditional Chinese Medicine (TCM). We have been building an ontology of Unified TCM Language System, which includes TCM concepts, objects, and their relationships with each other. We have chosen Protégé 2000 [7] as ontology editor to build the ontology in RDF(S). The TCM ontology is distributed across the web in more than twenty nodes throughout our country. They share a common ontology in the meta-information register center and are related by semantic links, URIs. Now we have finished in building the whole concept definition of the TCM ontology and edited more than 100,000 records of instances. Users of this TCM ontology can download SkyEyes from our server and install it. Then they can use it to acquire useful information they need from TCM ontology. TCM experts can solve practical problems with the help of SkyEyes, or they can take the result returned by SkyEyes as a reference. For example, if a doctor is not sure about the use of a medicine, he can turn to SkyEyes. 4.1 Semantic Browse Before starting to browse, the doctor can input the URL of the TCM ontology into the address field of SkyEyes and begin to perform Semantic Browse. The process of Semantic Browse can be divided into several steps: z First, given a URL, Sky Eyes will connect to knowledge server and get RDF(S) files. It will call Jena [8] API to parse RDF(S) files into some data model that can be understood and processed by client. SkyEyes will extract meta-information about RDF(S) and parse it into a class hierarchy tree to finish the initialization. z Next, the doctor can expand a tree node to browse its sub-classes, or click it to list its direct instances in the instance list area. The relationships between the class and properties are displayed as a semantic graph in the semantic graph area. z Then, if the doctor finds the instance of the medicine in the instance list, he can click the instance and then all its properties and property values will be displayed around the node standing for the medicine in a graph, including compositions, effect and so on, so the use of that medicine will be very clear. z If the doctor wants to know more about one composition of that medicine, he can click the node standing for the composition, then a detailed semantic graph about that composition will be displayed.

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During Semantic Browse, each user operation will send in a URI, acquire further related semantic information from server through this URI. SkyEyes then calls JGraph [9] API to draw semantic graph according to the information. If the doctor can’t find the information this way, he can turn to Semantic Query. 4.2 Semantic Query SkyEyes itself is unable to query, and query is done by accessing query services. SkyEyes just provides an easy-to-use interface to do Semantic Query and visualizes query results. Query results is returned from server as semantic information, which will be optimized by Semantic Query plugin and displayed as semantic graphs. For the moment, SkyEyes provides four kinds of Semantic Query, and for each, SkyEyes will display a type of corresponding semantic graph. z Class-class query returns semantic information about the specific class, its upclasses and its sub-classes. z Class-instance query returns semantic information about the specific class and its direct instances. z Instance-property query returns semantic information about the specific instance and its properties. z Correlative query returns semantic information about the specific class, its upclasses, its sub-classes, and their instances. It fits the condition that the class users queried has no direct instances at all but its sub-classes or up-classes have. The doctor can input restrictive information about the medicine to perform Semantic Query within the TCM ontology. The query result won’t be documents simply containing the keyword but a semantic graph focusing on the medicine, just like the semantic graph displayed when browsing. He can also set the depth of each query and control the semantic graph by configuring some parameters of SkyEyes. Deeper depth means there is more semantic information returned and displayed. 4.3 Reasoning If the problem is more than querying a medicine, for example, treating a patient, the doctor can take advantage of reasoning function to perform more complex work. Firstly, he can describe the symptoms of the patient in a particular format. Next, he could choose a case base that may contain a similar case with a rule set on diagnosis and treatment. The reasoning plugin of SkyEyes will access reasoning services to perform complex reasoning and the results will be returned as semantic information. At last, SkyEyes will display results as semantic graphs and the doctor could take the result as a useful reference when treating the patient.

5 Summary SkyEyes is a prototype Semantic Browser, which was implemented according to a generic architecture of Semantic Browser for KB-Grid. As an intelligent client to Semantic Web, it provides users with the major functions of Semantic Browser and a


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friendly user interface. SkyEyes owns several important features that distinguish itself from traditional web browsers: z Open. SkyEyes is based on Grid Service and works as part of KB-Grid, not subject to traditional C/S structure. z Exact. Browse and query utilize semantic links to locate and return more exact and useful information users require. z Intelligent. Accessing knowledge services to understand and solve more complex and practical problems, which previously call for domain experts. z Expandable. Expandable plugin mechanism allows expanding function dynamically according to the services delivered by knowledge server. z Universal. SkyEyes converts various formats of semantic information into uniform semantic graphs based on SGL. z Convenient and Operatable. Use of vectographic components provides users with excellent view of semantic information and a series of interactive functions. Our future work is to build a knowledge sharing and knowledge management platform towards Semantic Web. As part of this platform, the function of SkyEyes is still insufficient, so a series of knowledge services especially reasoning services and knowledge management services will be developed based on Grid Service and more Semantic Browser plugins will be added to SkyEyes to extend its functions. Besides, we will go on with the TCM Information Grid for TCM science research.

References [1] [2] [3] [4] [5] [6] [7] [8] [9]

Berners-Lee, T., Hendler, J., Lassila, O. The Semantic Web. Scientific American, May, 2001. WU ZhaoHui, CHEN HuaJun, XU JieFeng. Knowledge Base Grid: A Generic Grid Architecture for Semantic Web. JCST Vol.18, No.4, July, 2003. Resource Description Framework (RDF) Model and Syntax Specification. http://www.w3.org/TR/1999/REC-rdf-syntax-19990222/. Ideagraph, an Idea Development Tool for the Semantic Web. http://ideagraph.net/. IsaViz: A Visual Authoring Tool for RDF. http://www.w3.org/2001/11/IsaViz/. Peter Eklund, Nataliya Roberts, Steve Green. OntoRama: Browsing RDF Ontologies using a Hyperbolic-style Browser. The First International Symposium on CyberWorlds (CW2002), pp.405-411, Theory and Practices, IEEE press, 2002. Protégé 2000. http://protege.stanford.edu/. Jena Semantic Web Toolkit. http://www.hpl.hp.com/semweb/jena.htm. Jgraph. http://jgraph.sourceforge.net/.

Toward the Composition of Semantic Web Services Jinghai Rao and Xiaomeng Su Department of Computer and Information Science, Norwegian University of Science and Technology, N-7491, Trondheim, Norway {jinghai, xiaomeng}@idi.ntnu.no

Abstract. This paper introduces a method for automatic composition of semantic web services using linear logic theorem proving. The method uses semantic web service language (DAML-S) for external presentation of web services, and, internally, the services are presented by extralogical axioms and proofs in linear logic. Linear logic(LL)[2], as a resource conscious logic, enables us to define the attributes of web services formally (in particular, qualitative and quantitative value of non-functional attributes). The subtyping rules that are used for semantic reasoning are presented as linear logic implication. We propose a system architecture where the DAML-S parser, linear logic theorem prover and semantic reasoner can work together. This architecture has been implemented in Java programming language.

1

Introduction

The Grid is a promising computing platform that integrates resources from different organizations in a shared, coordinated and collaborative manner to solve large-scale science and engineering problems. The current development of the Grid has adapted to a services oriented architecture and, as a result, recently Grid technologies are evolving towards an Open Grid Services Architecture (OGSA).The convergence of Web services with Grid computing will accelerate the adoption of Grid technologies. [1] defines a Grid service as a Web service that provides a set of well-defined interfaces and follows specific conventions. As such, Grid service will inherently share some of the same problems and technical challenges of Web service in general. The ability to efficiently and effectively select and integrate interorganizational services on the web at runtime is a critical step towards the development of the online economy. In particular, if no single web service can satisfy the functionality required by the user, there should be a possibility to combine existing services together in order to fulfill the request rapidly. However, the task of web service composition is a complex one. Firstly, web services can be created and updated on the fly and it may be beyond human capabilities to analyze the required services and compose them manually. Secondly, the web services are developed by different organizations that use different semantic model to M. Li et al. (Eds.): GCC 2003, LNCS 3033, pp. 760–767, 2004. c Springer-Verlag Berlin Heidelberg 2004

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describe the features of services. The different semantic models indicate that the matching and composition of web service have to take into account on the semantic information. In this paper, we propose a candidate solution which we believe to contribute to solving the two challenges. We describe a method for automated web service composition which is based on the proof search in (propositional) Multiplicative Intuitionistic fragment of Linear Logic (MILL [3]). The idea is, given a set of existing web services and a set of functionality and non-functional attributes, the method finds a composition of atomic services that satisfies the user requirements. The fact that Linear Logic is resource conscious makes it possible to make proving on both qualitative and quantitative non-functional attributes of web services. Because of the soundness of the logic fragment correctness of composite services is guaranteed with respect to initial specification. Further, the completeness of logic ensures that all composable solutions would be found. The rest of this paper is organized as follows: Section 2 presents a system architecture for composition of semantic web services. Section 3 presents the methods on transformation between DAML-S documents and Linear Logic axioms. Section 4 discusses the usage of type system to enable semantic composition. Section 5 is the related works and the conclusion of the paper.

DAML-S Profile

DAML-S Parser

DAML-S Process

Logic Axioms Proof

Coordinator GUI

Linear Logic Planner el ba ir aV

Im pl ic at io n

Se m an tic

Semantic Reseasoner

Ontology Base

Fig. 1. Architecture for Service Composition

2

The Service Composition Architecture

Figure 1 depicts the general architecture of the proposed web service composition process. The approach is presented by the following process. First, a description of existing web services(in the form of DAML-S Profile) is translated into axioms of Linear Logic, and the requirements to the composite services are specified in form of a Linear Logic sequent to be proven. Second, the Linear Logic Theorem Prover determines whether the requirements can be fulfilled by composition of existing atomic services. On reading each propositional variable, the theorem prover requires the semantic reasoner to provide possible subtyping inference. The subtypings are inserted into the theorem prover as logic implications. If one or more proofs are generated the last step is the construction of flow models (written in DAML-S Process). The process is controlled by the coordinator,

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especially when components are distributely located. During the process, the user is able to interact with the system by GUI. In this paper, we pay special attention on the DAML-S Parser and the Semantic Reasoner. The detail on theorem proving part has been already introduced in [7]. The readers who have knowledge about Linear Logic or theorem proving are able to understand this part easily without referring to the separate publication.

3

Transforming from DAML-S to Linear Logic Axioms

In our system, the web services are specified by DAML-S profile externally and presented by LL Axioms internally. The transformation is made automatically by DAML-S Parser. The detail presentation of DAML-S can be found in [4]. Here, we focus on the presentation of LL axioms. Generally, a requirement to composite web service, including functionalities and non-functional attributes, can be expressed by the following formula in LL: Γ ; ∆1 (I O) ⊗ ∆2 where Γ is a set of logical axioms representing available atomic web services, ∆1 is a conjunction of non-functional constraints. ∆2 is a conjunction of nonfunctional results. We will distinguish these two concepts later. I O is a functionality description of the required composite service. Both I and O are conjunctions of literals, I represents the set of input parameters of the service and O represents output parameters produced by the service. Intuitively, the formula can be explained as follows: given a set of available atomic services and the non-functional attributes, try to find a combination of services that computes O from I. Every element in Γ is in form ∆1 (I O) ⊗ ∆2 , whereas meanings of ∆1 , ∆2 , I and O are the same as described above. Next, we describe the detail procedure to transform a DAML-S document into the linear logic expression. We present in sequence the transformation on functionalities and non-functional attributes. Afterwards, we present the whole process by an example. 3.1

Transforming on Functionalities

The functionality attributes are used to connect atomic services by means of inputs and outputs. The composition is possible only if output of one service could be transferred to another service as input. The web service is presented by DAML-S profile externally. The functionality attributes of the “ServiceProfile” specifies the computational aspect of the service, denoting by the input, output, precondition and postcondition. Below is an example of the functionalities for a temperature report service: Temperature_Report The service provides the temperature of the city with the given zip code


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ZipCode CelsiusTemp

From the computation point of view, this service requires an input that has type “&zo; #ZipCode” and produces an output that has type “&zo; #CelsT emp”, the value of temperature measured in Celsius. Here, we use entity types as a shorthand for URIes. For example, &zo; #ZipCode refers to the URI of the definitions for zip code parameter: http://www.daml.org/2001/10/html/zipcode-ont\#ZipCode When translating to Linear Logic formula, we translate the field “restrictedTo” (variable type) instead of the parameter name, because we regard the parameters’ type as their specification. Below is the example propositional linear logic formula that expresses the above DAML-S document: &zo; #ZipCode &service;#Temperature &tc; #CelsTemp 3.2

Non-functional Attributes

Non-functional attributes are useful in evaluating and selecting service when there are many services that have the same functionalities. In the service presentation, the non-functional attributes are specified as facts and constraints. We classify the attributes into four categories: – Consumable Quantitavie Attributes: These attributes limit the amount of resources that can be consumed by the composite service. The total amount of resource is the sum of all atomic services that formulate the composite service. – Non-consumable Quantitative Attributes: These attributes are used to limit the quantitative attributes for each single atomic service. The attributes can present either amount or scale. – Qualitative Constraints: Those attributes which can’t be expressed by quantities are called qualitative attributes. Qualitative Constraints are those qualitative attributes which specify the requirements to execute a web service. – Qualitative Facts: Another kind of qualitative attributes, such as service type, service provider or geographical location, specify the facts regarding the services’ environment. Those attributes can be regarded as goals in LL.

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The different categories of non-functional attributes are presented differently in logical axioms. The non-functional attributes can be described as either constraints or results. The constraints and results of the services can be presented as follows: – The constraints to the service: x

y

∆1 = Consumable ⊗ NonConsumable ⊗!Constraint

– The results produced by the service: y

∆2 = NonConsumable ⊗!Fact

3.3

Example

Here, we illustrate the LL presentation of the temperature report service example where both functionalities and non-functionalities have been taken into consideration. The complete DAML-S description of this example can be found at http://bromstad.idi.ntnu.no/services/TempService.daml. For the sake of readability, we omit the namespace in the name of the parameters. The available atomic web services in the example are specified as follows: NOK 10 LengthCM cm2inch LengthInch Γ = CA MICROSOFT (LengthInch ⊗ Brand ⊗ Model ski PriceUSD) ⊗ LOC NORWAY NOK 5 , QUALITY 2 (PriceUSD usd2nok PriceNOK ) ⊗ QUALITY 2

The formula presents three atomic services. name2code outputs the zip code of a given city. temp reports the Celsius temperature of a city, given the zip code of the city. trans transforms the Celsius temperature to the Fahrenheit temperature. N OK 10 in the left hand side of the name2code service denotes that 10 Norwegian Krones(NOK) are consumed by executing the service. The service trans costs 5 NOK and has a quality level 2. The quality level is not a consumable value, so it appears at both the left and right hand sides. In the specification it is also said that the temperature reporting service temp is located in Norway and it only responses to the execution request that has certificated by Microsoft. For other attributes which are not specified in service specification, the values are not considered. The required composite service takes a city name as input and outputs the Fahrenheit temperature in that city. It is specified by LL as follows: Γ ; ∆1 (LengthCM ⊗ Brand ⊗ M odel PriceNOK ) ⊗ ∆2

The non-functional attributes for the composite service are: ∆1 = NOK 20 ⊗ QUALITY 3 ⊗!CA MICROSOFT ∆2 = QUALITY 3 ⊗!LOC NORWAY

This means that we would like to spend no more than 20 NOK for the composite service. The quality level of all the selected services should be no higher than 3. The composite service consumer has certification from Microsoft (!CA MICROSOFT ) and it requires that all location-aware services are located


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within Norway (!LOC NORWAY ). ! symbol describes that we allow unbound number of atomic services in the composite service. For the qualitative constraints (location), the service uses LOC NORWAY to determine its value and we can determine in the set of requirements ∆1 whether a service meets the requirement. By now, we have discussed how DAML-S specification have been translated to LL extralogical axioms. Next step is to derive the process model from the specification of the required composite service. If the specification can be proven to be correct, the process model is extracted from the proof. we have stressed the proof in a separate publication [7] and therefore we don’t go into detail here. The result dataflow of the selected atomic service are presented through a graphic user interface. A screen shot is presented in figure 2. In figure 2, the interface of the user required service is presented in the ServiceProfile panel (upperright) and the dataflow of the component atomic services is presented in the ServiceModel panel (lowerright).

Fig. 2. The Screen Shot

4

Composition Using Semantic Description

So far, we considered only exact match of the parameters in composition. But in reality, two services can be connected together, even if the output parameters of one service does not match exactly the input parameters of another service. In general, if a type assigned to the output parameter for service A is a subtype of the type assigned to an input parameter for service B, it is safe to transfer data from the output to the input. If we consider resources and goals in LL, the subtyping is used in two cases: 1) given a goal x of type T , it is safe to replace x by another goal y of type S, as long as it holds that T is a subtype of S; 2) conversely, given a resource x of type S, it is safe to replace x by another resource y of type T , as long as it holds that T is a subtype of S. In the following we extend the subsumption rule for both resource and goal. Here we should mention that the rules are not extension to LL. The subtyping can be explained by inference figures of LL. We write in following < to

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emphasis that these inference rules are for typing purposes, not for sequencing methods, when constructing programs. First of all, the subtype relation is transitive.

In addition, subsumption rules state the substitution between types. ΣT

Γ T < S

Σ, Γ S goal subsumption

Σ, S G

Γ T < S

Σ, Γ, T G resource subsumption

Such subtyping rules can be applied to either functionality (parameters) or non-functional attributes. Here we use two examples to illustrate the basic idea. First, let us assume that the output of the temperature reporting service is air temperature measured by Celsius scale, while the input of temperature translation service is all Celsius temperature. Because the later is more general than the former, it is safe to transfer the more specific output to the more general input. Another example considers the qualitative facts. If an atomic service is located in Norway, we regard Norway is a goal in LL. Because Norway is a country in Europe, it is safe to replace Norway with Europe. Intuitively, if the user requires a service that is located within Europe, the service located within Norway meets such requirement. In this paper, we assume that the ontology used by service requester and that for the service provider are interoperable. Otherwise, the ontology integration is another issue which is beyond the scope of this paper.

5

Conclusion

This paper approaches the important issue of automatic semantic web service composition. It argues that Linear Logic, combined with semantic reasoning for relaxation of service matching (choosing), offers a potentially more efficient and flexible approach to the successful composition of web services. To that end, an architecture for automatic semantic web service composition is introduced. The functional settings of the systems are discussed and techniques for DAML-S presentation, Linear Logic presentation, and semantic relaxation are presented. A prototype implementation of the approach is proposed to fulfill the task of representing, composing and handling of the services. This paper concentrate on the automatic translation part and the semantic relaxation part, while the theorem proofing part has been stressed elsewhere[7]. Some works have been performed on planning based on semantic description of web services. In [5], the authors adapt and extend the Golog language for automatic construction of web services. The authors addressed the web service composition problem through the provision of high-level generic procedures and customizing constraints. SWORD[6] is a developer toolkit for building composite web services. SWORD uses ER model to specified the inputs and outputs of the web services. As a result, the reasoning is made based on the entity


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and attribute information provided by ER model. [8] presents a semi-automatic method for web service composition. The choice of the possible services are based on functionalities and filtered on non-functional attributes. The main difference between our methods and the above methods is we consider the non-functional attributes during the planning. Usage of Linear Logic as planning language allows us formally define the non-functional characteristics of web services, in particular, quantitative attributes. In addition, we distinguish the constraints and facts in qualitative attributes. The planner treats them differently in logic formulas. Also, as more and more organizations and companies embrace the idea of using web service interface as a cornerstone for future Grid computing architecture, the author hope that the revealing and discussing of semantic related issues will inform researchers in Grid computing of the intricate problem of service composition which might as well rise up in Grid service research. Our current work is directed to add the disjunction connective to the logical specification of service output. This is useful when we should consider exceptions or optional outputs of atomic services. By using disjunction, the planner is also able to generate control constructs such as choice and loop. Although the introduction of disjunction is easy in logic presentation, the proving speed is slowed down significantly. The mechanism to improve the computation efficiency of proving is also under consideration.

References 1. I. Foster, C. Kesselman, J. Nick, and S. Tuecke. The physiology of the grid. Online: http://www.gridforum.org/ogsi-wg/drafts/ogsa draft2.9 2002-06-22.pdf, January 2002. 2. J.-Y. Girard. Linear logic. Theoretical Computer Science, 50:1–102, 1987. 3. Patrick Lincoln. Deciding provability of linear logic formulas. In London Mathematical Society Lecture Note Series, volume 222. Cambridge University Press, 1995. 4. David Martin et al. DAML-S(and OWL-S) 0.9 draft release. Online: http://www.daml.org/services/daml-s/0.9/, May 2003. 5. Sheila McIlraith and Tran Cao Son. Adapting golog for composition of semantic web services. In Proceedings of the Eighth International Conference on Knowledge Representation and Reasoning(KR2002), Toulouse, France, April 2002. 6. Shankar R. Ponnekanti and Armando Fox. SWORD: A developer toolkit for web service composition. In The Eleventh World Wide Web Conference, Honolulu, HI, USA, 2002. 7. Jinghai Rao, Peep Kungas, and Mihhail Matskin. Application of linear logic to web service composition. In The First International Conference on Web Services, Las Vegas, USA, June 2003. CSREA Press. 8. Evren Sirin, James Hendler, and Bijan Parsia. Semi-automatic composition of web services using semantic descriptions. In Web Services: Modeling, Architecture and Infrastructure” workshop in conjunction with ICEIS2003, 2002.

A Viewpoint of Semantic Description Framework for Service* Yuzhong Qu Dept. of Computer Science and Engineering Southeast University, Nanjing 210096, P. R. China [email protected]

Abstract. The evolvement of Semantic Web Service technology is synergistic with the development of the Semantic Grid, reinforced by the adoption of a service-oriented approach in the Grid through the OGSI. However, the Semantic Web Service technology is far from maturity to pursue the vision of semantic service. This paper illustrates the semantic description framework of DAML-S by using RDF graph model, gives some thinking in improving DAML-S and designing “semantic” service description languages, presents a novel semantic description framework for service, and then illustrates the usage of our framework by two examples in describing web service and grid service.

1 Introduction Building on both Grid and Web services technologies, the Open Grid Services Infrastructure (OGSI) [1,2] defines mechanisms for creating, managing, and exchanging information among entities called Grid services. The main motivation of OGSI is the need for open standards that define the interaction and encourage interoperability between components supplied from different sources. Web/Grid services are represented and described using the WSDL, which uses XML to describe services as a set of endpoints operating on messages. Based on this description language, it is usually impossible for software agents to figure out the precise meaning of the service identifiers and functionality provided by the service. The lack of semantics in the capabilities of the service makes it difficult for machines to discover and use the service at the right time. To bring semantics to web services [3], the Semantic Web technologies such as RDF Schema, DAML+OIL or OWL, have been used to provide more explicit and expressive descriptions for web services. DAML-S [4,5] is a key component towards the Semantic Web Services vision. DAML-S can be used to characterize the serviceportfolio offered by a web service in a more expressive manner than the existing WSDL, thereby opening up the possibility of automatic service discovery and use. Recently, some applications of Semantic Web technologies in Grid applications [6] are well aligned with the research and development activities in the Semantic Web and Grid community, most notably in the areas where there is established use of on*

This paper is jointly supported by NSFC with project no. 60173036 and JSNSF with project no. BK2003001.


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tology. However, the application of Semantic Web technologies inside the grid infrastructure is less well developed. The emerging work on Semantic Web Services is synergistic with the development of the Semantic Grid, reinforced by the adoption of a service-oriented approach in the Grid through the OGSI, as pointed out by David De Roure [7]. We see the importance of semantic description for Grid Service in the vision of Semantic Grid [8], and observe the trend of convergence between Web Service and Grid Service in the future. We also notice that the Semantic Web Service technology (e.g. DAML-S) is far from maturity to pursue the vision of semantic service. Against this background, we focus on the semantic description framework for web service and grid service in this paper. It illustrates the semantic description framework of DAML-S by using RDF graph model, discusses some consideration in improving DAML-S and designing other “semantic” service description languages (SSDL, in short), presents a novel semantic description framework for service, and then illustrates the usage of our framework by two examples.

2 Semantic Description Framework of DAML-S DAML-S provides a set of basic classes and properties for declaring and describing services, as well as an ontology structuring mechanism inherited from DAML+OIL. The set of basic classes and properties is defined in DAML-S Service (an upper ontology) and three subparts including DAML-S Profile, DAML-S Process and DAMLS Grounding. An overview of DAML-S Ontology and the DAML-S Process Model will be discussed respectively in the following subsections by using RDF graph model. Some conventions in the RDF graph model is as follows:

The above notation means that the object labeled with “p” is a property, whose domain and range are specified to be class A and B, respectively. In addition, the label “m:n” is the multiplicity specification of the class A with the property “p” if the multiplicity presents.

The above notation means that the object labeled with “C” and the object labeled with “D” have the binary relationship denoted by the property labeled with “p”, in other words, object C has object D as a value of property p. Usually, a class or a property is assumed to be in a namespace. We use the prefixes such as “xsd”, “rdf”, “rdfs” and “owl”, to denote the commonly used namespaces. In addition, the prefixes such as “service”, “process” and “profile”, are used to denote the namespaces as defined in DAML-S. Note that the namespace prefix of a name may be omitted when there is no ambiguity.

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2.1 An Overview of the DAML-S Ontology An RDF graph model of the DAML-S ontology is roughly depicted in Fig.1. Each instance of Service "presents" zero or more instances of (a descendant class of) ServiceProfile, and may be "describedBy" at most one instance of (a descendant class of) ServiceModel. ProcessModel is a subclass of ServiceModel. Each instance of ProcessModel can has at most one process through the “hasprocess” property. It adopts the processes as classes approach, i.e. any application specific process should be defined as a subclass of (or, a descendant class of) Process. ProcessPowerSet is defined to be the class of all subclasses of Process. In addition, each instance of ProcessModel can have at most one instance of ProcessControlModel, and there exist some debates on the necessity of the process control model. Further, processes have IOPEs (inputs, outputs, preconditions, effects) properties to describe their functionalities, and more discussion will be given in section 2.2. Profile is a subclass of ServiceProfile, and each instance of Profile can point to at most one process through the functional property “has_process”. In addition, each instance of Profile has its IOPEs to present function description. But these IOPEs in profile just refer to the corresponding IOPEs in process, e.g. an input in a profile can only refer to a sub-property of input in the process model.

Fig. 1. RDF Graph Model of the DAML-S Ontology

In the Congo example of the DAML-S 0.9 [4], ExpressCongoBuyService is defined to be an instance of Service. This service presents Profile_Congo_BookBuying_Service (an instance of Profile), and is described by ExpressCongoBuyProcessModel (an instance of ProcesssModel). ExpressCongoBuyProcessModel has a user-defined process ExpressCongoBuy,


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which is defined to be a subclass of AtomicProcess, and then can be seen as a subclass of Process. 2.2 Framework of the DAML-S Process Ontology Within DAML-S Process Model, there are two chief components of a process model: the Process Ontology and the Process Control Ontology. The latter is out of the scope of this paper, and the former describes a service in terms of its IOPEs and where appropriate, its component sub-processes.

Fig. 2. RDF Graph Model of the DAML-S Process Ontology

An RDF graph model of the DAML-S process ontology is roughly depicted in Fig.2. Processes can have IOPEs and participants. Among them, input, output and participant are sub-properties of parameter in the process model. The ranges of precondition, effect and output are specified to be Condition, ConditionalEffect and ConditionalOutput respectively. The representation of preconditions and effects, as well as coCondition, ceCondition and ceEffect, depends on the representation of rules in the DAML language, but no proposal for specifying rules in DAML has been put forward. For this reason, they are currently mapped to anything possible. In addition to its action-related properties, a process has a number of bookkeeping properties such as name (rdfs:Literal) and address (URI). There are three different types of processes: atomic, simple, and composite. Composite process can be described as atomic processes chained together in a process model. A composite process must have only one composedOf property by which is indicated the control structure of the composite process. The use of control constructs, such as if-then-else, allows the ordering and the conditional execution of the subprocesses (or control constructs) in the composition. Again, take the atomic process ExpressCongoBuy [4] as an example. The ExpressCongoBuy process has two properties (congoBuyBookISBN and congoBuySignInInfo) as its input, two properties (congoOrderShippedOutput and con-

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goOutOfStockOutput) as its outputs, two properties as its preconditions (congoBuyAcctExistsPrecondition and congoBuyCreditExistsPrecondition), one property as its effect (ongoOrderShippedEffect). These concrete IOPEs for the ExpressCongoBuy process are specified to be sub-properties of input, output, precondition and effect, with some constraints on their domains and ranges (through using anonymous subclasses via the use of "Restriction").

3 Some Design Consideration DAML-S can be seen as a “semantic” service description language (in short, SSDL). We use the term SSDL to mean an ontology for describing services plus an ontology structuring mechanism inherited from the ontology defining language (base language). In the case of DAML-S, the base language is DAML+OIL, and the provided ontology is just what we discussed in previous section. The base language of DAMLS will be shifted to OWL in the next release. DAML-S is definitely a great propellant towards Semantic Web Service. However, we also learned some lessons from the evolvement of DAML-S. These lessons are helpful to the design of SSDL as well as the improvement of DAML-S. The followings are our corresponding considerations. (1) The Use of Meta-modeling Facilities of RDF Schema In the version of DAML-S 0.9, there are some misuses of the meta-modeling facilities of RDF Schema, e.g. the rdfs:range of “has_process” properties. In fact, ontology developers should take care when they define classes of classes or attach properties to classes. As we know, OWL provides three increasingly expressive sub-languages designed for use by specific communities of implementers and users. OWL Full can be viewed as an extension of RDF, while OWL Lite and OWL DL can be viewed as extensions of a restricted view of RDF. When using OWL Full as compared to OWL DL, reasoning support is less predictable since complete OWL Full implementations do not currently exist. Our position is as follows: The designer of a “semantic” service description language (in short, SSDL), such as DAML-S, may use some meta-modeling facilities of RDF Schema, and should be careful to do so, but users of the SSDL should not use any meta-modeling facilities in their applications. In the case of adopting OWL, the designer of a SSDL may use some constructs from OWL Full with carefulness, but users of the SSDL should just use ontology and constructs from SSDL and OWL DL to describe their web service and/or grid service. (2) The Functional Description of Services In DAML-S Process Model, as illustrated in section 2.1, any application specific process should be defined as a subclass of (or, a descendant class of) Process. In other words, the class Process is the super-class of every user-defined class representing an application-specific process. This representing processes-as-classes approach will bring some harm to the usage of DAML-S. Fortunately, DAML-S 1.0 will adopt the processes-as-instances approach.


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Secondly, an instance of Service can be described by at most one instance of ProcessModel, and each instance of ProcessModel can has at most one subclass of Process through the “hasprocess” property. This means that each service can be associated with at most one process. It lacks the capability to describe the service type having multiple functions. Of course, there is a trade-off between fine-grain and coarse-grain. We think a good description framework should be flexible enough to cover the coarse granularity. Thirdly, the DAML-S Profile also provides the functional description for service through the IOPEs. But these IOPEs in profile just refer to the corresponding IOPEs in process, and have little added value in principle. As we noted, in myGrid [6], additional properties such as performs_task and uses_resource, etc., are used with profiles to specify which task they perform, which resource they use. We think that the task being performed is an important aspect of a functional description, e.g. retrieving is an example of a generic task. The structure of the DAML-S Profile could be improved accordingly. Finally, the representation of preconditions and effects, as well as the condition of conditional output, depends on the representation of rules in the DAML language. A decision was made to more closely align the DAML Rules with the Rule Markup Language (RuleML [9], in short). Currently, RuleML supports user-level roles, URI grounding, and order-sortedness [10]. The rules will definitely play an important role in representing the functional behavior, however, the variable and scope issue should be resolved when integrating rules into DAML-S or other SSDLs, which will be discussed in more detail in section 4. (3) The Representation of Stateful Services There are some debates on “Service and State: Separation or Integration?” As we know, pure stateless services are rare and usually uninteresting. The question is through which mechanism to expose the state, through service, specific operation, session or contextualization? Within OGSI, the state of grid service is exposed directly through the service itself. We notice the trend of convergence between Web Service and Grid Service. A good description framework should be flexible enough to cover the requirement of grid service directly, including stateful grid services.

4 A Semantic Description Framework for Service Based on previous considerations, we propose a semantic description framework for service in this section. Our framework has following characteristics:

(1). Services are organized by service types using “services implements service types” mechanism;

(2). Operations (or processes) are associated with service types; (3). Each operation is represented as an instance; (4). Multiple inputs/outputs of an operation are aggregated by a message with part names, and then each operation has only one input message and only one output message;

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(5). Services state can be directly exposed at the service type level; (6). Rules are used to prescribe the behaviors of services at both of service type and operation levels, and three pseudo variables, i.e. “hostService”, “input” and “output”, are introduced to be used within rules. An RDF graph model of our description framework for service is roughly depicted in Fig.3. Note that the classes and properties within our description framework are assumed to be in a fiction namespace “mySSDL” to avoid name collision, and the prefix of a name would be omitted when there is no ambiguity.

Fig. 3. A Semantic Description Framework for Service

A service has service profiles, implements zero or more service types. A service type is an instance of the class ServiceType. ServiceType is defined to be a subclass of owl:Class, the intended meaning of ServiceType is the class of all service types just as owl:Class is the class of all OWL classes. Each service type may have service data templates, service rules and operations. A service data template describes the name and type of an exposed service data. A service rule prescribes the behaviors of a service type, such as the consistency constraint, the dependency and concurrency between the operations, etc. An operation is an instance of the class Operation, just as a property is an instance of the class rdf:Property. An operation has codomain and/or corange property, just as a property can have rdfs:domain and/or rdfs:range property. But, an operation must have exactly one codomain and corange property, and the codomain and corange property of an operation must be an instance of MessageType. In addition, an operation can have rules to describe functional behaviors of the operation. The class MessageType is defined to be a subclass of owl:Class, the intended meaning of MessageType is the class of all message types. Any message type (an instance of MessageType) can have zero or more part templates. A data template must have exactly one data name and one data type. The data type of a data template can be one of the built-in OWL datatypes including many of the built-in XML Schema datatypes, or anyone of other OWL classes including application specific data types


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and even user-defined service types. We define a specific message type (named as myssdl:Void) to be an instance of MessageType, with the constraint that there doesn’t exist any data template associated with Void. Rules are used to specify the behaviors of a service as well as the functional behaviors of an operation. As to the variable and scope issue, we propose: • The pseudo variable “hostService”, the service data names and operation names of a service type can be used within the service rules of the service type. • The service data names of the containing service type, three pseudo variables (“hostService”, “input” and “output”), as well as the data names of the co-domain and co-range message types can be used within operation rules of the function. The pseudo variable “hostService” denotes the service that implements the corresponding service type. Within operation rules of a function, “hostService” denotes the service requested to execute the function, while the pseudo variables “input” and “output” denotes the input message and the output message of the corresponding operation, respectively. It should be noted that further more research work should be taken to integrate RuleML and logic reasoning into our description framework for service, although some theoretical research results [10, 11] has been made. 4.1 Examples This subsection illustrates the usage of our framework by two examples. The first one is about describing web service, while the second one is about grid service. (1) The Congo Example We define a service type BuyBook as an instance of ServiceType, and a service congoBuyBook as an instance of Service. The service congoBuyBook implements the service type BuyBook, which has two or more operations: expressBuyBook, fullBuyBook and other possible operations exposed (e.g. createAcct, createProfile, locateBook). These operations are defined to be instances of Operation, with corresponding input/output message types and operation rules. As to the operation that uses other exposed operations, we can use the service rule to specify the control flow of the composition. Take the expressBuyBook operation as an example. It has an input message type with two parts: a String value with the name buyBookISBN and an instance of SignInData with the name buyBookSignInInfo. The operation has an output message type with one part: a String value with the name replyMessage. The operation has an operation rule, which prescribe that if the book is in stock, then the reply message indicates that the order is shipped; and that if the book is out of stock, then the reply message indicates that the book is out of stock. In the case that the quantity of available books is exposed as a service data, the rule could say more about the precondition and effect of the operation. (2) The GridService portType As we know from OGSI [2], the GridService portType MUST be implemented by all Grid services and thus serves as the base interface definition in OGSI. This portType

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is analogous to the base Object class within object-oriented programming languages such as Smalltalk or Java, in that it encapsulates the root behavior of the component model. The behavior encapsulated by the GridService portType is that of querying and updating against the serviceData set of the Grid service instance and managing the termination of the instance. Now let’s define a service type, also named as “GridService”, to reflect the above idea. First, we can define GridService as an instance of ServiceType. The GridService is defined to have many service data templates, including the ones with following data names: interface, serviceDataName, gridServiceHandle, gridServiceReference, findServiceDataExtensibility, setServiceDataExtensibility, factoryLocator, terminationTime. The first six ones have list types as their data types, the last two has ogsi:LocatorType and ogsi:TerminationTimeType as their data types, respectively. The mutability, modifiable and nillable of these service data can be described by service rules, and rules can also be used to describe the multiplicity constraints on the first six service data elements, e.g. a grid service has at least one interface and two setServiceDataExtensibility, as well as the other consistency constraint, e.g. initial setting of service data value. Second, we define the following operations for GridService: findServiceData, setServiceData, requestTerminationAfter, requestTerminationBefore and destroy, with their corresponding input/output message types. By doing so, every service that implements the service type “GridService” can be seen as a grid service. That’s just what service data and behaviors the OGSI requires every grid service should have. As to the domain-specific grid applications, the application domain community can design a couple of service types for reuse in their grid applications.

5 Conclusion The semantic description framework of DAML-S is illustrated by using RDF graph model in this paper. We discussed some consideration issues in improving DAML-S and/or designing another SSDL, and a novel description framework for service is presented. The six characteristics of our description framework are outlined, and it’s illustrated by two examples. There are some research works related to this paper. For example, Web Service Modeling Framework (WSMF) is proposed in [12] to enable fully flexible and scalable E-commerce based on web services, and IRS-II (Internet Reasoning Service) [13] is a framework and implemented infrastructure, whose main goal is to support the publication, location, composition and execution of heterogeneous web services, augmented with semantic descriptions of their functionalities. As compared to these related works, the system infrastructure and service composition are not the focus of this paper. Our main concerns include the semantic description framework for service and the design issues of the SSDL. We hope that the six characteristics of our description framework given in section 4 and our thinking on designing SSDL given in section 3 could push the improvement to DAML-S as well as the design of other SSDLs. To make the vision of Semantic Grid Service and Semantic Web Service a reality, a number of research challenges need to be addressed. Our further research work in-


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cludes the integration of rules into SSDL as well as the trust and provenance of Semantic Service. We believe, with the evolvement of the SSDL and the emerging of system infrastructure and framework, the Semantic Grid/Web Service will become a real life in the future.

References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

Foster, C. Kesselman, J. Nick, S. Tuecke, Grid Services for Distributed System Integration. IEEE Computer, 35(6):37-46, 2002. S. Tuecke, K. Czajkowski,I. Foster, J. Frey, S. Graham, C. Kesselman et al, Open Grid Services Infrastructure (OGSI), 2003, Available online at http://www-unix.globus.org/toolkit/draft-ggf-ogsi-gridservice-33_2003-06-27.pdf. Sheila A. McIlraith, David L. Martin: Bringing Semantics to Web Services. IEEE Intelligent Systems, 18(1): 90-93, 2003. DAML-S 0.9 Draft Release (2003). DAML Services Coalition. Available online at http://www.daml.org/services/daml-s/0.9/. DAML-S Coalition. DAML-S: Web Service Description for the Semantic Web. In First International Semantic Web Conference (ISWC) Proceedings, Sardinia (Italy), June, 2002, pp 348-363, 2002. C. Wroe, R. Stevens, C. Goble, A. Roberts, M. Greenwood, A suite of DAML+OIL Ontologies to Describe Bioinformatics Web Services and Data, International Journal of Cooperative Information Systems, Vol. 12, No. 2 (2003) 197-224. David De Roure, Semantic Grid and Pervasive Computing, available online at: http://www.semanticgrid.org/GGF/ggf9/gpc/ Roure, D., Jennings, N. and Shadbolt, N. 2001. Research Agenda for the Future Semantic Grid: A Future e-Science Infrastructure. Available online at http://www.semanticgrid.org/v1.9/semgrid.pdf Harold Boley, Said Tabet, and Gerd Wagner. Design Rationale of RuleML: A Markup Language for Semantic Web Rules. In Proc. Semantic Web Working Symposium (SWWS’01), pages 381–401. Stanford University, July/August 2001. Harold Boley, Object-Oriented RuleML: User-Level Roles, URI-Grounded Clauses, and Order-Sorted Terms, in Workshop on Rules and Rule Markup Languages for the Semantic Web (RuleML-2003), Sanibel Island, Florida, USA, 20 October 2003. Benjamin N. Grosof, Ian Horrocks, Raphael Volz, and Stefan Decker. Description Logic Programs: Combining Logic Programs with Description Logic. In Proc. 12th Intl. Conf. on the World Wide Web (WWW-2003). Budapest, Hungary, May 2003. Fensel, D., Bussler, C. (2002). The Web Service Modeling Framework WSMF. In 1st meeting of the Semantic Web enabled Web Services workgroup, 2002. Available at http://informatik.uibk.ac.at/users/c70385/wese/wsmf.bis2002.pdf. Enrico Motta, John Domingue, Liliana Cabral , and Mauro Gaspari, IRS-II: A Framework and Infrastructure for Semantic Web Services. In Proceedings of the 2nd International Semantic Web Conference 2003 (ISWC’2003), 20-23 October 2003, Sundial Resort, Sanibel Island, Florida, USA.

A Novel Approach to Semantics-Based Exception Handling for Service Grid Applications∗ Donglai Li,Yanbo Han, Haitao Hu, Jun Fang, and Xue Wang Software Division, Institute of Computing Technology, Chinese Academy of Sciences, 100080, Beijing, China Graduate School of Chinese Academy of Sciences, 100039, Beijing, China {ldl, yhan, huhaitao,fangjun, wxue }@software.ict.ac.cn

Abstract. Whenever the characteristics of a service grid environment are addressed, issues related to openness and dynamism pop out first. Such issues do affect the definition and handling of application exceptions, and traditional approaches to exception handling lack in proper mechanisms for capturing exception semantics and handling exceptions. In this paper, after analyzing the newly arisen problems of exception handling in a service grid environment, we focus on exceptions caused by runtime mismatches between user’s requests and underlying services, and propose a semantics-based approach to handling this kind of exceptions. The approach was first developed within the FLAME2008 project and some promising results have been achieved.

1 Introduction In a service grid environment, services evolve autonomously, their coupling is highly loose, and system boundaries are no longer clearly in control. Exceptions [8][10] may happen more frequently when building applications in such an open and dynamic environment, especially in connection with frequent changes of user requirements. Exception handling has always been an important topic and some previous efforts have led to remarkable achievements [3][9][11]. But in a service grid environment, new challenges appear: Applications may use any network-based service. Most users don’t have the ability to describe all the potential runtime mismatches, which lead to exceptions. We thus intend to detect such mismatches automatically. The handling process should be determined dynamically and mostly by the system instead of users. Often, because of lacking background knowledge and potential services, one couldn’t describe details of most mismatch exceptions. A mechanism is needed to judge what a mismatch exception is and whether it happens or not, and a mechanism is needed to determine the way of handling a mismatch exception. The connection among aspects of exception handling process is “tight” in current exception handling technology. To solve this problem, semantics is naturally introduced. In the information systems context, semantics can be viewed as a mapping between an object modeled, represented and/or stored in an information ∗

This paper is supported by the National Natural Science Foundation of China under Grant No. 60173018 and the Young Scientist Fund of ICT, CAS under Grant No. 20026180-22.


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system and the real-world object(s) it represents. [1]. Finding out the relationship between the semantics of a user’s request and the semantics of the services may help to solve the mismatch exception handling problems. Based on semantics, we propose an approach, named ASEED(a novel approach to semantics-based exception handling for service grid applications). We developed this approach within the FLAME2008[12], which adopts the service grid paradigm and develops a service integration platform targeted at a real-world application scenario – an information service provision center for the Olympic Games Beijing 2008. The paper organized as follow: section 2 discusses the prerequisites of the approach; section 3 illustrates the approach; section 4 shows and evaluates the approach; section 5 compares with related works; Last section lists some future directions.

2 Prerequisites of ASEED Although services are encouraged to be capsulated in a unified form, for example, Web Services facilitate open standards and a service is described by WSDL, there is no global system for publishing services in such a way as it can be easily processed by anyone else. The problem is that, in some contexts, it is difficult to use the services in the ways that their designers might want [4]. The unclear meaning of user’s requests and services makes the situation more complicated. Thus the meaning of user’s requests and services, so called semantics, needs to be exposed. In order to solve the mismatch exceptions, some prerequisites are needed. 2.1 Semantics Infrastructure We take ontology-based semantics as the infrastructure of our approach. An ontology is a formal, explicit specification of a shared conceptualization, which is mediator to share common concepts among different parties. We depict the relationship between two concepts. For example a single ontology includes some concepts: mammal, human, woman, etc. Woman is the subclass of human, human is the subclass of the mammal and woman inherits all properties of human and human inherits all properties of mammal. Thus we have different granularities of semantics. To illustrate these layered structural ontologies, we use a graph to demonstrate, nodes in the graph stand for specified semantics and the edges of the graph show the relations among specified semantics. Many research projects have produced large ontologies such as WordNet, which provides a thesaurus for over 100,000 terms explained in natural language. In a grid environment, resource sharing is done with resource providers and consumers defining clearly and carefully just what is shared, who is allowed to share, and the conditions under which sharing occurs. A set of individuals and/or institutions defined by such sharing rules form what we call a virtual organization (VO)[7]. Such rules are also the guidance and reference when experts construct ontologies besides their domain knowledge.

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2.2 Semantics of Services and User’s Requests Usually a service has two kinds of properties: functional ones and non-functional ones. Functional properties illustrate what the service could do, while non-functional properties show other information such as QoS. And they have their respective usage: - Semantics of functional properties: it manifests the service’s ability. Though languages like WSDL can describe it from syntax aspect, they don’t make it machineunderstandable. Services published with accessible semantics of their functional properties can be searched, invoked automatically. - Semantics of non-functional properties: services having same functional properties may be different from many other aspects, such as different QoS. Semantics of non-functional properties describe the constraints of the services so that the services can be understood more precisely. In a user’s request, each unit, so called activity, illustrates the user’s partial desire. Similar to a service, an activity usually has two aspects of semantics: Semantics of functional desire and Semantics of non-functional desire With the semantics infrastructure, it’s possible for users to describe their requests and for services suppliers to publish services from semantics level. The mismatch between them can be checked automatically by the system, because the machine understands feature of semantics.

3 The Semantics-Based Exception Handling Approach To ensure a smooth execution of service grid applications, mismatches between user’s requests and services should be observed and handled as mentioned before. Fig1 illustrates the central idea of our approach: Exception Signaling

Mismatch Analysis

Strategy Selection

Handling Strategy

Exception Context Semantics

Handling Pattern Infrastructure

Fig. 1. Reference model of ASEED

• When an exception is signaled, the semantics of user’s request and the services are derived form the Exception Context and the compatibility between them are analyzed by the Mismatch Analysis to find out whether it’s a mismatch exception. • There are common factors among different handling processes, such as the exception context, the strategy of handling exceptions, etc. The Handling Pattern contains patterns, which abstract out the common information of exception handling processes. Patterns are referred when similar exception happens.


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• When a mismatch exception happens, the Mismatch Analysis informs the Strategy Selection and Specified Handling Strategy is found out then by consulting the Exception Context and the Handling Pattern. The semantics infrastructure is the basis of connecting aspects of exception handling. Based on it, exceptions of mismatches between user’s requests and services can be observed and specified handling strategy is produced. Main components of the approach will be illustrated in the following sections. 3.1 Mismatch Exception Analysis During execution, the semantics of selected services should match the semantics of user’s requests from both the functional aspect and the non-functional aspect. When an exception is signaled, the compatibility between semantics of services and user’s requests will be calculated. The algorithm is: Cal_ Compatibility(s_s, s_u){ // s_s, s_u are the semantics of the selected services and user’s request. If (s_s is sub class of or equal to s_u) return compatible; If (s_s is ancestor of s_u) return incompatible; If (s_s and s_u belong to different branches of the semantics graph) return incompatible; } Compatibility of both functional and non-functional semantics would be checked, because either of them may lead to mismatch exceptions. With the semantics infrastructure, those kinds of mismatch exception can be distinguished automatically by the system when exceptions happen, and users don’t have to specify them when describing their requests. 3.2 Handling Pattern There are common factors among different handling processes. Pattern[2] is used to describe those common factors. A pattern may consist of many parts, and we use the following four to contain a minimum: name, problem, context and solutions: - Name: each pattern has its specified name, which identifies itself. - Problem: it describes what kind of mismatch exception the pattern tries to solve. - Context: a mismatch exception resides in a specified context, which consists of the structure of user’s requests, the semantics of user’s request or the semantics of the services and the context affects the exact meaning of an exception. - Solutions: solutions contain the necessary actions to handle the exceptions. It may contain one or more steps to guide the handling of a specified exception. The instantiations of patterns, which we call cases, have the solid information of the exception handling processes. For the context of a case, an expression is used to describe the structure: ax stands for a single activity; ∧ stands for Sequence; ∨ stands for Concurrent;

∨ stands for Choice; power stands for Loop:

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a2

a1

(

)

a 1 ∧ a 2n ∨ a 3 ∧ a 4

a4

a3

Fig. 2. Illustration of the a process’s structure

While the semantics of the user’s requests or services in the case are illustrated by a set of value pair. And the Solutions contain a set of semantics notations illustrating the handling process. Case will be populated during the exception handling processes. 3.3 Strategy Selection Handling strategy is the solutions property of the case. In order to solve the mismatch exceptions, information from the exception context will be consulted to retrieve the suitable case. The case matching consists of two parts: the structure matching and the semantics matching. - Structure matching: for the case and the user’s request, the structure can be described as an expression as mentioned above. Thus the structure matching then can be treated as an expression matching. Only those cases which have the same structure as the user’s request can be considered as matched. - Semantics matching: each activity in the case or in the user’s request maps with a (set of) specified semantics. Usually a single case or user’s request consists of many activities. The semantics matching results are defined by the least square distance function, which assumes that the best-fit is the one that has the minimal sum of the deviations squared from a given set of semantics, shown as below:

∑ w * Distance(a , b ) n

SM =

i

i

i

2

,

i =1

ai and bi are the corresponding activities in the user’s requests and the case; wi is the weight for bi, which illustrates the relativity of bi in the case. Distance(ai,bi) is the function which calculates the semantics matching degree of two corresponding activities from user’s request and the case: Distance (a,b){ // a and b are two corresponding activities from user’s request and the case set_aÅ the semantics set of a’s functional and non-functional properties; set_bÅ the semantics set of b’s functional and non-functional properties;

∑ Degree(c , d ) n

S=

j

j

2

;

j=1

// cj,dj are the corresponding semantics, in set_a and set_b. Degree(cj,dj) is //the function calculates the matching degree of two specified semantics by //depending on the level difference in the semantics graph. if there is no //corresponding semantics in set_b, a default semantics will be assigned


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return S; } Degree(c,d){ //c and d are two semantics to be calculated. if((c is ancestor of d) or (c is subclass of d) or (c equals d)) return level difference in the semantics graph between c and d; if(c and d belong to different branches of the semantics graph) return ∞; } The smaller SM is, the closer the semantics compatibility is. Structure similarity and semantics similarity make the Strategy Selection retrieve the suitable case, and the handling strategy is then retrieved and used to guide the exception handling.

4 Implementation in FLAME2008 4.1 Case Study A common travel scenario from Flame2008: first booking the flight, then reserving a hotel room and renting a car in parallel. If both are done, then booking a ticket for swimming game of Olympic Games 2008. Semantics Graph Travel User’ Request

Hotel Booking(HB)

Sports

Traffic

Sightseeing

Accomodation

Game Ticket Booking(GTB) Ticket booking

Flight Booking(FB) Car Renting(CR)

Swimming

Fencing

Fig. 3. Demonstration of travel scenario

Fig3 shows the scenario that illustrates the functional semantics mapping (two dotted broken line) and non-functional semantics mapping (doted line) for GTB. Before starting GTB, User changes his mind to watch fencing game instead of swimming game. So he modifies GTB by changing a non-functional semantics (lightcolored doted line). While the continuous execution of the application stops due to some wrong returning result, which signal an exception. The following shows the mismatch exception handling process: (1) Mismatch Analysis: For GTB, the semantics of the user’s new request (fencing ticket booking), the semantics of the user’s old request (swimming ticket booking) and the semantics of the selected service have the same functional semantics: “http://flame/KgB/travel.daml#sports.ticketbooking”, but their non-functional semantics are different:

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Activity name GTB (new) GTB (old)

Non-functional semantics http://flame/KgB/travel.daml#sports.ticketbooking.fencing http://flame/KgB/travel.daml#sports.ticketbooking.swimmin ging Selected Service http://flame/KgB/travel.daml#sports.ticketbooking.swimmin ging After the modification, the functional semantics of the service still satisfies the user’s request, but the non-functional semantics of the service is no longer compatible (they are siblings in the semantics graph). Thus a mismatch exception is observed. (2) Strategy Selection: in order to get the handling strategy, the structure similarity and the semantics similarity are calculated so as to retrieve suitable case: - Structure matching: shown in Fig3, the structure of the exception context is a1∧(a2∨a3)∧a4. By expression compare, Cases with same structure are selected out. - Semantics matching: Among the cases selected out by structure similarity comparing, the semantics similarity is calculated. Using the algorithm we have mentioned, a suitable case is selected out and it is: Name Service ReSelect Problems A new activity replaces the old one to satisfy a different goal. Context Structure: a1∧(a2∨a3)∧a4 Semantics: //semantics of non-functional properties are omitted here Solutions http://flame/KgB/task/Execute.daml#ServiceReSelect In the case, the solution is “http://flame/KgB/task/Execute.daml#ServiceReSelect”, which guides the execution of the application by allowing it to select another service. Meanwhile, if there is no suitable case available, a default case will be consulted. 4.2 Evaluation of ASEED Based on semantics, we proposed an approach to handle runtime mismatch exceptions of service grid applications. Semantics helps the system know what a runtime mismatch exception is. Also it helps to auto-detect these exceptions and handle them dynamically. In service grid environment, handling exception by using our approach has some promising effects: - The veracity of catching mismatch exceptions: The explicit semantics from some aspects of the application are used, such as the semantics of the services. Exceptions of mismatches between user’s requests and services could be detected precisely. - The flexibility of handling exceptions: as we have mentioned, exceptions may have different meaning and should be handled by different ways. Our approach tries to find out the suitable way each time an application encounters mismatch exceptions. And during handling process, the handling strategies are located dynamically. Still there are some points affecting the handling process: - How minute the semantics has been described: the precision of catching mismatch exceptions depends largely on the granularity of semantics description. If the granularity is rough, the catching result is not good.


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- The similarity-matching of the case: it’s easy to compare the structure similarity. But the semantics similarity may count on more factors so as to make more precise matching. - The handling patterns: in order to solve all kinds of mismatch exceptions, the handling patterns and their instances are needed to be enriched and better managed.

5 Related Works In a service grid environment, exception handling is still in its infant state. Some research groups have begun to pay their attention to this issue. IBM’s BPEL4WS[6] pays attention to the ability for flexible control of the reversal by providing the ability to define exception handling and compensation in an application-specific manner. But it mostly deals with local exceptions and exceptions (handlers) which are predefined by users themselves. If there are unexpected exceptions, the system cannot be aware of them. Globus[5] provides a range of basic services to enable the construction of application specific fault recovery mechanisms. It focuses on providing a basic, flexible service that can be used to construct a range of application-specific fault behaviors. But it is difficult to build those kinds of service for common use. There are some other works in related domains like workflow domain: [11] provides a model, which provides a rule base that consists of a set of rules for handling exceptions. But the rule base is a separate component functionally disjoint from the exception database. Sometimes the rules cannot describe the scenario even when an approach has been adopted to resolve many exceptions because of the disconnection between the two bases. METEOR[13] tried to solve the conflicts resolution in cross-organizational workflows. Their approach bundles knowledge sharing, coordinated exception handling and intelligent problem solving. The attention is paid to the conflictions between the handling participants not the mismatches we mentioned. But the case matching algorithm is worthy of reference. For most work, if the mismatch exceptions are not defined by users, they can’t be detected and handled. But it is quite difficult for users to describe all mismatch exceptions in a service grid environment due to its open and dynamic characteristics. Also the system lacks in the means of automatically detecting and handling the mismatch exceptions. And most work focuses on the problems within a bounded environment either intra-organization or inter-organization where exceptions and exception handling processes are stable in some sense.

6 Conclusions In this paper, we analyzed the upcoming problem of mismatch exceptions between user’s requests and the underlying services in service grid environment. In order to build reliable applications, these exceptions needed to be detected and handled in an effective way. We proposed a novel approach named ASEED, which adopts semantics as a dominant role for providing a basis throughout an exception handling process. Semantics makes the mismatch exceptions and the handling processes machine understandable. The approach makes it possible that these exceptions don’t

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have to be described by users and mostly they are detected and handled by the system itself. The approach has been implemented in the FLAME2008 project and some promising results have been achieved. Still some problems need to be solved to perfect the approach. In our future research, we will pay our attention to the following problems: the spectrum of exception context needs to be broadened and the details of context will be studied more thoroughly; Elements of a pattern will be under thorough consideration and a thorough classification of the patterns is needed to be done. Effective management of the cases is needed to offer better support.

References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

A. Sheth, Data Semantics: What, Where, and How?, in Data Semantics (IFIP Transactions), R. Meersman and L. Mark, Eds., Chapman and Hall, UK, (1996) 601-610. E. Gamma,R. Helm, R. Johnson, J. Vlissades, Addison-Wesley, Design Patterns Elements of Reusable Object-Oriented Software, ISBN: 0-201-63361-2, (1995) F. Casati, S. Ceri, S. Paraboschi, and G. Pozzi, Specification and Implementation of Exceptions in Workflow Management Systems, TODS, Vol 24, No. 3, (1999) 405-451 http://infomesh.net/2001/swintro/ http://www.globus.org/details/fault_det.html http://www-900.ibm.com/developerWorks/cn/webservices/ws-bpel_spec/index_eng.shtml I. Foster, C. Kesselman, and S. Tuecke. The anatomy of the Grid: Enabling scalable virtual organisations. International Journal of Supercomputer Applications, 15(3), (2001). J.B. Goodenough, Exception Handling: Issues and a Proposed Notation, Communications of the ACM, Vol. 18, No. 12 (1975)683-696 J. Eder and W. Liebhart, Contributions to Exception Handling in Workflow Systems, EDBT Workshop on Workflow Management Systems, Spain, (1998). J.L. Knudsen, Better Exception-Handling in Block-Structured Systems, IEEE Software, Vol. 17, No. 2 (1987) 40-49 S.Y. Hwang, S.F. Ho, J. Tang, Mining Exception Instances to Facilitate Workflow Exception Handling, Proc. of the Sixth International Conference on Database Systems for Advanced Applications, Taiwan, (1999) 45-52. Y. Han, H. Geng, H. Li, J. Xiong et al, VINCA – A Visual and Personalized Businesslevel Composition Language for Chaining Web-based Services, Proc. of International Conference on Service Oriented Computing , Italy (2003) Z. Luo, A. Sheth, K. Kochut and B. Arpinar, Exception Handling for Conflict Resolution in Cross-Organizational Workflows，Distributed and Parallel Databases Journal, Vol 11(2003)

A Semantic-Based Web Service Integration Approach and Tool* Hai Zhuge, Jie Liu, Lianhong Ding, and Xue Chen Knowledge Grid Research Group, Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 100080, Beijing, China [email protected], [email protected], [email protected]

Abstract. Integration of Web Services for large-scale applications concerns complex processes. Component technology is an important way to decompose a complex process and promote efficiency and quality. This paper proposes a service integration approach considering both the integration of service flows and the integration of data flows. The approach includes the component-based Web Service process definition tool, mechanism for retrieving services in a well-organized service space and UDDI repositories, algorithms for heterogeneous data flow integration, and rules for service verification. The proposed approach has been integrated into an experimental service platform and used in an online book sale business application. Comparisons show the features of the proposed approach.

1 Introduction Integration of Web Services for large-scale applications is a challenging issue due to unmanageable efficiency and quality issue of the involved complex service processes. Another issue arising from service integration is how to conveniently, accurately and efficiently retrieve services from the rapidly expanding and large-scale service repositories. Data returned from multiple services may be heterogeneous in semantics, structure and value [1, 4], so the third issue of service integration is how to integrate the heterogeneous data flows returned from different services so as to provide a unified view for users. Previous research on Web Service integration mainly concerns approaches for automatically integrating relevant services by using semantic markups [8], Petri-Netbased and ontology-based approaches for service description, simulation, verification and composition [3, 10], and languages for describing behavioral aspects of the service flow [6, 11]. However, these research works seldom address the above three issues. The current UDDI registry nodes only provide keyword-based service retrieval [9]. If users are not familiar with the pre-specified service categories, they usually could *

The research work was supported by the National Science Foundation of China (NSFC).


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not get the satisfied retrieval results. Applications show that the current UDDI repositories cannot meet the needs of the business processes in efficiency and accuracy. This paper solves the issue of complex service process construction by making use of component technology, an important way to decompose a complex service process. A component-based service process definition tool has been implemented to assist users to transform a business process into a service process and then to specify the requirements for the related service components, which are integrated by service flow and data flow. Interactions between the components in the service process are based on XML, SOAP and WSDL. We solve the issue of improving the accuracy and efficiency of service retrieval by making use of the service space model, which organizes Web Services in a normalized and multi-dimensional service space so that services could be retrieved efficiently. We solve the issue of heterogeneous data flow integration by establishing mapping between the global schema and the source schema. The semantic heterogeneity, the structure heterogeneity, and the data value heterogeneity are considered.

2 General Architecture The general architecture of the proposed Web Service integration approach is illustrated in Fig. 1, which mainly consists of the following modules: Process Definition, Definition Verification, Requirement Description, Web Service Retrieval, Integration, Integration Verification, and Registration.

Fig. 1. General architecture of the proposed approach

A Semantic-Based Web Service Integration Approach and Tool

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We have developed a component-based Web Service process definition tool to assist users to transform a business process into a service process. The process definition is accomplished by drawing nodes and arcs on the interface with the help of the operation buttons. After definition, the completeness and time constraints of the defined process components are verified. Modification is required in case errors occur. Otherwise, users can specify the requirements for the related service components by using the definition tool. A service space is a multi-dimensional space with a set of uniform service operations (http://kg.ict.ac.cn). A referential model for the service space can be expressed as Service-Space=(Classification-Type, Category, Registry-Node). In order to retrieve the required services effectively and efficiently, multi-valued specialization relationships and similarity degree between services are constructed [13]. Besides the GUI, the service space supports applications to retrieve services by using SOAP messages. If no matching services are retrieved, the service space will automatically communicate with the UDDI repositories through SOAP messages to get the required services. The components in the service process are integrated through service flow and data flow. The service flow reflects the control dependence, while the data flow denotes the data dependence among the service components. The Integration Verification module checks the accessibility, deadlock and the execution state of the service process. If no error occurs, the new service will be registered at the service space and also the UDDI repositories, otherwise modification of process definition is triggered.

3

Semantic Heterogeneous Data Flow Integration

In order to form a single semantic image for heterogeneous data returned by the service components [15], we use a triple DIS = to represent a data integration system, where G is the global schema  the XML schema defined by application developers, S is a set of the source schemas  the XML schemas of the data sources returned by service components, and M is the mapping from G into S. The process of heterogeneous data integration consists of the following four steps: The first step is global schema definition. The application developers define the basic information, the data dependence relationships (i.e., the semantic constraints), and the structure of the global schema according to the requirements. The basic information is represented by the structure: GNode (GnodeID, Gnode, Gtype), where GnodeID is the node identifier, Gnode is the node name, and Gtype is the node type. The data dependence relationship is represented by a set of pairs {GnodeA→GnodeB}, where GnodeA is the key just as the key in relational database systems. Paths in the global schema is expressed by the structure: GSchema (GpathID, Gpath, GpathLID, Gtype), where GpathID is the path identifier, Gpath is the label path (i.e., a sequence of slash-separated labels starting from the root to the current node), GpathLID is the identifier path (i.e., a sequence of slash-separated node identifiers starting from the root to the current node), and Gtype is the terminal node type.

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The second step is source schema extraction, which loads each data flow of the componential services, traverses the source schema recursively by the preorder sequence, and extracts the node name and the label path of the leaf node (or attribute node) from each source. The node information is kept by the structure: SNode (SourceID, SnodeID, Snode, Stype), and the label path information is recorded by the structure: SSchema (SourceID, SpathID, Spath, SpathLID, Stype), where SourceID is the source identifier, SpathID is the path identifier, Spath is the label path, SpathLID is the identifier path, and Stype is the terminal node type. The third step is mapping construction between the global schema and the source schemas, which solves the semantic conflict, structure conflict, and data value conflict among the involved service components. To solve the semantic conflict, such as the synonymy relationships between nodes naming, each node in the global schema is associated with a semantic attribute set (i.e., a set of semantically related terms) generated by making use of WordNet and can be added, modified and deleted on demand. Structure conflict is resolved through node mapping, path mapping, and tree mapping between the global schema and the source schemas. The node mapping is to map nodes in the global schema into nodes in the source schemas according to the established semantic attribute set. Human intervention is necessary in order to denote the mapping nodes on demand but they are not included in the semantic attribute set. The path mapping is to map the label paths in the global schema into paths in the source schemas. The tree mapping is to map the global schema as a tree into the source schemas. The tree structure sequence derived from the global schema is denoted as GSeq= (GPID1, GP1) … (GPID n, GPn), where GPIDi is the path identifier, and GPi is the label path from the root to the leaf node. The tree structure of the source schemas is denoted as SSeq=(SID1, SPID1, SP1,) …(SID1, SPIDu, SPu,) …(SIDn, SPID1, SP1) …(SIDn, SPIDv, SPv), where SIDi is the source identifier, SPIDi is the path identifier, and SPi is the label path from the root to the leaf node. The fourth step is data integration. To integrate the heterogeneous data flows satisfying users’ queries, the global query sequences including all the possible sub queries about the global schema are established. We use a set of triples to denote the global query sequences as QSeq=(QID1, QPath1, QCond1) … (QIDn, QPathn, QCondn), where QIDi denotes the sub-query identifier, QPathi is the path expression of the sub-query, and QCondi is the condition to be satisfied. Each user query corresponds to a set of non-continuous branches in the global query sequence, which further corresponds to non-continuous branches in each source tree and executes at each source. Data returned from the service components satisfying the sub-query branches with Boolean conditions is integrated. To solve the problem of data value conflict, the involved sources are ranked considering the reliability, data accuracy, and data quality. Data returned from the sources with higher rank has the higher priority.

4 Verification The Definition Verification module validates the completeness and time constraints of process definition. First, a component should be independent. The independency


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requires the component to reflect an independent business and to be able to execute independently. Second, a component should be encapsulated to interact with the rest components in the process through SOAP messages. Third, the start node and the end node should be unique and the internal process completeness should be satisfied as discussed in [14]. Considering the time factor and the logical relationships among services, the following rules are used for verification: − The start time of a single node must not be earlier than its predecessor’s end time, − The end time of a single node must not be later than its successor’s start time, − The start time of a node with “And-join” predecessors must not be earlier than any of its predecessors’ end time, − The start time of a node with “Or-join” predecessors must not be earlier than all of its predecessors’ end time, − The end time of a node with “And-split” successors must not be later than any of its successors’ start time, − The end time of a node with “Or-split” successors must not be later than all of its successors’ start time, − The start time of an arc must not be earlier than its predecessor’s end time, − The end time of an arc must not be later than its successor’s start time. The Integration Verification focuses on the following aspects: First, the components in the service process should be reachable from the start node, and, deadlock and loop should be checked and eliminated. Second, the components to be retrieved should be found in service space and UDDI repositories. Third, the execution condition of the components should be satisfied during the execution process, and data returned in the data flows should satisfy the requirements.

5 Application in Online Book Sale The purpose of this application is to demonstrate the integration of book information from multiple booksellers. According to the business process of book sale, users can use the definition tool to define service process and specify requirements for service components as shown in Fig. 2, where the background is the top-level service process, and the middle window is service component requirement specification. Clicking the “Search” button in the middle window will trigger the search process. Users will be asked to select services from a name list in the front window, and then information about service components will be automatically returned. The global schema defined by the application developers is shown in Fig. 3. The basic information of a book includes Book={ISBN, Title, Author, Publisher, Year, Abstract, Vendor, Price, Stock}. The semantic constraints is denoted as BookConstraint={ISBN→ (Title, Author, Publisher, Year, Abstract), (ISBN, VendorID) (Price, Stock)}, where ISBN is the key of Title, Author, Publisher, Year, and Abstract, and both ISBN and VendorID are the key of Price and Stock. The semantic attribute set consists of {ISBN (Book No, Book ID), Title (Name), Author (Writer),

→

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Publisher (Bookman), Abstract (Outline, Abstraction), Price (Cost), Stock (Inventory, Amount, Quantity)}.

Menu and Toolbar for Operating Service Process Service Process

Time Constraints Search Requirement

Scalable Componen t Hierarchy Service Node Definition Interface

Service Name List Returned

Fig. 2. An interface of the component-based service process definition tool

Fig. 3. The global schema of application in online book sale

After extracting the source schemas of the involved service components, the node mapping, path mapping, and tree mapping between the global schema and the source schemas can be constructed automatically. The global query sequence can be denoted as QSeq= {(Q1, Book//ISBN, "v1"), (Q2, Book//Title, "v2"), (Q3, Book//Author, "v3"), (Q4, Book//Publisher, "v4"), (Q5, Book//Year, "from v51 to v52" OR "after v51" OR "before v52"), (Q6, Book//Price, "less than v6" OR "v6" OR "larger than v6")}. Each user query corresponds to non-continuous branches in the global query sequence, which further corresponds to branches of the source schemas. We use the example


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“To retrieve books about Web Services under $60” to illustrate the query matching process. The above user query corresponds to two sub-query branches {(Q2, Book//Title, Keywords= "Web Services"), (Q6, Book//Price, "

Grid and Cooperative Computing: Second International Workshop, GCC 2003, Shanghai, China, December 7-10, 2003, Revised Papers, Part II

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Grid and Cooperative Computing: Second International Workshop, GCC 2003, Shanghai, China, December 7-10, 2003, Revised Papers, Part II