Grid and Distributed Computing - GDC 2011

Communications in Computer and Information Science

261

Tai-hoon Kim Hojjat Adeli Hyun-seob Cho Osvaldo Gervasi Stephen S. Yau Byeong-Ho Kang Javier García Villalba (Eds.)

Grid and Distributed Computing International Conference, GDC 2011 Held as Part of the Future Generation Information Technology Conference, FGIT 2011 Jeju Island, Korea, December 8-10, 2011 Proceedings

13

Volume Editors Tai-hoon Kim Hannam University, Daejeon, Korea E-mail: [email protected] Hojjat Adeli The Ohio State University, Columbus, OH, USA E-mail: [email protected] Hyun-seob Cho Chungwoon University, Chungnam, Korea E-mail: [email protected] Osvaldo Gervasi University of Perugia, Italy E-mail: [email protected] Stephen S. Yau Arizona State University, Mesa, AZ, USA E-mail: [email protected] Byeong-Ho Kang University of Tasmania, Hobart, TAS, Australia E-mail: [email protected] Javier García Villalba Universidad Complutense de Madrid, Spain E-mail: [email protected]

ISSN 1865-0929 e-ISSN 1865-0937 ISBN 978-3-642-27179-3 e-ISBN 978-3-642-27180-9 DOI 10.1007/978-3-642-27180-9 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: Applied for CR Subject Classification (1998): C.2, H.4, D.2, I.2, H.3, K.6.5

© Springer-Verlag Berlin Heidelberg 2011 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. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Foreword

Grid and distributed computing is an area that attracts many professionals from academia and industry for research and development. The goal of the GDC conference is to bring together researchers from academia and industry as well as practitioners to share ideas, problems and solutions relating to the multifaceted aspects of grid and distributed computing. We would like to express our gratitude to all of the authors of submitted papers and to all attendees for their contributions and participation. We acknowledge the great effort of all the Chairs and the members of Advisory Boards and Program Committees of the above-listed event. Special thanks go to SERSC (Science and Engineering Research Support Society) for supporting this conference. We are grateful in particular to the speakers who kindly accepted our invitation and, in this way, helped to meet the objectives of the conference. December 2011

Chairs of GDC 2011

Preface

We would like to welcome you to the proceedings of the 2011 Grid and Distributed Computing (GDC 2011) Conference—one of the partnering events of the Third International Mega-Conference on Future-Generation Information Technology (FGIT 2011) held during December 8–10, 2011, at Jeju Grand Hotel, Jeju Island, Korea. GDC 2011 focused on various aspects of advances in grid and distributed computing. It provided a chance for academic and industry professionals to discuss recent progress in the related areas. We expect that the conference and its publications will be a trigger for further related research and technology improvements in this important subject. We would like to acknowledge the great efforts of the GDC 2011 International Advisory Board, Special Session Organizers, as well as all the organizations and individuals who supported the idea of publishing this volume of proceedings, including the SERSC and Springer. We are grateful to the following keynote, plenary and tutorial speakers who kindly accepted our invitation: Hsiao-Hwa Chen (National Cheng Kung University, Taiwan), Hamid R. Arabnia (University of Georgia, USA), Sabah Mohammed (Lakehead University, Canada), Ruay-Shiung Chang (National Dong Hwa University, Taiwan), Lei Li (Hosei University, Japan), Tadashi Dohi (Hiroshima University, Japan), Carlos Ramos (Polytechnic of Porto, Portugal), Marcin Szczuka (The University of Warsaw, Poland), Gerald Schaefer (Loughborough University, UK), Jinan Fiaidhi (Lakehead University, Canada) and Peter L. Stanchev (Kettering University, USA), Shusaku Tsumoto (Shimane University, Japan), Jemal H. Abawajy (Deakin University, Australia). We would like to express our gratitude to all of the authors and reviewers of submitted papers and to all attendees, for their contributions and participation, and for believing in the need to continue this undertaking in the future. Last but not the least, we give special thanks to Ronnie D. Caytiles and Yvette E. Gelogo of the graduate school of Hannam University, who contributed to the editing process of this volume with great passion. This work was supported by the Korean Federation of Science and Technology Societies Grant funded by the Korean Government. December 2011

Tai-hoon Kim Hojjat Adeli Hyun-seob Cho Osvaldo Gervasi Stephen S. Yau Byeong-Ho Kang Javier Garcia Villalba

Organization

General Co-chairs Hyun-seob Cho Osvaldo Gervasi Stephen S. Yau

Chungwoon University, Korea University of Perugia, Italy Arizona State University, USA

Program Co-chairs Byeong-Ho Kang Javier Garcia Villalba Myoung-kwan Oh Tai-hoon Kim

University of Tasmania, Australia Complutense University of Madrid, Spain Hyejeon University, Korea GVSA and University of Tasmania, Australia

Publication Chair Keun-Wang Lee

Chungwoon University, Korea

Publicity Chair Min-sun Kim

Hyupsung University, Korea

International Advisory Board Hak-min Kim Jong-sun Lee Ok-hwan Kim

Soonchunhyang University, Korea Daejin University, Korea Kongju National University, Korea

Publication Committee Dae-Kyu Kim Hyun-Tae Jang Jong-Chan Lee Jung-Hwan Park Kwang-Hwan Kim So-Yeon Min Sun-Yeob Kim Tae-Seon Cho Young-Chul Joo

Sangmyung University, Korea Hanseo University, Korea Kunsan National University, Korea Jeju National University, Korea Konyang University, Korea Seoil University, Korea Namseoul University, Korea Chungwoon University, Korea Soonchunhyang University, Korea

X

Organization

Publicity Committee Dae-seok Rho Hyoung-keun Park Hyun Hahm Sang-young Oh Shin-hyeong Choi Young-choon Kim

Korea University of Technology and Education, Korea Namseoul University, Korea Chungwoon University, Korea Youngdong University, Korea Kangwon National University, Korea Kongju National University, Korea

Program Committee Albert Zomaya Bilha Mendelson BongHee Hong Chao-Tung Yang Chia-Han Lee Ching-Nung Cho-Li Wang Chun-Hsi (Vincent) Huang Damon Shing-Min Liu Dan Grigoras Dan Meng Daniel S. Katz Danilo Gonzalez Dimitrios Serpanos Domenico Laforenza Domenico Talia Eung Nam Ko Farrukh Nadeem Gail-Joon Ahn Geoffrey Fox George Bosilca

Gi-sung Lee Mohamed Jemni Gyoo-soo Chae Mohand-Said Hacid Hai Jin Nabil Abdennadher Heui-seok Lim Omer F. Rana Hung-Chang Hsiao Ramin Yahyapour Hyeong-Ok Lee Ronald Perrott Jan-Jan Wu Ruay-Shiung Chang Javid Taheri Shiow-Yang Wu Jean-Louis Pazat Si-ho Cha Jiannong Cao Susumu Date Jung-won Cho Tom`as Margalef Keecheon Kim Wei-Jen Wang Kenichi Takahashi Won-ho Lee Kuan-Ching Li Yangwoo Kim Kwang-hyoung Lee Yeh-Ching Chung Liria Matsumoto Sato Yeong-Tae Song Marcin Paprzycki Yong Man Ro Marian Bubak A.G.H. Krakow Yongik Yoon Mark Baker Yong-Kee Jun Minglu Li Young Choon Lee Min-Xiou Chen

Table of Contents

Appropriate Education to Improve Computer-Aided Patient Record Ability for Apprentice Doctors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seong-Ran Lee and Kwang-Hwan Kim

1

An Image Processing for Omnidirectional Image Based on the Bresenham Raster Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jin Shin and Soo-Yeong Yi

8

A Novel Half-Bridge Resonant Inverter for CCFL-Driving . . . . . . . . . . . . . Gang-Youl Jeong

17

Quorum Based Mutual Exclusion in Asynchronous Systems with Unreliable Failure Detectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sung-Hoon Park and Seon-Hyong Lee

25

Location Prediction for Grid-Based Geographical Routing in Vehicular Ad-Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Si-Ho Cha and Keun-Wang Lee

35

Hybrid Routing Algorithm Considering Moving Direction of Vehicles in VANETs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Min-Woo Ryu, Si-Ho Cha, Keun-Wang Lee, and Kuk-Hyun Cho

42

Development of an Active Ranging System Based on Structured Light Image for Mobile Robot Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jin Shin and Soo-Yeong Yi

49

A Study on Fault Analysis of Wind Power (DFIG) in Distribution Systems Based on the PSCAD/EMTDC . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jintaek Jeon, Joonho Son, Munseok Lee, Munkbaht, and Dae-seok Rho Poor Periodontal Health in Type 1 Diabetic Youth . . . . . . . . . . . . . . . . . . . Ji-Hye Park, Youn-Hee Choi, Cheol-Woo Ko, Heung-Sik Kim, Sang-Gyu Lee, Hyung-Gyoo Kim, and Keun-Bae Song Performance Evaluation of MapReduce Applications on Cloud Computing Environment, FutureGrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yunhee Kang and Geoffrey C. Fox Development of Evaluation Simulator of New Power Sources Using the LabVIEW Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kyung-Sang Ryu, Byung-ki Kim, Suk-hwan Song, Seob-han Kim, and Dae-Seok Rho

59

67

77

87

XII

Table of Contents

Evaluation Algorithm for Bi-directional Protection Coordination in Distribution System with Distributed Generations . . . . . . . . . . . . . . . . . . . . Sohee Kim, Seongho Gi, Bangwoon Song, Seungkyou Choi, and Dae-seok Rho Development of Remote Monitoring and Control Device for 50KW PV System Based on the Wireless Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jea-Bum Park, Byung-Mok Kim, Jian Shen, Sun-Young Kim, and Dae-Seok Rho

95

102

Operation Method for Protection Devices at the Primary Feeders with New Energy Sources Using EMTDC/PSCAD . . . . . . . . . . . . . . . . . . . . . . . . Hasan, Dae-seok Rho, Chanhyeok Kim, and Yongpeel Wang

110

The Design of AES-DN Embedded Cryptographic Processor for the RFID/USN Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seon-Keun Lee and Sun-Yeob Kim

120

Validity of Developmental Delay Screening Test; Using Latent Class Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seong-A. Lee, Chung-Yill Park, and Kang-Suk Lee

129

An Enhanced Data Privacy Mechanism Suitable for Ubiquitous Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jeong-Hyo Park, Yong-Hoon Jung, Kwang-Hyung Lee, Keun-Wang Lee, and Moon-Seog Jun Grinding Ability and Characteristics in Normal QT Heat Treatment vs. Carbo-nitriding Heat Teatment Bearing Steel under CBN Wheel Grinding Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jeong Jin Kim, Sung Hoon Oh, and Byung Moon So Correlation between Low Polarization and Roughness in Low-k SiOC Thin Films by Chemical Vapor Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . Teresa Oh A Semantic-Based Centralized Resource Discovery Model for Grid Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abdul Khalique Shaikh, Saadat M. Alhashmi, and Rajendran Parthiban Design of Synchronization Model Using Variance Buffer . . . . . . . . . . . . . . . Keun-Wang Lee and Si-Ho Cha Mobility Management Scheme for the Wireless Mesh Network Using Location Server and Mobility Anchor Point . . . . . . . . . . . . . . . . . . . . . . . . . Sung Hwan Jang and Gi Sung Lee

138

148

155

161

171

179

Table of Contents

XIII

The Comparative Study for ENHPP Software Reliability Growth Model Based on Mixture Coverage Function . . . . . . . . . . . . . . . . . . . . . . . . . Hee-Cheul Kim and Hyoung-Keun Park

187

A Study on Authentication and Access Control of Wireless Access Point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jong-Kyung Baek and Jae-Pyo Park

195

A Reliable Multicast MAC Protocol Based on Spread Spectrum Technique in Wireless Ad-Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jeonghun Kim, Junwoo Jung, and Jaesung Lim

202

Relationship between the Binding Energy and Boundary Condition in SiOC Film for ILD Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Teresa Oh, Chy Hyung Kim, and Chi Sup Jung

213

A Framework for Active Service Pattern Mining . . . . . . . . . . . . . . . . . . . . . Jeong Hee Hwang and Mi Sug Gu

219

Character Region Detection Using Structure of Hangul Vowel Graphemes from Mobile Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jong-Cheon Park, Byoung-Min Jun, and Myoung-Kwan Oh

228

Task Scheduling Algorithm with Minimal Redundant Duplications in Homogeneous Multiprocessor System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inseong Song, Wanoh Yoon, Eunmee Jang, and Sangbang Choi

238

RFID Mutual Authentication Protocols Using Key Exchange Based on Hash Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seung-Jae Jang, Young-Hoon Jung, So-Yeon Min, Keun-wang Lee, and Moon-Seog Jun The Development of Passive Filter Control System Based on Power Measurement and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jeong-Chay Jeon, Jae-Hyun Kim, and Jae-Geun Yoo

246

256

Preparation and Characteristics of Poly(St-co-MAA) Microgels . . . . . . . . Young Sik Kim, Sang Hyun Moh, Suk Hyung Cho, Young Jun Kim, and Jea Chul Cho

263

Identifying Organizational Culture for Patient Safety . . . . . . . . . . . . . . . . . Yoo Kyung Boo, Hyeon Sil Lee, Ji Won Park, and Seung Woo Ham

271

The Effect of a Support Program for Family Caregivers of Dementia on Stress Hormones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hyoshin Kim

278

XIV

Table of Contents

The Effect of Educational Game on the Intrinsic Motivation by Learner’s Traits: Focus on Personality Types . . . . . . . . . . . . . . . . . . . . . . . . Hyung-sung Park, Jung-hwan Park, and Young-Tae Kim

286

Detection and Recovery for Kidnapped-Robot Problem Using Measurement Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chuho Yi and Byung-Uk Choi

293

Factors Affecting the Turnover Intentions of Small and Medium Construction IT Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dong Hwan Cho and Haeng Nam Sung

300

Multiple Case Studies on the Critical Success Factors of Internet Banks: Implications for Korean Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dong Hwan Cho and Jin Hyuk Hong

309

Development Plan of Pet Dog Safe Transportation System Using IP-RFID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hyung-Rim Choi, Byoung-Kwon Park, Young-Jae Park, Min-Je Cho, Chang-Hyun Park, Jin-Wook Lee, and Gwang-Rok Kim The Security Process Model of Embedded Systems . . . . . . . . . . . . . . . . . . . Shin-Hyeong Choi

317

329

Electromagnetic Compatibility for U-Healthcare Remote Diagnosis System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eui-Bung Jeoung, Je-Ho Song, and You-Yub Lee

335

Workload Prediction and Weighted Rule-Based Task Scheduling for Face Certification System on Distributed Parallel Computing . . . . . . . . . . Tae Young Kim and Jong Sik Lee

342

A Robust Energy-Efficient Framework for Heterogeneous Datacenters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kittituch Manakul, Simon Chong Wee See, and Tiranee Achalakul

351

A Study on the Electrical Properties of the BaTiO3 Ceramic Thin Films . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ik-Tae Im, Hyun Seob Jo, Sung Hoon Oh, and Byung Moon So

361

Gait Recognition by Multidimensional Representation for Accumulated Silhouette . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seungdo Jeong and Jungwon Cho

368

The Method of Generating Assessment for Intelligent Tutoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Youngseok Lee, Jungwon Cho, and Byung-Uk Choi

376

Table of Contents

A Global Time Synchronization Scheme for Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bongkyo Moon and Jonggoo Bae The Correlations between Addicted Cell Phone Use of Adolescents and Mental Health: The Case of Middle School Students in Korea . . . . . . . . . . Choong-sook Ko, Dong-uk Cheong, Tae-su Park, Young-sik Kang, and Jung-hwan Park Security Requirements of Certificate Validation in Web Security . . . . . . . Hee Bong Choi, Yong Dae Kim, San Yun Han, and Hyuk Joong Yoon The Measurement for the Underwater Electric Field Using a Underwater 3-Axis Electric Potential Sensor . . . . . . . . . . . . . . . . . . . . . . . . . Jae-Hyun Kim, Jeong-Chay Jeon, and Jae-Geun Yoo Implementation of Web Services for ILBS . . . . . . . . . . . . . . . . . . . . . . . . . . . Changyong Han, Jaegeol Yim, and Gyeyoung Lee Petri Net Representation of Ontologies for Indoor Location-Based Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jaegeol Yim, Jaehun Joo, and Gyeyoung Lee A Platform for Real Time Brain-Waves Analysis System . . . . . . . . . . . . . . Eun-Gyu Jeong, Byungin Moon, and Yong-Hwan Lee

XV

383

392

398

408 415

423

431

Design of FCM-Based Fuzzy Neural Networks and Its Optimization for Pattern Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Keon-Jun Park, Dong-Yoon Lee, and Jong-Pil Lee

438

A File Assignment Strategy for Parallel I/O System with Minimum I/O Contention Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bin Dong, Xiuqiao Li, Limin Xiao, and Li Ruan

445

Replication and Checkpoint Schemes for Task-Fault Tolerance in Campus-Wide Mobile Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SookKyong Choi, JongHyuk Lee, HeonChang Yu, and Hwamin Lee

455

Dynamic Arrangement of Control in a Personalized Learning System Based on User Competency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Youngseok Lee, Jungwon Cho, Sungjae Han, and Byung-Uk Choi

468

Design and Implementation of Deduplication Storage Server Using Stride Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ho Min Jung, Jin Kim, Jin Haeng Cho, and Young Woong Ko

475

XVI

Table of Contents

Robust Gait Recognition Using Planar Homography and Shape Sequence Descriptor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seungdo Jeong, Youngseok Lee, Keun-Wang Lee, and Jungwon Cho

484

A Real-Time Distributed Architecture for RFID Push Service in Large-Scale EPCglobal Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Min Sung Kang and Do-Hyeun Kim

489

Heuristic Scheduling Strategies for Linear-Dependent and Independent Jobs on Heterogeneous Grids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Min-Yi Tsai, Ping-Fang Chiang, Yen-Jan Chang, and Wei-Jen Wang Fusion Context Model Based on User Scenario for Smart Service . . . . . . . Svetlana Kim, HagYoung Kim, and YongIk Yoon A Data Processing Framework for Cloud Environment Based on Hadoop and Grid Middleware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hyukho Kim, Woongsup Kim, Kyoungmook Lee, and Yangwoo Kim CloudTSS: A TagSNP Selection Approach on Cloud Computing . . . . . . . Che-Lun Hung, Yaw-Ling Lin, Guan-Jie Hua, and Yu-Chen Hu

496

506

515 525

The Study on Spatial Reuse Efficiency of Network Nodes Using Advanced Power Control Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seung-dae Lee

535

Agents for Cloud Resource Allocation: An Amazon EC2 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Octavio Gutierrez-Garcia and Kwang Mong Sim

544

An Offline Quality Management Strategy to Improve Online Quality of Service for Next Generation Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Calduwel Newton Pitchai, Arockiam Lawrence, and Gang-soo Lee

554

Hegemonic Power and Technology Advancement . . . . . . . . . . . . . . . . . . . . . O.F. Bahrisch and Jin-Suk Kim

562

Adaptive Hand Color Extraction Using Wave Motion Detection . . . . . . . . Kyungboo Jung, Il-Moek Kim, and Byung-Uk Choi

573

A Dynamic Resource Allocation Model for Virtual Machine Management on Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chao-Tung Yang, Hsiang-Yao Cheng, and Kuan-Lung Huang

581

Design of an Iterative Learning Controller of Nonlinear Dynamic Systems with Time-Varying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . In Ho Ryu, Hun Oh, and Hyun Seob Cho

591

Table of Contents

Adaptive Controller Design of the Nonlinear Dynamic Systems with a Neural Networks Compensator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hyun Seob Cho

XVII

597

Effects of Lotus Root Extract on Osteoblast and Osteoclast . . . . . . . . . . . Sang Hyun Moh, Tae Hoon Kang, Suk Hyung Cho, and Young Jun Kim

603

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

613

Appropriate Education to Improve Computer-Aided Patient Record Ability for Apprentice Doctors Seong-Ran Lee1 and Kwang-Hwan Kim2 1

Division of Health, Kongju National University, 182 Shinkwan-dong, Kongju, Chungnam, 314-701, South Korea [email protected] 2 Department of Hospital Management, Kon-yang University, 28 Gasuwon-dong, Seo-gu, Daejeon, 302-718, South Korea [email protected]

Abstract. This study was focused on appropriate education to improve computer-aided patient record ability for apprentice doctors. The records of 225 patients of a total of 4,519 patients, who had been discharged per a month from two tertiary care located in the area of metropolitan hospitals were reviewed to examine incomplete recording rate. In this work, the reduced value in incomplete recording rates after education were plotted as a function of time elapsed after education. The present research showed that although the incomplete recording rates in two hospitals were about 85.1-91.7%, it can be reduced to 43.952.4% by the education. In order to maintain the education effect well, it is very important to determine adequate education period and develop various education programs in consider hospital circumstances. Keywords: Computer-aided patient record, Apprentice doctors, Education, Ability.

1

Introduction

The key of the health information system lies in the computer-aided patient record, which contains all the data concerning diseases and practices[1],[2]. A computeraided patient record has been introduced starting from existing large hospitals and newly established hospitals and its use is now gradually increased in our country[3]. When introducing the computer-aided patient record, though the realization of user friendly system is considered to be important, the most important concern should be to ensure its completeness documented by the system. The goal of the computer-aided patient record may be to provide timely, reliable, complete information, while meeting standards for confidentiality[4]. So the JCAHO recommends the organization review the completeness, accuracy and timely completion of information in patient records at least quarterly for the management of information[4],[5]. The computer-aided patient record has complemented by apprentice doctors. Well prepared patient recording provides a hospital with medical treatment-related T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 1–7, 2011. © Springer-Verlag Berlin Heidelberg 2011

2

S.-R. Lee and K.-H. Kim

information that is useful for the improvement of medical service quality[6],[7], Nevertheless, many apprentice doctors do not their best in performing patient record that is their obligatory duty, resulting in various problems such as delay of medical service or misdiagnosis. Thus, this study was done to examine appropriate education to improve computer-aided patient record ability for apprentice doctors.

2

Materials and Methods

The computer-aided patient records of 225 patients of a total of 4,519 patients, who had been discharged from two tertiary care located in the area of metropolitan hospitals during a period from January 10, 2011 to February 10, 2011, were reviewed to examine incomplete recording rate based on the JCAHO’s Comprehensive Accreditation Manual for Hospitals[5] and some other references[6],[8],[9]. Further, in order to investigate whether doctors realize the importance of computer-aided patient recording, 146 doctors among a total of 477 doctors in study hospitals were survey. Based on the examination result, six clinical departments, which revealed the highest incomplete patient recording rates, were selected from two hospitals. Then 62 doctors were selected from the six clinical departments in two hospitals for education. A patient recoding expert taught doctors from A hospital from March 21, 2011 to April 21, 2011 and a patient recording researcher performed education of doctors from B hospitals from May 9, 2011 to June 9, 2011. The education was performed based on Video, CD-ROM, teaching, case study, discussion, and others for four hours during one month period. Then, the education effect was estimated by the reduction in incomplete recording rate after education, compared with that before education. In this work, the reduced value in incomplete recording rates after education were plotted as a function of time elapsed after education; 7 days, 30 days, 60 days, and 90 days. Then the education effect was characterized as a function of time elapsed after education for six departments of two hospitals. The pairwise t-test was done to compare the incompleteness before and after education in two hospitals.

3

Results

3.1

Basic Informations for Study Hospitals

Table 1 was done to investigate on the basic informations for two study hospitals. The number of beds was 831 for A hospital and 918 for B hospital. Average of bed occupancy rate per a month was 88.7% for A hospital and 93.2% for B hospital. The number of discharge patients per a month was 1,804 for A hospital and 2,715 for B hospital. Average recording times in progress note for a survey period was 4.81 times for A hospital and 4.29 times for B hospital.

Appropriate Education to Improve Computer-Aided Patient Record Ability

3

Table 1. Basic Informations for Study Hospitals

Variables No. of beds Average bed occupancy rate per a month No. of discharge patients per a month No. of apprentice doctors Average recording times in progress note Total numbers of patient records reviewed

3.2

Hospitals A B 831 918 88.7 93.2 1,804 2,715 176 301 4.81 4.29 93 127

A Survey of Computer-Aided Patient Record Incompleteness

Table 2 was done to examine the computer-aided patient record incompleteness in study hospitals. Serious problems, which were most responsible for incomplete patient recording, were the missing of therapeutic plan and the use of abbreviation on the admission and discharge record. The therapeutic plan took 87.1% of the incomplete recording rate for A hospital and the use of abbreviation was 88.2% for B hospital. Table 2. A Survey of Computer-Aided Patient Record Incompleteness Unit : No.(%)

Patient Records Operation and procedure Therapeutic result Therapeutic plan Abbreviation Final diagnosis Complication Laboratory result Therapeutic plan Admission note Impression after physical exam Follow plan Recording rate after 4 days based on length of stay Recording of subjective, objective, assessment and plan Total

Hospitals A 15(16.1) 5(5.4) 81(87.1) 69(74.2) 7(7.5) 45(48.4) 28(30.1) 30(32.3) 6(6.5) 10(10.8) 12(12.9) 9(9.7) 10(10.8) 93(100.0)

B 9(7.1) 27(21.3) 110(86.6) 112(88.2) 6(4.7) 79(62.2) 31(24.4) 28(22.0) 10(7.9) 16( 12.6) 11(8.7) 6(4.7) 8(6.3) 127(100.0)

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3.3

Comparison of the Incompleteness Before and After Education

Table 3 was done the pairwise t-test to compare the incompleteness before and after education in two hospitals. There was significantly higher in the lapse 30 days since the education than before education for A hospital(t=55.4, p 0

3. 4. 5.

key medium decision begin if !(Bover or (key medium playout)) then wait

6.

else

7.

if

8.

M ( p j ) < 1, ∀j : A( p j , t i ) > 0 fire

then

t j , t j : A(t j , p j ) > 0

9. 10.

end if end if

11.

M ( p j ) = M ( p j ) − 1, ∀j : A( p j , t i ) > 0

12.

M ( p k ) = M ( p k ) + 1, ∀k : A(t i , p k ) > 0

13. end 14. end

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As described in firing rule, if transition

ti is fired before removing a token from

its input place, the backtracking is performed for removing forcibly the remained tokens. 3.3

Presentation of Synchronization Model '

Fig. 1 shows an overall view of Petri-nets. 1 (125,3,1,1,1,0,4,1,1,1,1) indicates the information of a control medium. The first parameter 125 refers to the absolute time, 3 the number of key media, and 6 the number of input places. As Control indicates type, Control_in is applicable to all conditions. HS indicates a hierarchical sub module that goes to a lower level module for job processing before going to the next phase. HS goes to a relative duration routine - one of its lower level modules - to find the relative duration before executing After_duration. Therefore, HS has synchronization intervals three times, which results in effective synchronizations in a way that computes the relative duration before executing sync1, computes the compensation time for jitters before executing sync2. Control control_in

(125,3,1,1,1,0,4,1,1,1,1) Control

Check_control

Medium_on

variance_durat ion_time

variance_com pensate_time

HS

HS

Medium

Medium After_ duration

Au1

Au2

After_ jitter

Au2

Control Medium_on

variance_durat ion_time

variance_com pensate_time Medium

Medium I1

After_ duration

Medium_on

variance_durat ion_time

Control

I2 sync 1

After_ jitter

variance_com pensate_time

I3 sync 2

Medium

Medium Ani1

After_ duration

Medium_on

variance_durat ion_time

Ani2

After_ jitter

Ani3

Control variance_com pensate_time Medium

Medium V1

After_ duration

V2

After_ jitter

V3

Fig. 1. Presentation of the synchronization model

Verifications of the proposed multimedia synchronization model have been performed in order to make sure that it corresponds to the analysis methods of Petri-nets including a reachability graph and a matrix-equation.

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3.3.1 Verification of Reachability Graph The reachability tree represents the reachability set of Petri-nets. In the proposed model, the initial marking is (1,1,1,1,0,0,0,0,0,0,0,0) . The transition t1 is generated

t1 is fired, t1 [giving (0,0,0,0,1,1,1,1,0,0,0,0)] is obtainable, and a transition to t 2 is possible. If t 2 is fired, t 2 [giving (0,0,0,0,0,0,0,0,1,1,1,1)] is obtainable. The result of this tree is

from this initial marking. If

represented as follows:

(1,1,1,1,0,0,0,0,0,0,0,0) ↓ t1 (0,0,0,0,1,1,1,1,0,0,0,0) ↓ t2 (0,0,0,0,0,0,0,0,1,1,1,1) 3.3.2 Verification of Matrix-Equation Verifications of the proposed model were performed using matrix-equations as the

D − and D + indicating input function and output function, respectively. Matrices D − and D + are expressed as follows:

second analysis method of the petri-nets. The following are two matrices

111100000000  000011110000 + , D = D− =   . 000011110000  000000001111 And the matrix D is expressed as follows:  − 1 − 1 − 1 − 11111 D = D+ − D− =  . 0000 − 1 − 1 − 1 − 1 The result of applying the equation the following:

μ' = μ + x⋅ D

to the matrices is addressed by

 − 1 − 1 − 1 − 11111 (0,0,0,0,0,0,0,0,1,1,1,1) = (1,1,1,1,0,0,0,0,0,0,0,0) + x  , 0000 − 1 − 1 − 1 − 1  − 1 − 1 − 1 − 11111 (−1,−1,−1,−1,0,0,0,0,1,1,1,1) = x  . 0000 − 1 − 1 − 1 − 1

x is ( x1 , x 2 ) and − 1 = −1 ⋅ x1 , 0 = x1 − x 2 , 1 = x 2 . For this, x1 = 1 , x2 = 1 . Therefore, x1 − x2 = 0 .

Where

Through the verification described above, the proposed synchronization model proved to be consistent with the reachability graph and matrix-equation.

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Synchronization Scheme

This paper proposes a jitter strategy complementing the shortcomings of both the I-strategy and E-strategy in which belatedly arrived frames wait for as long as delay jitter times until they are presented, instead of being deleted unconditionally or waiting indefinitely until they are presented at the next playout time. Fig. 2 shows the proposed jitter strategy that complements the shortcomings of both the I-strategy and E-strategy. As depicted in Fig. 2, the frames b and c are played out in the units 4 and 6 by compensating for the maximum latency or jitter. The frame f indicates that it has not arrived within the variable delay jitter time. In this case, the frame f cannot be played out even if it waits for as long as the variable latency or jitter times. Therefore, the unit 8 in the frame f indicates that it cannot be compensated for the maximum delay jitter time due to excessive delay.

Fig. 2. Proposed strategy applied under reduced network traffic

Fig. 3 shows that the unit 4 in the frame c was skipped due to excessive delay and the unit 5 in the frame d was compensated by applying the maximum delay jitter time.

Fig. 3. Proposed strategy applied under heavy network congestion

Design of Synchronization Model Using Variance Buffer

5

177

Performance Evaluation

With focus on the playout time and loss time, we performed a comparative analysis between the existing scheme and the delay jitter & playout scheme using the maximum delay jitter time. Comparisons among the I-strategy, E-strategy, and the proposed strategy were made in consideration of the following two cases; Circumstances of reduced network traffic and circumstances of heavy network congestion. As a result, the proposed strategy proved to be vastly superior to other two strategies. For the purpose of this paper, we have assumed that the average delay is 100ms and the variance is 20ms in the event of delay in the audio stream. Fig. 4 shows the result of comparisons of playout rates among the I-strategy, E-strategy, and the proposed strategy that were applied under reduced network traffic. The playout rates of the proposed strategy were obtained by conducting experiments ten times. The proposed strategy showed more improved playout rates than the I-strategy and E-strategy by 18.36% and 8.83%, respectively. As shown in Figure 5, under heavy network congestion, the proposed strategy showed more improved playout rates than the I-strategy and E-strategy by 16.57% and 7.38%, respectively.

Fig. 4. Comparison of playout rates under reduced network traffic

6

Fig. 5. Comparison of playout rates under heavy network congestion

Conclusion

This paper has proposed a scheme for implementing intra-media and inter-media synchronizations by means of smooth buffering at the BS in mobile environments. The proposed scheme delivered optimized synchronizations without causing any degradation of quality of service. The superiority of the proposed scheme was demonstrated by extending intra-media synchronization intervals using the maximum delay jitter time of the audio media as a key medium, as well as by synchronizing irregularly arriving packets within the extended intervals through applications of the

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said maximum delay jitter time to inter-media synchronizations. Furthermore, it allows us to take 3D animation training for electrical safety while on the road. Future work needs to focus not only on standard schemes for mobile multimedia synchronizations which take interactions with users into account, but also on optimized synchronization mechanisms which employ minimum buffering.

References 1. Oh, M.-K., Lee, K.-W.: Design and Verification of Multimedia Synchronization Model using PetriNet. Journal of The Korea Academia-Industrial Cooperation Society 11(2) (2010) 2. Boukerche, A., Hong, S., Jacob, T.: MoSync: A Synchronization Scheme for Cellular Wireless and Mobile Multimedia Systems. In: Proc. of the Ninth International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems. IEEE (2001) 3. Lee, K.-W., Lee, J.-H., Lee, K.-H.: Multimedia Synchronization Model for Two Level Buffer Policy in Mobile Environment. In: Yazıcı, A., Şener, C. (eds.) ISCIS 2003. LNCS, vol. 2869, pp. 1060–1068. Springer, Heidelberg (2003) 4. Blakowski, G., Steinmetz, R.: A Media Synchronization Survey: Reference Model, Specification, and Case Studies. IEEE J. Selected Areas in Communications 14(1) (1996) 5. Nam, D.-H., Park, S.-K.: A Smooth Playback Mechanism of Media Streams in Mobile Computing Environment. In: ITC-CSCC 1998 (1998) 6. Yang, C.-C., Huang, J.-H.: A Multimedia Synchronization Model and Its Implementation in Transport Protocols. IEEE J. Selected Areas in Communications 14(1) (1996) 7. Steinmetz, R.: Human Perception of Jitter and Media Synchronization. IEEE J. Selected Areas in Communications 14(1) (1996) 8. Lee, K.-W., Cho, H.-S., Lee, K.-H.: Mobile-Based Synchronization Model for Presentation of Multimedia Objects. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3036, pp. 381–388. Springer, Heidelberg (2004)

Mobility Management Scheme for the Wireless Mesh Network Using Location Server and Mobility Anchor Point Sung Hwan Jang and Gi Sung Lee School of Information & Communication Engineering, Sungkyunkwan University, 300 Cheoncheon-Dong, Jangan-Gu, Suwon, Gyeonggi-Do 440-746, Republic of Korea School of Computer Game, Howon University, 64 Wolha-ri, Impi-mveon, Gunsan-si, Jeollabuk-do 573-718, Republic of Korea [email protected], [email protected]

Abstract. Some WMN researches support mobility using router protocol such as AODV and DSR because WMN has the similar features with Ad-hoc network in terms of operation. However, WMN has got different operations with additional functions of multi-interface and internet connect, it can't be a good method to manage efficient mobility any more. This study suggests method to apply Location Server and MAP function of HMIPv6 to WMN as a way to manage WMN mobility. Through simulation, operation was verified and function was appreciated. Through the results of evaluation, the suggest mobility management had shorter handover delay and packet loss reduced, which improved whole network throughput rate, compared with mobility management using AODV. Keywords: Wireless Mesh Network, Hierarchical Mobile IPv6, Mobility Anchor Point, Mobility Management Scheme, Location Server.

1

Introduction

Compared with the existing wired infrastructure, Wireless Mesh Network (WMN)[1] can be quickly established at inexpensive cost by making use of wireless communications technologies. As shown in Fig. 1, WMN comprises Mesh Routers (MR) and Mesh Clients (MC). A MR, which has usually minimum mobility, forms a wireless backbone and serves as a bridge and a gateway, which help to connect different networks. A MC, which can be stationary or mobile, connects to a MR to receive services. WMN is similar to ad hoc network in the aspect that it features multi hop communications. But, ad hoc network, which communicates between mobile nodes without infrastructure, is connected to WMN and thereby uses the existing single-hop wireless network’s ad hoc routing protocols such as Ad-hoc On-demand Distance Vector (AODV)[2] and Dynamic Source Routing (DSR)[3], so it has difficulties in using handover as it was. Accordingly, it is necessary to find a mobility management scheme suitable to WMN.

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Fig. 1. WMN Structure

In order to provide handover for WMN, this thesis proposes a mobility management scheme for WMN on the basis of Location Server (LS) and Hierarchical Mobile IPv6[4]’s Mobility Anchor Points (MAP). Since the existing client WMN is classified as subnet and MR manages its subnet information, handover delay and packet losses, which are caused by MC’s mobility, reduces. Following this introduction, chapter 2 of this thesis consists of related works, which are conducted on HMIPv6, and the current WMN mobility management schemes. Chapter 3 will explain the algorithm proposed in this thesis, and chapter 4 will show the analysis of the performance results about the proposed method. Lastly, chapter 5 sums up and concludes this thesis.

2

Related Works

HMIPv6 provides regional mobility by defining MAP with new functions and making a hierarchical network structure, and it also defined Regional CoA(RCoA), which a mobile node creates based on MAP domain’s prefix, and On-Link CoA (LCoA), which a mobile node creates based on Access Router(AR)’s prefix. RCoA us a temporary address presenting MAP’s subnet position and LCoA is a temporary address on the basis of a physical location of Access Router (AR). Studies on schemes for WMN other than general mobility management schemes have been conducted, and they are largely divided into Tunnel-based, Routing-based, and Multicast-based schemes. Tunnel-based scheme has Ant[5] and Mesh Mobility Management(M3)[6]; Routing-based scheme has iMesh[7] and Mobile Party[8]; Multicast-based scheme has SMesh[9]. Details about each scheme are presented in Table 1.

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Table 1. 1 Existing Mobility Management Schemes

Type Layer LocationServer Routing Macro mobility Overhead reason Overhead

3

Ant Tunnelbased Layer-3 Yes

M3 Tunnelbased Layer-3 Yes

iMesh Routingbased Layer-2+3 No

Mobile Party Routingbased Layer-3 No

SMesh Multicasttbased Layer-3 No

OLSR No

No No

OLSR No

Mobile Party No

No Yes

Location update Normal

Hierarchical delay Normal

Routing

Routing

Normal

High

Group managemeent High

Mobility Manag gement Scheme in the WMN

Fig. 2 presents a logical stru ucture of WMN proposed in this thesis. Internet compriises Gateway Mesh Routers refflecting MAP function (MAP-GMR) and Location Servver, and Infrastructure/Backbon ne WMN consists of MAP-GMR and MR. Client WM MN consists of MCs and is linkeed to MR to receive communications support.

Fig. 2.. Logical Structure of the proposed WMN

Table 2 indicates a messsage newly proposed and changed for the mobility m management scheme of this thesis. As MC enters the networrk, MR receives Router Advertisement messages which are periodically broadcast. If MR M fails to receive the messages, MC broadcast a Rouuter Solicitation message. Wh hen MR receives the message, it transmits MC C a RouterAdvertisement messaage including MAP-GMR’s prefix and its own prefix. M MC uses MR and MAP-GMR’s prefix to create LCoA and RCoA.

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Message

Field/Flag

Signification

I

Intermediate MCs LCoA update

C

Intermediate MCs Hop Count update

Path Solicitation

S

Destination MC RCoA request

Location Update

R

RCoA update

S

Destination MC RCoA request

A

Acknowledgement (Destination MC RCoA)

Local Binding Update

Location Solicitation Tunnel Setup

T

Tunnel Setup

R

RCoA update

After that, the Local Binding process in which MC is registered in MR and MAPGMR is displayed. MC which entered the network transmits a Local Binding Update message to MR. If it is linked to MR through multi hops, it updates both LCoA and hop count of relay MCs into the message’s I field and C flag and then transmit the updated message. MR updates MC’s LCoA and the LCoA and hop count of relay nodes into Visitor List, and transmits a Local Binding Update Message to MAP-GMR. MAP-GMR performs Duplicate Address Detection (DAD) process regarding RCoA, and updates MC’s RCoA and LCoA into Binding Cache. After that, MAP-GMR sends MC a Local Binding Acknowledgement, and if MR is linked with MC through multi hops, relay MCs’ LCoA is updated into relay MCs’ Binding Cache. In this process, a tunnel between MAP-GMR and MR is established. After that, MAP-GMR sends LS a Location Update message containing MC’s RCoA. LS saves MC’s RCoA into its Location Table, and sends MAP-GMR a Location Update Acknowledgement. The process to start communicating between MCs is presented as follows. To communicate with target MC, source MC sets the S flag of a Path Solicitation message as 1 and then sends it to MAP-GMR. MAP-GMR identifies if the target MC’s RCoA exists in its own Binding Cache, and unless the target MC belongs to its own network, then the MAP-GMR transmits a Location Solicitation message to LS. The LS sends MAP-GMR a Location Solicitation Acknowledgement message containing the information on the target MC. After that, MAP-GMR sends the source MC a Path Solicitation Acknowledgement message containing the target MC’s RCoA. The source MC which received the message updates the target MC’s RCoA into its own Binding Cache, and then transmits the packet. When MC performs micro handover to other client WMN, it receives a Router Advertisement Message which nMR periodically broadcasts. MC creates a new LCoA with nMR’s prefix, but does not create RCoA since the received prefix is equal to the MAP-GMR’s prefix of the MC. And then, the Local Binding process is performed just as the process on the network entrance was. When Binding Life Time expires, pMR prior to MC’s movement and the paths saved into relay MCs’ Visitor List and Binding Cache are removed.

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In the case of Macro handover, MC uses nMR’s prefix and nMAP-GMR’s prefix of a Router Advertisement message received from nMR to create a new LCoA and RCoA. And then, Local Binding process is to be performed. The nMAP-GMR sends LS a Location Solicitation. The LS checks out Location Table, and sends nMAP-GMR a Location Solicitation Acknowledgement message containing pMAP-GMR’s information. The nMAP-GMR sets T flag as 1, saves the targe MC’s new RCoA into R field, and then sends pMAP-GMR a Tunnel Setup message. The pMAP-GMR updates a previous RCoA and a new RCoA into its Binding Cache, and sends a Tunnel Setup Acknowledgement message to the nMAP-GMR. In this process, a tunnel between the pMAP-GMR and the nMAP-GMR is established. When the target MC performs handover, it does not let the source MC register the target MC’s new RCoA, and packets are sent to the target MC through the tunnel between MAP-GMRs.

4

Performance Analysis

The ns-2[10] was used to evaluate the performance of the mobility management scheme proposed in this study. Multi-interfaces and multi-channel allocation function was applied to the existing 802.11 based network to implement WMN, and AODV was used as a routing protocol. In addition, MAP function was applied to mobilwan [11] based WMN to implement the proposed WMN. Fig.3 presents the simulation composition.

Fig. 3. Simulation Structure

Constant Bit Rate (CBR) is used as a packet type, and a packet with 200byte is transmitted in a 20ms cycle. As for Wireless MAC, IEEE 802.11b was applied. The target MC is linked to MR1 one second after the simulation starts; 16 seconds after the simulation, the target MC performs micro handover from MR1 to MR2; at the 31 seconds after the simulation, a macro handover is performed from MR2 to MR3; and finally, at the 46 seconds after the simulation, a micro handover is performed from MR3 to MR4.

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4.1

Performance Analysis

Fig. 4 displays the comparative graph about a delay as the existing WMN and the WMN reflecting the proposed mobility management scheme perform handover, respectively.

Fig. 4. Handover Delay

In the case of the WMN using AODV, when the target MC performs its mobility in the same network, a delay records about 165.28ms and 227.32ms, and when it moves to a different network, a delay records about 1173.79ms. The reason for the high record of delay in the latter case is that the AODV searches for the paths of the entire network again to explore a new path, so the time loss increases. In the case of the proposed mobility management scheme, when the target MC performs micro handover, a delay records about 82.17ms and 82.16ms, when it performs macro handover, a delay records about 102.34ms. The reason for the high record of delay on the performance of macro handover is that the target MC’s network changed, and thus the time of registering mobility information to LS and of establishing a tunnel between MAP-GMRs was added. Fig. 5 presents the graph about packet losses caused by handover.

Fig. 5. Packet losses caused by handover

In the case of the existing WMN using AODV, packets are lost a lot as a node moves to a different network. When the distance between MRs is close, searching

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paths goes fast, but when it is far, the path searching time increases because the entire network should be sheared for a path. In the case of the proposed mobility management scheme, there was no difference in packet losses caused both by micro handover and by macro handover. That is because nMAP-GMR does not let the source MC register the target MC’s mobility, but let only pMAP-GMR register it, and so the time of reconfiguring a path reduces.

5

Conclusions

This thesis proposed MAP function to manage mobility in the existing WMN and a method of applying LS to the existing WMN, and analyzed the performance of the mobility management in the existing WMN and of the proposed mobility management in the WMN. The proposed mobility management scheme reflecting MAP function showed no big difference of handover delay regarding micro handover and macro handover, as MC performs handover. Regarding packet loss rate, the proposed scheme also had better rate than the WMN using the existing ad hoc routing protocol. In addition, in the case of the proposed scheme, the delay of micro handover and macro handover recorded about 82 ~ 102ms, a figure which satisfies the maximum delay of 150ms [12] necessary to provide real-time service. Therefore, the proposed mobility management scheme turned out to be suitable for real-time service. Acknowledgements. This work was supported by the Howon University Fund. 2011.

References 1. Akyildiz, I.F., Wang, X., Wang, W.: Wireless mesh networks: a survey. Computer Networks 47, 445–487 (2005) 2. Perkins, C., Royer, E.B., Das, S.: Ad hoc On-Demand Distance Vector (AODV) Routing. IETF RFC 3561( 2003) 3. Johnson, D., Hu, Y., Maltz, D.: The Dynamic Source Routing Protocol (DSR) for Mobile Ad Hoc Networks for IPv4. IETF RFC 4728 (2007) 4. Soliman, H.: Hierarchical Mobile IPv6 mobility management. Internet Draft (2004) 5. Wang, H., Huang, Q., Xia, Y., Wu, Y., Yuan, Y.: A network-based local mobility management scheme for wireless mesh networks. In: IEEE Wireless Communications and Networking Conference, pp. 3792–3797 (2007) 6. Huang, R., Zhang, C., Fang, Y.: A mobility management scheme for wireless mesh networks. In: IEEE Global Telecommunications Conference, pp. 5092–5096 (2007) 7. Navda, V., Kashyap, A., Das, S.R.: Design and evaluation of iMesh: an infrastructuremode wireless mesh network. In: Sixth IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, pp. 164–170 (2005) 8. Sabeur, M., Sukkar, G.A., Jouaber, B., Zeghlache, D., Afifi, H.: Mobile party: A mobility management solution for wireless mesh network. In Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, pp. 45–53 (2007)

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9. Amir, Y., Danilov, C., Hilsdale, M., Musaloiu-Elefteri, R., Rivera, N.: Fast handoff for seamless wireless mesh networks. In: Proceedings of the 4th International Conference on Mobile Systems, Applications and Services, pp. 83–95 (2006) 10. Network Simulator version 2, http://www.isi.edu/nsnam 11. MobiWan: NS-2 extensions to study mobility in Wide-Area IPv6 Networks, http://www.inrialpes.fr/planete/pub/mobiwan 12. The VOIP quality standard applying the wideband network, Telecommunications Technology Associations, TTAK.KO-01.0136 (2008)

The Comparative Study for ENHPP Software Reliability Growth Model Based on Mixture Coverage Function Hee-Cheul Kim and Hyoung-Keun Park Department of Industrial & Management and Electronics Engineering, Namseoul University, Korea {kim1458,phk315}@nsu.ac.kr

Abstract. Finite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. Accurate predictions of software release times and estimation of the reliability and availability of a software product require quality of a critical element of the software testing process: test coverage. This model called Enhanced non-homogeneous Poisson process (ENHPP). In this paper, exponential coverage and S-shaped (Yamada-Ohba-Osaki) model was reviewed, proposes modified mixture model, which make out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on SSE statistics for the sake of efficient model, was employed.1 Keywords: Test Coverage, ENHPP, Mixture model, SSE.

1

Introduction

In the software testing phase, if the access repair software failure phenomenon by number of software failures and time between failures, the valuation of the software can be more easily. Reliability assessment measures (the number of software failures, software fault occurs every time, software reliability and failure rate) is estimated that in the future failure times may predict. To modeling the number of software failures in the observation time, non-homogeneous Poisson process (NHPP) is widely have been using qualitative. These NHPP, intensity function and the mean function are dependent on [1]. In order to release times of software forecast or predict the effectiveness and reliability, using important factor, test coverage, in the process of software testing, effective testing can work. These models from the existing NHPP models included test coverage. In this model, ENHPP (Enhanced non-homogeneous Poisson process) is called [2]. In this study, the lifetime distribution for finite failure ENHPP model with the exponential model and the Goel-Okumoto S- shaped pattern and mixture models used, present a measure to estimate the reliability based on this to evaluate the characteris1

Funding for this paper was provided by Namseoul University.

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tics of reliability. Section 2 of this paper as a test-related research coverage, simple coverage and proposed mixture model was described. Parameter estimation methods of ENHPP model in section 3 was listed. In section 4, the actual failure data for each model was analyzed. Finally, conclusion was listed in section 5.

2

Related Researches

2.1

Test Coverage on Software Reliability

Test coverage on software reliability can be applied to finite failure NHPP. This applies to the extended model is called ENHPP. ENHPP model developed states that the rate at which the faults are detected is proportional to the product of the rate at which potential fault sites are covered and the expected number of remained faults, the ENHPP model is based on the following basic assumptions [2]: (A1) Faults are uniformly distributed over all potential faults sites. (A2) When a potential fault- site is covered, any fault present at that site is detected with probability K (t ) . (A3) Repairs are affected instantly and without the introduction of new faults. This assumption is the same as in case of finite NHPP models. (A4) Coverage is a continuous monotonic non-decreasing function of testing time. d m(t )  d c(t ) = a K (t ) dt dt

(1)

or t

 K (τ ) c ' (τ ) dτ

m(t ) = a

,

(2)

0

Where a is defined as the expected number of faults that would be detected given infinite testing time, perfect faults detection coverage implying K (t ) = 1 , and complete test coverage leading to lim t →∞ c(t ) = 1 . If K (t ) is assumed to be a constant value K , denoted by m(t ) is given by m(t ) = a K c (t )

(3)

Equation (3) is intuitively simple – the expected number of faults detected by time is equal to the expected total number of faults in the product times the probability of detecting a fault given that it is covered times the fraction of potential faults sites covered by time . Substituting a = a K , Equation (3) can be written as m(t ) = a c(t )

This results in a failure intensity function λ , given by

(4)

The Comparative Study for ENHPP Software Reliability Growth Model λ (t ) =

d m(t ) = ac '(t ) dt

189

(5)

The failure intensity function can also be rewritten as [2, 3]: λ (t ) = [a − m(t )]

c '(t ) 1 − c(t )

(6)

Equation (6) clearly shoes that failure intensity depends not only on the number of remaining potential fault sites are covered divided by the current population of uncovered potential fault sites. From Equation (6), the failure occurrence rate per fault or hazard function h(t ) is given by h(t ) =

c '(t ) 1 − c(t )

(7)

Therefore, the testing process models as ENHPP is each rate of failure that can occur because of the time-varying time-dependent as the rate of failures can occur. In addition, this hazard function at time t that corresponds to the distribution function can be evaluated in the coverage. These ENHPP model for (incomplete) coverage for the function can be applied to practical situations c(∞) < 1 . As a result, the conditional reliability R(t | s) is known as follows [2, 3]: R(t | s ) = e

Where

2.2

t denote

−

s +t

s

mission time and

λ (t ) dt

s is

=e

−

s +t

s

a c '(t )

= e − a[c( s + t ) − c( s )]

(8)

the last failure time.

NHPP Model with Coverage Function (ENHPP)

2.2.1 Exponential Coverage Function Well-known Goel-Okumoto model [2, 3] is the basic model in this field. The failure occurrence rate per fault is constant. If access to these NHPP model, the mean value function and intensity function is known as follows: m(t | a, g ) = a (1 − e − g t ) (a > 0, g > 0)

λ (t | a , g ) = a g e − g t

(9) (10)

Where a can be denoted the expected number of faults that would be detected given infinite testing time in case of finite failure NHPP model and g is search ratio rate per fault. Therefore, the coverage function c (t ) and the hazard function h (t ) are derived as follows: c (t ) = 1 − e − g t

(11)

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h(t ) =

c '(t ) =g 1 − c(t )

(12)

2.2.2 S- shaped Coverage Function The S-shaped coverage function was known as Yamada-Ohba-Osaki the model [2, 4]. If access to these NHPP model, the mean value function and intensity function is known as follows: m(t | a, g ) = a[1 − (1 + gt )e− g t ] (a > 0, g > 0)

(13) (14)

2 −g t

λ (t | a , g ) = a g t e

Therefore, the coverage function function is derived as follows:

c (t )

and the hazard function

h (t )

for the S-shaped

c(t ) = 1 − (1 + gt )e − g t

h(t ) =

2.3

(15)

2

c '(t ) g t = 1 − c(t ) 1 + g t

(16)

Proposed Mixture (Transformation) Coverage Function

Software systems grow in complexity, it may cause a malfunction of one does not happen only by the intensity function is several possible causes could be detected. In this case, if you know the intensity function and the mean value function can be modeled. In other words, case of the distribution of the lifetime of components or systems with different groups of k subpopulations, mixture model can be applied to in field. For example, a combination of the entire population with parts or manufacturing defects early failure group and the normal group, consisting of two groups of cases are those in the mixed model. In this mixed-model, probability density function and the distribution function consist of the form of a weighted sum. In other words, the distribution function and probability density function is expressed as follows [6]: F (t | Θ, p ) = Pr(T < t ) =

Where



k i =1



k i =1

pi Fi (t | θi )

(17)

pi = 1 .

Therefore, the mean value function and intensity function of the coverage mixed pattern for exponential and S- shaped can be expressed as follows: −g −g λ (t ) = pλ 1(t ) + (1 − p)λ 2 (t ) = a[ pg1 e 1t + (1 − p) g22 t e 2 t ]

m(t ) = pm 1(t ) + (1 − p) m 2 (t ) = ap (1 − e

Where a > 0 , p ≥ 0

g1 > 0

and g 2 > 0 .

−g t 1

) + a(1 − p)[(1 − g2 t ) e

−g t 2

(18) )]

(19)

The Comparative Study for ENHPP Software Reliability Growth Model

Therefore, the coverage function c(t ) and the hazard function sition function is derived as follows: c(t ) = ( p − 1)(1 + g 2 t ) e

h (t ) =

pg1 e pe

3

−g t 1

−g t 1

−g t 2

− pe

−g t 1

2

−g t 2

+ (1 − p ) g 2 t e

+ (1 − p )(1 + g 2 t ) e

191

for the superpo-

h(t )

+1

(20) (21)

−g t 2

Parameter Estimation for ENHPP

Given the time truncated model testing until time t , the order epochs of the observed n failure times are denoted by x1 , x2,, xn . The likelihood function is given by

∏

LNHPP ( a, g | Dt ) = [

n k =1

λ ( xk )exp(− m(t )]

(22)

Where D t = {x1 , x2,, xn ; t} is the data set? In this paper, using the maximum likelihood estimation (MLE) parameter estimation is carried out. Log likelihood function on exponential coverage model, using equation (22) is expressed as follows:

∏

LE xp (a, g | Dxn ) = (

n k =1

age

−g x

k )exp[− a (1 − e − g xn )]

(23)

To use the MLE this log likelihood function is derived as follows. ln LE xp (a, g | Dxn ) = n ln a + n ln g − g

Using equation (24), the likelihood estimators following equation:



aˆ MLE

n − gˆ xn =1− e aˆ n = gˆ



n k =1

xk + aˆ xn e

n k =1

xk − a(1 − e

and

−g x

gˆ MLE

n)

(24)

must satisfy the

(25) − gˆ xn

(26)

Using (25) and (26) equation, numerically solve the two parameters can be obtained Similarly, the likelihood estimators aˆ MLE and gˆ MLE for S- shaped (Yamada-OhbaOsaki) must satisfy the following equation: n − g xn = 1 − e− g − gˆ xn e a n = g



n k =1

xk + a gxn 2 e

− gxn

(27) (28)

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When using equation (18) and (19), the likelihood function for mixture model is expressed as follows:

∏

LMix (a, g | Dxn ) = [

n k =1

pλ 1( xk ) + (1 − p )λ 2 ( xk )] exp[ pm 1( xn ) + (1 − p ) m 2( xn )]

(29)

−g −g Where λ 1(t ) = a g1 e 1t , m 1(t ) = a (1 − e 1t ) , a > 0 , g1 > 0 and g 2 > 0 .

λ 2 (t ) = a g 2 2 t e

−g t 2

, m 2(t ) = a[1 − (1 + g2 t ) e− g 2 t ] ,

p is

constant.

Using equation (29), the likelihood estimators aˆ MLE , pˆ MLE satisfy the following equation:

, gˆ1MLE and

gˆ 2MLE

−g x −g x n = ( p − 1)(1 + g 2 xn ) e 2 n − pe 1 n + 1 a

  4

−g x −g x 1 k − pg 1 k −g x 1xk e = pa xn e 1 n −g x −g x −g x k =1 2 2 g1 e 1 k p − p g 2 xk e 2 k + g 2 xk e 2 k

( p − 1)[ g 2 xk e

n k =1

(30)

pe

n

g1 e

−g x 1 k

2

−g x 2 k

p − p g 2 xk e

(2 − g 2 x k )]

−g x 2 k

2

+ g 2 xk e

−g x 2 k

= a (1 − p )(1 − g 2 xn 2 ) e

must

−g x 2 n

(31)

(32)

Software Failure Data Analysis

In this chapter, using actual fault data is to analyze the model. Failure data NTDS [10] (Naval Tactical Data System) is caused by a software failure data Goel and Okumoto [3], Mazzuchi and Soyer [7, 8 and 9] and using the failure data has proposed a software model. This study will also want to use this data. In this paper, for the parameter estimation was performed using the MLE estimation. Nonlinear equations, numerical analytical method of calculating was used to bisection method. These calculations and the initial value 1 and 10−5 , tolerance for width of interval 10−5 , ensuring the availability of sufficient convergence and repeated 100 times, using C-language, parameter estimation was carried out. In table 1, result of parameter estimation for Goel-Okumoto model coverage, S- shaped (Yamada-Ohba- Osaki) coverage, depending upon the mixed is summarized. Table 1. Parameter Estimators of each model

Table 2. SSE of each model

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In general, model selections in this area as a way of model bias trend (mean squared error; SSE [9]) are available. In Table 2, this result of SSE is summarized. In this table, mixture model are relatively efficient compared to Goel-Okumoto model and S- shaped coverage as p-value decreases.

Fig. 1. Hazard function of each model

Fig. 2. Hazard function of mixture model

Fig. 3. Coverage function of each mode

Fig. 4. Reliability of each mode

In Figure 1, the hazard function for each model is a drawing. In this figure, the exponential coverage model is constant, S-coverage model and mixed model (p = 0.5) have the form of increases shows. In Figure 2, the hazard function for the mixed model as p-value decreases have a high probability pattern. In Figure 3, the coverage function for each model is a drawing. In this figure, ENHPP assumed in the model as a continuous forging is shown in the form of non-decreasing function. In Figure 4, patterns for the reliability are discussed. This figure, too, has shown highly mixed model.

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Acknowledgment. Funding for this paper was provided by Namseoul University, Korea. Dep. Industrial & Management and Electronics Engineering is also gratefully acknowledged.

5

Conclusions

In this paper, ENHPP of mixed model, existing NHPP models to include test coverage, were studied. In other words, the superposition and mixed model of the failure occurrence rate per fault with reduction form were suggested. Using time between failure data, maximum likelihood estimation for existing models, mixed model were carried out. Numerical example, the relatively large number of faults measured data, NTDS, was analyzed. Using mean squared error, the comparing results of efficient model, mixed model than existing models have shown that better. Thus, mixed ENHPP model that can be used effectively in the field of the model was able to find that.

References 1. Lawless, J.F.: Statistical Models and Methods for Lifetime Data. John Wiley & Sons, New York (1981) 2. Gokhale, S.S., Trivedi, K.S.: A time/ structure based software reliability model. Annals of Software Engineering 8, 85–121 (1999) 3. Goel, A.L., Okumoto, K.: Time-Dependent Error-Detection Rate Models for Software Reliability and Other Performance Measures. IEEE Trans. on Reliability R-28(3), 206–211 (1979) 4. Yamada, S., Ohba, M., Osaki, S.: S-Shaped Reliability Growth Modeling for Software Error Detection. IEEE Trans. on Reliability R-32(5), 475–485 (1983) 5. Cinlar, E.: Introduction to Stochastic Process. Prentice-Hall, New Jersey (1975) 6. Berry, D.A., Stangl, D.K.: Bayesian Bio-statistics, pp. 601–616. Marcel Dekker Inc. (1996) 7. Mazzuchi, T.A., Soyer, R.: Bays Empirical Model for Software Reliability. IEEE Transactions on Reliability 37, 248–254 (1988) 8. Gokhale, S.S., Philip, T., Marinos, P.N., Trivedi, K.S.: Unification of Finite Failure NonHomogeneous Poisson Process Models through Test Coverage. In: Proc. of Int. Symposium on software Reliability Engineering, White Plains, NY (1996) 9. Horgan, J.R., London, S., Lyu, M.R.: Achieving Software Quality with Testing Coverage Measure. IEEE Computer, 60–69 (September 1994) 10. Kuo, L., Yang, T.Y.: Bayesian Computation of Software Reliability. Journal of the American Statistical Association 91, 763–773 (1996)

A Study on Authentication and Access Control of Wireless Access Point Jong-Kyung Baek1 and Jae-Pyo Park2 1

Course of Computer Science, Soongsil University, 1-1 Sangdo-Dong, Donggak-Ku, Seoul, Korea 2 Graduate School of Information Science, Soongsil University, 1-1 Sangdo-Dong, Donggak-Ku, Seoul, Korea {jkbaek,pjerry}@ssu.ac.kr

Abstract. When a client is accessing a wireless AP, many methodologies which prevent from external attack or internal information leakage using only authorized wireless AP after deciding authorized/unauthorized have proposed. But there are securities and cost problem in server based authentication, and availability problem in client based authentication. In this paper, we use MAC Address to get an authentication from an AP. It searches the NDIS Intermediate Driver from the wireless network card, and then controls the packets after operating the scope of IP and PORT. We have implemented the proposed model, and it showed no more security and cost problem. It also showed that client based authentication availability is very flexible and has high scalability. Keywords: Wireless Authentication, Access Control, Network Driver, MAC Address, Access Point.

1

Introduction

These days, Smartphone, Wibro(Wireless Broadband Internet), HSDPA(High Speed Downlink Packet Access), and other wireless technologies are developed and widely used. Because of that, the number of attacking wireless device vulnerability continues to grow. Leaking internal information to external case has also found using wireless AP mobility and scalability. There are proposals such as server based control against wireless AP attack, wireless sensor used, client side control, etc. But these have security vulnerabilities and less controlling availability in external network. In this paper, we propose that the client decides authorized/unauthorized AP by authenticating wireless AP and we increase strength of the security by controlling the unauthorized AP packets and also increase availability by using part of IP and PORT.

2

Wireless Access Point Control

Server based wireless AP controlling method has authentication server, and the server is monitoring packets and checking the packets if those are wireless packets which T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 195–201, 2011. © Springer-Verlag Berlin Heidelberg 2011

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J.-K. Baek and J.-P. Park

goes out externally. Only internal wireless AP is controllable but external wireless AP. It is hard to control if an internal authorized person takes a notebook out to external and send information. We need additional device to monitor packets, therefore it is expensive than client based. The controlling method using device controls by placing a sensor which can control wireless AP in controlling area. 500 AP's can be controlled per a sensor and it is possible to defend around 20 wireless threats at the same time. Because of the sensor are placed in each areas, it is easy to track problems. But it is very vulnerable when the sensor goes out of the range or internal authenticated person sends information after taking the notebook to the external area. It costs a lot by adding sensors to broadening the range. Client based control is needed to supplement the limitation and the cost. Client decides if the AP is authorized or not with MAC Address whenever client tries to get access from the AP. But it has less availability by using only permit/block against wireless AP. An event though unauthorized wireless AP, it needs availability in secure way. Also, because of the client based, it has vulnerable security by attacking malicious code, hacking tool and reversing tool.

3

Wireless AP Authentication and Control Method

3.1

Security Model Configuration and Definition

In this paper, we have configured security model to authenticate and control wireless AP. The model is shown as follows in Fig.1

Fig. 1. Wireless AP Authentication Security Model

There are three different modules such as Server(AW-Server), Client User Lever(AW-Client), Client Kernel Level(NDIS Intermediate). Network transmitting packet goes out like User-level Client, TDI Layer, NDIS Layer, Network device in this order. AW-Server receives requested wireless authenticating AP information from AWClient and send only authorized AP information to AW-Client. AW-Client receives authorized wireless AP list from AW-Server, after that client decide if the AP is

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197

authorized when it access to wireless AP. It controls network packets by the policy after receiving unauthorized wireless AP diver name. 3.2

Wireless Authenticating Process in Server

After checking authorized list from AW-Client, and it authorized internal network wireless AP, and then it updates and send it to AW-Client. Client update the list as soon as it receives, it deletes unauthorized wireless AP information. 3.3

Wireless AP Authentication Process in Client

When SW-Client is initiating, it receives authorized wireless AP information and stores it in the memory. If it monitors wireless AP access and detect access request, it checks the network card and finds connected network card. It requests MAC Address to related wireless network card and decide if it is authorized wireless AP. In case of unauthorized wireless AP, if it finds device name by searching network card, it sends network card's MAC Address to NDIS Filter Driver using the device name. When NDIS Filter Driver sending a data, if it is unauthorized network card MAC Address, it controls the network packet by the unauthorized policy. 3.4

NDIS Intermediate Driver Packet Control Policy

Unauthorized wireless AP controlling scope is calculating the value of the OR and AND operations with each policy IP and PORT scope. If it blocks unauthorized wireless AP unconditionally, the availability lowers so that it increase the availability by permitting part and decide the policy to maintain the security. This is an example of the control policy operation as follows. We made unauthorized wireless AP to use internal network FTP Server (192.168.1.224, 21 PORT) and internet(80 PORT) Possible IP scope goes in the first operation and PORT scope in second. Next condition will be decided in third, and if there is anymore next decision, then decide the control scope after AND operation. First IP scope and second PORT scope set the control scope after OR operation. When sending packets, it detects packet from NDIS Intermediate Driver, and it blocks if it is violated the policy by scanning IP and PORT, in other case, it permits and makes the availability high.

4

Implementation and Performance Analysis

4.1

Implementation Environment and Method

The test environment configured as table 1, except wireless AP device, we have added the smart phone which switches in AP device using WiBro tethering technology.

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J.-K. Baek and J.-P. Park Table 1. Test Enviroment Detail

O/S

Server

Windows 2008 R2

Client Wireless AP Smart Phone Wibro

Windows 2000NT ~ Windows 7 (x86/x64) I-OS/ Android -

Language

Note

C/C++/JSP

MS-SQL 2008

C/C++/ASEM/ Kernel C

DWA-140/Dell Wireless 1520 Wireless-N WLAN Mini-Card

-

DWL-3200AP

-

I-Phone 4/ Galuxy-S

-

DM-MR100

Client side shows wireless AP authentication requested list, and we configured the WEB(JSP) to authenticate for the requested wireless AP. Authenticated wireless AP is stored in file and DB(MS-SQL 2008), and stored information sends to AW-Client. We have used C/C++ language to configure the AP authentication process in AWClient, and ASEM/Kernel C language was used to control unauthorized wireless AP. Smart phone and WiBro device was used as connecting device which can turn wireless AP into AP device. After implemented DB and WEB server in the server, we have set unauthorized wireless AP control policy. The policy reflects if these Usage, explanation, IP scope, PORT scope, used time, used date are set. We need to set the test client PC area, but because we need to assort authorized and unauthorized, we need more than 2 of them. It the policy is established, we need to install client SW-Client. Wireless authentication registration and unauthorized wireless AP scenario are as follows.

① Install wireless LAN card more than 1 in client. ② Request wireless AP to authenticate to SW-Server. ③ Bring the possibly connecting list and signal through wireless LAN card if the

wireless AP search button in clicked. And after selecting the wireless LAN on the list, it request to the server by clicking. The requested wireless AP to the server is able to check on the WEB page as Manager click delete button if it is inappropriate wireless AP, if it is not, manager checks in check box and reflects the policy.

④

Fig. 2. Wireless AP Registration in Server

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199

⑤ Client tries to contact wireless AP, and unauthorized wireless AP is controlled by policy Blocked wireless AP requests to use if it is wireless AP, it leads to use different IP and PORT by changing controlling policy. 4.2

The Implementation Result and Performance Evaluation

The test environment configured as table 2, except wireless AP device, we have added the smart phone which switches in AP device using WiBro tethering technology. We have checked if it controls by connecting to authorized wireless AP and unauthorized wireless AP. As soon as the AW-Client module start, it bring the OS version and list of wireless AP. If the wireless AP is connected, it performs the authentication process after wireless network card. If the wireless AP is connected, it compares with authenticated list. And it blocks unauthorized wireless AP and inform to user by using alert window if it is not on the list. Control methods for known wireless AP; one is blocking relevant frequency using electronic jamming device in server, and the other one is placing authentication server to control in NAC level. In client side, there is control method for wireless AP by installing program to user PC. We have compared securing method in the server and controlling method by using proposed model in table 2. Table 2. Control Method and Suggestion Model in Server Detail control method scope limitatino external network normal scalability cost

frequency based frequency high

NAC based NAC high

proposed model Client control none

normal

low

high

low high

low low

high low

Frequency based range is until the disturbing frequency, and NAC based scope is in internal network range, if a user goes out with a notebook it can't be controlled. But, because of the proposed model controls in client level, it is possible to control external packets. Therefore, if it goes out externally, frequency and NAC based external networks have security vulnerability. Because it is limiting frequency and NAC based scope, scalability is very low, cost will incurred by installing the devices, but the proposed model doesn't need the cost because it controls the program itself. In table 2, Server based controlling method is very vulnerable if it is located outside, client side controlling method was chosen. We have compared client controlling method and proposed model in table 3.

200

J.-K. Baek and J.-P. Park Table 3. Control Method and Suggestion Model in Client Detail confidentiality availability scalability program security

client based high low low low

proposed model high high high high

Between client based and proposed model show high confidentiality, but it also showed many differences in availability, scalability and program security part. The client based show high security strength on unauthorized AP by controlling permit/block, it can't control every each packet. But security model can use internet, FTP while maintaining the security strength by manager setup and can monitor after saving used logs. Because of client based only blocks, it cannot expand, but proposed model can expand additional functions such as mail, messenger, WEB hard, etc Because of the proposed model only authenticates User-Level and controls in Kernel-Level, it doesn't get attacked by debugs. Proposed model doesn't have limitation compared with frequency, NAC based, it also can secure in external network, and it is cost effective. Also, the availability is higher than client based model, it is also high in scalability and it has strong security in program.

5

Conclusion

In wireless authentication, existing methods have security weaknesses and low availability in limited scope. Therefore, in this paper, we proposed client based model which can authenticate vulnerable server based external network and beyond the scope. And also, we have installed network driver to increase availability by controlling packets instead of permit/block method. Proposing models can have various polices depends on the packet algorithm, and it can have high availability by users having different policies. Wireless based services have continuously developed, whereas various attacks have increased. We should control the wireless packets and prevent various attacks and leakage from the internet information by mixing server based authentication and client authentication method.

References 1. Industrial Network Team : Windows Device Driver Programming. SamYangBook, pp. 571–578 (2000) 2. ANSI/IEEE Std 802.11 : Wireless LAN Medium Access Control(MAC) and Physical Layer(PHY) Specification (1999)

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3. Lee, J.H., Lee, M.S., Ryou, J.H.: Implementation of a Secure Wireless LAN System using AP Authentication and Dynamic Key Exchange. Korea Information Processing Society 11-C(4), 497–508 (2004) 4. Kwo, J.H., Park, J.T.: User Pre-Authentication Method for Support of Fast Mobility in IEEE 802.11 Wireless LAN. The Institute of Electronics of Korea 44(11), 191–200 (2007) 5. Kim, M.H., Lee, J.W., Choe, Y.G., Kim, S.J.: DoS-Resistance Authentication Protocol for Wireless LAN. Korea Institute of Information Security 14(5), 3–10 (2004) 6. Go, Y.M., Kwon, K.H.: Detecting and Isolating a Cloned Access Point IEEE 802.11. The Korea Contents Association 10(5), 45–51 (2010) 7. Song, C.L., Jung, B.H.: Wireless LAN Security Mechanism. Korean institute of Information Scientists and Engineers 20(4), 5–13 (2002) 8. IEEE Standard for Port Based Network Access control, IEEE Draft P802.1X/Dll (1998) 9. IEEE Standard 802.11i: Medium Access Control(MAC) Security Enhancements, Amendment 6 to IEEE Standard for Information technology - Wireless LAN Medium Access Control (MAC) and Physical Layer(PHY) specifications. IEEE (2004) 10. Im, S.C.: A Study of Hand-off Scheme Using Mutual Authentication between APs in Wireless LAN Environments. Korean Institute of Information Technology 8(9), 95–101 (2010)

A Reliable Multicast MAC Protocol Based on Spread Spectrum Technique in Wireless Ad-Hoc Networks Jeonghun Kim, Junwoo Jung, and Jaesung Lim Ajou University, Woncheondong san 5, Suwon, Korea

Abstract. We propose a reliable multicast MAC protocol based on the directsequence code-division multiple access (DS-CDMA) scheme to enhance channel efficiency in wireless ad-hoc area networks. In conventional multicast MAC protocols based on IEEE 802.11 standards using RTS-CTS handshake exchange CTS and ACK packets consecutively for reliable transmission. Although this protocol guarantees transmission reliability by avoiding the hidden node (HN) problem, multiple CTSs and ACKs degrade network efficiency. The multicast sender must receive multiple CTSs and ACKs from all multicast receivers in its own multicast group for reliable multicast. In the proposed protocol, CTS/ACK messages of multiple receivers can be transmitted concurrently through the DS-CDMA mechanism. The proposed protocol reduces the total transmission delay due to the reduction of the overhead caused by consecutive CTSs and ACKs. The proposed protocol is thoroughly examined by simulation and theoretical methods. Results show that the proposed protocol significantly reduces the aforementioned overhead, and thus improves the performance of wireless ad-hoc networks. Keywords: DS-CDMA, Multicast MAC, Reliability, Wireless ad-hoc networks.

1

Introduction

Mobile Ad hoc networks (MANETs) have recently been the topic of extensive research. The lack of infrastructure and the peer-to-peer nature of ad hoc networking make random access protocols the natural choice for medium access control in ad hoc networks. Indeed, most proposals of MAC protocols for ad hoc networks are based on the random access paradigm; in addition, the CSMA/CA scheme was selected by the IEEE802.11 committee as the basis for its standards due to the inherent flexibility of this scheme [1]. Multicasting is an efficient communication service for supporting multi-point applications in the Internet. In mobile Ad hoc network (MANET), the role of multicast services is potentially even more important due the bandwidth and energy savings that can be achieved through multicast packet delivery [2].

T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 202–212, 2011. © Springer-Verlag Berlin Heidelberg 2011

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Various multicast schemes have recently been introduced into wireless communication systems to improve channel efficiency. The multicast technique achieves better channel efficiency in transmitting the same packets to receivers than the unicast method. Unlike broadcast, whose transmission targets all devices within radio range, multicast targets devices belonging to a particular receiver group. Several issues, including reliable multicast MAC protocol design, challenge the design of a multicast MAC protocol. IEEE 802.11 standards specify multicast services, as well as unicast services in layer 2. However, most parts of the protocol specifications are limited to the unicast environment, and multicast has mainly been studied to focus on the relatively higher layers, such as the network and transport layers, as seen in [3-4]. Several MAC layer multicast protocols [5-8] have been introduced. However, they have some problems including MAC overhead and reliability. Until now, there was a tradeoff between guaranteeing reliability and reducing overhead in multicast MAC protocols. In this paper, we propose a new reliable multicast MAC protocol assisted by DSCDMA, to reduce the multicast MAC overhead with guaranteeing reliability. When multiple receivers receive the RTS/DATA message from a sender, each receiver sends a CTS/ACK message spread with a given DS-CDMA code. This MAC protocol can reduce the MAC overhead of multiple CTSs/ACKs via concurrent transmissions. The proposed protocol is thoroughly examined by simulation and analytical methods. The results show that it significantly reduces the aforementioned overhead, and thus improves the performance of the system. The remainder of the paper is structured as follows. Section 2 reviews previous wireless multicast research. In section 3, the proposed protocol is detailed. In section 4 and 5, we evaluate the proposed protocol with mathematical and simulation methods, respectively. Finally the conclusion is given in section 6.

2

Related Works

2.1

Multicast MAC Protocol Based on IEEE 802.11

Multicast aware MAC protocol (MMP) [6] and a multiple access collision avoidance protocol for multicast services [7] were proposed for reliable multicast. These kinds of protocol use multiple CTSs and ACKs sent by each receiver for reliable transmission, as shown in Fig. 1(a). This can be reliable. However, it increases overhead caused by the reception time of multiple CTSs and ACKs. Leader-based protocol (LBP) was proposed for multicast to reduce the overhead [8]. In this work, when a sender transmits a packet, it selects a leader station among the multicast group member stations. Then, only the leader responds with the CTS and ACK corresponding to the RTS/DATA message. If any member station receives the packet with an error, it transmits a NACK message to the sender. This NACK causes a collision with the ACK. LBP, as shown in Fig. l(b), can reduce overhead.

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However, when one of the member stations fails to demodulate the multicast RTS, the leader CTS, or the MAC header, it cannot send a NACK, since it cannot recognize if the received packet is multicast and if it is the destination of the packet. Since the station does not send a NACK, no collision is experienced at the sender. Therefore the LBP may not be reliable in terms of the detection of failed transmission.

(a)

(b) Fig. 1. Example scenarios of (a) Reliable multicast with multiple CTSs and ACKs and (b) LBP (Leader Based Protocol)

Orthogonal frequency division multiplex (OFDM) based multicast ACK (OMACK) was proposed to reduce MAC overhead and to guarantee reliability [9]. This protocol uses one OFDM symbol for ACKs from all member stations. Each member station indicates its packet reception status by utilizing a sub-carrier within the OFDM symbol. This can reduce MAC overhead. However, this can only be adopted for small-sized packet transmissions. Until now, the research on the multicast MAC protocol based on IEEE 802.11 standards has been conducted to reduce overhead or to guarantee reliability. There is a tradeoff between the overhead reduction and guaranteeing reliability. 2.2

Research on Wireless Ad-Hoc Networks Based on Spread Spectrum Technique

In a CDMA system, each user signal is spread over a large bandwidth using PN (Pseudo-random Noise) sequence, or codes with low cross-correlation, before transmitting over the channel. It can help multiple stations can transmit their own packet concurrently. However, the use of spread spectrum technique is restricted by the constraints imposed due to power control, synchronization, hardware complexity of the medium access protocol. When CDMA is used in ad-hoc networks, where multiple contending links try to transmit simultaneously, the near-far effect causes multiple access interference (MAI) and leads to collisions and packet loss.

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Most of the earlier schemes suggested for medium access in CDMA networks did not consider the effect of multiple access interference, and they assumed that two overlapped transmissions spread using two unique spreading codes do not collide with each other [10, 11, 12, 13]. Orfanos et al. [14] propose a broadcast scheme using Multi-Carrier CDMA (MC-CDMA) where available frequency channel is divided into multiple narrow-band sub-channels, and a node which has to send packets to multiple nodes in the neighborhood, can use all of the sub-channels to simultaneously transmit packets to all of such nodes. In contrast to the model assumed in the above schemes, in practice, two or more simultaneous links can concurrently communicate if and only if the SIR at the receiver of each link is above the required threshold. Due to the near-far effect and the asynchronous channel, spreading of the signal only minimizes the effect of interference but cannot completely eliminate it. Minimum separation and power control of the contending links is thus necessary for them to communicate concurrently. ElBatt and Ephremides [15] propose a centralized link scheduling policy for power control in CDMA/TDMA ad-hoc networks. The effect of multiple access interference has been considered by Muqattash and Krunz [16] in the design of CDMA-based medium access protocol for ad-hoc networks. Muqattash et al. make use of the multiple access interference suppression provided by the binary phase code modulated CDMA channel employing random spreading sequences [17], for increasing the link-packing capability of the network. [18 ] assume the use of multiuser receiver, specially minimum mean square equalizer (MMSE) receivers, by each node in the network. Multiuser receivers are known for their near-far effect resistance and asymptotic efficiency.

3

Proposed MAC Protocol

The previous section mentioned the multicast MAC problem that we want to solve. In this section, we propose a new multicast MAC protocol, to reduce the multicast MAC overhead, while guaranteeing reliability, assisted by the DS-CDMA scheme. We adapt the assumption of [ 18]. To lessen MAI effect, each station use MMSE for multi user detection. And let us assume the chip synchronization of the allocated codes coincide with each other for the successful reception of CTS/ACK messages, and each station is able to control the power of the transmission, as the processing gain varies. The mechanism of the proposed multicast MAC protocol is as follows. When multiple receivers receive a RTS/DATA message from a sender, each receiver spreads a CTS/ACK message with the allocated code based on DS-CDMA. Each code allocated to multiple receivers is given by the multicast RTS (mRTS) message from the sender. After waiting for the SIFS time, each receiver sends the CTS/ACK message concurrently. The specific feature of the proposed protocol compared to conventional protocol using multiple CTSs/ACKs is as follows. First, the proposed MAC protocol can reduce the overhead of multiple CTSs and ACKs through concurrent transmissions, as shown in Fig 2(a). However, a single CTS/ACK time spread with the given code is

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longer than the previous ones. Additionally, the number of multicast group members is restricted by the codelength. If the number of multicast group members exceeds the supportable number, the CTS/ACK is sent at least twice. If the codelength is enlarged, the CTS/ACK time is longer. We will compare this influence in later section in this paper. Also, this protocol can reduce both transmission and carrier sensing range of multiple receivers through the processing gain of the codes as shown in Fig. 2(b). This can elevate the spatial reuse to avoid packet collision between stations.

(a)

(b) Fig. 2. (a) Proposed MAC Protocol (b) Mitigating carrier sensing range assisted by DS-CDMA

In the following section, we will show the margin between the multiple consecutive CTS time and the proposed spread CTS time and find the appropriate codelength, as the number of multicast group members is varied. The contribution of the proposed multicast MAC protocol is this protocol can reduce the MAC overhead while guaranteeing reliability.

4

Performance Evaluation

4.1

Throughput Analysis

An analytical model is suggested for throughput calculation under saturation. That is, each station immediately has a packet immediately available for transmission [19].

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This work models the MAC events of successful transmission, collision and idle times. The backoff process is then modeled by a two-dimensional Markov chain. τ is probability that the station transmits a packet in a randomly chosen slot time. p is probability of a collision seen by a packet being transmitted on the channel. The following two equations are found for τ and p.

τ=

2(1 − 2 p) (1 − 2 p)(W + 1) + pW (1 − (2 p) m ) p = 1 − (1 − τ ) n −1

(1) (2)

where n is the number of contending stations, W = CWmin, Let m, “maximum backoff stage”, be the value, such that CWmax = 2mW, and let us adopt the notation Wi = 2iW, where i ∈ (0,m) is termed the “backoff stage”. Solving these equations using numerical methods, one can find τ and p based on the known values of W, m, and n. PT = 1 − (1 − τ ) n

PS =

nτ (1 − τ ) n −1 nτ (1 − τ ) n −1 = PT 1 − (1 − τ ) n

(3)

(4)

Let PT be the probability that there is at least one transmission in the considered slot time. The probability Ps that transmission occurring on the channel is successful, We now express throughput, S as the ratio S=

E[payload informatio n transmit ted in a slot time] E[ slot ]

(5)

where, E[slot] denotes the length of a slot time. As E[P] is the average packet payload size, the average amount of payload information successfully transmitted in a slot time is PTPSE[P]. E[slot] is obtained considering that, with probability 1 − PT, the slot time is empty; with probability PTPS it contains a successful transmission, and with probability PT(1 − PS) it contains a collision. Hence, (5) becomes

S=

PT PS E[ P] (1 − PT )σ + PT PS TS + PT (1 − PS )TC

(6)

Here, TS is the average time the channel is sensed busy (i.e., the slot time duration) due to a successful transmission and TC is the average time the channel is sensed busy by each station during a collision. σ is the duration of an empty slot time. In conventional multiple CTS/ACK systems, like [6]-[7], we can express TS and TC as follow. TSbas = RTS + n( SIFS + δ + CTS ) + SIFS + δ + H + E[ P ] + n( SIFS + δ + ACK ) + DIFS + δ TCbas

= RTS + DIFS + δ

(7)

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Let H = PHYhdr + MAChdr be the packet header, and δ be the propagation delay. In the proposed protocol, TS is modified. TSmc = RTS + SIFS + δ + CTS mc + SIFS + δ + H + E[ P] + SIFS + δ + ACKmc + DIFS + δ

(8)

If the number of receivers (R) exceeds the codelength (LC), the CTS/ACK has to transmit more than twice. If LC < R, equation (8) has to be modified.  R  TSmc = RTS +   ( SIFS + δ + CTS mc ) + SIFS + δ  LC  R + H + E[ P] +  ( SIFS + δ + ACK mc ) + DIFS + δ  LC  4.2

(9)

Packet Delay Analysis

We use an approach similar to [20] to derive the average delay of the successful packet transmissions. The average delay for a successfully transmitted packet is defined to be the time interval from the time the packet is at the head of its MAC queue ready to be transmitted, until an ACK for this packet is received. If a packet is dropped, as it has reached the specified retry limit, the delay time for this packet will not be included in the calculation of the average delay. The average packet delay E[D] for packets is given by

E[ D ] = E[ X ]E[ slot ]

(10)

Where, E[slot] is given by (6) and (7), E[X] is the average number of slot times required for successfully transmitting a packet and is given by W −1

 Wi + 1  i bi k =  ⋅ 2  k =0 i =o  m

E[ X ] =









 Wi + 1   Wi + 1    ⋅bi.0  = 2 2     i =o m

=

W −1

i  Wi − k   Wi + 1      ⋅ bi.o  W  2  i  i =0  k =0 

m



2

 Wi + 1    ⋅ bi.0 2   i =o m



2  W + 1  2  b W ⋅ 2 m + 1  m  i 0.0  p p b ⋅ ⋅ + ⋅ ⋅    i 0.0    2  2  1 − p   i =o   m

=

=



m −1 

 i  Wi + 1   τ ⋅ (1 − p ) p ⋅   2  i =0  



2

 m  Wm + 1    +τ ⋅ p   2  

2

2 2  m−1   W i + 1   W +1     + p m ⋅  m = τ  (1 − p ) p i ⋅    2     2        i =0 



Where, bi.k value can be referred to [19].

(11)

A Reliable Multicast MAC Protocol Based on Spread Spectrum Technique

5

209

Numerical Results

The analytical model and simulation results are implemented by a simulator to verify the performance of the proposed protocol. We use the MATLAB simulator. Table 1 summarizes the values of the parameters used to obtain the numerical results for the analytical model and the simulation runs. The values of these parameters are based on the IEEE 802.11a standard [21]. The proposed protocol is compared to the conventional protocol using multiple CTSs and ACKs, such as [5] and [6]. We only compare this basic multiple CTSs/ACKs protocol, since we are only interested in a reliable transmission, as explained in section 1. Among the protocols explained in section 2, the only protocol supporting a reliable service with RTS-CTS handshake is the MAC protocol using multiple CTSs/ACKs, such as [5] and [6].

Table 1. Simulation Parameter Values

Parameter

Value

Parameter

Value

CWmin

15μsec

MAC header

272bits

CWmax

1023

PHY header

46bits

RTS time

52μsec

Preamble

16μsec

SIFS time

16μsec

CTS/ACK time

44μsec

DIFS time

34μsec

Data rate

6Mbps

Slot time

9μsec

Packet payload

8192bits

δ

1μsec

Fig. 3 shows the variation of the throughput of the proposed MAC and the multiple CTS/ACKs MAC with the number of multicast receivers (R), where n = 50, the codelength is 8,16 and 32, the R is varied from 5 to 50. The performance of the proposed MAC is better than for the basic one. Furthermore, as the R value is higher, the gain difference between the basic protocol and the proposed one gets bigger and bigger. The throughput of both decreases as the number of multicast receivers increases. The curve of the proposed scheme is bent at a point (the point that the number of multicast receivers exceeds the codelength) of multiple numbers of codelength, because the CTS/ACK is sent more than twice.

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bas Lc=8 Lc=16 Lc=32

Throughput

0.6

0.4

0.2

10

20

30

40

50

Number of Multicast Receivers(R) Fig. 3. Number of multicast receivers and throughput 0.8

bas Lc=8 Lc=16 Lc=32

Delay(sec)

0.6

0.4

0.2

10

20

30

40

50

Number of Multicast Receivers(R)

Fig. 4. Number of multicast receivers and packet delay

Fig. 4 shows the packet delay time, calculated by equations (10) and (11), as R increases from 5 to 50. Fig. 4 shows the delay time increases with increasing R. The curves of the proposed scheme are also bent at the point of the multiple number of the codelength.

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211

Conclusion

In this paper, we proposed a new reliable multicast MAC protocol over the IEEE 802.11-based ad-hoc networks. The major innovation over previous reliable multicast protocols using consecutive CTSs/ACKs is that this can send multiple CTS/ACK messages concurrently via the spread spectrum technique. When multiple receivers receive a RTS/DATA message from a sender, they respond with a CTS/ACK message spread with the pre-assigned code in the same time interval. This can reduce the overhead of multiple CTSs/ACKs. Our proposed protocol and the multiple CTSs/ACKs based multicast protocol were compared and evaluated using an analytical method and simulation. The proposed protocol outperforms the conventional protocol based on multiple CTSs/ACKs in terms of throughput and delay. That is, our proposed protocol provides an efficient reliable multicast MAC mechanism with minimum overhead. Acknowledgments. "This research was supported by MKE (The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency" (NIPA-2011-C1090-0902-0003).

References 1. Chlamtac, I., Conti, M., Liu, J.: Mobile ad hoc networking:imperatives and challenges. Ad Hoc Networks J. 1(1) (January-March 2003) 2. De, M.C.C., Gossain, H., Agrawal, D.P.: Multicast over wireless mobile ad-hoc networks: present and future directions. IEEE Network (2003) 3. Obraczka.: Multicast transport protocols: A survey and taxonomy. IEEE Commun. Mag. 36(1), 94–102 (1998) 4. El-Sayed, A., Roca, V., Mathy, L.: A survey of proposals for an alternative group communication service. IEEE Netw. 17(1), 46–51 (2003) 5. Sum, M.T., Huang, L., Arora, A., Lai, T.H.: Reliable MAC layer multicast in IEEE 802.11 wireless networks. Wireless Commun. Mob. Comput. 3(4), 439–453 (2003) 6. Gossain, H., Nandiraju, N., Anand, K., Agrawal, D.P.: Supporting MAC layer multicast in IEEE 802.11 based MANET’s: Issues and solutions. In: IEEE LCN, pp. 172–179 (2004) 7. Lee, K.-H., Cho, D.-H.: A Multiple access collision avoidance protocol for multicast service in mobile ad hoc networks. In: Vehicular Technology Conference (2003) 8. Kuri, J., Kasera, S.K.: Reliable multicast in multi-access wireless LANs. Wireless Netw. 7(4), 359–369 (2001) 9. Kim, B., Kim, S.W., Ekl, R.L.: OFDMA-based reliable multicasting MAC protocol for WLANs. IEEE Transactions on Vehicular Technology 57(5), 3136–3145 (2008) 10. Bertossi, A.A., Bonuccelli, M.A.: Code assignment for hidden terminal interference avoidance in multihop packet radio networks. IEEE/ACM Transactions on Networking 3(4), 441–449 (1995) 11. Jiang, S., Hsiao, M.-T.T.: Performance evaluation of a receiver-based handshake protocol for CDMA networks. IEEE Transaction on Communications 43(6), 2127–2138 (1995)

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12. Jao-Ng, M., Lu, I.-T.: Spread spectrum medium access protocol with collision avoidance in mobile ad-hoc networks. In: IEEE INFOCOM, vol. 2, pp. 776–783 (1999) 13. Rodoplu, V., Meng, T.H.: Position based CDMA with multiuser detection (PCDMA/MUD) for wireless ad hoc networks. In: Proceedings of IEEE International Symposium on Spread Spectrum Techniques and Applications, vol. 1, pp. 336–340 (2000) 14. Orfanos, G., Habetha, J., Liu, L.: MC-CDMA based IEEE 802.11 wireless LAN. In: Proceedings of 12th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (MASCOTS 2004), pp. 400– 405 (2004) 15. ElBatt, T., Ephremides, A.: Joint scheduling and power control for wireless ad-hoc networks. In: Proceedings of IEEE INFOCOM, vol. 2, pp. 976–984 (2002) 16. Muqattash, A., Krunz, M.: CDMA-based MAC protocol for wireless ad hoc networks. In: Proceedings of the 4th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2003, June 1-3, pp. 153–164. ACM, New York (2003) 17. Sousa, E., Silvester, J.A.: Optimum transmission ranges in a direct-sequence spreadspectrum multihop packet radio network. IEEE Journol on Selected Areas in Communications 8(5), 762–771 (1990) 18. Hasan Raza Naqvi, S., Patnaik, L.M.: A medium access protocol exploiting multiuserdetection in CDMA ad-hoc networks. Wireless Netw. 16, 1723–1737 (2010) 19. Bianchi, G.: Performance analysis of the IEEE 802.11 distributed coordination function. IEEE J. Select. Areas Commun. 18, 535–547 (2000) 20. Chatzimisios, P., Boucouvalas, A.C., Vitsas, V.: IEEE 802.11 packet delay – a finite retry limit analysis. In: IEEE Globecom, San Francisco, USA (2003) 21. Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Higher-Speed Physical Layer in the 5 GHz Band, IEEE Std. 802.11a (1999)

Relationship between the Binding Energy and Boundary Condition in SiOC Film for ILD Application Teresa Oh1,*, Chy Hyung Kim2, and Chi Sup Jung3 1

Semiconductor Engineering Department of Applied Chemistry 3 Department of Laser and Optical Information Engineering, Cheongju University 36 Naeduckdong Sangdangku, Cheongju 360-764, Korea [email protected] 2

Abstract. Low dielectric constant SiOC film was deposited by the chemical vapor deposition. To research the change of polarization by the chemical reaction between opposite polar sites, the synthesized SiOC films at various flow rates of BTMSM were studied by Fourier Transform Infrared Spectroscopy, X-ray diffraction patterns, X-ray photoelectron spectroscopy, and Scanning Electron Microscope. By using these analytical methods, the correlation between the chemical shift and boundary condition on surfaces of SiOC film was observed. The binding energies of Si 2p, C 1s, and O 1s in SiOC film with the low polarization were higher than other films, and the 2θ value in X-ray diffraction pattern was also moved to the higher degree in the film of the lowest polarity. The chemical shift was caused by the polarization due to the lowering of the ionic polarization by the chemical reaction between alkyl and hydroxyl group as polar sites. The Scanning Electron Microscope of SiOC film showed an image of flat surface without pin-holes due to the low surface energy, which improves the properties of SiOC film to become an ideal low-k material. On the other hand, SiOC film with low binding energies of Si 2p, C 1s, and O 1s displayed the pin-holes in SEM images owing to the existence of polar sites. The grain boundary such as pin-holes indicates that an unbalance exists in the uniformity of electron distribution on a surface and a low degree of amorphism. Keywords: SiOC film, FTIR spectra, SEM, XRD, XPS spectra.

1

Introduction

To get high-speed integrated circuits, insulating materials with low-k (low dielectric constant) is required, which can be replaced instead of SiO2 film because of a lot of problems such as signal noise, cross talk, and power consumption in small size devices. SiOC film as a low-k material is made by the SOD (spin on coating) or CVD (chemical vapor deposition). SiOC film as a promising low-k material possesses a lower dielectric constant due to the presence of lighter C and H atoms, as opposed to Si and O atoms [1-4]. The reduction of dielectric constant in the SiOC film by the *

Corresponding author.

T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 213–218, 2011. © Springer-Verlag Berlin Heidelberg 2011

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CVD originates from the decrease of the polarization in the film. Low polarization is essential to low-k materials, which become the amorphous structure to a great degree because of the reduction of the electron density that resulted from the C-H bond elongation effect and the carbon insertion in Si-O network. Therefore, SiOC film must satisfy a number of requirements including a low dielectric constant of the film to be successfully integrated. The dominant fraction of the C-H bond length behavior comes from the straightforward electrostatic polarization of the proton donor. Regarding to the chemical shift of hydrogen bonds, electrostatic interactions illustrates a major portion of the shift in FTIR spectrum, with polarizabilities increasing the shift further. The C-H bond should also be weakened upon the hydrogen bond formation and elongated. The extent of the chemical shift has been correlated with the strength of the hydrogen bond, the proton donor-acceptor distance, and the ionization potential of the proton acceptor [5-7]. SiOC film is usually prepared by using the CVD such as ICP (inductive coupled plasma)-CVD, CCP (capacitive coupled plasma)-CVD, and TCP (transformer coupled plasma)-CVD. Each pattern of FTIR spectrum has some differences in accordance with the plasma energy during the deposition. However, in order to understand the C-H bonding effect, the results obtained from the FTIR spectra of SiOC films in previous studies are not enough to understand the bonding structure. Moreover, for the analysis of SiOC film more than two kinds of analyzers are needed [8-10]. The CVD-SiOC film was analyzed by the Fourier transform infrared spectroscopy, X-Ray Photoelectron Spectroscopy, Scanning Electron Microscope, and X-ray diffraction. The results were utilized focusing on the relationship between boundary conditions and physical-chemical properties based on the incorporation of carbon compounds at the SiOC film formation. To research the surface properties of SiOC film, the pentacene was deposited on the SiOC film, because the trend of pentacene growth provided us the information such as electron distribution in SiOC films depending on the roughness of surface or weak boundary condition.

2

Experiments

The carbon doped silicon oxide films were obtained by the capacitive coupled plasma chemical vapor deposition (CCP-CVD) using the mixed gases of oxygen and bistrimethylsilylmethane (BTMSM). The precursor of bistrimethylsilylmethane was purchased from the Applied Materials Corporation. The deposition on substrate was carried out at 100℃ for 10 sec. The BTMSM was vaporized and transferred by argon gas at 35 ℃ through a thermostatic bubbler. Although the carbon doped silicon oxide films were prepared at various flow rate ratios of BTMSM precursors, the oxygen gas flow rate was kept at 60 sccm. The base pressure was 3 Torr and the rf power was 450 W in each experiment. To study on the surface properties of SiOC film, the pentacene was deposited on SiOC film by thermal evaporation at the base pressure of 1 Torr approximately, due to the dependence of the substrate properties on the growth patterns of pentacene molecule. The substrates were held at 80 °C and the pentacene was deposited at 0.1~0.3 nm/s, with a final average thickness of 40 nm. The chemical shift

Relationship between the Binding Energy and Boundary Condition in SiOC Film

215

was researched by Fourier Transform Infrared spectrometer (FTIR, Galaxy 7020A) and X-Ray photoelectron spectroscopy (XPS, ESCALAB 210). The surfaces of films were measured by using the Scanning Electron Microscope (HITACHI S-4200) and the crystalline structure was studied by utilizing X-ray diffractometer (D/MAX2200H, Rigaku).

3

Results and Discussion

Figure 1(a) shows the FTIR spectra of SiOC film at various flow rate ratios. The BTMSM flow rates are 24~28 sccm respectively, and the oxygen gas flow rate is 60 sccm. Sample number indicates the BTMSM gas flow rate of 24~28. In the range of 1400~1800 cm-1, the figure 1(b) is the normalized FTIR spectra. The intensity of Si-CH3 bond near 1250 cm-1 decreased at the sample 25. The reduction of intensity of Si-CH3 bond is due to the dissociation of the bond by plasma energy and then the nucleophilic reaction between alkyl group and hydroxyl group because dissociated sites induce to decrease the polarization and the chemical shift in FTIR spectra. The main peak near 1110 cm-1 observed in FTIR spectra originates from the dissociation of Si-CH3 groups broken by the oxygen of neighboring high electronegative atom. Then the Si-O bond or O-CHn(n=1, 2, 3) in SiOC film is formed. The O-CH bond changes to the Si-O-CH bond because the Si atom attacks O-CH bond in accordance with the increase of BTMSM flow rate. Thus, the weak Si-O-C bond displays the chemical shift in FTIR spectra. The main bond of 970~1190 cm-1 of samples 26~28 is the Si-O-C broad bond due to the recombination of the dissociated radicals. The chemical shift observed between 25 and 26 samples, which showed the red shift owing to the increase of the intensity of left shoulder in the main bond of Si-O-C. The chemical shift of SiOC film provides the information about the polarity of the final materials. Non-polarization is one of the conditions to make an amorphous structure as a low-k material. In terms of the wavelength, the red shift results from the decrease of the polarization and low electron density due to the C-H bond elongation, which results in the weak boundary condition and changes the surface energy as described in Figure 2. (b)

Absorbance (arb.units)

Absorbance (arb.units)

(a) as deposited films O2 = 60 sccm

BTMSM(sccm)= 24 25 26 27

24 25

26 27

28

28 750

1000

1250

1500 -1

Wave number (cm )

1750

700

800

900

1000

1100

1200

1300

-1

Wave number (cm )

Fig. 1. FTIR spectra of SiOC films at various flow rate ratios, (a) samples of 24~28 in the range of 650~2000 cm-1, (b) normalized FTIR spectra

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After the pentacene deposition, the SEM shows the images of many grains on SiOC film at various flow rate ratios. The pentacene is deposited with gradient growth depending on the surface’s properties of the substrate. The gradient growth strongly influences the grain formation on the surface of SiOC film in accordance with the polarity [8]. The samples 24 and 25, which have the characteristic of polarization produced by hydroxyl, show the SEM images of a number of grains involving the pinholes after the pentacene deposition on SiOC film. The pin-holes induce the electronic localization inside the film causing a higher surface energy and providing less flat surface. The samples 27 and 28 display images of the small grains. These grain boundaries and pin-holes prevent the carrier transfer for the devices and reduce the mobility of the carrier [10]. However, sample 26 observed first in the chemical shift displays a relatively weak boundary and large grain due to the coalescence of small grains, so the surface becomes flatter than other samples. This weak boundary condition is strongly related to the low polarization. So, the non-polar property of the SiOC film results from a nucleophilic reaction and C-H bond elongation due to the electron deficient group, improving the surface property by lowering the density of the grain boundary.

Fig. 2. SEM images after pentacene deposition on SiOC film in accordance with the increase of the precursor, BTMSM

Figure 3 indicates the Si 2p, C 1s, and O 1s spectra obtained by using X-Ray photoelectron spectroscopy (XPS). Of the samples 24~28, sample 26 in which the chemical shift occurs first, increases the binding energies of the Si 2p, C 1s and O 1s the most, owing to the reduction of polarization that causes the fine cross link structure and flat surface. So, the bonding structure of sample 26 is stronger than that of other samples. Therefore, the sample 26 demonstrating the chemical shift has a low surface energy due to the decreased polarity and weak boundary condition, which improve the hardness of the film surface. Figure 4 is the X-ray diffraction of SiOC film at various flow rate ratios of BTMSM. X-ray diffraction pattern near 54.4 degree of 2 theta (2θ) changed with increasing the flow rate of precursor, BTMSM.

Relationship between the Binding Energy and Boundary Condition in SiOC Film

217

104.8 286.8

(a) Si 2p

535.0

(b) C 1s

(c) O 1s 534.8

104.4

104.2

104.0

286.6

Binding Energy (eV)

Binding energy (eV)

Binding Energy (eV)

104.6

286.4

286.2

534.6 534.4 534.2 534.0 533.8

103.8 286.0 22

22

24

26

28

24

26

533.6

28

22

BTMSM flow rate (sccm)

BTMSM flow rate (sccm)

24

26

28

BTMSM flow rate (sccm)

Absorbance (arb.units)

Fig. 3. X-Ray photoelectron spectroscopy of SiOC film at various flow rate ratios, (a) Si 2p electron orbital spectra, (b) C 1s electron orbital spectra, (c) O 1s electron orbital spectra

23 26 27 29 34 51

52

53

54

55

56

57

58

o

2 Theta ( )

Fig. 4. X-ray diffraction of SiOC films at various flow rate ratios of BTMSM

The XRD pattern of sample 26 shows the highest peak intensity near 54.4 degree, and most of the samples display the XRD peaks below 54.4 degree. The bonding structure between alkyl and hydroxyl group fabricated by the chemical reaction becomes finer cross link structure like amorphous structure of non-polarization and induces the reduction of the dielectric constant of SiOC film. Therefore, there is a close relationship between the low polarization and dielectric constant in low-k materials.

4

Conclusion

The red chemical shift in FTIR and the increase of the binding energy of C 1s orbital in XPS occurred in accordance with changing the flow rate ratios of precursor in the CVD-SiOC film. The growth of pentacene on surface of SiOC film depends on the properties of polarity of SiOC film. The terminal bond of Si-CH3 in the precursor (BTMSM) was dissociated by the plasma energy. When the terminal bond was not dissociated and deposited on the substrate surface, the polarity of SiOC film increased. Thus, the SEM of pentacene/SiOC film with polar sites showed the images of many pin-holes caused by the electron rich group. As lowering the polarity from sample 25 to sample 26, the grain size decreased and then the coalescence of the grains were induced, so the final film became the flat surface because of the weak boundary

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condition and improvement of the uniformity, which is one of the important factors to fabricate the small sized-semiconductor device. The binding energy of C 1s orbital in XPS also increased in SiOC film as the polarization was lowered. Low polarity is an important key in synthesizing SiOC film, which induces the flat surface owing to the low surface energy and weak boundary condition, thus forming a strong bonding structure in the film.

References 1. Navamathavan, R., Choi, C.K.: Journal of the Korean Physical Society 48, 1675–1679 (2006) 2. Cho, S.J., Bae, S., Boo, J.H.: Thin Solid Films 518, 6417–6421 (2010) 3. Oh, T., Choi, C.K.: Journal of the Korean Physical Society 56, 1150–1155 (2010) 4. Grill: Diamond and Related Materials 10, 234–239 (2001) 5. Oh, T., Kim, C.H.: IEEE Trans. Plasma Science 38, 1598–1602 (2010) 6. Damayanti, M., Widodo, J., Sritharan, T., Mhaisalkar, S.G., Lu, W., Gan, Z.H., Zeng, K.Y., Hsia, L.C.: Materials Science and Engineering B 121, 193–198 (2005) 7. Yu, L.D., Lei, S., Dong, Z.S., Yi, W., Yan, L.X., Qi, H.R.: Chin. Phys. Soc. 16, 240–245 (2007) 8. Huang, W.J., Li, B.Q., Zuo, J.M.: Surface Science 12, 157–160 (2005) 9. Li, H.M., Zhang, G., Yoo, W.J.: Thin Solid Films 518, 6382–6384 (2010) 10. Oh, T.: IEEE transactions on Nanotechnology 5, 23–29 (2006)

A Framework for Active Service Pattern Mining* Jeong Hee Hwang1 and Mi Sug Gu2 1

Department of Computer Science, Namseoul University Database/BioInformatics Laboratory, Chungbuk National University [email protected], [email protected] 2

Abstract. It is important to consider both location and time information which is related to all object and user activity to supply suitable services to users in ubiquitous computing environments. In this paper, we design a spatial-temporal ontology considering user context and propose system architecture for active mining user activity and service pattern. The proposed system is a framework for active mining user activity and service pattern by considering the relation between user context and object based on trigger system. Keywords: Ontology, Context-awareness, Spatio-temporal Ontology, Active Mining.

1

Introduction

In the ubiquitous environment, each computer which has the functions such as information collection, process, and communication, is connected functionally and spatially each other, so it needs the method to provide the necessary information or services to the users immediately[2], [3]. In the previous works, there have been context ontology generation which considers the user context[1], [4], and also researches about the data mining technologies using the ontology[7], [8]. In this paper, to provide the behavior patterns and the optimum services according to the user context, we construct the ontology about the spatio-temporal information which is related with the user behavior. And also we propose active mining framework based on spatiotemporal ontology which uses trigger function of the event based intelligent movement performance. This framework is the architecture which provides the most suitable services according to the user context and can automatically find out the user behavior and the service pattern which is provided to the users, so it can be a basic research to provide the intelligent services. The proposed framework has a characteristic that includes ECA(Event-Condition-Action) rule based trigger system responding automatically to the events according to the user context which is collected from sensor devices. Ubiquitous computing is the basic technology which connects with all the objects including people functionally and spatially and then provides the necessary information or services immediately [1], [2]. *

Funding for this paper was provided by Namseoul university.

T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 219–227, 2011. © Springer-Verlag Berlin Heidelberg 2011

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In Ubiquitous computing there are software architectures for the context recognition service such as ECA pattern[9], multi agent architecture, blackboard pattern and so on. ECA pattern of them has some advantages for the event based process. Therefore, our mining engine combined with trigger system is based on the context information and performs the active mining of the user behavior and service pattern by the trigger event. Accordingly, this paper is not only basic research for providing the intelligent information service needed in Ubiquitous environment but also can be useful for basic information which can provide the optimum service according to the user behavior pattern. This paper comprises as follows. In chapter 2, we explain the context ontology which considers the user activities and context proposed in this paper. And in chapter 3, we propose the active service pattern mining framework and show the example of active rule. And in chapter 4, we explain the mining algorithm and the experimental result. Finally, in chapter 5 conclude this paper.

2

Context Ontology Design

Recognizing the context information is modeling the environment and the objects that are around the users and providing the service based on this. Time exists with space together. The action of the people is related to the time and space[10], [11]. Spatio-temporal movement pattern is the location pattern of the moving objects, and also it is the spatio-temporal rules that are personalized according to the user location and can provide the suitable contents and service[6], [10]. Therefore, through the ontology which considers the time and space together, it is needed to provide the service to the user behavior. The temporal topology relation which analyzes the temporal relation includes the similar definition to the topology relation of the space, Allen[11] defines thirteen temporal relation operators such as before, after, starts, started_by, finishes, finished_by, during, contains, meets, met_by, equals, overlaps, overlapped. In this paper, we used eight major basic temporal relation operators. Spatio-temporal ontology considers temporal phase relation, spatial phase relation [10] and also the relationships between the user behavior and the objects about the spatio-temporal phase relation. Fig. 1 shows the whole domain of the context ontology to provide the service recognizing the context information of the user in home. Sptio-temporal ontology is classified into SPACE ontology and TIME ontology. and the spatio-temporal information of the user is generalized through relation ontology which connects the relationships between time and space. To express the spatiotemporal based user information we define the spatio-temporal information, the user domain and the bounds as follows. Definition 1. The sptio-temporal based context information of the user, U includes three phases such as object, spatio-temporal information, behavior, and also contains the identifier for recognizing of the only object. It is represented as U={OID, (S, T), A}. Here, OID is the object identifier used by the user, S is the space, T is time, and A is the user activity and context.

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The spatio-temporal information which identifies the user space and time comprises the object identifier, OID for the user behavior and the location information according to the object movement. Location information is composed of a generalized value about spatial coordinate and the valid time which the real event occurs. It is represented as {(S1, T1), (S2, T2), ...,and (Sn, Tn)}. Here, (Sn, Tn) means nth location and time information about the user and the object movement. Sn is a generalized location information about the spatial coordinate (xi, yi).

Fig. 1. Context Ontology

The generalization of the location identifies the location of the user and the objects, generalizing the coordinate of the space as a certain zone. For example, if the user U is watching the television in the living room, the coordinate of the user is generalized into the zone value of the living room area. The general domain of the time is divided into two such as the effective time which means that the object exists in the real world, and the transaction time which is recorded in the system. That is, within the domain of each phase there is a scope. The time domain has timestamp, interval, and period of the temporal object on the line of time. For example, there are the timestamp which the user comes into home, the time interval which it takes for the user to have meal, and the periodic occurrence of the event. And the user context information contains the user activity. The detail information of user activity is the behavior attribute that represents speed, direction, and status. For example, if there are two switches in a row, the habitual action that turns the light on the left is stored as detail information through the attribute information and then

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the user can be provided the suitable services. To represent the context ontology, we used OWL(Web Ontology Language)[12] suggested by W3C. And Fig. 2 shows the generation of the ontology using protege[13].

Fig. 2. Ontology generation using protege

3

Service Pattern Mining Framework

Fig. 3 shows the framework of active service pattern mining, which intelligently provides the service according to the user environment and context. The structure of the system comprises the application layer, the service provider layer, and the physical layer. The application layer provides the users with the optimum services according to the query from the users. And the service provider layer comprises the context ontology composed of the basic context information, the data mining engine which finds out the user behavior patterns and the service rules, and the database which generates new service and stores the existing service. The physical layer is the lowest layer that collects the context information through the sensor network. And ECA trigger engine of the proposed system is the system that combines the ontology engine including the spatio-temporal ontology with the data mining engine to perform data mining and provide the service intelligently. Through the events such as the user behavior, time, location recognition and so on, the condition of the previously given rules is evaluated, and the exact service to the user is searched and

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provided automatically. Also the trigger system searches the service and performs the data mining according to the user behavior event. Data mining engine finds not only the user behavior but also the related service based on the spatio-temporal ontology. Data mining generates the repetitive pattern rules about the user behavior and the provided service in given interval period and the special context. Here, the given interval period means the time interval period over the collected data amount or the window size. Data mining is defined by the window size based on the number of transaction and then is performed. And performing the data mining using the ontology can find the more concrete rules.

Fig. 3. Service pattern mining architecture

The ontology engine stores the previous service history provided to the user and also a new rule from the data mining results is added and stored consistently. The ontology provides the spatio-temporal ontology information for performing the data mining and is the base for extracting the suitable service to the user. That is, the ontology database stores not only the previously provided service rules but also a new rule generated by the data mining. And if a new is searched, the priority is given to the new rule. so the newly searched rules is performed over others. The trigger rule performs the data mining if the time is fixed or a given number of transaction is collected to perform the data mining periodically by the trigger event. An example of the trigger rule(INVOKE_MINING_TRIGGER) is defined in Fig. 4. To provide the service automatically if the user behavior event happens, the related service from the service set is searched and stored into the service providing table. The other in Fig. 4 is the trigger rule(SEARCH_SERV_TRIGGER) for storing into the service table. Trigger monitors the operation(insert, delete, update) about the target table and defines the suitable action. If the previously defined event(data operation)

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on target table is searched, and then performs the action, checking the condition defined in the rule. In this paper, using INSERT trigger the condition and the action of the trigger rule is performed. And the type of a newly inserted service is same to the existing service, the priority is given to the new service rule and then it is inserted. If the inserted new rule recognizes the same context event of the user related to the service, the service that has the top priority is provided to the user. // INVOKE_MINING_TRIGGER CREATE OR REPLACE TRIGGER INVOKE_MINING_TRIGGER AFTER INSERT ON TRANSACTION_TABLE FOR EACH ROW BEGIN IF DATE_TIME = RESERVED_TIME OR TRAN_SEQ_NUM ≥ SET_NUM THEN ALERT MINING_MANAGER END IF; END; // SEARCH_SERV_TRIGGER CREATE OR REPLACE TRIGGER SEARCH_SERV_TRIGGER AFTER INSERT ON EVENT_TABLE DECLARE w_service_id INT; w_user_id INT; BEGIN IF ACT_CONTEXT(OID, Space, Time, Activity) EXIST IN Rsrv_Table THEN w_service_id:= Rsrv_Service_id; w_user_id:= Rsrv_User_id; INSERT INTO SERV_TABLE VALUES (w_user_id, w_service_id); ENDIF; END; Fig. 4. Trigger rule example

4

Algorithm and Experiment

Data mining algorithm which searches the user activity pattern is as follows. We use the sequential pattern algorithm[14] which searches the relationship over time. And we can predict the following action by the searched rules Algorithm ActPatterns Input : Transaction Database(TD)

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Output : Activity Sequential Patterns Initialize Activity Patterns For each transaction t TD Activity Patterns = PrefixSpan(α,β,S|α) //extract activity patterns using PrefixSpan algorithms If Activity Patterns not exists in RuleBase then add Activity Patterns to ActivityRuleBase End if End for

∈

Data mining in designing the active mining framework proposed in this paper is important for extracting the basic data to provide the user the optimum service. Accordingly, in the experiment, we will show the efficiency of the proposed system through the number of the service searched by the data mining. For the experiment we used Java and Oracle9i in Window XP with Pentium 4 CPU 2GHz and 1GB RAM. And we assumed that the service rule (Si) provided according to the user behavior (Ai) is stored in the ontology rule base. Table. 1 shows a part of the rules. Table 1. Service rule Activity

Service Rule

A1

S111, S103

A2

S110, S125, S101

A3

S115, S106, S118

A4

S107, S102

A5

S104, S126, S109

To find out the service relation rules, the service history information is regarded as a transaction, and we let each transaction contain 2 to 3 of basic service information randomly to 10000 transactions. and then using Apriori algorithm the related service rules are searched. Fig. 5 is the result compared the number of the service rules (ServRule) which

can be stored and provided in the ontology with the number of the service rules (ServMineRule) which can be provided by data mining. The experimental result shows that the number of the service rules by data mining can provide on average 2.5 times more than the number of the service rules which are stored in the ontology. It means that in addition to the service rules previously stored in the ontology a new relation service rules can be found based on the service history. And it is possible to provide and recommend better service to the user, adding the service combination or the mined service. In the following experiment, to find out a variation on the number of the service, we inserted the transaction of 1000 units sequentially and then measured the change of the number of the searched service. In Fig. 6 it is proven that the more the number of the transaction is over time, the more the number of the searched service is. That is, if the number of the transaction is increasing, the number of the related service is also so increasing that the number of the service relation rules with a given threshold is increasing.

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Fig. 5. The number of services

5

Fig. 6. Variation of the number of services

Conclusion

In this paper, to provide the automatic service we proposed a framework of the active service pattern mining which includes ECA trigger system. It is based on the ontology which includes the spatio-temporal information, and mines the service pattern rules relating to user activity. And we proved the efficiency of finding out the service pattern rules through the experiment. In the future, we will evaluate the quantity about the efficiency of the service system using the trigger.

References 1. Harry, C., Tim, F.: An Ontology for Context-aware Pervasive Computing Environments. In: Workshop Ontologies and Distributed Systems. IJCAI Press (2003) 2. Khedr, M., Karmouch, A.: Negotiating Context Information in Context-aware Systems. IEEE Intelligent Systems (2004) 3. Strimpakou, M., et al.: Context Modeling and Management in Ambient-Aware Pervasive Environments. In: Workshop on Location and Context-aware (2005) 4. Strimpakou, M.A., Roussaki, L.G., Anagnostou, M.E.: A Context Ontology for Pervasive Prevision. National Technical University of Athens (2004) 5. Lee, C.H., Helal, S.: Context Attributes:An Approach to Enable Context-Awareness for Service Discovery. In: Symposium on Applications and the Internet, pp. 22–30 (2003) 6. Maffioletti, S., Mostefaoui, S.K., Hirsbrunner, B.: Automatic Resource and Service Management for Ubiquitous Computing Environments. In: The Second IEEE Annual Conference on Pervasive Computing and Communications Workshops (2004) 7. Brisson, L., Collard, M.: An Ontology Driven Data Mining Process. In: The Tenth International Conference on Enterprise Information Systems (2008) 8. Bellandi, A., Furletti, B., Grossi, V.,, R.: Ontology-driven Association Rules Extraction: a Case of Study. In: The International Workshop on Contexts and Osntologies: Representation and Reasoning (2007)

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9. Beer, W., et al.: Modeling Context-Aware Behavior by Interpreted ECA Rules. In: Kosch, H., Böszörményi, L., Hellwagner, H., et al. (eds.) Euro-Par 2003. LNCS, vol. 2790, pp. 1064–1073. Springer, Heidelberg (2003) 10. Abraham, T.: Knowledge Discovery in Spatio-Temporal Databases. School of Computer and Information Science, University of South of Australia, Ph. D dissertation (1999) 11. Allen, J. F., Kautz, H. A.:A Model of Native Temporal Reasoning. In: Formal Theories of The Commonsense World (1985) 12. http://www.w3.org/2004/OWL 13. http://protege.stanford.edu 14. Pei, J., Han, J., et al.: PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth. In: The International Conference on Data Engineering (2001) 15. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: The 20th International Conference on Very Large Data Bases (1994)

Character Region Detection Using Structure of Hangul Vowel Graphemes from Mobile Image Jong-Cheon Park1,*, Byoung-Min Jun1, and Myoung-Kwan Oh2 1

Department of Computer Engineering Chungbuk National University [email protected], [email protected] 2 Department of Digital Service, Hyejeon College [email protected]

Abstract. Recently, many researchers have been proposed utilizing mobile images, whereupon the various smartphone applications have been developed on the basis of images. These applications have analyzed the images and mined the information, so they can let people search without typing keywords on the web. However, most of conventional methods for character region detection are based on clustering technique without using structural feature of character. Therefore, if a character in mobile image has complex backgrounds, these methods are difficult to localize the character region. We proposes the method to detect the Hangul character region from mobile image using structure of Hangul vowel graphemes and Hangul character type decision algorithm. First, we transform a mobile image to a gray-scale image. Second, feature extraction performed with edge and connected component based method, Edge-based method use a Canny-edge detector and connected component based method applied the local range filtering. Next, if features are not corresponding to the heuristic rule of Hangul character, extracted features filtered out and select candidates of character region. Next, candidates of Hangul character region are merged into one Hangul character using Hangul character merging algorithm. Finally, we detect the final character region by Hangul character type decision algorithm. Experimental result, proposed method could detect a character region effectively in images that contains a complex background and various environments. As a result of the performance evaluation, the recall rate is 82.33% and the proposed method showed the advanced results about detection of Hangul character region in mobile image. Keywords: Character Region, Detection, Hangul Vowel Grapheme Structure.

1

Introduction

Mobile devices have been spreading like a digital camera, a cell phone, a smartphone, PDA, and so on and users have made good use of the images recorded with a camera built in these devices. The image is combined with network technology and computer vision technique and used as major data of diverse application. A letter of the natural *

Corresponding author.

T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 228–237, 2011. © Springer-Verlag Berlin Heidelberg 2011

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image needs to be recognized and detected the information of the region. So, it is an important matter to detect the region in it precisely. These research fields are helping system for visually impaired person [1], indoor mobile robot navigation [2], a Robust License-Plate Extraction [3], video or image coding/ index on contents [4], etc.. A letter of the natural image can be damaged, overlapped each other, made up with any other color, distinguished from background color because it is mixed with structure, trade mark, white noise. So, it sometimes has a low contrast with these differences. And as it has the feature changed the color of letter due to various font, size and slope of a letter and lighting and shadow, detection method should be considered of various feature of character and character region [5]. The old research detecting the character region from natural images couldn’t make full use of structural feature of Hangul but had usually used the group feature of general letters. As a result, the existing method had a difficulty in detecting Hangul in the intricate background effectively. So, this article suggests the method to detect the character region by using the structural feature in order to solve this problem. New method detects the character region by checking whether that is a Hangul letter or not with algorithm to merge character region by using the location of vowel, the structural feature and the Hangul geometric relation consisting of initial consonant, vowel and final consonant and to detect the type of the letter. Overall procedure on this article is shown blow Fig. 1.

Input image G Image transform & preprocessing

Detection based on the edge

Detection based on connecting elements

Detection of the edge & labeling Filtering feature value of edge Distinction and detection of candidates region

Detection of connecting elements & labeling Filtering feature value of connecting elements Distinction and detection of candidates

Merging candidate regions

Detection of the character region

Fig. 1. Character Region Detection Procedure

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Detection of Hangul Character Region

The first step for the detection of Hangul region is that we convert RGB 24 bit color image into gray-scale image with brightness value 256, do with median-filter to remove the noise in the pre-process and use the end-in searching method to improve the image contrast. In the second step, the procedure extracting character was conducted with edge and connecting elements. Canny-edge detector was used in extracting edge character [6], it applies the threshold value to the outcome after conducting the range filter finding the remainder between minimum and maximum of the 8 directions neighbor pixel value adjacent to pixel in order to extract the character of connecting element. Extracted value is detected by labeling edge and connected component as objects. In the third step, filtering is conducted to remove anything not to correspond with the character region of labeling the detected edge and connected component. In [7], we comment on the method to filter feature value by using an edge and the edge and connected component based method. This is to make the character value selected as a filtering procedure using the algorithm that merges each element of Hangul into the one character region. Merged character region detects the final character region by testing whether the character is with algorithm that tells Hangul character type.

Fig. 2. Hangul Character Structure. C1: First Consonant, C2: Final Consonant, V1: Vertical Vowel, V2: Horizontal Vowel

2.1

Fig. 3. Hangul Character Font Face

Hangul Grapheme Merging

Old researches related with merging each element of Hangul have dealt with the method to isolate Hangul, written English, numbers, and special characters on documentary image mainly [8]. We suggest two algorithms merging character to detect character region with color and leaning of the character from natural images after detecting connecting elements with these features [9]. Hangul consists of first consonant, vowel and last consonant and can have sense to merge every element into one character. Accordingly, extracted value is merged into one character region using an algorithm of merging Hangul graphemes. Hangul grapheme merging algorithm is made by merging Hangul graphemes repeatedly from top-down and left to right fashion. Table 1 shows the location of vertical vowel, the

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number of Runs and the position of stroke as a threshold standard. Hangul letter is classified into 6-types as depicted in Fig. 2. We can correctly merge one Hangul letter among neighboring letters with the feature of vertical vowels considering that a letter always starts with a consonant. Hangul grapheme merging algorithm uses the geometric structural feature, distance value between features and slope of the detected candidate feature values. But every Hangul character is not detected as Hangul character region merged with the element merging algorithm. Fig. 3 shows the various Hangul font, ‘ ’, ’ ’, ’ ’ in the second line have already combined in one letter with first consonant, vowel, last consonant. Characters with like this structure cannot detect with this algorithm. Instead, they should be detected using Hangul character type decision algorithm, the next grade.

당 구 장

Table 1. Features of Hangul Vowel Vertical Vowel

ㅣ ㅏ ㅑ ㅓ ㅕ ㅐ ,ㅒ 2.2

The Number of Run Horizontal Vertical 1 1

Vowel Position Vertical left

Stroke Position Horizontal null

left

center up center /below

1

1

1

2

left

1

1

right

2

1

right

[1, 2]

2

left, right

center up center /below center, up center /below

Hangul Character Type Decision

The detected candidate character region with Hangul grapheme merging algorithm can be detected using the 6-type-character decision algorithm. In table 2, the features are presented according to the Hangul character 6-types. It is showing the feature of position and length of Run in the horizontal and vertical direction. The number of Runs means the number of black pixel equated to character in binary image. That is, it means the number of pixel that consists of the character region. The number of Runs marked with [minimum value, maximum value]. As Hangul character has structurally horizontal or vertical vowel surely, we can distinguish whether the character is in Hangul character region by extracting the feature value of horizontal or vertical vowel according to each Hangul character type and confirming that position. Too small region should be excluded from the character region by setting minimal size of the character region in order to remove but this noise not the character region detected generally. On this article, a minimal size of width and height is set up 16 pixels. The character region is removed to consider of noise region unless it meets the condition that the rate of width versus height is bigger than 0.4 and smaller than 2.5.

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If the character region is detected with the edge-based method, more than 2 edges in the character region should be consisted of less than 50% of width and height. We could divide the noise region from the character region by setting threshold value for the standard deviation calculating the standard deviation of the character region as the character region has big differences in the brightness value and the background. As an experimental result, the standard deviation is set up ’25’ as a threshold value. Firstly, the noise should be removed as a feature condition of the character region and then it is detected using Hangul character type decision algorithm. Table 2. Features of Hangul Character 6-Types 6-Types

3

Number of Run Horizontal Vertical

Vowel Position Vertical Horizontal

I

[2,3]

[2,4]

right

null

II

[2, 4]

[2, 4]

null

middle below

III

[2, 3]

[2, 5]

up right

null

IV

[3, 3]

[2, 6]

null

middle

V

[2, 3]

[2, 4]

right

middle

VI

[2, 4]

[4, 5]

up right

middle

Experimental Result

We presented the method using the feature of Hangul vowel grapheme structure and Hangul character type decision algorithm in natural image captured by mobile phone camera. Experimental image data base for detection of proposed character region was used from KAIST artificial intelligence laboratory site [10]. It was taken with mobile camera and made of images taken in various backgrounds like indoor, outdoor, lighting and shaded image. We evaluated detection performance with precision rate in formula 1 and recall rate formula 2 [11]. They are revised with pixel to apply formula 1 and 2 to image- based. Precision rate is refined to formula 3 recall rates to formula 4. That is, it means the evaluation is conducted for the square detected as character region. p r p

∑

E

|E|

E T ,T

(1) (2) (3)

Character Region Detection Using Structure of Hangul Vowel Graphemes

r

∑

T

|T|

,T

233

(4)

The c is the number of the character region(c) in formula 1and 2 and it is refined into formula 3 and 4 and marked with number in the detected square. |E| in the formula 3 means the number of detected character region and |T| in the formula 4 means the number of all character regions existing in the image. So, precision rate stands for the correctness of character region detected from the experiment and recall rate stands for how many character regions are detected from the image. Fig. 4 is the detected result in natural image taken in various environments as remarked above. We could find out that character region which couldn’t be detected with the edge-based method is able to detected with connecting element-based method. Like this, they can be done complementarily. Recall rate as experimental result was improved. But precision rate had detected a noise as the character region and the more detected noise region, the lower precision rate by combining with results of the edge-based and connected component-based method. In Fig. 4(a) indicates that indoor image has a high recall rate with a relatively simple background. Because the most characters in indoor image have a small size and connecting element-based method cannot get a feature value of the character region during the pre-procedure, it makes precision rate and recall rate low. We found out the skill to detect the feature of the small character region effectively with connecting element-based method. In Fig. 4(b) is as an image taken outdoors and we detected the character region when the background is simple. It is easy to detect the character region having a small character, too. But precision rate is lower than indoor image outdoor image has various environments and a complex background. In Fig. 4(c) has feature that the contrast value of the character region is high as the character region is shaded. So, recall rate is high when the bigger differences of the character region are, the more highlighted it is; however, there is an error in detecting the character region and recall rate becomes lower because the noise region is highlighted in accordance with the change of contrast. In Fig. 4(d) shows the character region is in the lighting and surroundings around the character are blurred. But because there is difference of the brightness between the character region and surroundings, rather the character region is detected easily. Therefore, it is the highest recall rate of the experimental results; on the contrary, precision rate is low with other effect of lighting. In Fig. 4(e) indicates recall rate and precision rate of proposed character region detecting methods are low entirely as a vertical character image is that the character we usually see on the store sign is vertical aligned. The reason is that the character aligned toward the right side has the unique feature not to start with vertical vowel as structural feature during the Hangul element merging procedure but the character aligned vertically doesn’t have the feature like that. Because the character region aligned vertically is affected according to the distance with other letters, this kind of trouble is arose when elements are not merged correctly if the character is relatively close to aligned letters in the procedure merging elements. This trouble needs to be solved by applying additional Hangul structural feature to vertical character merging procedure.

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Fig. 4. Result of Performance Evaluation about Character Region Detection

Performance evaluation for the result detecting the character region of the image taken from various environments is shown with each image type using the bar graph in Fig. 4. The result of indoor image is like that recall rate which is 84.6% and precision rate is 74.8% respectively. Indoor image has a higher detection rate relatively than other images due to a simple background. The image with the lowest performance is the vertical character image and it is resulted because the feature of merging Hangul element is not distinguished in the character region aligned vertically. As an overall experimental result, it turns out that the edge-based method has a high recall rate. It is because the edge feature of character region can be detected relatively easily, on the other hands, the connecting elements are affected from many environments and they cannot get the enough feature value. Instead, the edge-based method has a weakness that precision rate lowers by detecting not the edge of the character but the noise when the background is complex or thickness is distinct from each other. The edge-based method is affected less from the lighting and shadow and because a small sized character region can be detected with an edge feature, it turns out it is a major fact. The weaknesses of this method are that the character loses the structural feature as the edge is not connected and the edge is connected with noise around the character. These problems can be supplemented by detecting connecting elements using connecting element-based method. Proposed methods in this article have weaknesses that while recall rate goes up, precision rate goes down by merging two results of the edge-based method and connecting element-based method. The letter is low when the character region is not detected correctly with two methods by taking merging or if two regions are not overlapped with each other, the matching rate of the detected region goes down to half. Accordingly, we can reduce these problems if we conduct with merging the

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(a)

(b)













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(d)

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Fig. 5. Result of character region in various natural images. Left images are test images, Right images are results of character region detection. (a)Indoor (b)Outdoor (c)Shadow (d)Lighting (e)Vertical Text.

edge-based and connected component-based method before the detection of the character candidates. Therefore, the research should be conducted like above. Because the character region has local grouping generally, another method should study on the local grouping of the character region for detection result with the edge-based and connected component-based method or if there is any grouping like that, it can make precision rate high by detecting not a character region. Also, it can be improved by distinguishing the character region from background set up the threshold value after detecting distribution feature because pixel has a high contrast rate with the background.

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Conclusion

On this article, the method to detect the character region with Hangul structural feature was considered as the method to detect the Hangul in the natural image. Hangul elements merging algorithm was suggested to detect the character region among extracted values after detecting feature value of the character region using the method based on an edge and connected component. It could merge precisely Hangul elements in the character region as a special feature due to the feature which is distinguished from surroundings in merging procedure. The character region detected with Hangul elements merging algorithm was detected by procedure detecting Hangul character type. To detect Hangul character region, the 6 - type - detection algorithm was suggested. We got the result similar to the character detection with that of Hangul through Hangul character region detection algorithm by testing whether the character is in Hangul character region with using the location information and the number of Run from Hangul vertical vowels. After testing image taken with the mobile phone in diverse environments, whereupon the character region detection performance is 82.3%, the relatively high reproduction rate, but we got the relatively lower performance as the correctness rate is 57.9%. The next research project needs to be conducted on the method that merges the outcomes into one using the edge and revision connecting element-based method after extracting the character value. We should do the research on the slop detection and revision to detect the when the natural scene image is tilted slightly. Finally, we merged Hangul elements effectively in the character region based on the structural features of Hangul elements for the detection of character region. And then, we proposed the algorithm of vertical vowel detection for merging Hangul elements. The vertical vowel is needed to find out the unique feature value of the additional Hangul character.

References 1. Ezaki, N., Bulacu, M., Schomaker, L.: Character detection from natural scene images: towards a system for visually impaired persons. In: Proceedings of the 17th International Conference on, vol. 2, pp. 683–686 (2004) 2. Liu, X., Samarabandu, J.: An Edge-Based Character Region Extraction Algorithm for Indoor Mobile Robot Navigation. International Journal of Signal Processing 3(4), 273–280 (2006) 3. Kim, S., Kim, D., Ryu, Y., Kim, G.: A Robust License-Plate Extraction Method under Complex Image Conditions. In: Proceedings of International Conference on Pattern Recognition, vol. 3, pp. 216–219 (2002) 4. Smith, M.A., Kanade, T.: Video Skimming for Quick Browsing Based on Audio and Image Characterization. Carnegie Mellon University, Technical Report CMU-CS-95-186 (1995) 5. Lee, C.-M., Kankanhalli, A.: Automatic Extraction of Characters in Complex Scene Images. Pattern Recognition 9(1), 67–82

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6. Canny, J.: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8(6), 679–698 (1986) 7. Cheon, P.J.: Detection Method of Character Region Using Hangul Structure from Natural Image, Department of Computer Engineering, Doctoral Thesis, University of Chungbuk National (1993) 8. Oh, I.G.: Study on the Extraction of Character and Special Character from Hangeul Documents with English, Master’s Thesis, Department of Computer Science, University of Kwangwoon (1993) 9. Yi, C., Tian, Y.: Character String Detection from Natural Scenes by Structure-based Partition and Grouping. IEEE Transactions on Image Processing, PMID:21411405 (2011) 10. KAIST Scene Character Database, http://ai.kaist.ac.kr/home/DB/SceneCharacter 11. Lucas, S.M., Panaretos, A., Sosa, L., Tang, A., Wong, S., Young, R.: CDAR 2003 Robust Reading Competition. In: 7th International Conference on Document Analysis and Recognition (ICDAR), pp. 682–687 (2003) 12. Lucas, S.M., Panaretos, A., Sosa, L., Tang, A., Wong, S., Young, R., Ashida, K., Nagai, H., Okamoto, M., Yamamoto, H., Miyao, H., Zhu, J., Ou, W., Wolf, C., Jolion, J.-M., Todoran, L., Worring, M., Lin, X.: ICDAR 2003 Robust Reading Competitions: Entries, Results and Future Directions. International Journal on Document Analysis and Recognition (IJDAR) 7(2-3), 105–122 (2005)

Task Scheduling Algorithm with Minimal Redundant Duplications in Homogeneous Multiprocessor System Inseong Song, Wanoh Yoon, Eunmee Jang, and Sangbang Choi Department of Electronics Engineering, Inha University, Incheon, Korea [email protected], [email protected]

Abstract. The performance of a homogeneous multiprocessor system depends on an algorithm which schedules input DAG. Among various task scheduling algorithms, ones that are using full duplication provide superior performance by duplicating tasks recursively. However, redundant duplications lead to increase of total execution time and decrease of processor idle time. In this paper, we propose GDRA which reduces total execution time and increases processor idle time by eliminating redundant duplications. Comparisons with other full duplication algorithms, such as CPFD, SD, and MDJ, show that GDRA provides the better performance as the CCR increases. Keywords: DAG, full duplication, homogeneous, multiprocessor, scheduling.

1

Introduction

Homogeneous multiprocessor system, one of the parallel and distributed computing systems, consists of multiple numbers of processors which have the equal processing capabilities and interconnected by a network. The performance of a homogeneous multiprocessor system depends on a task scheduling algorithm which allocates a task on the optimum processor. But as the task scheduling algorithm is an NP-complete problem which cannot obtain the optimal solution in polynomial time, most of studies concentrate on obtaining sub-optimal solution with affordable time complexity. Task duplication based algorithms consist of task prioritizing phase and processor allocation phase. The task prioritizing phase calculates priority of each task, and the processor allocation phase allocates those tasks on the optimum processor based on their priorities. Task duplication based algorithms duplicate tasks to eliminate communication overhead. Of all the task duplication based algorithms, ones with full duplication, which duplicate the tasks recursively, provide superior performance though they have large time complexity. CPFD (Critical Path Fast Duplication)[1], SD (Selective Duplication)[2], and K. S. Shin et al.[3] are the algorithms that are using full duplication. We named the algorithm on K. S. Shin et al.[3] as MDJ (Minimized Duplication at Joint node) in this paper. In this paper, we propose GDRA (Generalized Duplication Reduction Algorithm), which removes redundant duplications not only on join nodes but on fork nodes. Leveling method is adapted in task prioritizing phase to enhance the performance. T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 238–245, 2011. © Springer-Verlag Berlin Heidelberg 2011

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And the method that pre-confirms redundant duplications on join nodes and fork nodes before their actual allocation is adapted to increase processor idle time. The more idle time stands for the more opportunities for task insertion and as a result, total execution time can be reduced. Input graphs created with various parameters are applied to CPFD, SD, MDJ, and proposed GDRA for a performance evaluation. The results show that GDRA outperforms CPFD, SD, and MDJ, in terms of total execution time.

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Problem Definition

An input task graph, which models parallel application that is to be executed in homogeneous multiprocessor system, is generally modeled as a DAG (Directed Acyclic Graph) G = (V, E). A DAG consists of nodes and edges. V is a set of n nodes which stand for tasks, and E is a set of e communication edges which stand for relations between two tasks. If there exists a directed edge from ni to nj, denoted by ei,j, then ni is called as a parent node of nj, and nj is called as a child node of ni. The set of all parent nodes of ni is called as pred(ni), and the set of all child nodes of ni is called as succ(ni). In Fig. 1, each node is represented by a circle, and each edge is represented by an arrow. The numbers in the upper and lower half denote the node number ni and the computation cost of the node, respectively. A number on an edge denotes a communication cost from ni to nj. A node without a parent node, such as n1, is called as a start node, and a node without a child node, such as n13, is called as an exit node. If there is more than one start or exit node, they have to be connected to a pseudo start or exit node with a pseudo edge which has zero computation cost and zero communication cost, so as not to affect the schedule. The cp(n) is a parent node from which the data arrives at the latest time. By duplicating the cp(n), the node n’s finish time can be reduced[4].

Fig. 1. An example of directed acyclic graph

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Homogeneous multiprocessor system consists of a set P of p homogeneous processors, fully connected with each other. Assume that all the inter-processor communications are done without contention, and task executions are nonpreemptive. Also, assume that computation and communication can be overlapped. Task ordering is carried out according to a node’s ranku(ni) and rankd(ni) value. The ranku(ni) is the sum of computation costs and communication costs along the longest path from an exit node to the ni, and is calculated as follows[5]: ranku(ni) = c(ni) + maxn

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nj) + ranku(nj)} .

(1)

The rankd(ni) is the sum of computation costs and communication costs along the longest path from a start node to the ni, and is calculated as follows[5]: rankd(ni) = maxn

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+ c(nj) + d(ni, nj)} .

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Proposed Algorithm

In this paper, we propose a novel task scheduling algorithm which removes redundant duplications in homogeneous multiprocessor system, called GDRA. GDRA consists of three major phases; task prioritizing phase, RN (Redundant Node) selection phase, and processor allocation phase. 3.1

Task Prioritizing Phase

In task prioritizing phase, the given DAG is traversed in a top-down fashion in order to group the nodes that are independent of each other. This is called the DAG leveling. As a result, nodes in the same group can be executed in parallel. Given a DAG, level 0 contains start node. Level i consists of all nodes nk such that, for all ej,ks, node nj is in a level less than i and there exists at least one ej,k such that nj is in level i-1. The last level comprises of exit node. The DAG leveling is carried out with the breadth first search. Then, calculate each node’s ranku(ni) and rankd(ni) with equation (1) and (2) to determine a critical path. Nodes with the equal ranku(ni) and rankd(ni) constructs critical path. In each level, a node on the critical path gains the highest priority and the other nodes gain priorities according to their ranku(ni). The higher ranku(ni), the higher priority. 3.2

RN Selection Phase

In RN selection phase, the candidate nodes that may cause redundant duplications are selected. RN selection phase consists of two minor stages: RJN (Redundant Join Node) selection stage, and RFN (Redundant Fork Node) selection stage. In the RJN selection stage, the join nodes ns which may cause the redundant duplication of cp(n) are selected and grouped into set RJNS (Redundant Join Nodes Set). Followings are the conditions of selecting the RJN.

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Condition 1: A join node that is the only child of at least one of its parent. Condition 2: The communication cost between the node, the node which satisfies the condition 1, and its parent with the highest priority is larger than the computation cost of the parent. In the RFN selection stage, the fork nodes which may cause redundant duplication of themselves are selected and grouped into set RFNS (Redundant Fork Nodes Set). Following are the conditions of selecting the RFN. Condition 1: A fork node that is a RJN is not a RFN. Condition 2: A node which has at least one child node whose level is larger than the fork node as much as 1. Condition 3: The communication cost between the node, the node which satisfies the condition 1 and 2, and its child with the highest priority is larger than the computation cost of itself.

Group the nodes that are independent to each other Calculate ranku(ni) and rankd(ni) of all nodes Determine critical path for each level do Select and queue the node that are on the critical path Queue nodes in decreasing order of ranku(ni) endfor Make a final ordered list for input DAG Find RJN and group into RJNS Find RFN and group into RFNS for each node n in final ordered list do if duplication of n’s parent is possible then if the node n is not the parent of RJN or not the RFN then Assign the node with duplication technique else Assign the node with end technique endif elseif insertion is possible then Assign the node with insertion technique else Assign the node with end technique endif endfor Fig. 2. The pseudo code for the proposed GDRA

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Processor Allocation Phase

In processor allocation phase, the nodes are assigned to the processor which provides the fastest execution finish time with task insertion technique or duplication technique[6], considering RN. First, the GDRA selects a first node n in the task priority list created in task prioritizing phase. Then GDRA tries to assign the node n with duplication technique considering RN, if it satisfies duplication conditions. At here, if the current node n is a parent of RJN or the current node n is a RFN, duplication does not take place. And if the node n does not satisfy duplication conditions, then GDRA tries to assign the node with insertion technique. If the node n cannot be assigned with both duplication technique and insertion technique, then the node is assigned with end technique[6]. Fig. 2 shows the pseudo code for the proposed GDRA, and Fig. 3 shows the flowchart for the proposed GDRA. DAG leveling

Calculate priority of each node Determine critical path

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Fig. 3. The flowchart for the proposed GDRA

Assign the node with duplication

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Performance Evaluation

The performance of GDRA is compared with previous algorithms, CPFD, SD, and MDJ. Input DAGs are from STDGP (STanDard task Graph Project)[7-8]. The number of nodes, the number of processors, and CCR (Communication to Computation Ratio) are used as the input parameters for the simulations. Input parameters are restricted to the following values in the Table 1. Table 1. Input parameters Parameters The number of nodes The number of processors CCR

Values 50, 100, 300, 500, 750 4, 8, 16, 32, 64 1.0, 2.0, 3.0, 4.0, 5.0

The number of nodes refers to the number of nodes in a DAG. STDGP provides standard DAGs with 50, 100, 300, 500, 750 nodes, and so on, 180 DAGs for each graph size. The number of nodes in a DAG can be regarded as a size of a parallel application. The number of processors refers to the number of processors in the homogeneous multiprocessor system, which greatly affects the performance. CCR is the ratio of the average communication cost to the average computation cost. Lower CCR value indicates that it is computation-intensive, and has lower chance of duplication. Total execution time, the Makespan, is used as the comparison metric. Makespan is the time that execution of given input DAG is finished. That is, the finish time of the exit node. The task scheduling algorithm that gives the lowest Makespan is the best algorithm in terms of the performance. For each graph size, 180 DAGs are used. By combining the number of processors and CCR, the total number of 22,500 graphs is used in this simulation. CPFD SD MDJ GDRA

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Fig. 4. Average Makespan with respect to the number of nodes

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Fig. 4 shows the average Makespan with respect to the number of nodes. As shown in the figure, GDRA outperforms CPFD, SD, and MDJ. Furthermore, GDRA shows better performance as the number of nodes increases. Fig. 5 shows the average Makespan with respect to the CCR. As above, GDRA shows more improved performance as CCR increases. Since higher CCR stands for larger communication costs, this leads to more chances of duplication. GDRA is an algorithm which enhances performance by reducing redundant duplications, and as a result, it shows better performance as CCR increases. CPFD SD MDJ GDRA

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Fig. 6 shows the average Makespan with respect to the number of processors. The result shows that GDRA outperforms CPFD, SD, and MDJ.

5

Conclusion

In this paper, we presented the GDRA (Generalized Duplication Reduction Algorithm) for homogeneous multiprocessor system. GDRA reduces the total execution time and increases processor idle time by removing redundant duplications on join nodes and fork nodes. Decreased number of redundant duplications and increased processor idle time led to increase of insertion possibilities. Since insertion can enhance the performance of the algorithm significantly, GDRA was able to provide improved performance. As shown in the comparison results, the proposed algorithm, GDRA, provides better performance when compared to CPFD, SD, and MDJ with varying the number of nodes, CCR, and the number of processors in terms of total execution time, the Makespan. Acknowledgment. This work was supported by Key Research Institute Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology (2011-0018394).

References 1. 2.

3.

4. 5.

6. 7. 8.

Ahmad, I., Kwok, Y.K.: On Exploiting Task Duplication in Parallel Program Scheduling. IEEE Transactions on Parallel and Distributed Systems 9, 872–892 (1998) 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 15 (2004) Shin, K.S., Cha, M.J., Jang, M.S., Jung, J.H., Yoon, W.O., Choi, S.B.: Task Scheduling Algorithm Using Minimized Duplications in Homogeneous Systems. J. Parallel Distrib. Comput. 68, 1146–1156 (2008) Sinnen, O., To, A., Kaur, M.: Contention-aware scheduling with task duplication. J. Parallel Distrib. Comput. 71, 77–86 (2011) Topcuoglu, H., Hariri, S., Wu, M.Y.: Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing. IEEE Transactions on Parallel and Distributed Systems 13, 260–274 (2002) Sinnen, O.: Task Scheduling For Parallel Systems. Wiley (2007) Tobita, T., Kasahara, H.: A Standard Task Graph Set for Fair Evaluation of Multiprocessor Scheduling Algorithms. Journal of Scheduling 5, 379–394 (2002) http://www.kasahara.elec.waseda.ac.jp

RFID Mutual Authentication Protocols Using Key Exchange Based on Hash Functions Seung-Jae Jang1, Young-Hoon Jung1, So-Yeon Min2, Keun-wang Lee3, and Moon-Seog Jun1 1

Department of Computer Science, Soongsil University, Sangdo-Dong, Dongjak-Gu, Seoul 156-743, Korea {hezc81,jyh,mjun}@ssu.ac.kr 2 Department of Internet Information, Seoil University/Seoildaehak-gil 22 Jungnang-Gu Seoul 131-702, South Korea [email protected] 3 Department of Multimedia Science, Chungwoon University, Namjang-ri 29, Hongseong-eup, Hongseong-gun, Chungcheongnam-do 350-701, South Korea [email protected]

Abstract. A RFID system is a key technology used to create a ubiquitous society through non-contact radio identification technology, expected to provide us with information far above time and space. However, several security issues including a matter of privacy have been raised on account of restricted features of RFID communication environment. A variety of studies have been underway to bring solutions to them, but existing studies require excessive processing capabilities for security problems, tags and back-end database. To solve out this problem, this paper suggests mutual authentication RFID protocols that can guarantee safety from spoofing and re-transfer attack and also reduce the number of comparative operations in the back-end database, with the use of hash functions, XOR operation and key exchange. Keywords: RFID, Authentication, Key Exchange, Hash, XOR.

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Introduction

Recently, brisk studies regarding the ubiquitous environment have been in progress. RFID is an object recognition technology for a radio frequency that can uniquely identify all things most fundamental to ubiquitous computing. If a RFID tag has been planted in an object, it becomes possible to decode, trace and manage information about the object. Hence, it is highly expected to be used in many applications, covering not only logistics industry but also medical treatment, banking and transport. As a RFID tag, however, has a property of identifying an object uniquely, tag information may leak by someone else without the tag owner noticing it, and location information may be disclosed by tracking down tag ID [1]. Various studies are underway to solve out the matter of privacy, but it was difficult to apply algorithms used in the existing fixed and mobile network, due to the T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 246–255, 2011. © Springer-Verlag Berlin Heidelberg 2011

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restricted resources and features of low-cost RFID tags, creating a necessity of protocols which are simple and enable privacy protection. In particular, though a RFID tag enables various attacks, it is essential to have a mutually authenticable mechanism between the tag and the reader, and between the reader and the tag, as it can carry out an attack to acquire tag information illegally and deceive tag information by using an unauthorized and unjust tag or reader. Of them, existing main authentication mechanisms using hash functions include hash-lock technique, extended hash-lock technique, hash chain technique and hash ID modification technique. But these techniques are very vulnerable to spoofing or re-transfer attack. This paper explains in Chapter 3 it gives an insight into an introduction to and problems with the existing authentication techniques. In Chapter 4, the paper suggests light weight and highly stable protocols strong against spoofing and re-transfer attack while exchanging keys between tags and readers, not requiring much of the operation on back-end database, with the use of hash functions and XOR operations to be possibly embodied in tags. Chapter 5 of this paper suggests an analysis of the stability in authentication protocols and performance assessments, followed by Chapter 6 dealing with a conclusion.

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2.1

Hash-Lock Technique

Fig. 1. Hash-Lock Technique

The operating process of a hash-lock technique [2,3] is shown in Figure 1. Tags in the hash-lock technique store the metaID = H(key) with a random key hashed. When the reader requests an approach to the tag, it sends a metaID to the reader. The reader delivers metaID through the safe channel to the back-end database system, and receives the key to the tag and an ID value. The reader forwards the tag key again to the tag which then calculates hash values for the key from the reader compare the values to its metaID for identification and then delivers the identical ID to the reader. This technique uses a hash just one time, but it can trace down the tag location as metaID values are always fixed. When the legitimate metaID is received and metaID is sent to the reader afterward, it may bring a problem of acquiring Key and ID.

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Extended Hash-Lock Technique

Fig. 2. Extended Hash-Lock Technique

The extended hash-lock technique [2,3], as shown in Figure 2, allows the tag to generate a random number R which is connected to its ID, calculate a hash value and then transmit R, H(ID||R) to the reader. Back-end database receives the value from the reader, make comparison to see if there is an identical value to what the tag operates, using all ID values and a random number R in its DB. Then it forwards the identical ID value to the reader, if any. This technique is strong against the spoofing attack as it uses a random number which changes information transferred each time from the tag to the reader. On the other hand, the IDk value can be exposed and when re-transferring the R, H(IDk || R) it can be posed as an legitimate tag. This technique also causes server overload as the back-end database needs to work on hash operation and comparison operation for all identification information to see if any of the information is identical to the value from the tag. Besides, the number of gate is too insufficient to embody the random number generator in the tag. 2.3

Hash-Based ID Modification Technique

Fig. 3. Hash-based ID Modification Technique

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Figure 3 shows Hash-based ID Modification technique[4]. A hash-based ID modification technique keeps changing ID in each session using hash functions. Tags include the following information : current ID, current session number TID, and last success session number LST. The back-end database system contains the same information as tags and H(ID) values at the same time. Once the system begins to operate, ID and TID are initialized to a random value, while △TID and TID hold the same value. This technique does not guarantee the integrity of ID and contains a disadvantage of necessarily maintaining the modified ID continually if applied to other RFID systems. In addition to this, it enables getting an approval as a legitimate tag when carrying out a re-transfer attack on the reader, pretending to be a tag prior to the ID value being changed, by posing as a legitimate reader to acquire H(ID), H(TID ⊕ ID), and △TID values, followed by a legitimate tag making the next session. H(ID) values remain the same anytime until the legitimate session is complete, so the technique holds a weakness of allowing the location tracking. 2.4

Improved Hash-Based ID Modification Technique

An improved hash-based ID modification technique [4] is a technique with the addition of partial security to the re-transfer and spoofing attack in a hash-based ID modification technique.

Fig. 4. Improved Hash-based ID Modification Technique

Figure 4 shows improved hash-based ID modification technique [5]. Like the hashbased ID modification technique, this technique still contains problems, such as allowing the location tracking as H(ID) values remain the same until the legitimate session is complete, as well as ID integrity and compatibility with other RFID systems.

3

Proposed RFID Mutual Authentication Protocol

The structure of a proposed RFID mutual authentication protocol, as shown in Figure 5, enables safe communications by renewing periodically the key value for the

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tag and back-end database through a mutual authentication process between the reader and the tag, which makes the key useless in the following session though a hacker takes the key.

Fig. 5. Proposed Protocol Structure

3.1

Initial Information and Assumptions in Each Component

A tag stores ID, a unique identification number, previously hashed H(ID) and KeyT, the key value. DB has IC, the key value KeyDB and H(KeyDB) with KeyDB hashed, putting the KeyDB value in DB and the KeyT of a tag in the same initial configuration. Besides, a tag is able to do hash and XOR operations, while the reader enables hash operation, generating a random number, timer generation, XOR and a connection, supposing DB contains hash functions. 3.2 • • • • • • • • • •

Term Definitions

H() : Unidirectional hash functions ID : Unique bit identifying object information KeyDB : Key transferred from DB KeyT : Key transferred from a tag ⊕ : eXclusive-OR operation || : Connection C : H(ID||Timestamp) value used to create the following session key R : Random number H(ID)’ : H(ID) calculated from a tag Timestamp : Time generated by clock

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Operating Process of Proposed Protocol

Fig. 6. Operating Process of Proposed Protocols

As shown in Figure 6, the operating process of proposed protocols is as follows. 1. The reader transfers a request message to the tag. 2. The tag transfers H(KeyT) to the reader. H(KeyT) is used as a primary key to find out individual identification information and keys in DB. 3. The reader transfers H(KeyT) to DB. DB finds out, using its H(KeyDB) previously calculated, the requested key and ID in comparison with the H(KeyT) received from the reader. 4. DB transfers KeyDB and ID to the reader which then uses the KeyDB and ID received from DB and generate a random number to create a random number R and H(KeyDB||R), and create C = H(ID||Timestamp) using a timer. C is used as a new key for an authentication and hashes ID and timestamp as it should be a unique value. The reader hashes the C value to keep the KeyDB=H(C) value renewed. 5. The reader transfers a random number Rand H(KeyDB||R) and C⊕KeyDB to a tag. The tag hashes and compares its KeyT and R before authenticating the reader. Once the reader has been authenticated, the value C is extracted via C=(C⊕KeyDB)⊕KeyT) using the KeyT, then the KeyT=H(C) value of the tag is renewed. 6. The tag transfers H(ID)’⊕KeyT to the reader. The reader, which shares the same key with the tag, uses its KeyDB to extract H(ID)’. 7. The reader delivers H(ID)’, KeyDB to DB. DB compares H(ID)’ from the reader to H(ID) of its own before authenticating if it is a proper tag. If the tag has been authenticated, renew KeyDB and H(KeyDB).

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Stability Analysis and Performance Assessments

4.1

Stability Analysis

(1) Re-transfer Attack A reader may forward the information in phase 5 to the tag without any suspicion, if a malicious attacker is posed as a legitimate reader, as shown in Figure 5, receives information H(KeyT) in phase 2, send the H(KeyT) received by pretending to be a legitimate tag as a response to a reader request. Besides, information in phase 6 will forward the information also to the reader if the reader stays near the tag. This way, all the information in phases 2, 5, 6, communications between the reader and the tag, this information cannot be used for a re-transfer attack whatsoever. Information (H(ID)'⊕KeyT) in phase 6 is the combination of the past key and data, so the reader extracts the wrong H(ID)' when the reader needs to extract it following the transfer made to the reader. Then, DB stops the authentication process when it fails in receiving this data and comparing it to H(ID)'=H(ID). (2) Spoofing Attack As discovering all information in phases 2, 5, 6 does not stop the data including key values different from random numbers in each session from being transferring, the tag posed as legitimate is not authorized to keep using this data until the following session. (3) Search Speed of Back-end Database DB prepares previously hashed. H(KeyDB) on a table. Like the extended hash-lock technique, if there are over 10,000 DB documents to search, all in hash formatting each time, it may increase overhead and bring the system shutdown. Previously hashed H(KeyDB) compares only data values which is therefore advantageous in the aspect of speed. This advantage is applied to the hash-based ID modification technique and improved hash-based ID modification technique, except for the hashlock technique and extended hash-lock technique. It features a fast –pacing search, but has a limitation of allowing a location tracking. (4) ID Integrity Maintenance The hash-based ID modification technique and improved hash-based ID modification technique modify ID values. Therefore, when applied to the RFID systems different from each other, one weakness is that they need to keep the modified ID values continually. ID integrity was maintained by modifying the key value, metadata, rather than changing the unique ID value. As the key value should be a unique value, H(ID||Timestamp) was used to maintain the unique value. (5) Growing Complexity in Attacks Using XOR Operation XOR operation is a technique used for stream coding, showing a weakness of allowing one operand known while the other becomes known automatically.

RFID Mutual Authentication Protocols Using Key Exchange Based on Hash Functions

253

All information between the tag and the reader contains all different operands, so it is difficult to analogize ID or key values. (6) Increase in Security via Mutual Authentication Regarding the hash-lock technique, it has a structure where a tag simply has provided meta information one-sidedly without checking if it is legitimate. But, proposed protocols improved security through mutual authentication in which the tag clearly certifies the reader and the reader certifies the tag, using key values. (7) Damage by Key Leak If a key is lost while used continually without getting renewed, numerous tags may have to be wasted, so the key renewal may cause an overhead but the damage by a key loss may be minimized. 4.2

Performance Assessment

In the RFID system, tags have restrictions on space and cost with respect to hardware design. The reader and back-end database, on the other hand, have no space limitations in hardware design, and it is possible to embody, without demanding great expense, such simple functions as hash functions, random number generation and XOR. Therefore, operation processing capability of a tag should be primarily considered in designing protocols. Table 1. Compare the Function of Each Protocol

Function

Classification

HL

Tag Reader DB Tag Reader DB Tag Reader DB Tag Reader DB

-

-

Authentication Step

6

5

Hash Function Random Number Generator ⊕

||

EHL

Proposed Protocols

HIdM

IHIdM

-

-

-

-

-

-

-

-

-

-

-

5

5

7

254

• • • •

S.-J. Jang et al.

HL : Hash-Lock EHL : Extended Hash-Lock HIdM : Hash-based ID Modification IHIdM : Improved Hash-based ID Modification

Table 1 shows a comparison of functions needed for each authentication protocol. Proposed protocols, compared to other protocols in the tag, do not need a special operation device, but need a timer for generating the timestamp in the reader and a random number generator for creating a random number. The reader, however, has better operation ability than the tag, so it is not too much to have a timer and a random number generator loaded extra. With respect to phases of authentication process, an element called a key was added to them for security reinforcement, which shows a couple of more phases than the conventional techniques. As the authentication between the reader and the tag is processed within a very fast period of time and the recognition speed of a microchip and the reader is growing with the development of hardware, seven phases of operation process in authentication do not work as a major obstacle.

5

Conclusions

RFID technology, a core technology to establish the ubiquitous computing environment, is applied in overall industry yielding a lot of profits. Regarding its adverse, however, greater damages may occur due to threats to the RFID system. To troubleshoot this, many conventional authentication techniques have been studied, each of which contains several problems. The mutual authentication protocol based on a key exchange as suggested in this paper is designed to be free from re-transfer attack, spoofing attack and location tracking attack which are the biggest issues in previously studied RFID authentication techniques. This was confirmed through the stability analysis and performance assessment and strengths of other proposed protocols were described beforehand. When applying the proposed protocols, it is expected to provide various utilities under the environment in demand of authentication and security which uses RFID including the RFID radio authentication system and the RFID-based home network system.

References 1. Sarma, S.E., Weis, S.A., Engels, D.W.: RFID Systems and Security and Privacy Implications. In: Kaliski Jr., B.S., Koç, Ç.K., Paar, C. (eds.) CHES 2002. LNCS, vol. 2523, pp. 1–19. Springer, Heidelberg (2003) 2. Weis, S.A., Sarma, S.E., Rivest, R.L., Engels, D.W.: Security and Privacy Aspects of Low-Cost Radio Frequency Identification Systems. In: Hutter, D., Müller, G., Stephan, W., Ullmann, M. (eds.) Security in Pervasive Computing. LNCS, vol. 2802, pp. 201–212. Springer, Heidelberg (2004)

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3. Weis, S.A.: Security and Privacy in Radio-Frequency Identification Devices: MS Thesis. MIT (May 2003) 4. Henrici., D., Muller, P.: Hash-based enhancement of location privacy for radio-frequency identification devices using varying identifications. In: PerSec 2004, pp. 149–153 (March 2004) 5. Lee, S., Hwang, Y., Lee, D., Lim, J.: Efficient Authentication for Low-Cost RFID Systems. In: Gervasi, O., Gavrilova, M.L., Kumar, V., Laganá, A., Lee, H.P., Mun, Y., Taniar, D., Tan, C.J.K. (eds.) ICCSA 2005. LNCS, vol. 3480, pp. 619–627. Springer, Heidelberg (2005) 6. Ohkubo, M., Suzuki, K., Kinoshita, S.: Hash-chain based forward-secure privacy protection scheme for low-cost RFID. In: Proceedings of the SCIS 2004, pp. 719–724 (2004) 7. Yang, J., Park, J., Kim, K.: Security and Privacy on Authentication Protocol for Low-Cost Radio. In: The 2005 Symposium on Cryptography and Information Security (2005) 8. Juels, A., Rivest, R., Szydlo, M.: The blocker tag: Selective blocking of RFID tags for consumer privacy (2002) 9. Sarma, S.E., Weis, S.A., Engels, D.W.: Radio Frequency Identification: Secure Risks and Challenges. RSA Laboratories Cryptobytes 6(1), 2–9 (2003) 10. Rhee, K., Kwak, J., Kim, S., Won, D.H.: Challenge-Response Based RFID Authentication Protocol for Distributed Database Environment. In: Hutter, D., Ullmann, M. (eds.) SPC 2005. LNCS, vol. 3450, pp. 70–84. Springer, Heidelberg (2005)

The Development of Passive Filter Control System Based on Power Measurement and Analysis Jeong-Chay Jeon, Jae-Hyun Kim, and Jae-Geun Yoo #27 Sangcheon-ri, Cheongpyeoung-myeon, Gapyeong-gun, Gyeonggi-do, 477-814, Rep. of Korea [email protected]

Abstract. The passive filter is economic and efficient in suppressing harmonics. But the effect of the passive filter is constantly dependent on power system impedance or working conditions of loads. This paper presented the control system can automatically control the passive filter according to working conditions of loads and measured harmonics, reactive power and power factor. Experimental results in the power system with passive filter are presented in order to verify the performance of the control system. Keywords: Harmonics, Passive filter, Control, Reactive power, Power Factor.

1

Introduction

Proliferation of power electronics loads in the electrical power system produce harmonics distortion as well as many other troubles. In particular, motor drives among nonlinear loads produce a significant amount of harmonics during power converting processing and induce many obstacles [1][2][3]. Active and passive filters could be considered as concrete countermeasures to suppress harmonics [4][5][6]. Active filters, which are based on non-sinusoidal currents to meet the harmonic current requirement of the nonlinear load, have become an attractive harmonic countermeasure in recent years. However, the costs involved are quite high. Passive filters, providing a shunt path of low impedance to the harmonic frequencies, generally experience many problems such as harmonic amplification and the fact that its performance is constantly dependent on system impedances. There is also the probability of generating series or parallel resonance with the power system [7], but if those problems are solved, passive filters could be considered as a harmonic countermeasure due to their low cost. Although the passive filter is designed and produced by considering several conditions such as the harmonic field data, power system impedance, frequency fluctuation, filter size and target values of harmonic repression, the effects of the passive filter could be changed according to operating conditions of the loads. It is essential to control the passive filter according to operating conditions of harmonic loads, harmonic magnitude and reactive power, power factor and so on. Because the lead power factor in the power system is generated by compensation of T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 256–262, 2011. © Springer-Verlag Berlin Heidelberg 2011

The Development of Passive Filter Control System

257

the reactive power of the passive filter when some harmonic generation loads are stopped, there are concerns regarding the increase of power losses of the power system and transformer, and rise of power system voltage. In particular, in the event that power system impedance is varied and the harmonic component in the power source is included, there is a need to protect the filter by monitoring it because of series and parallel resonance problems. The passive filter must be controlled to avoid overload of the other branches when troubles with some branches of the passive filter are generated. In order to solve the problems in dealing with passive filters, this paper developed a control system to automatically control the passive filter according to operating conditions of loads, along with a program to monitor operating states of the passive filter as well as the control system, harmonics, power factor, voltage, and current in the power system.

2

Passive Filter

Passive filters are largely classified into a tuned filter to eliminate low order harmonics and a high pass filter to eliminate high order harmonics (e.g. 17th up) . The parameter to be considered prior to the selection of R, L and C during passive filter design is the quality factor (Q), and the quality factor determines the sharpness of tuning. In this respect filters may either be of high Q type or low Q type. The high Q type filter is sharply tuned to one of the lower harmonic frequencies and a typical value is between 30 and 60. The low Q type filter, typically in the region of 0.5 5, has a low impedance over a wide range of frequencies [1]. A single tuned filter as shown in Figure 1 consists of a series R-L-C circuit and is generally tuned to a lower characteristic harmonic. Its impedance ( Z f ) and quality

～

factor are given by equations (1) and (2), which at the resonant frequency (

X0 )

reduces to R

1   Z f = R + j  wL −  wC   X Q= 0 R where

(1) (2)

X 0 is reactance of inductor or capacitor in ohms at the tuned frequency, and it

is given by equation (3).

X 0 = wn L =

L 1 = wnC C

where wn is tuned angular frequency in radians per second.

(3)

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In the high pass filter, impedance and quality factor (Q) are equations (4) and (5) respectively. −1

1 1  1  − Z f =  + jwC  R jwL 

(4)

R R = wn L X 0

(5)

Q=

3

The Passive Filter Control System

The passive filter control system, which is proposed in this paper, consists of a signal input voltage component, current measuring sensor and filter, A/D conversion part converting analog signal into digital signal, digital signal processing element controlling circumference installations and performing operation functions, and an output part sending signals to control the passive filter as shown in Figure 1.

Fig. 1. Block-diagram of passive filter control system

The passive filter control system in Figure 1 adjusts the analog signal into a signal suitable to the A/D converter after inputting voltage and current signal by voltage and current sensor and converting it into digital signal by the A/D converter. In order to simultaneously convert the analog signal of voltage 3 and current 4 channels into a digital signal, the A/D conversion component was designed using Analog Devices 14bit A/D converter AD 7865 having an analog input 4 channel of AC level and conversion start signal input ability. A/D converted data is transmitted to Microchip’s microprocessor PIC24 HJ256GP210A of the digital signal processing part, and the microprocessor performs frequency analysis by Fourier conversion on each cycle of voltage and current, and calculates power (active, reactive and apparent power), power factor and so on. Picture 1 shows the developed passive filter control system.

The Development of Passive Filter Control System

259

Pic. 1. The developed passive filter control system

4

Test and Results

4.1

Measurement Accuracy Test of the System

First, we tested accuracy of voltage and current measurement by the proposed system. Test system, which is consisted of standard AC power source (Fluke 6100A) and digital oscilloscope (Tektronix TDS7104), was constructed as shown Figure 2. Table 1 and 2 show the test results that the proposed system yielded less than 0.3 % error in all case.

G Fig. 2. Test Circuit Table 1. Voltage measurement results Measurement Value and Accuracy

Vref [V]

Vr[V]

Accuracy[%]

Vs [V]

Accuracy [%]

Vt [V]

Accuracy [%]

12

12.0

100.0

12.0

100.0

12.0

100.0

25

25.0

100.0

25.0

100.0

25.0

100.0

50

49.9

99.8

49.9

99.8

49.9

99.8

110

109.8

99.8

109.8

99.8

109.8

99.8

220

219.8

99.9

219.8

99.9

219.8

99.9

330

330.0

100.0

330.0

100.0

330.0

100.0

380

379.6

99.9

379.6

99.9

379.4

99.8

260

J.-C. Jeon, J.-H. Kim, and J.-G. Yoo Table 2. Current measurement results

Iref[A]

Measurement Value and Accuracy Ir [A] Accuracy[%] Is [A] Accuracy[%] It[A] Accuracy[%] In [A] Accuracy[%]

5.0

5.1

102.0

5.1

102.0

5.1

102.0

5.1

102.0

10.0

10.0

100.0

10.0

100.0

10.0

100.0

10.0

100.0

20.0

20.0

100.0

20.0

100.0

20.0

100.0

20.0

100.0

40.0

40.1

100.3

40.1

100.3

40.0

100.0

40.0

100.0

60.0

60.0

100.0

60.0

100.0

60.0

100.0

60.0

100.0

4.2

Field Test of the Developed System

Table 3 lists the design and production specifications of the passive filter for test of the developed system. The passive filter consists of a tuned filter for third, fifth, seventh order and a high-pass filter for the 11th and high orders. SSR (Solid State Relay) was used to switch each branch of the passive filter by the signal outputted in the passive filter control system. Table 3. Passive filter design and production specification

Section Capacitor (kVAR) Inductor (mH)

Third order Fifth order 6.1 2.6

5.7 0.95

Seventh order

Hi-pass

5.5 0.4

10 0.09

As shown in Figure 3, the control system developed in this paper was connected with a passive filter in the power system using a 100HP DC motor, an extruding machine, an air blower and other loads. The power system of Figure 3 used a DC motor to produce automobile soundproofing material, and it was difficult to optimize effects of the passive filter due to repeated start and stop of the DC motor.

Fig. 3. Wiring diagram of the passive filter control system

The Development of Passive Filter Control System

261

Figure 4 shows reactive power magnitude variation according to load operation condition variation. In Figure 4, because the passive filter is automatically closed by the control system when the DC motor is being operated, reactive power is reduced to 5kVAR by compensation effectiveness of the passive filter. Also, there is no reactive power after a set time because the passive filter is automatically opened by the control system after a set time (five minutes according to the operating pattern of the DC motor in this paper) when the DC motor is stopped.

s –ˆ‹ –

s–ˆ‹ –•

s–ˆ‹ – m“›Œ™ vu

m“›Œ™ vmm

Fig. 4. Variation of reactive power magnitude

s–ˆ‹ –• s –ˆ‹ –

s–ˆ‹ –

m“›Œ™ vu m“›Œ™ vu

m“›Œ™ vmm m“›Œ™ vmm

Fig. 5. Variation of power factor

5

Conclusion

This work developed a passive filter control system that can automatically control the passive filter according to operating conditions of loads, harmonics, reactive power, power factor and so on. The passive filter control system was tested with the passive filter. Test results illustrated that the control system suppressed the occurrence of reactive power by automatically opening the passive filter when the DC motor is

262

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stopped and reduced reactive power by automatically closing the passive filter when the DC motor is operated. It is judged that the passive control system could solve both the over compensation problem of reactive power and the resonance problem with the power system by automatically controlling the passive filter.

References [1] Arrillaga, J., Bradley, D.A., Bodger, P.S.: Power System Harmonics, pp. 41–109. John Willy & Sons (1985) [2] IEEE Std. 519-1992, IEEE Recommended Practices and Requirements for Harmonic Control in Electric Power Systems (1993) [3] McGRanaghan, M.F., Mueller, D.R.: Designing harmonics filters for adjustable-speed drives to comply with IEEE-519 harmonic limit. IEEE Trans. on Industry Application 35(2), 312–318 (1999) [4] Sato, Y., Kawase, T., Akiyama, M., Kataka, T.: A Control Strategy for General-Purpose Active Filters Based on Voltage Detection. IEEE Trans. on Industry Application 36(5), 1405–1412 (2000) [5] Chang, T.-T., Cang, H.-C.: An Efficient for Reducing Harmonic Voltage Distortion in Distribution Systems with Active Power Line Conditioners. IEEE Transaction on Power Delivery 15(3), 990–995 (2000) [6] Peng, F.Z.: Harmonic sources and Filtering Approaches. IEEE Industry Application Magazine, 18–24 (August 2001) [7] Wu, C.J., Chiang, J.C., Yen, S.S., Liao, C.J., Yang, J.S., Guo, T.Y.: Investigation and Mitigation of Harmonic amplification problems caused by single-tuned filters. IEEE Trans. on Power Delivery 13(3), 800–806 (1998)

Preparation and Characteristics of Poly(St-co-MAA) Microgels Young Sik Kim1,*, Sang Hyun Moh2, Suk Hyung Cho3, Young Jun Kim4, and Jea Chul Cho5 1

Department of Chemical Engineering, Kangwon National University, 1 Joongang-ro, Samcheok, Gangwon, Korea 245-711 2 BIO-FD&C Co., Ltd, Phytomecca Co., Ltd, Pilot plant 7-50, Songdo-Dong, Yeonsu-Gu, Incheon, Korea 406-840 3 Department of Fire Safety and Management, Hyejeon University, Hongsung-up Chungnam, Korea 350-800 4 Department of Cosmetic Science, Chungwoon University, Hongsung-up Chungnam, Korea 350-800 5 Department of Hotel Culinary Arts & Food Service, Hyejeon University, Hongsung-up Chungnam, Korea 350-800 [email protected]

Abstract. By using suspension polymerization, molecularly imprinted porous poly(St-co-MAA) gels were prepared from styrene (St), methacrylic acid (MAA) monomer and ethylene glycol dimethacrylate (EGDMA) as a crosslinking agent. The polymerization condition was chosen that the formation of solution complexes between methacrylic acid and the template(caffeine) prior to polymerization gave better results. Polymerization parameters such as mole ratio of monomers, concentration of monomers, concentration of a crosslinking agent and concentration of a blowing agent on the average particle size and pore size distribution were investigated. Keywords: suspension polymerization, imprinted porous polymer, microgels microgels.

1

Introduction

As every manufactured products are diversified with industrial development, analysis of product ingredients and separation and refinement of compounds demand a high level of accuracy. Thus, analysis methods such as HPLC and GPC are widely used. Porous poly gels are usefully used as fillings of column for HPLC and GPC [1-5]. Monodisperse polymer microgels have a variety of size, pattern and structure of particles upon preparation method. Thus, polymer microgels with appropriate characteristics can be manufactured with monodisperse polymer microgels according to the purpose of use. These polymer microgels are rather easy to functionalized by employing manufacturing method and functional groups and a lot of studies on its *

Corresponding author.

T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 263–270, 2011. © Springer-Verlag Berlin Heidelberg 2011

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application is being tried due to its large surface area. Especially, the studies on porous polymer microgels with uniformed particle size are popular: such as column fillings of HPLC and GPC, carriers for enzyme fixations and protein adsorption [6-11], chelate resin that selectively adsorbs and separates ionic materials in heavy metal and seawater[12-13], adsorption and separation of protein and enzyme, adsorption and separation of metal ions, and development of application as catalyst[14-16]. Recently, poly gels are used for cosmetic fillings, toner for digital images, gap controller for liquid crystal display panel and standard particle of coulter counter. Studies have been focused on application of poly gels to biomedical area, e.g. as a diagnostic reagent and drug carrier. To produce polymer microgels, emulsion polymerization, dispersion polymerization and suspension polymerization are mostly used. According to polymerization methods, poly gels have different characteristics in particle size, dispersion of particles and molecular weight of polymers. Generally, monodisperse particles of polymers with are produced Emulsion polymerization, while polydisperse particles under 1 are made by dispersion polymerization, which particles in size within 1-10 molecular weight cannot be controlled [17-18]. Seed polymerization is the way to synthesize poly gels with uniformed dispersion of particles which monodisperse polymer seed, linear or cross-linked synthesized by emulsion polymerization and dispersion polymerization are increased in particle size by swelling by monomer or organic solvent [19-21]. Also, particles over 10 can be obtained by suspension polymerization. However, its polydisperse particles may cause reduced column efficiency, decreased pressure and shortened polymer life due to fine particles if it is used as column fillings. On the other hand, its manufacturing method is simple and various vinyl monomers can be used rather than other polymerization methods. Also, it is widely used for various area by employing functional groups to polymer particles. In this study, porous foamed polymer microgels with uniformed size of particles were synthesized by suspension polymerization. Also, the experiments were performed on mol ratio of monomer as factors that affect the characteristics of poly gels, particle size and particle size distribution by the amount of cross-linking agent.

㎛

㎛

㎛

2

Experiment

2.1

Reagent

Monomer styrene (St) used in this study was purchased from Junsei Chemical, Japan. After washed with 10% NaOH, it was vacuum distilled under nitrogen stream and stored at -5°C. and Methacrylic acid (MAA) was purchased from Aldrich and vacuum distilled under nitrogen stream. Ethlene glycol dimethacrylate (EGDMA), a grosslinking agent was purchased from Aldrich. After washed with 10% NaOH, it was vaccum distilled under nitrogen steam. Benzoyl peroxide (BPO), a polymerization initiator was purchased from Wako Pure Chemical and recrystallized with methanol. Also, polyvinyl pyrrolidinone (PVPd) as suspension stabilizer, n-hexane as blowing agent, toluene as swelling agent, and acetonitrile as porogen were purchased from Junsei Chemical.

Preparation and Characteristics of Poly(St-co-MAA) Microgels

2.2

265

Synthesis of Copolymer Gel

Synthesis of Poly(St-co-MAA) gel. dissolve PVPd the suspension stabilizer in 200ml of distilled water. Then, add cross-linking agent, EGDMA and stirred in the water. And then, dissolve benzoyl peroxide in the water and add monomer styrene and methacrylic acid. Put this liquid into 500ml erlenmeyer flask. Add nitrogen stream to remove dissolved oxygen and stir and polymerize it at 300rpm at 70°C for six hours. At this time, fix monomer concentration in 10wt.% and change mol ratio of St/MAA to 95/5, 90/10, 85/15 and 80/20 each and copolymerize it. Change EGDMA concentration to 0.25 5 wt% and synthesize poly(St-co-MAA) gel. To remove unreacted reactants, wash them more than three times using methanol and distilled water and vacuum dry it at 50°C.

～

2.3

Characteristics of Microgels

Analysis of chemical composition. Furrier Transfer Infrared Spectroscope(IFS66/FRA 106S, Bruker) was used to examine IR spectrum of microgel in order to find the composition of prepared poly gels. Measurement of particle pattern and particle size distribution. Scanning electron microscope(LEO-1530FE) was used to observe particle size and the surface pattern at 10 kV. The prepared poly gel was spreaded on the sample plate of the microscope and observed. Also, ultrasonic generator and agitator were used to uniformly distribute specific amount of poly gel. Particle analyzer (Malvern Mastersizer 2000) was used to measure particle size of suspension. The particle size distribution was calculated by Span value using the following equation: Span=represents volume per cent that microcapsule accounts for and Dn% indicates average particle size in the area. Accordingly, small span value indicates monodisperse microcapsule.

3

Results and Discussion

Poly(St-co-MAA) gel was prepared by suspension polymerization changing mol ratio of St/MAA and using BPO as initiator and EGDMA as cross-linking agent. while monomer concentration was changed to 10, 15 and 20 wt%; St/MAA mol ratio was to 95/5, 90/10, 85/15, 80/20; cross-linking agent, EGDMA concentration was changed to 0.5, 1.0, 1.5, 2.0, and 2.5 wt%, particle size of prepared poly gel was measured by SEM image and PCS method. Sphere type of fine particles were obtained with different particle sizes upon mol ratio of monomer and concentration of cross-linking agent. Prepared poly(St-co-MAA) gel particles are distributed within the range of 93-643 . SEM images of the gel particles prepared upon each reacting condition were shown in Figure 1, Figure 3 and Figure 5. As shown in Figure 1 and 2, as St/MAA mol ratio changed to 95/5, 90/10, 85/15 and 80/20, particle size of microgel tended to increase.

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Fig. 1. SEM images of the microgels prepared with various molar ratio of monomer: (a) St/MAA(95/5); (b) St/MAA(90/10); (c) St/MAA (85/15); (d) St/MAA(80/20)

Exp. No

Particle size ( )

㎛

Span

a

134

1.66

b

164

1.39

c

206

1.99

d

271

1.95

Fig. 2. Particle size and size distribution of the microgels prepared with various molar ratio of monomer: (a) St/MAA(95/5); (b) St/MAA(90/10); (c) St/MAA(85/15); (d) St/MAA(80/20)

Preparation and Characteristics of Poly(St-co-MAA) Microgels

267

Also, as shown in Figure 3, if St/MAA mol ratio was 90/10, as EGDMA increased, particle size decreased. As shown in Figure 4, as cross-linking agent increased, the range of particle size distribution got narrow. When the amount of hydrophilic MAA increased than that of hydrophobic St which forms nucleus, gel particles are polydispersed and the average size of particle increased. This is because swelling

Fig. 3. SEM images of the microgels prepared with various concentration of crosslinker: (a) 0.5 wt%; (b) 1.0 wt%; (c) 1.5 wt%; (d) 2.5wt%

Exp. No

Particle size ( )

㎛

Span

a

634

2.29

b

367

1.99

c

180

1.56

d

93

0.84

Fig. 4. Particle size and size distribution of the microgels prepared with various concentration of crosslinker: (a) 0.5 wt%; (b) 1.0wt%; (c) 1.5 wt%; (d) 2.5 wt%

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occurs when polymerized when more hydrophobic MAA were contained and cohesion event occurred while swollen gel was drying. Also, as shown in Figure 3 and 4, when cross-linking agent increased to 0.5, 1, 1.5, 2.0 and 2.5 wt% while preparing poly (St-co-MAA) gel, the particle type got uniformed and the size got smaller as crosslinking agent increased. It is because cross-linking density increased and particle internal structure got dense as cross-linking agent increased.

Fig. 5. SEM images of the microgels prepared with various concentrations of monomer: (a) 10 wt%; (b) 15 wt%; (c) 20wt%

Exp. No

Particle size ( )

㎛

Span

a

164

1.39

b

334

8.84

c

473

7.86

Fig. 6. Particle size and size distribution of the microgels prepare with various concentration of monomer: (a) 10 wt%; (b)15wt%; (c) 20 wt%

Preparation and Characteristics of Poly(St-co-MAA) Microgels

269

When St/MAA mol ratio was 90/10 and cross-linking agent was 2 wt%, monomer to solvent was changed to 10, 15 and 20 wt%. As shown in Figure 5 and 6, as the level of monomer increased, particle size increased and particle distribution also increased. As dispersed medium increased, monomer bubbles relatively decreased. Thus, the opportunity for them to cohere each other increased and more stable and uniformed bubbles are created. Figure 7 indicates FT-IR results that indicates the structure of poly(St-co-MAA) prepared. Absorption peak by C-H bond was shown in 3100-3000 cm-1; the absorption peak by C=C and =CH by double bond of aromatic series was shown in 1600-1400 cm-1 and 800-700 cm-1; the characteristic peak of vinyl group (CH2=CH-) was shown in 1600 cm-1. Crossed-linked poly(St-co-MAA) was proved in that peak of C=O bond of MAA was shown in 1700 cm-1 and absorption peak of C-O of EGDMA was shown in 100-1100cm-1.

Fig. 7. FT-IR spectrum of poly(St-co-MAA) gels

4

Conclusion

In this study, St and MAA were used as monomers and EGDMA was used as crosslinking agent to produce poly(St-co-MAA) gel upon mol ratio of monomers and the level of cross-linking agent. 1. When monomer level was 10 wt%, mol ratio of St/MAA, 90/10 and cross-linking agent, 25 wt%, poly(St-co-MAA) with the most uniform particle size was obtained. Here, relatively monodisperse poly gel was produced with 93 in particle size and 0.84 in span value.

㎛

㎛

㎛

to 271 2. As mol ratio of St/MAA increased, particle size increased from 134 while as level of crossing agent increased, particle size decreased from 643 to 93 .

㎛

㎛

3. When mol ratio of St/MAA was 90/10, level of cross-linking agent, 2.0 wt%, and blowing agent level, 20 wt% and swelling agent level, 20 wt%, foamed poly(Stco-MAA) gel with uniformed particle size was obtained. Here particle size was 943 and foaming magnitude was approx. 5.8.

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References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27.

日本高分子論文集

Ogino, K., Sato, H.: , 46, 667 (1989) Kulin, L.I., Ellingsen, T., Ugelstad, J.: J. Chromatogr 514, 1 (1990) Ellingsen, T., Aune, O., Ugelstad, J., Hagen, S.: J. Chromatogr 535, 147 (1990) Cheng, C.M., Vanderhoff, J.W., El-Asser, M.S.: J. Polym. Sci. 30, 245 (1992) Ugelstad, J., Kaggerud, K.H., Hansen, F.K., Berge, A.: Markrom. Chem. 180, 737 (1979) Gehrke, S.H., Andrews, G.P., Cussler, E.L.: Chem. Eng. Sci. 41, 2153 (1986) Kawaguchi, H., Hoshinon, F., Ohtsuka, Y.: Makromol. Chem. Rapid Commun. 7, 109 (1986) Freitas, R.F.S., Cussler, E.L.: Chem. Eng. Sci. 42, 97 (1987) Okubo, M.: Polymer International 30, 469 (1993) Park, T.G., Hoffman, A.S.: J. Biomed. Mater. Res., 24, 21 (1990) Park, T.G., Hoffman, A.S.: Biotech. Bioeng. 35, 152 (1990) Egawa, H., Nakayama, M., Nonaka, T., Uemura, K.: J. Appl. Polym. Sci. 34, 1557 (1987) Kim, K.S., Kang, S.H., Park, M.H., Lee, S.H.: J. Ind. and Eng. Chem (Korea) 2, 45 (1996) Kawaguchi, H.: Gendai Kagaku 170, 34 (1985) Kawaguchi, H.: Kobunshi Kakou 30, 477 (1984) Scott, C.D.: Clin. Chem. 14, 521 (1986) Shiroya, T., Yasui, M., Fujimoto, K., Kawaguchi, H.: Colloid and surfaces B: Biointerfaces 4, 267 (1995) Achiha, K., Ojima, R., Kasuya, Y., Fujimoto, K., Kawaguchi, H.: Polym. for Adv. Tech. 6, 534 (1995) Kawaguchi, H.: Prog. Polym. Sci. 25, 1171 (2000) Pelton, R.H., Chibante, P.: Colloids Surf. 120, 247 (1986) Clarke, J., Vincent, B.: J. Chem. Soc., Faraday Trans. 77, 1831 (1981) Liu, Z.S., Xu, Y.L., Yan, C., Gao, R.Y.: Anal. Chem. Acta 523, 243 (2004) Chen, X., Yia, C., Yan, X.Q., Wang, X.R.: J. Chromatohr, B 812, 149 (2004) Kugimiya, A., Takeuchi, T.: Anal. Chim. Acta 395, 251 (1999) Yano, K., Karube, I.: Trends in Anal. Chem. 13(3), 199 (1999) Kempe, M., Mosbach, K.: J. Chromatogr. A 664, 276 (1997) Cheng, S.H., Oh, C.Y., Seo, J.I., Park, J.K.: Korean J. Bioeng. 16, 115 (2001)

Identifying Organizational Culture for Patient Safety Yoo Kyung Boo1, Hyeon Sil Lee2, Ji Won Park3, and Seung Woo Ham4 1

Department of Healthcare Management, Eulji University Department of Health Administration, Korea University 3 Department of Medical Management Administration, Gimcheon University 4 Department of Medical Record, Korea Institute of Radiological & Medical Sciences, 215-4, Gongneung-Dong, Nowon-Gu, Seoul 139-706, Korea [email protected] 2

Abstract. A survey on organizational culture for patient safety was conducted among a total of 391 physicians, nurses, and health professionals working at four general hospitals in Seoul, using the Hospital Survey on Safety Culture instrument developed by AHRQ of the U.S. The results of the study showed that the patient safety awareness significantly differed by gender, age, career, average work hours, work units, contact with patients, and frequency of reported events. A good work climate and the immediate supervisor’s high level of patient safety awareness had positive influences on patient safety, and the hospital management’s low patient safety awareness had a negative influence thereon. To ensure patient safety in a hospital, the management’s leadership is required. In addition, the education of the medical professionals who come in direct contact with the patients should be reinforced, and a patient safety management system that is linked to the hospital information system should be developed. Keywords: patient safety, quality of care, organizational safety culture, medical error reporting, health information technology.

1

Introduction

Patient safety and quality of care improvement has recently emerged as an important issue for medical institutions. Patient-safety-related accidents occurring in hospitals may seriously affect both the hospital staff and the patients, and may lower the quality of care offered by the hospitals[1]. It was reported that most of the medical errors that had been committed so far were related with the defective systems of medical institutions and were not the results of the indifference or mistake of an individual [2]. Thus, an open organizational culture and hospital climate should be established for the prevention and management of medical errors, and the medical-service delivery system should be improved [3]. The Joint Commission on Accreditation of Health Organizations (JCAHO) of the U.S. issued patient safety standards in 2003, and reviewed medical institutions [4]. In South Korea, the Korean Institute for Healthcare Accreditation carries out accreditation of medical institutions with regard to patient safety and quality of T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 271–277, 2011. © Springer-Verlag Berlin Heidelberg 2011

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care [5]. Data on medical errors, however, are very scarce. As such, there is a need to lay the foundation for the fast establishment of a patient safety culture in local medical institutions by investigating the medical institutions’ climate, organizational culture, and medical errors regarding patient safety. This study aimed to provide the basic data required for the early establishment of a patient safety culture through the investigation of the hospital climate, organizational culture, and current status of medical errors in various medical professions’ reports with regard to patient safety.

2

Materials and Methods

2.1

Subjects

This study was conducted on physicians, nurses, and health professionals (pharmacists and medical technicians) of four randomly sampled general hospitals with over 200 beds in Seoul City. 2.2

Instruments

The survey questionnaire that was used in this study for the investigation of the medical professionals’ patient safety awareness was based on the Hospital Survey of Patient Safety Culture instrument developed by the Agency for Healthcare Research and Quality(AHRQ) of the U.S. as well as on other related studies and literature [6]. The survey questionnaire consisted of three areas (hospital climate, organizational culture, and medical errors) and 53 items. The survey questionnaire items were graded based on a 5-point Likert scale, where “1” corresponds to strongly disagree, “2” to disagree, “3” to neither agree nor disagree, “4” to agree, and “5” to strongly agree. 2.3

Data Collection and Analysis

The survey questionnaire was self-administered by the subjects from May 10 to June 9, 2011, in cooperation with their respective hospitals. A total of 500 questionnaire forms were distributed, and 400 (80%) were retrieved. Of the 400 retrieved forms, 391 (78.2%) were used for the final analysis, excluding nine that contained what were suspected as being insincere answers, or where many items were not answered. For the reliability of the survey, Cronbach’s alpha was 0.945. Frequency test, chi-square test, t test, one-way ANOVA, multiple-regression analysis, and logistic-regression analysis were conducted on the results of the survey, using SPSS 18.0.

3

Results

3.1

General Characteristics of the Survey Respondents

The general characteristics of the survey respondents are presented in Table 1. The test of the association of patient safety awareness with medical profession showed significant

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differences in gender (p=0.000), age (p=0.000), entire career (p=0.000), career in the current work unit (p=0.000), work hours per week (p=0.000), work units (p=0.000), contact with patients (p=0.000), and number of reported events (p=0.014) (Table 1). Table 1. General characteristics of respondents

Nurse

Health Prof.

25(.065) 50(.131)

265(.695) 4(.010)

19(.049) 18(.047)

52.5 31.1 16.4 17.8 40.7 19.3 22.2

37(.102) 33(.091) 5(.013) 39(.100) 39(.100) 1(.002) 0(.000)

141(.391) 70(.194) 43(.119) 29(.074) 99(.255) 71(.183) 71(.183)

11(.030) 9(.025) 11(.030) 1(.002) 20(.051) 3(.007) 14(.036)

86 203 73 24

22.3 52.6 18.9 6.2

44(.113) 32(.082) 2(.005) 0(.000)

41(.106) 148(.383) 68(.176) 13(.033)

1(.002) 23(.059) 3(.007) 11(.028)

20~39 40~59 60 ~ Surgery Medicine ICU1) Ancillary Other Dept

26 296 64 146 110 55 40 34

6.7 76.7 16.6 28.6 37.9 14.3 10.4 8.8

2(.005) 17(.044) 59(.152) 20(.051) 22(.057) 3(.007) 11(.028) 22(.057)

21(.054) 245(.634) 4(.010) 78(.202) 124(.322) 52(.135) 3(.007) 12(.031)

3(.007) 34(.088) 1(.002) 12(.031) 0(.000) 0(.000) 26(.067) 0(.000)

Contact with patient

Yes No

351 36

90.7 9.3

12(.031) 67(.173)

11(.028) 259(.669)

Frequency of reported event

No 1~2 3~5 6~

176 151 52 9

45.4 38.9 13.4 2.3

48(.125) 19(.049) 10(.026) 1(.002)

113(.294) 113(.294) 35(.091) 8(.020)

variables Gender Age

Career - years Current specialty - years Working hours per week Work area

*pb>c

12.665

.000

a>b>c

2.550

.079

.448

.640

Physician(a) Nurse(b) Health Prof.(c)

79 270 38

2.51 1.92 1.99

.588 .298 .204

77.377

.000

a>c, b

Factors That Had an Influence on the Assessment of Patient Safety

The stepwise selection of multiple-regression analysis showed that the hospital climate (p=0.000) and the immediate supervisor’s patient safety awareness (p=0.006) were independent variables with a positive (+) influence on the assessment of patient safety, and that the hospital management’s patient safety awareness (p=0.000) was an independent variable with a negative (-) influence on the assessment of patient safety (Table 3). Table 3. Influence factors on the assessment of patient safety dependent variable Patient safety grade

independent variable (constant) x1 x2 x3

β

t

-.105 .210 -.238

20.892 -1.814 3.582 -.3.116

P

Tolerance

.000 .000 .000 .002

.706 .681 .404

VIF

1.417 1.468 2.473

R=.340, R2-square=.116, Adj-R2=.106, F=12.305, p=.000 x1) Frequency of Events Reported, x2) hospital management’s patient safety awareness, x3) Hospital climate.

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275

Influence Factors on the Patient Safety in Relation with Patient Contacts

The logistic-regression analysis that was performed to analyze the influence of the medical professionals’ contact with patients on patient safety predicted that the possibility of a medical professional coming in contact with a patient will increase by e1.341=3.823-fold in “one team helps another team if the work unit is really busy”, by e-.979=.376-fold in “a work unit’s procedure or system is helpful for preventing errors”, and by e-.859=.424-fold in “things fall between the cracks when transferring patients from one unit to another” compared with when a medical professional does not come in contact with a patient if one unit increases in each variable, when it is assumed that the scores of the other independent variables were equal (Table 4). Table 4. Prediction of patient safety according to contact with patients B x1 x2 x3 constant

1.341 -.979 -.859 3.038 -2LL=92.921

S.E,

Wals

p

.403 .430 .407 1.310

11.066 5.185 4.460 5.377

.001 .023 .035 .020

Exp(B) 3.823 .376 .424 20.861

X2(Intercept-Theoretical model)=19.026, (df=3 ,p=.000), Nagelkerke R2=.220

x1: one team helps another team if the work unit is really busy, x2 : a work unit’s procedure or system is helpful for preventing errors, x3: things fall between the cracks when transferring patients from one unit to another.

4

Discussion

Patient safety can be defined as the avoidance and prevention of patient injuries or adverse events resulting from the process of healthcare delivery [7]. Nowadays, patient safety has become the minimum principle that the entire area of medical services should comply with. In South Korea, studies on patient safety have been extensively conducted due to the influence of the accreditation and assessment of medical institutions and the increased awareness of medical errors in developed countries [8, 9]. This study was conducted to identify hospital climate and organizational culture for patient safety as well as the current status of medical-error reporting, to improve the quality of medical care by establishing a patient safety culture. There was nearly no difference in patient safety awareness between the physicians, nurses, and health professionals, and the longer the career and work hours were, and the higher the number of reported events was, the higher the patient safety awareness, although the overall patient safety awareness was low. In this study, the patient safety awareness was low possibly because the hospitals that were included in this study were not fully prepared for medical-institution accreditation as they were general hospitals, even though patient safety awareness is

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increasing in the society due to the accreditation of medical institutions. Thus, hospital climate improvement is needed to ensure patient safety. The results of this study showed that an increase in hospital climate improvement and in the immediate supervisor’s patient safety awareness had positive influences on patient safety, whereas a decrease in the hospital management’s patient safety awareness had a negative influence on patient safety. Thus, it was revealed that the leadership of the hospital management is needed to establish a patient safety culture, as has been shown in other studies, both local and abroad [10, 11, 12, 13]. To prevent patient-safety-related accidents, a patient safety management system that can remove and prevent risk factors in advance should be established. The patient safety management system should be linked to the hospital information system to ensure its expeditious and effective operation [14]. Enhanced prescription delivery and clinical-decision support systems and clinicalpractice guidelines should be interlinked within the hospital information system. In addition, a voluntary medical-error reporting and feedback system where the confidentiality is ensured is required for the effective prevention of near-miss and medical errors. To create a patient safety culture at the soonest possible time, a climate that can ensure patient safety, safety education of the staff members, a change in the level of patient safety awareness, establishment of patient safety and medical-error reporting systems, cooperation between work units, and open communication are required. By doing all these, the quality of medical services could be improved.

References 1. Blendon, R.J., DesRoches, C.M., Brodie, M., Benson, J., Rosen, A., Schneider, E., Altman, D., Zapert, K., Herman, M., Steffenson, A.: Views of Practicing Physicians and the Public on Medical Errors. N Engl. J. Med. 347, 1933–1940 (2002) 2. Institute of Medicine: To Err Is Human: Building a safer Health System. National Academy Press, Washington, DC (2000) 3. Joint Commission on Accreditation of Health Organizations, http://www.jointcommission.org/ 4. Radecki, R.P., Sittig, D.F.: Application of electronic health records to the Joint Commission’s 2011 National Patient Safety Goals. JAMA 306(1), 92–93 (2011) 5. Park, J.H.: Understanding of the new Korea Healthcare Accreditation System. J. Korean Med. Assoc. 54(2), 142–145 (2011) 6. Agency for Healthcare Research and Quality (AHRQ) Hospital Survey on Patient Safety, http://www.ahrq.gov/qual/patientsafetyculture/ hospscanform.pdf 7. Kim, M.R.: Concept Analysis of Patient Safety. J. Korean Acad. Nurs. 41(1), 1–8 (2011) 8. Nam, M.H., Kang, S.H.: The Cognition Level on the Patient Safety and Safe Nursing Activities among Hospital Nurses in Busan. J. Health and Social Sciences 28, 197–221 (2010) 9. Kim, J.E., Kang, M.A., An, K.E., Sung, Y.H.: A Survey of Nurses’ Perception of Patient Safety Related to Hospital Culture and Reports of Medical Errors. Clinical Nursing Research 13(3), 169–179 (2007)

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10. Sammer, C.E.: What is patient safety culture? A review of the literature. J. Nurs. Scholarsh 42(2), 156–165 (2010) 11. Ruchlin, H.S., Dubbs, N.L., Callahan, M.A.: The Role of Leadership in Instilling a Culture of Safety: Lessons from the Literature. J. Healthc Manag. 49(1), 47–58 (2004) 12. Ginsburg, L.R.: The Relationship between Organizational Leadership for Safety and Learning from Patient Safety Events. Health Serv. Res. 45(3), 607–632 (2010) 13. Firth-Cozen, J., Mowbray, D.: Leadership and the Quality of Care. Quality in Health Care 10(suppl. II), ii3–ii7 (2001) 14. Bates, D.W., Gawande, A.A.: Improving safety with information technology. N. Engl. J. Med. 348(25), 2526–2534 (2003)

The Effect of a Support Program for Family Caregivers of Dementia on Stress Hormones* Hyoshin Kim Dept. of Nursing, Chungwoon University, San29, Namjang-ri, Hongseong-eup, Hongseong-gun, Chungnam, 350-701, Republic of Korea [email protected]

Abstract. This study aims to evaluate the effect of a support program for family caregivers of seniors with dementia on stress hormones (Epinephrine and Norepinephrine). A total of 44 family caregivers participated in the experiment: 25 were in the experimental group, and 19 were in the control group. The experimental group took part in a support program once per week for 8 weeks, with 2-3 hours per session. Blood Epinephrine and Norepinephrine of both groups were measured before and after the program. As a result, Norepinephrine increased significantly less in the experimental group than in the control group after the support program. This results shows that the support program had the positive effect of reducing the increase of Norepinephrine. In conclusion, it was found that the support program can be used as a safe and effective strategy for managing stress control for the family caregivers of seniors with dementia. Keywords: Dementia, Family Caregiver, Stress Hormone, Epinephrine, Norepinephrine.

1

Introduction

1.1

Necessity

The elderly population is increasing rapidly due to a number of factors, including the extension of the average life span, economic and medical development, and the declining birthrate. By 2026, Korea is expected to be a super-aged society, with seniors representing 20.8% of the population [1]. Corresponding to this, the prevalence of dementia, one of the typical diseases experienced by the elderly, has increased sharply. The prevalence rate is reported to be between 6.3 ~ 13% in many epidemiologic surveys in Korea [2]. Family caregivers for a person with dementia suffer from chronic psychological problems, including depression, anxiety and tension. In certain cases, they have to be treated for depression, and visit hospitals or are administered with medicines due to *

This study was supported by a research grant of the Life Insurance Philanthropy Foundation.

T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 278–285, 2011. © Springer-Verlag Berlin Heidelberg 2011

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their physical sacrifices, such as skipping meals or working too hard to care for their patients.[3]. Economic burdens are also frequently increased, as a family member may have to quit a job or withdraw from social activity to support their seniors with dementia, or to pay the related care service cost [4]. As dementia, unlike other diseases, is a cognitive and behavioral disorder that requires 24-hour protection and care, family caregivers experience huge physical and mental stress [5]. In particular, the frequent problematic behavior and low social support for elderly sufferers of dementia increase the burden and depression of caregivers [6][7]. The support program is designed to reduce the burden to family caregivers, based on the knowledge and demand of dementia by caregivers. Previous studies on family caregivers developed programs mainly based on a literature survey [8][9][10]. But this study attempted to develop a customized support program through interviews, a literature survey and the participation of professionals. In previous studies, the effect of these programs were measured mainly as social and psychological variables, but rarely was any attempt made to measure stress hormones. Thus, in this study Epinephrine and Norepinephrine were measured. 1.2

Purpose of the Study

The purpose of this study is to develop a support program and to verify its effects on Epinephrine and Norepinephrine of family caregivers of senior citizens with dementia.

2

Methodology

2.1

Design

This study is quasi-experimental research employing the nonequivalent control group pretest-posttest design. The support program (SP) is an independent variable, and the Epinephrine and Norepinephrine of family caregivers are dependent variables. It was developed using interview, literature survey, experience of caregivers, and participation of professional groups. The experimental group received the support program once per week for 8 weeks, with one 2-3 hour session per week. The control group did not receive any intervention. 2.2

Subjects

The experimental group consisted of family caregivers who consented to join the support program and to take the 8-week support program and blood test before and after the program. The control group was made up of family caregivers who also agreed to join the experiment and took a blood test before and after the program. Both group had blood tests at the same time and period before and after the test.

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The homogeneity test of both groups showed no difference in general characteristics (Table 1). Before the test, there was no difference between the two groups in the Epinephrine and Norepinephrine levels (Table 2). 2.3

Instruments

2.3.1 The Blood Test of Epinephrine and Norepinephrine The blood test was carried out at 9:30~10:30 a.m. in a comfortable state after a 12hour fast from 7:00 p.m. of the previous day, as scheduled in advance. A trained examiner and a blood-gathering nurse controlled the process and the subjects. For the blood Epinephrine and Norepinephrine content, the High Performance Liquid Chromatography (HPLC) method was applied, and the sample was analyzed using an HPLC-Acclaim device. 2.4

Resource Collection Method and Research Procedure

Family caregivers were recruited with the assistance of workers in dementia support centers, day centers for the elderly, and domiciliary care facilities in Seoul, as well as through internet and leaflet advertisement. The program content was constructed by interviewing 44 family caregivers of senior citizens with dementia. This was followed by the advice of a professional group. The program included information on the concept and symptoms of dementia, diseases related to dementia, home care methods, health management of family caregivers of senior citizens with dementia, exercise therapy, art therapy, resolution of family conflict caused by senior citizens with dementia, skills for communication with senior citizens with dementia, handling problematic behavior by senior citizens with dementia, stress control for family caregivers, information about local community resources, and an information exchange among family caregivers of senior citizens with dementia. Subsequently, the program was given to 26 Experimental group members for 8 times with one session per week, and 2-3 hours per session. The questionnaire survey and blood test were performed in the preliminary meeting, and the program was carried out 8 times. The total number in the experimental group became 25 as one dropped out during the test. The control group was 19 after excluding 3 who did not join the post test. The test was carried out from July to November of 2009. 2.5

Resource Analysis

A total of 44 cases were analyzed. The collected resources were analyzed using SPSS 18.0k for Windows. (1) The general characteristics of family caregivers were analyzed by calculating frequency and percentage. (2) The general characteristics of family caregivers and the homogeneity test of Epinephrine and Norepinephrine were analyzed using the χ2-test.

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(3) In the preliminary homogeneity test of family caregivers, all general characteristics such as gender, age, residence, marital status, number of family members, number of children, religion, education level, occupation, monthly income, relation with the senior with dementia, years caring for the senior with dementia, and symptoms showed no difference between the experimental group and the control group. Therefore, the paired t-test was employed within the group before and after the test, and the unpaired t-test was used to analyze the two groups before and after the test.

3

Research Result

3.1

Homogeneity Test

3.1.1 General Characteristics of Family Caregivers of Seniors with Dementia Table 1 shows the preliminary general characteristics and homogeneity of subjects in the experimental group and control group. In all general characteristics, such as gender, age, residence, marital status, number of family members, number of children, religion, education level, occupation, monthly income, relation with the demented elderly, caring years of the demented elderly, and symptoms, there was no difference shown between the experimental group and the control group (Table 1). Table 1. General characteristics and homogeneity of subjects in the experimental and control groups

(N=44) General characteristics

Category

Gender

Age(yr)

Place of Residence Marital status Number of family members (living together) Number children Member Religion

of of

a

Female Male Below 40 40-49 50-59 60-69 70-79 80 and above Seoul Gyeonggi Unmarried Married 1 2-4 5-6 0 1-3 4 or more Yes No

Exp. N (%) 24(96.00) 1(4.00) 2(8.00) 4(16.00) 10(40.00) 8(32.00) 0(0.00) 1(4.00) 19(76.00) 6(24.00) 4(16.00) 21(84.00) 3(12.00) 18(72.00) 4(16.00) 3(12.00) 20(80.00) 2(8.00) 21(84.00) 4(16.00)

Cont. N (%) 16(84.21) 3(15.79) 0(0) 2(10.53) 3(15.79) 10(52.63) 1(5.26) 3(15.79) 18(94.74) 1(5.26) 1(5.26) 18(94.74) 6(31.58) 10(52.63) 3(15.79) 1(5.26) 16(84.21) 2(10.53) 15(78.95) 4(21.05)

Total N (%) 40(90.91) 4( 9.09) 2( 4.55) 6(13.64) 13(29.55) 18(40.91) 1( 2.27) 4( 9.09) 37(84.09) 7(15.91) 5(11.36) 39(88.64) 9(20.45) 28(63.64) 7(15.91) 4( 9.09) 36(81.82) 4( 9.09) 36(81.82) 8(18.18)

χ2

p

1.816

0.300

7.989

0.145

2.833

0.119

1.236

0.370

2.660

0.341

0.638

0.858

0.185

0.710

282

H.S. Kim Table 1. (Continued)

(N=44) Education level

Employed Monthly income (unit: 10,000 won)

Relation with the demented senior

Years caring for the demented senior Does the caregiver have symptoms

Elementary school or below Middle or High school College or above Yes No Below 100 100~Below 200 200~Below 300 300 or higher Daughter-in-law Son Daughter Spouse Sister 5 or less 6-15 16 or more Yes No

1(4.00)

5(26.32)

6(13.64)

12(48.00)

9(47.37)

21(47.73)

12(48.00) 6(24.00) 19(76.00) 4(16.00) 5(20.00) 5(20.00) 11(44.00) 6(24.00) 1(4.00) 10(40.00) 4(16.00) 4(16.00) 20(80.00) 4(16.00) 1(4.00) 21(84.00)

5(26.32) 3(15.79) 16(84.21) 9(47.37) 5(26.32) 1(5.26) 4(21.05) 5(26.32) 1(5.26) 3(15.79) 9(47.37) 1(5.26) 9(47.37) 6(31.58) 4(21.05) 17(89.47)

17(38.64) 9(20.45) 35(79.55) 13(29.55) 10(22.73) 6(13.64) 15(34.09) 11(25.00) 2( 4.55) 13(29.55) 13(29.55) 5(11.36) 29(65.91) 10(22.73) 5(11.36) 38(86.36)

4(16.00)

2(10.53)

6(13.64)

5.257

0.093

0.447

0.710

7.172

0.072

6.893

0.121

5.660

0.058

0.275

0.684

3.1.2 Preliminary Measurement of Epinephrine and Norepinephrine of the Experimental Group and the Control Group In the preliminary measurement of Epinephrine and Norepinephrine, there was no significant difference between the experimental group and the control group [Table 2]. Table 2. Homogeneity of Epinephrine and Norepinephrine of subjects in the experimental and control group

(N=44) Exp.

Cont.

t

p

Epinephrine (pg/mL)

27.84±15.48

29.60±9.00

0.47

0.661

Norepinephrine (pg/mL)

303.01±108.92

349.24±151.07

1.18

0.245

Exp. : Experimental group (n=25) Cont. : Control group (n=19) Data represents mean±S.D.

3.2

Hypothesis Testing

3.2.1 Epinephrine Epinephrine in the experimental group was 27.84±15.48 pg/mL before the test and became 28.66±15.01 pg/mL after the test, which was not a statistically significant difference. Epinephrine in the control group was reduced from 29.60±9.00 pg/mL to 28.87±13.90 pg/mL, which was also not significant. After the test, Epinephrine was

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increased by 0.83±17.32 pg/mL in the experimental group and decreased by 0.73±15.96 pg/mL in the control group. Nonetheless, both tests showed no significant difference between the two groups (Table 3). Table 3. Epinephrine and Norepinephrine before and after support program in the experimental and control groups

(N=44) Before

After

t

p

Difference (After-Before)

t

p

0.06

0.953

2.63

0.012

Epinephrine (pg/mL) Exp.

27.84±15.48

28.66±15.01

0.24

0.816

0.83±17.32

Cont.

29.60±9.00

28.87±13.90

0.20

0.844

-0.73±15.96

Norepinephrine (pg/mL) Exp.

303.01±108.92

337.76±98.46

1.77

0.090

34.76±98.21

Cont.

349.24±151.07

514.72±337.81

2.24

0.038

165.50±321.32

Exp. : Experimental group (n=25) Cont. : Control group (n=19) Data represents mean±S.D.

3.2.2 Norepinephrine Norepinephrine in the experimental group was 303.01±108.92 pg/mL before the test and became 337.76±98.46 pg/mL after the test, which was not a statistically significant difference. Norepinephrine in the control group was increased from 349.24±151.07 pg/mL to 514.72±337.81 pg/mL, which was statistically significant (p=0.038). After the test, Norepinephrine was increased by 34.76±98.21 pg/mL in the experimental group, and by 165.50±321.32 pg/mL in the control group, which showed significant differences between the two groups (p=0.012) (Table 3) (Fig. 1).

Fig. 1. Stress hormone (Norepinephrine) changes before and after support program in the experimental and control groups

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Discussion

Stress is a generic term for an individual's physiological, psychological and behavioral reactions to control and to adapt to internal and external pressures. Basically, a stress reaction is an adjustment reaction for survival. If this reaction is over-activated, it can cause anxiety and depression. If it continues chronically, it can damage the body [11]. According to Wellis-Federeman et al. (1995) [12], stress is a non-specific response of the body against a stressor, and may cause a negative psychological reaction such as anxiety when environmental stimulation (the stressor) is recognized as threatening. Stress can cause stress reaction through the central nervous system, the autonomic nervous system, and the hypothalamus - pituitary gland - adrenal that increases stress hormones. There are three catecholamines - Epinephrine, Norepinephrine, and Dopamine - secreted from the adrenal medulla, of which Epinephrine and Norepinephrine are present in the greatest amount. As dementia is an irreversible disease that chronically progresses after its occurrence, it imposes a very serious psychological, physical and economical burden to the family. Kim and Jeon (1996) [13] defined dementia as a disease that devastates whole families, and as the disease of the century. Park (1997) [14] enumerates five common burdens to the families of seniors with dementia: first, the restriction of social activity; second, an increased psychological burden; third, an increased economic burden; fourth, an increased burden on the health of caregivers; and fifth, the aggravation of conflict between family members. Among the factors that reduce the burden of family caregivers, the importance of support has been proven in previous studies. Support helps to improve individual psychological adaptation, to promote problem-solving ability and to achieve stability [15]. The support program for family caregivers described in this study is a program designed to reduce stress to family caregivers and to improve adaptation. In this study, Norepinephrine levels in the experimental group showed a significantly smaller increase than in the control group, when measurements taken before and after the test were compared. This demonstrates that the support program contributed to reducing the increase of Norepinephrine, a factor of stress hormone, in the experimental group, as has been shown in similar studies [16][17]. In conclusion, the support program for family caregivers had a positive effect of reducing the increase of Norepinephrine, and was proven to be safe and suitable to contribute to stress control. Therefore, if the support program is applied to family caregivers of the demented elderly, it seems to have a positive effect through stress hormone control.

References 1. Statistics Korea Estimating future population (2006), http://sgis.kostat.go.kr 2. Cho, M., et al.: The Prevalence and Risk Factors of Dementia in the Korean Elderly. Ministry of Health and Welfare (2008)

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3. Parks, M., Novielli, K.D.: A practical guide to caring for caregivers. American Family Physician 62, 2613–2620 (2000) 4. Song, M.I., Choe, G.G.: The Decision Factor on Primary Care-giver’s Burden for Senile Dementia Elderly. Journal of Welfare for the Aged. 37, 131–160 (2007) 5. Lee, S.J., Park, H.A., Cho, N.O., Oh, J.J.: Study on the Effectiveness of Caregiver Education Program on the Home Care of Senile Dementia Patients. Journal of Academy of Nursing 25, 45–60 (1995) 6. Donalson, C., Tarrier, N., Burns, A.: The Impact of the Symptoms of Demetia on Caregivers. British Journal of Psychiatry 170, 62–68 (1997) 7. Lee, Y.M., Yoo, I.Y.: Care Burden, Depression and Social Welfare Service Utilization among Family Caregiver for Demented Elderly. Journal of Korean Gerontological Society 25(3), 111–121 (2005) 8. Kim, S.Y., Chang, J.K.: A Study on Education Programs for Senile Dementia Family. Living Science Research Institute 16, 13–39 (2001) 9. Han, H.S.: The Development and Its Effectiveness of the Group Program on Empowerment for the Senile Dementia Caregivers, Master’s thesis of Honam university, Korea (2004) 10. Lee, E.H.: Effects and Development of Cognitive-behavioral Group Intervention in Reducing Psychological Distress in Primary Caregivers of Persons with Dementia: Caregiving Burden, Depression, and Anxiety, Master’s thesis of Chonnam national university, Korea (2005) 11. Yu, B.H., Jong, M.W.: Neural Circuits Mediating Stress 9(1), 81–92 (2001) 12. Wellis-Federman, C.V., Stuart, E.M., Deckro, J.P., Mandle, C.L., Baim, M., Medich, C.: The Mind-body Connections: The Psychophysiology of Many Traditional Nursing Intervention. Clinical Nurse Specialist 9(1), 59–66 (1995) 13. Kim, T.H., Jun, G.Y.: A Study on Caregiving Situations and Resources for Adjustment among Family Caregivers of the Elderly with Dementia. Journal of the Korean Home Economics Association 34(4), 145–160 (1996) 14. Park, T.R.: Senile Dementia and Support Countermeasure for the Family. Journal of Social Science Research 4(2), 251–269 (1997) 15. Suh, M.H., Oh, K.S.: A Study of well-being in Caregivers Caring for Chronically Ill Family Members. Journal of Academy of Nursing 23, 467–486 (1993) 16. Yun, S.Y., Choi, B.J.: Effect of Horticultural Therapy on the Stress and Serum Cortisol of Demented Elders. Korean Journal of Horticultural Science and Technology 28(5), 891–894 (2010) 17. Yoo, K.H.: The Effects of Massage on Stress Hormone in Prematuer Infants. Journal of Korean Academy of Child Health Nursing 11(1), 125–131 (2005)

The Effect of Educational Game on the Intrinsic Motivation by Learner’s Traits: Focus on Personality Types Hyung-sung Park, Jung-hwan Park, and Young-Tae Kim Korea National University of Education, Jeju National University, Wonkwang University [email protected], [email protected], [email protected]

Abstract. The purpose of this study was to identify the effect of the action levels of educational game on the intrinsic motivation of the learners by personality types. In this study the action levels of the educational game and the learners’ personality types are main variables to be explored. The result of this research, there are meaningful difference in intrinsic motivation between learners who use games on the higher level of action and learners on the lower level of action in game-based learning. And also, the intrinsic motivation of extrovert learners' improved more than that of introvert learners' in game-based learning. It is expected that the results of this study would suggest how to utilize educational games in teaching and learning. It is considered that the result can be enough information for learners, parents of students and teachers who try to use and develop educational games. For example, it can be an idea to construct menus considering characteristics of learners in various steps of designing and developing educational games. And it can help to improve learners' intrinsic motivation especially if games are developed, distinguishing games for extrovert learners from those for introvert learners. Keywords: Educational Game, Personality Type, Intrinsic Motivation.

1

Introduction

Digital game is a popular activity in nowadays. Former studies on video games typically focuses on negative aspects surrounding video games like aggressive behavior (Gentile, Lynch, Linder, & Walsh, 2004), violence(Thompson, 2001), and addiction (Chiu, Lee, & Huang, 2004), Sherry(2001) performed a meta-analysis of the video gaming literature and found only a minute relationship between hostile behavior and violent video games (Beedle & Wright, 2007). On the other hand, in recent years there has been no shortage of efforts to design and develop educational games for learning (Amory, 2001; Barab et al., 2005; Gros, 2007). However, as many educators already foresee, it is not an easy task to achieve the two objectives of education and fun at the same time. The more fun the game has, the weaker the learning becomes and vice versa. In fact, it is difficult to imagine and then devise a scenario where both T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 286–292, 2011. © Springer-Verlag Berlin Heidelberg 2011

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learning contents and gaming situations occur simultaneously while balancing fun and learning. This balancing act between enjoyment and educational value continues to be a hot topic in discussions about developing educational games (Kim, Park, & Baek, 2007). With the attention of the games deeply, educators begin to focus on the educational potentials of games. First game as a media, there are sound music, vivid animations, intricate plots the characters with distinct personality, so the involvement of game can be called a kind of enjoyment of art. Second, the player through various designed circumstances, and to complete the task, in the process of playing, their ability of resolving problems and corporation promote increasingly. Specially, the MMORPGs which can incorporate some education content, players can obtained in the course of the geography, history, culture, daily life, animals and plants, tourism, a few interesting physical and chemical knowledge, because such knowledge is relatively easy to express by the scenes, maps, tasks, skills and other elements of the game. Regardless of how the game was demonized, if we can objectively understand the positive effects of games, and make the best use of it to promote learning, it should have great advantages and potential. Many educators and scholars at home and abroad have initiated research and development of the educational game based on the pros and cons, they try to explore application model, and already have achieved some positive results (Dickey, 2007; Kafai, 2006; Killi, 2005; Prensky, 2001; Squire & Jenkins, 2003; Squire, 2003). Like this, much effort to use educational games for students’ learning is being made actively at educational filed. Roles of learners and instructors are undergoing considerable changes in current view of the constructivism, and the changes in the teaching-learning method are dynamic, correlating with changes in the whole contemporary socialites. For this reason, this research was started. In this study the action levels of the educational game and the learners’ personality types are main variables to be explored. It is expected that the results of this study would suggest how to utilize educational games in teaching and learning.

2

Educational Game, Motivation and Learning by Doing

Games provide an engaging and immersive learning environment which is different from traditional learning. Many digital video games create a sense of sensory and physical immersion by using technologies resulting in one’s feeling “inside” an environment. These environments are psychologically immersive in ways that the world-to-the-desktop interface is not (Oblinger, 2006). In the environment game create which interactive fantasy, motivated, the player can play, practice actively. game-based learning environment proved to increase participants' interest and motivate participants in exploring and engaging in the learning activities(Kuo, 2007). Digital game as a learning environment is a powerful pedagogy that allows for practice, operate, rehearse immersive in safe environment. Squire (2003) examines the history of games in educational research, and argues that the cognitive potential of games have been largely ignored by educators. Contemporary developments

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in gaming, particularly interactive stories, digital authoring tools, and collaborative worlds, suggest powerful new opportunities for educational media. These elements we argued will contribute to a good learning environment. Games provide a rich learning environment in which students involved in and can operate by themselves with mentoring and apprenticeships in communities of practice. Begoña Gros (2003) argued game with tremendous potential for reaching, motivating, and fully involving learners. Through the gaming, students can learn by doing and work with others through games, game can support children’s learning inside and out of school. Learning by doing, this is great for Digital game-based learning, because “it turns out that doing is something that computer games are especially good at- they allow us to interact with them” (Prensky, 2001, p.158). Learn-by-doing is permeated into digital games, drill and practices, exploring, discovery, and problem solving are forms of doing required in games, in Game-based learning, students pursue a goal by practicing target skills and using relevant content knowledge to help them achieve their goal. The essential components of games: the goal, mission, storytelling scenario, operation, resources, feedback and ubiquitous coach are good at supporting learning by doing in video games. Most often, the more fun a game is the less effective it is as a learning tool and vice versa. In fact, the quintessential combination of fun and learning seems almost impossible to achieve. This ideal scenario is an equal mixture of both learning and game content. It’s not easy to determine the intrinsic balance where both learning and gaming happen simultaneously. This has continued to be a hot issue in the development of educational games. In the constructivist point of view on cognition, knowledge is generated from experience with tasks in authentic contexts rather than from isolated activities like memorizing facts and practicing skills separately (killi, 2005). Skills and knowledge are thus best acquired within a complex problem-rich context such as those presented by educational game, where a variety of problems can vary greatly in complexity and difficulty. As far as constructivists are concerned, using games for learning can play an important role in knowledge construction and composition and as an environment for problem solving skills.

3

Characteristics of Personality Type

The Myers-Briggs Type Indicator (MBTI), developed by Isabel Briggs Myers and her mother, Katharine Cook Briggs, is a well known and widely used personality inventory based on the psychological theories of Carl Gustav Jung. Jung's theory of psychological types assumes that much apparently random human behavior is really quite orderly and consistent and reflects the different ways persons take in information and make decisions. The Myers-Briggs Type Indicator is a self-report instrument based on Jung's theory of psychological types. The instrument returns the respondent's preferences on each of the four dimensions extraversion/introversion, sensing/intuition, thinking/feeling and judging/ perceiving (Rosati, 1998). The classification scheme is based on four dimensions or scales of preferences: (1) extraversion-introversion

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(EI)-whether to direct perception outward toward the environment or toward the inner world of ideas; (2) sensing or intuition (SN)-whether the person perceives things directly through one or a combination of the five senses (sensory), j or whether he/she tends to add ideas or associations to the incoming information (intuitive); (3) thinking or feeling (TF)-whether the person prefers to make judgments (or decisions) based on an impersonal or true-false basis (thinking), or based on personal values (feeling); and (4) judging-perceiving (JP), which “reflects whether the person relies primarily upon a judging process [thinking or feeling] or upon a perceptive process [sensing or intuition] in her/his dealings with the outer world” (MYERS, 1967).

4

Research Methods

4.1

Main Research Hypothesis

Hypothesis I: There will be a meaningful difference in intrinsic motivation between learners who use games on the higher level of action and learners on the lower level of action in game-based learning. Hypothesis II: The intrinsic motivation of extrovert learners' will be improved more than that of introvert learners' in game-based learning. Hypothesis III: There will be interaction effects between the level of action and the learner's personality type on learners' intrinsic motivation in game-based learning. 4.2

Participants, Experimental Design and Questionnaire

The learners were divided into four groups, considering the educational games' levels of action and the learners' personality types and were treated with the experiment process in the same condition for six weeks. The educational games were selected through operational definitions and expert examination. Those had same subject matters but different levels of action. The learners' personalities were evaluated by extraversion (E) or introversion (I) test of MBTI. Intrinsic Motivation Assessment Scale which was made by Harter’s(1981) assessment scale to have examined middle/high school students' intrinsic motivation, was used to check the learners' changes in their intrinsic motivation. That scale was reconstructed for questions suitable for educational games through expert examination, the reliability of the questions was satisfying as Cronbach's α = .84.

5

Results and Conclusion

The followings are results of the experiment. First, as shown Table 1, according to the pre-test result, high action level group and low action level group could make sure that there is no significant between experimental group and control group as statistical by action levels of educational game (t=1.545, p>.05). The result of the post-test of intrinsic motivation indicates that there is a 0.38 points difference between learners

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who use games on the higher level of acting(M=3.68, SD=.385, t=5.649, p.05). Extrovert learners (M=3.21, SD=.47, t=2.924 p "Control Panel" -> Performance and Maintenance" -> "Adminirtrative Tools" -> "Internet Information Services" - Keep clicking "+" under "Internet Information Services" until "Default Web Site" appears - Right-click "Default Web Sites" - Select "New Virtual Directory" - Specify "alias", KNN for example, on the wizard window - Browse to the directory, K_NN in this case, and press "NEXT" - Click "Finish" Once we have put our web method file in the virtual directory, we can use the method in our web program by the following procedure. - Right-click the name of the project on the Solution Explorer panel. - Click "Add Web Reference" - type in the URL, http://203.247.239.92/mhKnn for example. 3.2

Web Services for Trilateration

We have discussed two methods of indoor positioning, trilateration and K-NN in section 2. This section describes our web methods for trilateration. As we discussed before, the trilateration can determine the location of the mobile terminal if at least three distances from base stations to the mobile object are given. We also showed that we can estimate the distance with RF (Radio Frequence) signal strength. If we use existing equipments to obtain RF signal strength, then we can implement indoor positioning without any additional cost. WLAN (Wireless Local Area Network) provides such equipments and many WLAN based indoor positioning methods have been published [9-10]. WLAN based trilateration method scans signal strengths of APs (Access Point) and estimates the distances by evaluating Expression 2 with the signal strengths. Scanning signal strength should be done on a mobile terminal. Therefore, we have implemented a dynamic link library which reads signal strengths of APs. There are many ways of representing RF signal strength. Among them WLAN card uses RSSI (Received

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Signal Strength Indicator). So, our library is a RSSI reader. We have implemented our RSSI reader in C# with Microsoft Visual .NET 2005 on a Samsung laptop SENS M40 equipped with an Intel(R) PRO/Wireless 2200BG Network Connection. We named the RSSI reader WirelessManager.dll. Using our WirelessManager, a mobile terminal can scan RSSIs and send them to the service provider. By evaluating expression 2, the service provider can estimate the distances between the mobile terminal and the APs. If the locations of the APs are known to the service provider, the service provider can perform the trilateration procedure to estimate the location of the mobile terminal. Therefore, we also have implemented a web method, uploadApLocation(), with which a user can upload the location information of APs. We named our web method which performs the trilateration procedure TrilaterationPositioning(). 3.3

Web Services for K-NN

Deployment of K-NN consists of off-line phase and real-time phase. It builds a lookup table during its off-line phase. If a client sends its location and RSSIs to the server then the server can add the data to the look-up table to make the table one step closer to perfect. Therefore, we have implemented a web method called uploadRSSIs. During the real-time phase, if a client sends RSSIs to the server, then the server returns the location of the client by looking up the look-up table. For real-time phase, we have implemented a web method called KnnLocation. 3.4

Web Services for RFID Positioning

Deployment of RFID positioning is also consists of off-line phase and real-time phase. This also builds a look-up table during the off-line phase. A row of the look-up table is a (tag-ID, location) pair. This information should be provided by a client. Therefore, we have implemented a web method called uploadTagId. During the realtime phase, if a client sends a tag-ID to the server, then the server returns the location of the client by looking up the look-up table. To do this, we have implemented a web method called RfidLocation. 3.5

Rendering Drawings

One of the most important elements of ILBS's user interface is a drawing. Rendering a drawing should be done on the client system. So, we have implemented a DLL which renders a drawing given a list of object types (called objectList) and another list of data (called dataList). The i-th element of dataList contains the necessary and sufficient information to draw a figure of the type specified by the i-th element of objectList. For example, if the i-th element of objectList is Line, then the i-th element of dataList consists of two points. Our rendering function also provides zoom-in, zoom-out, up, down, move, and so on. Fig. 3 shows experimental results of the rendering program. It is hard to imagine an ILBS that does not render drawings and a drawing should be provided by a client. Therefore, we have implemented a web method called

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Fig. 3. Execution results of our rendering program

Fig. 4. List of implemented web services

uploadDrawing with which a client can upload a drawing. We have also implemented a web method with which a client can select a drawing from a list of drawing names. Fig. 4 shows the web methods we have implemented. [4] proposed a ”generalized Petri net,” a type of modified Petri net for modeling rule-based systems. There are many formalized ways to represent Petri nets, including the metamodel and UML (Unified Modeling Language). [5] introduced a Petri net ontology written in UML, RDFS, and OWL (Web Ontology Language). This work shares many similarities with [6], which introduced a method of transforming OWL DL (Description Logic) into predicate/transition nets. Because the RDF model in the present paper is a subset of OWL DL, there is an overlap between the objectives of [6] and those of this paper. However, our paper proposes a method that can transform RDF models into CPNs, not into predicate/transition nets. Furthermore, we actually built a CPN model with CPNTools to verify the proposed method.

4

Implementation of an Application

Making use of those web methods and DLLs, we have implemented another DLL named "ViewControl.DLL". Then we have created an ASP page as follows. Fig. 5 shows a typical test result of the ASP page.

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Fig. 5. A typical test result of our web service

5

Conclusions

Web service is one of the easiest ways of publishing our programs to the world so that anybody can call the program as if it is a function. Therefore, web service can improve the productivity of programming. This paper introduced several web methods which provide essential functions of ILBS. This paper also introduced a web program which uses the web methods we have published. We are planning to develop more web methods and develop a web service providing all the functions need for ILBS. We are also planning to expand the web methods to make them more versatile.

References 1. Alonso, G., Casati, F., Kuno, H., Machiraju, V.: Web Services: Concepts, Architectures and Applications. Springer, Heidelberg (2004) 2. Stal, M.: Web Services: Beyond Component-Based Computing. Comm. ACM 55(10) (2002) 3. Vogels, W.: Web Services Are Not Distributed Objects. IEEE Internet Computing 7(6) ( November/December 2003) 4. Booth, D.: Web Service Description Language (WSDL) Version 2.0. Technical report (2006) 5. W3C: UDDI technical white paper (2000) 6. Box, D.: Simple Object Access Protocol (SOAP) 1.1, W3C note (2000) 7. Bahl, P., Padmanabhan, V.: RADAR:An In-Building RF-Based User Location and Tracking System. In: INFOCOM 2000, pp. 775–784 (2000) 8. Harter, A., Hopper, A.: A New Location Technique for the Active Office. IEEE Personal Communications 4(5), 43–47 (1997) 9. Yim, J.: Introducing a decision tree-based indoor positioning technique. Expert Systems with Applications 34(2), 1296–1302 (2008) 10. Yim, J., Park, C., Joo, J., Jeong, S.: Extended Kalman Filter for Wireless LAN Based Indoor Positioning. Decision Support Systems 45, 960–971 (2008)

Petri Net Representation of Ontologies for Indoor Location-Based Services* Jaegeol Yim1, Jaehun Joo2, and Gyeyoung Lee1 1

Dept. of Computer Engineering, Dongguk University, GyeongJu, Gyeongbuk, 780-714 Korea {yim,lky}@dongguk.ac.kr 2 Dept. of Information Management, Dongguk University, GyeongJu, Gyeongbuk, 780-714 Korea [email protected]

Abstract. This paper presents a Petri net representation of an ontology for indoor location-based services. The ontology is described within the Resource Description Framework (RDF). Therefore, this paper proposes a method for transforming the RDF model into a CPN (colored Petri net). The proposed method represents the semantics of the RDF model in the CPN by mapping the classes and properties of the RDF model onto CPN places and representing the relationships between those classes and properties as token transitions in the CPN. To represent an RDF statement in the CPN, the method introduces a transition that produces a (complex) token composed of two ordered (simple) tokens: one corresponding to the subject and the other corresponding to the object of the statement. Applying the proposed method, we build a sample CPN for an RDF model and perform simulations using the model to answer RDF queries. This paper also introduces a simple ontology for an indoor locationbased service. Using the proposed transformation method, we transform the ontology into a CPN. Finally, we introduce our inference algorithm for the CPN and a prototype database system for demonstrating the practicality of our method. The results indicate that the proposed database system can resolve the semantic ambiguities in the query by using the ontology. Keywords: Petri net, colored Petri net, ontology, location-based services, indoor moving objects database.

1

Introduction

This paper introduces a method that can transform an RDF (Resource Description Framework) model into a colored Petri net (CPN) model. Performing a simulation on the CPN, we can obtain the answers of an RDF query. The study demonstrates the practicality of the proposed method by introducing a simple ontology for an indoor *

“This research was supported by ‘Industry, Academy and Research Institute Co-development of Techniques’ funded by the Small and Medium Business Administration”.

T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 423–430, 2011. © Springer-Verlag Berlin Heidelberg 2011

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location-based service described in the RDF and showing how the proposed method can be used in the moving object database system’s query processing for the service. The RDF is a family of World Wide Web Consortium (W3C) specifications that can be used as a general method of modeling information. An RDF model consists of an RDFS (RDF Schema) layer and an RDF layer. The vocabulary used in RDF models is defined in the RDFS layer. A user defines the vocabulary, specifies the properties to be applied to each object, and describes the relationships between objects in the RDFS. That is, a user represents his knowledge in the RDFS. This implies that a wellformed RDF model is a type of ontology. The basic component of an RDF layer is a statement consisting of a subject, a predicate, and an object. A statement expresses a known fact The Petri net was first introduced in 1962, and since then, it has been widely used in computer science and engineering for system performance tests, communication protocol consistency and validation tests, and so forth. The Petri net has been popular because constructing a Petri net is easy and because mathematical methods of analyzing Petri net models are readily available [1]. The colored Petri net is a modified Petri net; it was developed so that the explosive increment of the size of the Petri net can be avoided when a complex phenomenon is modeled. There are several software tools that can provide convenient environments for constructing a Petri net model and for performing simulations. Among such tools, CPNTools [2] is designed to run on a desktop computer. This paper proposes a method that can transform an RDF model into a Petri net model. Applying the proposed method, we build a sample CPN for an RDF model and perform simulations using the model to answer RDF queries. An RDF model describes web resources in a machine-readable form. That is, it is possible to develop a software tool that interprets RDF models and inferences valuable information implied in those models. In other words, developing an RDF model is one thing, and developing an inference software tool is another. On the other hand, both the RDFS and RDF layers are mixed in a CPN model; the RDFS layer is reflected in the topology of the CPN model, whereas the RDF layer is reflected in the dynamics of the CPN model. As a result, we can extract inferential information by simulating CPN dynamics. To demonstrate the practicality of the proposed method, a simple indoor locationbased service system and an ontology for the system are considered. Applying the abovementioned transformation algorithm to the ontology, we construct a Petri net model. Then we introduce an inference engine for the Petri net model based on the ontology. We show that by using the inference engine, the moving object database of the indoor location-based service resolves ambiguities in queries.

2

Related Work

2.1

RDF

XML (Extensible Markup Language) is a general language that can be used for defining markups. Therefore, XML documents are typically used to exchange data between

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applications. However, XML cannot represent data semantics. The RDF is a data model in which this shortcoming of XML is reinforced. That is, a user can define his or her own vocabulary, properties of objects, and relationships between the objects by using RDFS language. The purpose of the RDF is to describe web resources, and the basic component of RDF is a statement consisting of a subject, a predicate, and an object. For example, “The name of the resource whose ID is ‘352’ is ‘David’” can be specified as follows: David , Where “uni” is a predefined name space. The subject of this statement is “352,” the predicate is the name, and the object is David. We assume here that the reader is familiar with the RDF. Please refer to References [3] for more information on the RDF. The sample RDF model shown in Fig. 1 is from [3]. We refer to this model frequently in the present paper

Fig. 1. RDF and RDFS layers

2.2

Colored Petri Net

Petri (1962) was the first to introduce the concept of the Petri net. Since then, the Petri net has been widely used for modeling and analyzing concurrent, nondeterministic, asynchronous, parallel, and/or distributed information processing systems. A Petri net is both graphical and mathematical. That is, we can visually represent the information and data flow of systems and establish state equations and mathematical models governing the behavior of systems by using the Petri net. A number of researchers have examined the applications and theory of Petri nets. Petri nets have been successfully applied in performance evaluation and communication protocols. Promising Petri net application areas include the modeling and analysis of distributed database systems, distributed software systems, flexible manufacturing control systems, concurrent and parallel programs, and so forth. We assume here that the reader is familiar with Petri nets. Please refer to References [1, 2] for more information on Petri nets.

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Other Related Research Results

CPNTools [2] is a software tool that can be used to easily build CNP models and perform simulations. CPNTools has been used to develop a number of Petri net models and validate their performance. In this study, we built a CPN model by using CPNTools and performed simulations to validate the proposed method. Petri nets have been used in a wide range of areas. [4] proposed a ”generalized Petri net,” a type of modified Petri net for modeling rule-based systems. There are many formalized ways to represent Petri nets, including the metamodel and UML (Unified Modeling Language). [5] introduced a Petri net ontology written in UML, RDFS, and OWL (Web Ontology Language). This work shares many similarities with [6], which introduced a method of transforming OWL DL (Description Logic) into predicate/transition nets. Because the RDF model in the present paper is a subset of OWL DL, there is an overlap between the objectives of [6] and those of this paper. However, our paper proposes a method that can transform RDF models into CPNs, not into predicate/transition nets. Furthermore, we actually built a CPN model with CPNTools to verify the proposed method.

3

Our Method of Transformation

We propose a method that can transform an RDF model into a CPN. The proposed method consists of two phases: the transform of the RDFS layer and that of the RDF layer. In the “transform of the RDFS layer” phase, we identify the core classes, map them onto CPN places, and identify the core properties. For each property, we introduce a transition so that the tokens at the place corresponding to the subject of the property can be transferred to the place corresponding to the object of the property. We then identify the domains and ranges of properties, and for each pair of the domain and range of a property, we introduce a transition that transfers an ordered pair of tokens: the first one from the place corresponding to the domain to the place corresponding to the property at a time and the second one from the place corresponding to the range to the same. This completes the topology of the CPN model. During the “transform of the RDF layer” phase, RDF statements are reflected in the CPN. For each statement, we identify its subject, predicate, and object and then do the following. First, if the predicate is ref:type, then we introduce a transition that generates a token representing the subject and adds it to the place corresponding to the object. Second, if the object is a container (e.g., rdf:Bag, rdf:Seq, or rdf:Alt), then for each case, we do the following. For rdf:Bag, we separate the statement into many statements and go to the third step. For rdf:Seq, we put all the elements of rdf:Seq into a list in order. For rdf:Alt, by using the random number generation function, we make the transition to produce one of the tokens corresponding to the elements of rdf:Alt. Third, we finally map the subject, predicate, and object onto places ps, pp, and po, respectively, and map the sentence onto the transition ts. Then we put a guard on ts so that it consumes a token x from ps and a token y from po and adds a token of (x, y) to pp when it fires. In this manner, we can also transform rdf:resource attributes and reifications into a CPN. Our transformation method is summarized in Table 1.

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Table 1. Algorithm for Transforming an RDF Model into a CPN

Input: an RDF model Output: CPN model 1. Identify the core classes, resources, classes, literals, properties, and statements of the RDFS and map them onto CPN places. 2. Represent the properties of RDFS:subClassOf and subPropertyOf by introducing a transition that transfers a token from the place corresponding to the subject class/property to the place corresponding to the object class/property when it fires. 3. Identify the domains and ranges of RDFS properties. For each property and its domain and range, we introduce a transition that consumes a token x from the place corresponding to the domain and a token y from the place corresponding to the range and adds a token of (x, y) to the place corresponding to the property. 4. For each RDF statement consisting of a subject, a predicate, and an object, we map its subject, predicate, and object onto places and the statement onto a transition. Then we put a guard to the transition so that it consumes tokens x and y from the places corresponding to the subject and the object, respectively, and adds a token of (x, y) to the place corresponding to the predicate. (A) In case of the predicate rdf:type, instead of mapping the predicate onto a place, we introduce a transition that transfers a token representing the subject to the place corresponding to the object class. (B) In case of the “Container,” we represent the Container by using the approach explained earlier in the discussion about the “transform of the RDF layer” phase.

4

Example of Transformation

A CPN model for the RDF model shown in Fig. 1 is shown in Fig. 2. The topology of the CPN in Fig. 2 is constructed during the “transform of the RDFS layer” phase (Steps 1 to 3 of the algorithm shown in Table 2), except for the source transitions (t1 and t2). In Step 1, we identify all the classes (e.g., Professor, Academic_staff, Literal, etc.) and properties (e.g., ID, Phone, isTaughtBy, etc.) and introduce a place for each. In Step 2, we introduce the transitions for subClassOf (t3, t4, t5, …) and subPropertyOf (t6). In Step 3, we introduce t7 and t8 to represent the domains and ranges. In Step 4, we introduce t1 and t2 to represent that David is a professor and DisMath is a course. We then put a guard to represent “DisMath isTaughtBy David.”

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Fig. 2. A CPN model for the RDF in Fig. 1

Drawing an inference is easy with a CPN model and we can extract answers for all RDF queries in CPN.

5

Ontology for Indoor Location-Based Services

Consider an indoor location-based service (ILBS) provided by a small, imaginary museum exhibit area shown in Fig. 3. In the area, there are 7 booths (Booth1 to 7), 2 vending machines (one for hot coffee (d2) and the other for soft drinks (d1)), and 12 chairs (c1 to c12). One of the main parts of the ILBS is the moving object database (MODB) in which the spatio-temporal information of moving objects is recorded. The ILBS translates questions such as “Who is visiting Booth 1?” into a query of the MODB and displays the answer to the query on the user interface of the ILBS. The MODB records the information in a relational database. A sample ontology for the imagined exhibit area is shown in Fig. 4. Three classes “people,” “place,” and “object” are used for the ontology for the museum’s LBS. The class “object” has various subclasses such as “vending-machine,” “chair,” “lounge,” “corridor,” and so on. C1, C2, …, Cn are the instances of “chair,” and their type is “chair.” The object property C-locatedIn relates the class “chair” to the class “sittingplace.” As shown in Fig. 5, the domain of “c-loacatedIn” is “chair,” and its range is “sitting-place.” It can indicate that people can sit on a chair; a person sitting on a chair is taking a break and not moving; a booth is a place where an exhibit is displayed and can be viewed by people; a booth is something a person cannot walk through, and so forth.

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Fig. 3. A Museum Exhibition Room

Fig. 4. An ontology for the ILBS on Fig. 3

Fig. 5. Object property c-locatedIn

As discussed in Section 3, the ontology can be transformed into the Petri net shown in Fig. 6. All places (e.g., staff, visitors, chairs, and booths) have only two input transitions in the figure, but in the real world, they would have many input transitions.

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For example, if there are 10 chairs, then the place chair has 10 input transitions. An object property in an ontology is represented as exactly one place in the Petri net. There are many object properties in the ontology, and there should be that many places representing properties; however, in Fig. 6, only one property is represented, and others are omitted. We have integrated the Petri net model of the ontology with our ILBS on the museum. For example, our system interprets “Find places to sit” as “Find all available chairs” by referring to the ontology.

Fig. 6. A Petri net representation of Fig. 5

6

Conclusions

This paper proposed a method that can transform RDF models into CPN models. To test the practicality of the proposed method, we introduced an ontology for a small, imaginary exhibit area in a museum. For the location-based service, we built a prototype Indoor Moving Objects Database (IMODB) system for the exhibit area. Our test results indicated that the IMODB can successfully resolve ambiguities in queries by referring to the proposed ontology.

References 1. Murata, T.: Petri nets: Properties, analysis and applications. Proceedings of the IEEE 77(4), 541–580 (1989) 2. http://wiki.daimi.au.dk/cpntools/cpntools.wiki 3. Antoniou, G., Harmelen, F.: A Semantic Web Primer. MIT Press (2004) 4. Shih, D., Chiang, H., Lin, B.: A Generalized Associative Petri net for Reasoning. IEEE Transactions on Knowledge and Data Engineering 19(9), 1241–1251 (2007) 5. Gasevic, D.: Petri Nets on the Semantic Web Guidelines and Infrastructure. ComSIS 1(2), 127–151 (2004) 6. Zhang, G., Meng, F., Jiang, C., Pang, J.: Using Petri Net to Reason with Rule and OWL. In: IEEE International Conference on Computer and Information Technology (CIT 2006), vol. 42 (2006)

A Platform for Real Time Brain-Waves Analysis System* Eun-Gyu Jeong1, Byungin Moon2, and Yong-Hwan Lee1,** 1 School of Electronic Engineering, Kumoh National Institute of Technology, Gumi, Korea {kyou4848,yhlee}@kumoh.ac.kr 2 Gyeongbuk National University, Daegu, Korea [email protected]

Abstract. There are many methods for human-computer interface. Recently, many researchers are studying about brain-signal. This is because not only the disabled can use a computer by their thought without their limbs but also it is convenient to general people. In this paper, we propose a platform for the real time electroencephalogram(EEG) and magnetoencephalography(MEG) analysis system. The signals on the scalp are so weak. Thus, the signals must be amplified by the amplifier. Filters are used to filter the incoming continuous signal before it is sampled by the analog-to-digital converter(ADC). The filtered EEG and MEG are analyzed and compacted by using FFT and JPEG2000, respectively. The platform is easy to reuse because of its simple block architecture and is implemented as a single chip on the Xilinx Virtex5 FPGA development board. Keywords: Electroencephalogram, EEG, Magnetoencephalography, MEG, Brain-computer interface, BCI.

1

Introduction

The brain-waves test is obtained by recording the brain's electric activities from the scalp electrodes. The electrodes are placed on the scalp to detect the electroencephalogram(EEG) and magnetoencephalography(MEG) in order to detect the brainwaves[1]. Since the frequency of EEG is different depending on a human state[2], the value of EEG is determined as delta, theta, alpha, beta and gamma by its frequency. Alpha brain-waves are typically the dominant cycle during dream states and light meditation. The alpha waves are oscillations with between 8 and 13 cycles per second. Beta brain-waves are dominant during normal waking consciousness, and range from 14 to 30 cycles per second. Theta brain-waves are considered to be 4 *

This work was sponsored by ETRI System Semiconductor Industry Promotion Center, human resource development project for SoC convergence. ** Corresponding author. T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 431–437, 2011. © Springer-Verlag Berlin Heidelberg 2011

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to 7 cycles per second and dominate during normal sleep and meditation. Delta brainwaves range from less than 1 cycle per second to about 3 cycles per second. These slow brain-waves tend to be dominating during the deepest sleep or deepest meditation states. Gamma brain-waves typically dominate during periods of advanced problem solving or critical thinking. They oscillate at between 30 and 60 cycles per second. The Table 1 shows the state of brain according to the frequency of EEG[3]. Table 1. Main EEG indicators Indicator Alpha wave(α) Beta wave(β) Gamma wave(γ) Theta wave(θ) Delta wave(δ)

Frequency Definition 8-13Hz 14-30Hz 30-60Hz 4-7Hz 0.5-3.5Hz

State Awake Concentration, Activity Excitement Sleep Deep Sleep

MEG is unique and effective diagnostic tool for evaluating brain function in a variety of surgical planning applications. This, when pooled with other information from the patient, forms the basis for determining whether surgery is the best option for treatment and, if so, how to plan it. MEG can determine exactly which bit of your brain controls actions such as speaking or moving arms and legs. In this paper, the architecture of the real time EEG and MEG analysis system is proposed. The architecture consists of an analog part and a digital part. The analog part is composed of a band-pass filter and amplifiers to process analog signal. The digital consists of analog-to-digital converter (ADC) which converts EEG from time domain to frequency domain and FPGA.

2

Electroencephalogram Analysis System

2.1

System Architecture

The block diagram of real time EEG and MEG analysis system is shown in Fig. 1. The sensor modules are on the scalp to measure EEG or MEG depending on the external mode selection. Since the amplitude of both signals is small, the amplifier of which gain is large enough and the band-pass filter are needed to measure them before signals are converted to digital codes though ADC. The digital codes are serialized to be transmitted to the chip that is shown in Fig. 1. The serialized data are transformed to parallel signals which are connected to JPEG2000 and FFT in order to be analyzed and be compacted for transmission at Data Transmitter module. The data are stored at FIFO. And the data are sent to the host PC through RS232 and USB (Universal Serial Bus).

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Fig. 1. Block diagram of EEG and MEG analysis system

Fig. 2. Sensor module structure and digital MPW structure

2.2

Filter

High pass filter and low pass filter structure is shown in Fig. 3. The frequency of EEG signal of scalp is from 0.5Hz to hundreds of Hz with dozen of milli-voltage (mV) and the frequency of MEG signal is from 0.1Hz to several KHz with several micro-voltage(uV). The signals are different frequencies and voltages. Thus, EEG and MEG signal analyzed circuit get each different Amp and filter[4], as shown in Fig. 2.

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Fig. 3. High pass filter and low pass filter structure

2.3

Fourier Transform

EEG signal analysis FFT(Fast Fourier Transform) processor IP(Intellectual Property) for the diagnosis of brain function, EEG signals delta(δ), theta(θ), alpha(α), beta(β) and gamma(γ) frequency bands are analyzed. EEG signals using a Fourier transform is converted to the frequency band. The Fourier transform is defined by the equation(1). N

G k

Gn

cos

j2πnk N

j sin

j2πnk N

,k

0, 1, … , N

1

(1)

Fig. 4. 8-channel EEG signal analysis FFT processor IP structure

We use 256 point decimation-in-frequency as radix-2 fast Fourier transform algorithm[5] to improve the speed of the calculation. The EEG signal as input data has been broken into eight parallel data stream. In our implementation, input data is stored to a single port RAM whose size is 8x256x12. To avoid read and write conflict, we control data flow in memory using double buffering with two single ports RAMs. The output data is stored to a single port RAM whose size is 1x256x12 and sent to USB or UART FIFO.

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The Hamming window function as shown in equation (2) is applied to the samples before calculating the FFT. It's basically a smoothing function that will make our samples look a bit nicer. The FFT class has a function by which we can enable Hamming window smoothing. ωn 2.4

0.54

0.46 cos 2πn/N ,

0

n

N

1

(2)

JPEG2000

64-channel MEG data from the sensors are for interface use serial to parallel interface module connected to JPEG2000 to be MEG data compacted for transmission at the data transmitter module.

Fig. 5. JPEG2000 IP for MEG compression structure

JPEG2000 IP structure system is shown in Fig 5. 8-bit MEG data is entered 64 times consecutively. The MEG data from DCT blocks divided up into four frequency bands and EBCOT tier-1 block through the EEG data compression, EBCOT tier-2 compressed stream of data to be packetized[6]. And then, EEG data is compressed in frames. 2.5

Brain-Signal Analysis Embedded Platform

The block diagram of embedded hardware architecture is shown in Fig. 6. (a). ARM11-based embedded real-time brain-waves monitor system configuration using analog brain-waves signal analysis chip and digital brain-waves analysis chip. ARM11-based embedded board is Embedded Linux to configure system to use for displays brain-waves at 7 inches TFT-LCD. The brain-waves are analyzed and compressed. The data stored in an external storage device. The block diagram of embedded software architecture is shown in Fig. 6. (b). Embedded software be modular, each functions. Data Collector data received from the external interface of the packet by analyzing the collected packets will be put to a temporary buffer stack. The received data is stored through the database. It also stores measured information for experimenter and environment.

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(a)

(b)

h architecture (b) Embedded software architecture Fig. 6. (a) Embedded hardware

3

Result

Embedded brain-waves monitoring system is EEG signals the delta, theta, alpha, bbeta, and gamma power able to see the size as shown in Figure 7 is a graph. EEG signnals the delta, theta, alpha, beta,, and gamma is displayed red, yellow, green, light blue and blue. Absolute power of eacch wave is displayed with the color of the area.

Fiig. 7. EEG signal frequency spectrum

MEG signals are composed of 64 channels. The size of each component represeents the EEG activity. The sizee value is displayed in a row, as shown in Fig. 8. (a) for analysis should be possiblle. Alternatively, Fig. 8. (a) MEG signal brain activiities graph showed according to each part of the brain activity.

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(a)

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(b)

Fig. 8. (a)MEG sig gnal raw graph (b) MEG signal brain activities graph

4

Conclusion

We designed the platform for real time brain-waves analysis system in this papper. This platform is composeed of the analog part and digital part. The analog ppart processes brain-waves sign nals that swings within few microvolt at particular frequuency. The digital part analyzes that the brain-waves signal using FFT and JPEG20000. was This platform is easy to reeuse due to its simple block structure. The platform w implemented as FPGA deveelopment board with Virtex5 LXT FFG FPGA board. Real time brain-waves an nalysis system will contribute greatly to health care, eduucation and forensic science in n the future. Later, the brain-waves analysis system cann be utilized for that gaming, auttomotive and portable devices. Acknowledgements. This work was sponsored by ETRI System Semiconducctor SoC Industry Promotion Centter, Human Resource Development Project for S Convergence.

References 1. Casson, A., Yates, D., Sm mith, S.: Wearable Electroencephalography. IEEE Engineerinng in Medicine and Biology Maagazine: The Quarterly Magazine of the Engineering in Mediccine & Biology Society 29(3), 44–56 (2010) 2. Samei, S., Chamber, J.: EEG signal Processing. John Wiley & Sons, Ltd. (2007) 3. Oregon Health & Sciencee University Information, http://www.ohsu.edu 4. Haifeng, C., Donghee, Y., Y Jungtae, L.: Development of a Portable EEG Monitooring System based on WLAN. IEEE Circuits and Systems Society, 460–465 (2010) 5. Budiardja, R.D., Cardalll, C.Y.: Parallel FFT-based Poisson solver for isolated thhreedimensional systems. Com mputer Physics Communications 182(10), 2265–2275 (2011) 6. Auli-Llinas, F., Bilgin, A., A Marcellin, M.W.: FAST Rate Allocation through Steeepest Descent for JPEG2000 Video Transmission. IEEE Signal Processing Society 200(4), 1166–1173 (2011)

Design of FCM-Based Fuzzy Neural Networks and Its Optimization for Pattern Recognition Keon-Jun Park1 , Dong-Yoon Lee2, and Jong-Pil Lee3 1

Department of Information and Communication Engineering, Wonkwang University, 344-2, Shinyong-dong, Iksan-si, Chonbuk, 570-749 South Korea [email protected] 2 Department of Electrical Electronic Engineering, Joongbu University, 101, Daehak-ro, Chubu-myeon, Geumsan-gun, Chungnam, 312-702 South Korea [email protected] 3 Korea Electric Institute, 147-11 Sachang-dong, Heungdeok-gu, Cheongju-city, Chungbuk, 361-831 South Korea [email protected]

Abstract. In this paper, we introduce a new category of fuzzy neural network with multi-output based on fuzzy c-means clustering algorithm (FCM-based FNNm). The premise part of the rules of the proposed network is realized with the aid of the scatter partition of input space generated by FCM clustering algorithm. The number of the partition of input space is composed of the number of clusters and the individual partitioned spaces describe the fuzzy rules. Due to these characteristics, we may alleviate the problem of the curse of dimensionality. The consequence part of the rule is represented by polynomial functions with multi-output. And the coefficients of the polynomial functions are learned by BP algorithm. To optimize the parameters of FCM-based FNNm we consider real-coded genetic algorithms. The proposed network is evaluated with the use of numerical experimentation. Keywords: Fuzzy Neural Networks, FCM clustering, Scatter partition, Optimization, Genetic Algorithms.

1

Introduction

Fuzzy neural networks (FNN) [1] [2] have emerged as one of the active areas of research in fuzzy inference systems and neural networks. These networks are predominantly concerned with the integration of these two fields. There have been many approaches to synthesize FNNs. Typically, FNNs are represented by fuzzy “ifthen” rules while the back propagation (BP) is used to optimize the parameters. The generation of the fuzzy rules and the adjustment of its membership functions were done by trial and error and/or operator’s experience. The designers find it difficult to develop adequate fuzzy rules and membership functions to reflect the essence of the data. Moreover, some information gets lost or ignored on purpose when human operators articulate their experience in the form of linguistic rules. As a T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 438–444, 2011. © Springer-Verlag Berlin Heidelberg 2011

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consequence, there is a need for an optimization environment to construct and/or adjust a collection of linguistic rules. In this paper, we present the structure of fuzzy neural networks with multi-output by means of fuzzy c-means clustering algorithm [3] (FCM-based FNNm). The premise part of the rules is realized with the aid of the scatter partition of input space generated by FCM clustering algorithm. The consequence part of the rule is represented by polynomial functions with multi-output for pattern recognition. And the coefficients of the polynomial functions are learned by BP algorithm. We also optimize the parameters of the networks using real-coded genetic algorithms [4]. The proposed network is evaluated through the numeric experimentation.

2

Design of FCM-Based FNN with Multi-output

In this section, the form of fuzzy clustering if-then rules along with their development mechanism is discussed. More specifically, we elaborate on the three types of fuzzy inference and present the learning algorithm. 2.1

FCM Clustering Algorithm

The premise part of the FCM-based FNNm is developed by means of the Fuzzy CMeans clustering algorithm [3]. This algorithm is aimed at the formation of ‘c’ fuzzy sets (relations) in Rn. Consider the set X, which consists of N data points treated as vectors located in some n-dimensional Euclidean space. In clustering we assign patterns xp∈X into c clusters, which are represented by its prototypes vi∈Rn. The assignment to individual clusters is expressed in terms of the partition matrix U = [uip] where c

u

N

ip

i =1

= 1, 0 <  uip < N , 1 ≤ p ≤ N , 1 ≤ i ≤ c

(1)

p =1

The objective function Q guiding the clustering is expressed as a sum of the distances of individual data from the prototypes v1, v2, …, and vc, c

N

Q =  uipmi x p − v i

2

.

(2)

i =1 p =1

Here ⋅ denotes the Euclidean distance; ‘m’ stands for a fuzzification coefficient, mi>1.0. The resulting partition matrix is denoted by U = [uip]. The minimization of Q is realized through successive iterations by adjusting both the prototypes and entries of the partition matrix, that is min Q(U, v1, v2, …, vc). The corresponding formulas used in an iterative fashion read as follows. N

vi =  u x p mi ip

p =1

N

u p =1

mi ip

 x p − vi  , u =1  ip  xp − v j j =1  c

2

 mi −1  .  

(3)

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The Structure of the FCM-Based FNNm

The structure of the FCM-based FNNm involves FCM clustering algorithm in the premise part and neural networks present in the consequence part of the rules. The overall topology of the network is illustrated in Fig. 1.

yˆ1

yˆ q

Fig. 1. The structure of FCM-based FNNm

FCM-based FNNm is implied by the fuzzy scatter partition of input spaces. In this sense, each rule can be viewed as a certain rule of the following format

Then ysj = f ( x1 , … , xd ) .

R j : If x1 and " and xd is F j

(4)

As far as inference schemes are concerned, we distinguish these cases Case 1 (Simplified Inference):

f = w sjo

(5)

f = w sj 0 + k =1 w sjk xk

(6)

Case 2 (Linear Inference): d

Case 3 (Modified Quadratic Inference):

f = w sj 0 + k =1 w sjk xk + k =1 i =k +1 w sjz xk xi , d

d

here, z = d + 1," d ( d + 1) / 2.

d

(7)

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To be more specific, R j is the j-th fuzzy rule, while Fj denotes j-th membership grades using FCM clustering algorithm. w sjk , k = 0, " , d ( d + 1) / 2 are consequent parameters of the rule and s is the number of output. The functionality of each layer is described as follows. [Layer 1]. The nodes in this layer transfer the inputs. [Layer 2]. The nodes here are used to calculate the membership degrees using FCM clustering algorithm. [Layer 3]. The nodes in this layer realize a certain inference process. n

hsj =  fˆ j y sj

(8)

j =1

[Layer 4]. The nodes in this layer compute the outputs. n

yˆ s =  hsj

(9)

j =1

2.3

The Learning Algorithm

The parametric learning of the network is realized by adjusting connections of the neurons and as such it could be realized by running a standard Back-Propagation (BP) algorithm. The performance index is based on the Euclidean distance. As far as learning is concerned, the connections are adjusted in a standard fashion,

w sj 0 ( p + 1) = w sj 0 ( p) + Δw sj 0

(10)

where this update formula follows the gradient descent method. Quite commonly to accelerate convergence, a momentum coefficient α is being added to the learning expression. Then the complete update formula reads as follows

(

Δw sj 0 = η ( y ps − yˆ ps ) fˆ j + α w sj 0 ( p ) − w sj 0 ( p − 1)

)

(11)

(

)

(12)

(

)

(13)

Δw sjk = η ( y ps − yˆ ps ) fˆ j xk + α w sjk ( p ) − w sjk ( p − 1)

Δw sjz = η ( y ps − yˆ ps ) fˆ j xk xi + α w sjz ( p ) − w sjz ( p − 1) .

3

Optimization of FCM-Based FNNm

It has been demonstrated that genetic algorithms (GAs) [4] are useful in a global population-based optimization. GAs are shown to support robust search in complex search spaces. Given their stochastic character, these methods are less likely to get trapped in local minima in comparison to the performance offered by gradient-descent techniques. GAs start with a randomly generated population of l chromosomes positioned in solution (parameter) space. The population is evolved repeatedly toward achieving a

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better overall fitness value. The search in the solution space is completed with the aid of several genetic operators. Let us briefly elaborate on the essence of these operators. Reproduction is a process in which the mating pool for the next generation becomes selected. Individual strings are copied into the mating pool according to their fitness function values. Crossover usually proceeds in two steps. Firstly, members from the mating pool are mated at random. Secondly, each pair of strings undergoes crossover as follows; a position l along the string is selected uniformly at random from the interval [1, l-1], where l is the length of the string. Swapping all characters between the positions k and l creates two new strings. Mutation is a random alteration of the value of a string position. In real coding, mutation is defined as an alternation at a random value in special boundary. Mutation occurs with a small probability. Those operators, combined with the proper definition of the fitness function, constitute the main body of the genetic computing. In order to optimize the parameters of the FCM-based FNNm, we determined the fuzzification coefficients associated with the corresponding clusters, the learning rate, and the momentum coefficient as the parameters.

4

Experimental Studies

In this paper, we use the Iris dataset. The Iris dataset is a collection of 150 Iris flowers of 3 kinds, with four attributes, leaf and petal width and length in cm. Three classes are the setosa, versicolor, and virginica. For the evaluation of the performance of the network, the random sub-sampling method was applied. The random sub-sampling was performed with 5 data splits of the data set. Each split was randomly selected from the training examples and the test examples with the ratio of 7:3. We experimented with the proposed network using the parameters outlined in Table 1 with the weight factor θ = 0.5 [5]. Table 2 summarizes the performance for FCM-based FNNm before optimization and Table 3 shows the performance for FCM-based FNNm using genetic optimization. From these tables we know that the optimized FCM-based FNNm is better than before optimization. From Table 3 we select the network that has ten fuzzy rules and linear inference engine. This network exhibits CR=98.29±0.43, PI=0.031±0.00 for training datasets and CR=99.56±0.99, PI=0.026±0.00 for testing datasets. Table 1. Initial parameters for GAs and FNNm

GAs

FNNm

Parameter Generation Population size Crossover rate Mutation rate Fuzzification coefficients Learning rate Moment coefficient

Value 100 50 0.65 0.1 1.0<mi≤2.5 0.0≤η≤0.01 0.0≤α≤0.001

Design of FCM-Based Fuzzy Neural Networks Table 2. Performance of the FCM-based FNNm No. of Inference Clusters (Case) 1 5 2 3 1 10 2 3 1 2 15 3 1 20 2 3

CR Training 93.33±5.13 95.43±0.80 96.76±1.44 94.67±1.28 95.81±1.44 95.24±0.67 95.24±1.35 96.00±1.41 95.43±1.70 96.38±0.80 96.19±1.35 96.57±1.09

PI Testing 90.67±4.82 96.44±1.99 96.89±1.99 98.22±1.86 96.44±1.99 97.78±2.22 96.89±1.22 94.67±3.72 96.44±2.98 96.00±2.43 97.33±1.86 96.00±3.65

Training 0.035±0.02 0.026±0.00 0.025±0.00 0.024±0.00 0.019±0.00 0.022±0.00 0.023±0.00 0.022±0.00 0.021±0.00 0.021±0.00 0.023±0.00 0.022±0.00

Testing 0.040±0.01 0.028±0.01 0.027±0.01 0.022±0.01 0.025±0.01 0.024±0.01 0.023±0.01 0.023±0.01 0.022±0.01 0.026±0.01 0.023±0.01 0.024±0.01

Table 3. Performance of the optimized FCM-based FNNm No. of Inference Clusters (Case) 1 5 2 3 1 10 2 3 1 15 2 3 1 2 20 3

CR Training 96.95±1.56 99.05±0.67 98.86±0.43 96.76±0.52 98.29±0.43 98.67±0.52 98.67±0.85 99.05±0.67 99.05±0.00 98.67±0.85 98.67±0.85 99.62±0.52

PI Testing 96.89±3.72 97.78±1.57 97.78±1.57 99.56±0.99 99.56±0.99 98.67±1.22 96.89±2.53 97.78±1.57 97.78±0.00 97.33±1.86 99.56±0.99 97.78±1.57

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In this paper, we have introduced fuzzy neural networks based on fuzzy c-means clustering algorithm and discussed its optimization using real-coded genetic algorithms. The input spaces of the proposed networks were divided as the scatter form using FCM clustering algorithm to generate the fuzzy rules. From this method, we could alleviate the problem of the curse of dimensionality and have designed fuzzy neural networks compact and easy. And genetic algorithms were also used for parametric optimization of the proposed networks. From the result in the previous section, we were able to design good networks. Through the use of performance we were able to achieve a balance between the approximation and generalization abilities of the resulting network. Finally it could be possible to apply to many fields.

References 1. Yamakawa, T.: A Neo Fuzzy Neuron and Its Application to System Identication and Predition of the System Behavior. In: Proceeding of the 2nd International Conference on Fuzzy logic & Neural Networks, pp. 447–483 (1992) 2. Buckley, J.J., Hayashi, Y.: Fuzzy neural networks: A survey. Fuzzy Sets Syst. 66, 1–13 (1994) 3. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981) 4. Golderg, D.E.: Genetic Algorithm in search, Optimization & Machine Learning. Addison wesley (1989) 5. Oh, S.K., Yoon, K.C., Kim, H.K.: The Design of Optimal Fuzzy-Neural Networks Structure by Means of GA and an Aggregate Weighted Performance Index. Journal of Control, Automation and Systems Engineering 6(3), 273–283 (2000)

A File Assignment Strategy for Parallel I/O System with Minimum I/O Contention Probability Bin Dong, Xiuqiao Li, Limin Xiao, and Li Ruan School of Computer Science and Engineering, Beihang University, Beijing 100191, P.R. China {bdong,xiuqiaoli}@cse.buaa.edu.cn, {xiaolm,ruanli}@buaa.edu.cn

Abstract. Many solutions have been proposed to tackle the problem of assigning files in a parallel I/O system. The primary objective of the existing solutions is either to balance the load among disks or to minimize the service time variance at each disk, whereas the dynamic characteristics of the file requests which would access these files are ignored. The studies on the dynamic I/O behaviors of applications show that the file requests targeted on the different popular files situated in the same disk may temporally compete with each another for the same disk. Consequently, the performance gained from the parallelism of multiple disks is degraded because this type of I/O contention turns the parallel I/O into sequential one. Hence, how to minimize the I/O contention among the file requests should become one of the new objectives which the file assignment strategy should take into consideration. In order to address this issue, this study proposes a new static file assignment algorithm named MinCP for parallel I/O system. Through assigning files sorted in their access rates onto multiple disks in round-robin fashion, the MinCP aims to minimize the I/O contention probability among file requests, thereby optimizing the mean response time of these requests. The experiment results show that the MinCP achieves optimal performance on mean response time among the existing schemes for comparison. Keywords: Parallel I/O system, file assignment algorithm, I/O contention probability, heuristic algorithms.

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The volumes of data that need to be stored grow rapidly in the recent past [1], whereas the rate at which the program can access data from single disk is still increasing much slowly [2]. As a result, the I/O has become the performance 

The work described in this paper is supported by the fund of the National Natural Science Foundation of China under Grant No. 60973007 and No. 61003015, the Doctoral Fund of Ministry of Education of China under Grant No. 20101102110018, and the Fundamental Research Funds for the Central Universities under Grant No. YWF-10-02-058.

T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 445–454, 2011. c Springer-Verlag Berlin Heidelberg 2011 

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bottleneck of dedicated applications when they read or write files [3,4]. Meanwhile, since the parallel I/O system permits the accesses from applications to these data executing concurrently, the parallel I/O systems like Redundant Array of Inexpensive Disks (RAID) [5] have received a lot of attention. While the partitioning of a file and then spreading the partitions onto different disks allow the single requests of the file to be serviced by multiple disks, the allocation of all files onto the disks plays an equally important role in the parallel I/O system performance. The reason is that proper allocation of all files permits the independent requests targeted on different files ( and even partitions ) to be concurrently served by the parallel I/O system. Thus, in order to reap the benefits of parallel I/O system, the files should be carefully assigned onto multiple disks before they are accessed. The file allocation problem (FAP) of the parallel I/O system has been extensively investigated in the previous literature [6,7,8,9,10] and the solutions to this problem fall into two camps: optimal and heuristic. The optimal file allocation problem actually is a NP-complete problem and can be transferred to other resource allocation problem [7]. The heuristic file assignment algorithm, on the other hand, owns low computational complexity and therefore becomes an effective and practical file allocation approach. The well-known heuristic file allocation algorithms include Greedy[6], Sort Partition (SP)[8], Hybrid Partition (HP)[8], Static Round-robin (SOR) [9], Balanced Allocation with Sort (BAS) [10], and Balanced Allocation with Sort for Batch (BASB) [10]. Typically, these heuristic file assignment algorithms try to optimize the mean response time of file requests, either through balancing the load among disks or via minimizing the service time variance at each disk, whereas most of them ignore the dynamic characteristics of the file requests targeted on the assigned files. Several previous studies [11,12,13,14] which focus on the dynamic I/O behaviors for both the scientific and enterprise applications show that the I/O requests from these applications own certain correlation. Specifically, the I/O requests from applications may temporally compete with each other for the same disk but leaving other disks idle [15,16,17]. Consequently, the load among the disks becomes imbalanced and the parallelism of multiple disks is underutilized [17]. One of the important reasons for the I/O contention is that the popular files are assigned onto the same disk and thus more requests from the applications are directed to this disk. Because the I/O contention turns the parallel I/O into sequential one [6], the performance gained from the parallelism of multiple disks would be reduced. Meanwhile, the file assignment algorithm which is responsible for allocating files onto disks decides the location and distribution of files among the disks [8]. Thus, it is necessary to redesign and implement a new and more efficient file assignment strategy which can take the I/O contention among the file requests into consideration. To achieve this target, this study proposes a new static heuristic file allocation algorithm named minimum I/O contention probability (MinCP). Firstly, we explore the relationship between the file access rate and I/O contention probability. The I/O contention probability theory proven in this study states that the I/O

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contention probability between the popular files is higher than that between the unpopular files when the file size follows the Zipfian distribution [18] or the Uniform distribution [9,11]. In other words, the file requests targeted on the popular files compete with each other with higher probability than the requests targeted on the unpopular files do. The I/O contention probability theory also indicates that using the file access rate as the file allocation metric can help minimize the I/O contention probability among the file requests. Then, we design and implement the MinCP file assignment algorithm. The idea behind the MinCP is to assign all the files sorted according to their access rates onto disks in roundrobin fashion. As a result, the popular files should be distributed evenly among the disks and thus the parallelism of the multiple disks is fully utilized. Finally, we compare MinCP with the state of arts under the synthetic. When the file size follows Zipfian distribution, MinCP and SOR [9] own equal performance and they outperform other algorithms for comparison. When the file sizes obeys Uniform distribution, the MinCP consistently performs best among the existing algorithms for comparison. The rest of the article is organized as follows. The MinCP algorithm, including its mathematic model, design and analysis, is presented in Section 2. In Section 3, we evaluate the MinCP algorithm with the synthetic workload. Section 4 concludes this article and presents the future work.

2 2.1

The MinCP Algorithm Mathematic Model Description

The file allocation algorithm can be incorporated into the logical of disk array controller, a file system or the logical volume manager (LVM) [19] . Suppose that there are m non-partitioned files F = f1 , f2 , . . . , fm and n disks d1 , d2 , . . . , dn . The access information for each file includes the access rate λ, the file size s and the expected service time E(s). The expected service time of a file is assumed to be proportional to the file size because the rotation latency and seek time are negligible compared to the transfer time when the file is big and entirely accessed [9]. The previous studies [20,9,11] show that the file accesses among files obeys either the Zipfian ( also called Zipf-like ) distribution or the the Uniform distribution. Suppose that there are overall p requests which would access this parallel I/O system and the R = r1 , r2 , . . . , rp denotes the request set. During the time duration  t, the expected total number of requests submitted to the system is t×( m i=1 λi ), and the expected number of requests targeted on the kth file is denoted with E(Ckt ): E(Ckt ) = t × λk .. Take ri , the ith request in the R and fk , the kth file in F , as example. We use ri = fk to denote the event that the request ri accesses the file fk . During the time duration t, the probability with which the request ri accesses the kth file is:

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E(Ckt ) λk P (ri = fk ) = m = m . t) E(C h=1 h h=1 λh In order to define the I/O contention probability and explore the relationship between the file access rate and the contention probability among I/O requests, we firstly define the following two access probability: Consecutive access probability, denoted with P s , is the chance that two files are accessed by two adjacent requests in the R. For example, the P s for s the fk and fj is: Pk,j = P (ri = fk , ri+1 = fj ). Overlapping access probability, denoted with P o , is the likelihood at which the expected service time of the first request covers the expected average arrival interval of the second request when the two files are accessed by two o o consecutive requests. For example, the Pk,j for the fk and fj is: Pk,j = 1 2

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Thus, the I/O contention probability, denoted with P c , can be defined as the file consecutive access probability multiplied with the I/O overlapping probability. In terms of the fk and fj , it can be computed with the following equation: s o s o P c (k, j) = Pk,j Pk,j + Pj,k Pj,k .

Theorem 1 (I/O Contention Probability Theorem). F is one file set and the file size obeys the Uniform or Zipfian distribution. Let f1st be the file with the most significance access rate and f2nd be the second. For two random files fr1 and fr2 , exclusive the file f1st and the file f2nd , in F , P c (ff1st , ff2nd ) > P c (ffr1 , ffr2 ). Proof (of theorem). Firstly, we prove the theory when the file size follows the Uniform distribution. For the f1st and the file f2nd , we have P c (f1st , f2nd ) = P sf1st ,f2nd Pfo1st ,f2nd + Pfs2nd ,f1st Pfo2nd ,f1st According to definition of the P s and P o , we can obtain Pfs1st ,f2nd > Pfsr1 ,fr2 , Pfs2nd ,f1st > Pfsr2 ,fr1 , Pfo1st ,f2nd > Pfor1 ,fr2 , Pfo2st ,f1nd > Pfor2 ,fr1 . Therefore, P c (f1st , f2nd ) >Pfsr1 ,fr2 Pfor1 ,fr2 + Pfsr2 ,fr1 Pfor2 ,fr1 , and the right of this inequality is equal to P c (fr1 , fr2 ). Thus, we obtain P c (f1st , f2nd ) >P c (fr1 , fr2 ), which completes the proof when the file size follows Uniform distribution.

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Secondly, we prove the theorem when the file size obeys the Zipfian distribution. For the file f1st and the file f2nd , we have P c (f1st , f2nd ) = P sf1st ,f2nd Pfo1st ,f2nd + Pfs2nd ,f1st Pfo2nd ,f1st . According to the definition of the Pfs1st ,f2nd and Pfo1st ,f2nd , we have Pfs1st ,f2nd Pfo1st ,f2nd

  Pfs1st ,f2nd (λf2nd )2 E(sf1st )λf1st m = + . 2 ( h=1 λh )2 × 2

When the file size obeys Zipfian distribution, we can assume that λf1st E(sf1st ) =   C and λfr1 E(sfr2 ) = C . So   Pfs1st ,f2nd (λf2nd )2 E(sfr1 )λfr1 s o m Pf1st ,f2nd Pf1st ,f2nd = + . 2 ( h=1 λh )2 × 2 Combined with the inequalities: λf2nd > λfr2 and Pfs1st ,f2nd > Pfsr1 ,fr2 , we obtain Pfs1st ,f2nd Pfo1st ,f2nd

  Pfsr1 ,fr2 (λfr2 )2 λfr1 E(sfr1 ) m > + , 2 ( h=1 λh )2 × 2

the right part of which is equal to Pfsr1 ,fr2 Pfor1 ,fr2 . Thus, Pfs1st ,f2nd Pfo1st ,f2nd >Pfsr1 ,fr2 Pfor1 ,fr2 . By employing the same approach, we can prove Pfs2nd ,f1st Pfo2nd ,f1st >Pfsr2 ,fr1 Pfor2 ,fr1 . Combined with these two inequalities, we obtain P c (ff1st , ff2nd ) >P c (ffr1 , ffr2 ), which completes the proof under the Zipfian file size distribution. 2.2

 

MinCP File Assignment Strategy Description

The MinCP file allocation algorithm is outlined in the Fig. 1. It tries to minimize the I/O contention probability among file requests through assigning file sorted in descending order of their access rates among multiple disks. Be different from most existing solutions [8,9,10] which employ the expected service time or the heat of the files as the allocation metric, the MinCP allocates files according to their access rates. This is because, according to the theorem 1, the I/O contention probability between the popular files is much larger than that between the unpopular files and assigning file with access rate as metric can help reduce the I/O contention probability.

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Input:

m = number of files n = number of disks λi = access rate of file i si = expected service time of file i Output: {A1 , A2 , . . . , An } = assignment of m files to n disks 1: Compute the height hi ← λi × si for each file 2: Compute the average disk load ρ : ρ ← n1 m i=1 hi 3: Sort files in descending order of their access rate λi 4: d ← 1 5: for each file fi sorted by access rate do 6: if loadd + hi < ρ then 7: Ad ← Ad ∪ fi // assign file fi to disk d 8: loadd ← loadd + hi 9: d ← (d + 1)%(n − 1) // next disk 10: else 11: Search a disk t with enough free load space to store file fi 12: if Successful then 13: At ← At ∪ fi 14: loadt ← loadt + hi 15: else 16: An ← An ∪ fi // assign file fi to last disk n 17: loadn ← loadn + hi 18: end if 19: end if 20: end for Fig. 1. MinCP File Assignment Algorithm

After computing the average load of all the files in Step 2 and 3, the MinCP algorithm sorts files in descending order of their access rates. Then, all files are assigned onto disks in the round-robin way. When the popular files are allocated evenly among disk, the total load and requests targeted on these files would be distributed among disks evenly. As the step 6 indicates, when the load of the target disk plus the load of the being allocated file is less than the average load ρ, the file is assigned onto this disk. Otherwise, the MinCP would try to find one disk with enough free load space to hold this files. But, if there is no disk owning enough free load space, the file should be very large and own low access rate [8,9,10] and thus this file is allocated onto the last disk. The reason why we use the last disk to hold the large files is that the larges files would block the response for the requests for small files when larges files and small files are stored together. Table 1. Configurations of the disk Attributes Value

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To compare the MinCP algorithm with the existing solutions for the nonpartitioned file assignment problem, we develop an event-driven parallel I/O system simulator with OMNet++ [21]. The disk simulated is the Cheetah disks [9], and the main configurations of it are presented in the Table 1. Table 2. Simulated synthetic workload characteristics Parameters

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5000 (16) – (8, 16, 24, 32, 40, 48, 56, 64) Zipfian: X:Y=(70:30) – (50:50, 60:40, 70:30,80:20, 90:10) Uniform: ([1:1000]) – ([1:1100], [1:1200], [1:1300], [1:1400], [1:1500], [1:1600], [1:1700], [1:1800], [1:1900], [1:2000]) Zipfian: X:Y=(70:30) – (50:50, 60:40, 70:30,80:20, 90:10) Aggregated access rate (100) – (25, 50, 100, 200, 300, 400, 500, 600, 700, 800, 900,  ( 5000 1000) i=1 λi ) Simulation duration 500 seconds

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In this section, the MinCP is compared with the existing static file algorithms under the workload with the Uniform file size distribution. When the file size obeys the Uniform distribution, the SOR outperforming both the SP and Greedy has been proven in the previous work [9]. So, to the best of our knowledge, SOR should be the most optimal static file assignment strategy under the Uniform file size distribution. In summary, four static file assignment algorithms, including SOR [9], SP [8], BAS [10], and MinCP, are compared in this test. The average response time with different aggregated access rates for the evaluated four algorithms are presented in the Fig. 2(a). Among the four algorithms, MinCP offers optimal average response time in all cases. This is because the I/O contention probability between the file requests is minimized when the MinCP assigns the files sorted in descending order of their access rates onto disks in round-robin fashion. In such case, the requests from the clients is distributed more evenly among disks, which can be verified by the Fig. 2(b). As a result, the parallelism of multiple disks is effectively utilized and the performance of the parallel I/O system can be efficiently reaped. Although SOR also allocates files in the round-robin way, it assumes the file access rate and service time are correlated. Thus, different popular files may be assigned onto same disk by SOR. As a result, the performance offered by SOR is degraded. The performance of both SP and BAS is worse than that of the MinCP or the SOR. This reason is that the SP and BAS group files with same service time on the same disk and therefore

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In this section, we evaluate the MinCP under the workload with the Zipfian file size distribution. Note that, the performance MinCP algorithm should be equal to that of SOR when the file size obeys Zipfian distribution. Thus, we only compare the proposed MinCP with BAS in this test. The experiment results on the average response time of all the I/O requests are presented in the Fig. 3(a). As the results show, the MinCP offers better performance than BAS in all cases. This is because that the SOR allocates file in the round-robin fashion and therefore popular files are more evenly distributed among disks. As a result, the I/O contention probability among the file requests is reduced. For the unpopular file, the BAS allocates one file with the most unpopular file onto one dedicated disk, but the MinCP proposed in this study employs one dedicated disk to hold more than one unpopular files. Thus, MinCP inherits all the advantages of SOR when file size follows Zipfian distribution. Furthermore, we also explore the impact of skew parameter (Fig. 3(b)) and the number of disk (Fig. 3(c)) on these algorithms. Among the four algorithms for comparison, the MinCP always achieves optimal performance in all cases. Thus, the MinCP can offer performance improvement for for a wide of skew parameter θ of the Zipfian distribution and own high scalability.

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This study proposes a static nonpartitioned file assignment strategy called MinCP for parallel I/O system. The idea behind the MinCP to assign the files sorted in the descending of their access rates onto disks in the round-robin way. The experimental results show that the MinCP outperforms the existing static file allocation schemes when the file size follows the Uniform distribution or Zipfian distribution. Thus, the MinCP proposed in this study is one effective strategy to allocate the non-partitioned files in the parallel I/O system. Future studies can be performed in the following directions. Firstly, the MinCP should be extended to support the intra-request parallelism of single request [6]. Secondly, since some file statistic information is not known in advance in dynamic environment, one dynamic version of MinCP should be developed to assign files without further file access characteristics known and work online.

References 1. Short, J.E., Short, J.E., Bohn, R.E., Baru, C.: How Much Information? 2010 Report on Enterprise Server Information. Technical report, Global Information Industry Center (2011) 2. Deng, Y.: What is the future of disk drives, death or rebirth? ACM Comput. Surv. 43 (2011) 3. Isaila, F., Garcia Blas, J., Carretero, J., Latham, R., Ross, R.: Design and Evaluation of Multiple-Level Data Staging for Blue Gene Systems. IEEE Transactions on Parallel and Distributed Systems 22, 946–959 (2011)

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4. Lang, S., et al.: I/O performance challenges at leadership scale. In: SC 2009: Conference on High Performance Computing Networking, Storage and Analysis, pp. 1–12. ACM, New York (2009) 5. Chen, P.M., Lee, E.K., Gibson, G.A., Katz, R.H., Patterson, D.A.: RAID: highperformance, reliable secondary storage. ACM Comput. Surv. 26, 145–185 (1994) 6. Scheuermann, P., Weikum, G., Zabback, P.: Data partitioning and load balancing in parallel disk systems. The VLDB Journal 7, 48–66 (1998) 7. Dowdy, L.W., Foster, D.V.: Comparative Models of the File Assignment Problem. ACM Comput. Surv. 14, 287–313 (1982) 8. Lee, L.W., Scheuermann, P., Vingralek, R.: File Assignment in Parallel I/O Systems with Minimal Variance of Service Time. IEEE Transactions on Computers 49, 127–140 (2000) 9. Xie, T., Sun, Y.: A file assignment strategy independent of workload characteristic assumptions. Trans. Storage 5, 10:1–10:24 (2009) 10. Zhu, Y., Yu, Y., Wang, W., Tan, S., Low, T.C.: A Balanced Allocation Strategy for File Assignment in Parallel I/O Systems. In: 2010 IEEE Fifth International Conference on Networking, Architecture and Storage (NAS), pp. 257–266 (2010) 11. Wang, F., Xin, Q., Hong, B., Brandt, S.A., Miller, E.L., Long, D.D.E., Mclarty, T.T.: File System Workload Analysis for Large Scale Scientific Computing Applications. In: 12th NASA Goddard Conference on Mass Storage Systems and Technologies, pp. 139–152 (2004) 12. Nisar, A., Keng Liao, W., Choudhary, A.: Scaling parallel I/O performance through I/O delegate and caching system. In: SC 2008 International Conference for High Performance Computing, Networking, Storage, pp. 1 –12 (2008) 13. Feng, D., Zou, Q., Jiang, H., Zhu, Y.: A novel model for synthesizing parallel I/O workloads in scientific applications. In: 2008 IEEE International Conference on Cluster Computing, pp. 252–261 (2008) 14. Zoll, Q., Zhu, Y., Feng, D.: A study of self-similarity in parallel I/O workloads. In: IEEE / NASA Goddard Conference on Mass Storage Systems and Technologies, pp. 1–6 (2010) 15. Reddy, A.L.N., Wyllie, J.C.: I/O issues in a multimedia system. Computer 27, 69–74 (1994) 16. Carretero, J., No, J., Chen, P.: COMPASSION: A parallel I/O Runtime System Including Chunking and Compression for Irregular Applications. In: Ferreira, A., Rolim, J.D.P., Teng, S.-H. (eds.) IRREGULAR 1998. LNCS, vol. 1457, pp. 262– 273. Springer, Heidelberg (1998) 17. Dong, B., et al.: Self-acting load balancing with parallel sub file migration for parallel file system. In: Third International Joint Conference on Computational Science and Optimization, vol. 2, pp. 317–321. IEEE Computer Society, Washington (2010) 18. Cunha, C., Foster, D.: Characteristics of WWW client-based traces. Technical report, Boston University (1995) 19. Verma, A., Anand, A.: General store placement for response time minimization in parallel disks. J. Parallel Distrib. Comput. 67, 1286–1300 (2007) 20. Kwan, T., Mcgrath, R., Reed, D.: Ncsas world wide web server design and performance. Computer 28, 67–74 (1995) 21. Omnest Global, Inc. (OMNeT++ User Manual for Version 3.2)

Replication and Checkpoint Schemes for Task-Fault Tolerance in Campus-Wide Mobile Grid* SookKyong Choi1, JongHyuk Lee1, HeonChang Yu1, and Hwamin Lee2 1

Dept. of Computer Science Education, Korea University, Anam-Dong, Seongbuk-Gu, Seoul, Korea {csukyong,spurt,yuhc}@korea.ac.kr 2 Dept. of Computer Software Engineering, Soonchunhyang University, 336-745, Asan-si, Korea 1 [email protected]

Abstract. Mobile grid computing is a computing environment that incorporates mobile devices to an existing grid environment and supports users’ mobility. But this environment is not stable, so methodologies to cope with the reliability issue are needed. Fault tolerance approaches for task execution in grid computing can be categorized into replication and checkpoint. We apply these techniques to a SimGrid simulator to provide a fault tolerance for a mobile environment and show the results in this paper. The results demonstrate that the best solution for fault tolerance in mobile grid computing depends on the situations of the network. The contribution of this paper is the use of real-life trace data to simulate fault tolerance in a mobile grid computing. Keywords: mobile grid, replication, checkpoint, fault tolerance, reliability.

1

Introduction

Grid computing is a technology to offer high scalability, multitasking, and multitenancy. Grid computing provides an environment that effectively execute tasks by sharing and integrating computing resources in heterogeneous computing systems. Traditional grid environment is mostly implemented with resources in physically fixed locations. But this environment is evolving and extending the scope of resources into movable resources, by using mobile devices, to take advantage of the exploding population of mobile gadgets such as laptops, netbooks, and even smartphones. More recently, the mobile devices are equipped with high-performance computing engines with inexpensive price tags, attracting more and more users. Mobile grid computing is a computing environment that incorporates mobile devices to an existing grid environment and supports users’ mobility. In mobile grid computing environment, mobile devices can act as both a service provider and a service requester. *

This work was supported by National Research Foundation of Korea Grant funded by the Korean Government (2009-0070138)

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As a service provider, it can provide its computing resources as a part of a mobile computing infrastructure. That is, mobile devices can execute tasks in mobile computing environment, like desktops or servers in a traditional wired computing. As a service requester, a mobile device can provide an interface to request services and use resources in mobile grid computing. The mobile computing environment exhibits its own characteristics, unlike the wired computing. Users of mobile devices may freely move in and out of a mobile network, which causes unstablility in network connection. Even worse, mobile devices may experience sudden power-offs by user or dead battery. We regard these cases that may results in outcome loss as faults. Therefore, the environment of mobile grid is not stable, so it should provide methodologies to cope with the reliability issue. Fault tolerance approaches for task execution in grid computing can be categorized into replication and checkpoint. Replication is used to improve reliability, fault tolerance, or accessibility using redundant resources. If the same data is duplicated and stored on several storages, it is referred to as data replication. If the same task is duplicated and executed several times, it is referred to as computation replication. Computation task can be executed on several resources simultaneously (replicated in space) or can be executed many times on one resources (replicated in time). Most of studies for replication have been on how to replicate data to improve accessibility and minimize access latencies. Checkpoint is used to minimize efforts of reexecuting long task from the beginning when the task is interrupted by faults such as a hardware failure, a software failure or resource unavailability. Checkpointing is taken periodically at specified intervals. Some information like a machine status, intermediate results and logs, are stored at every checkpoint interval on non-volatile storage. In case a fault occurs, information stored at the latest checkpoint is retrieved and used to execute task from the checkpoint. In the checkpoint scheme, it is crucial to figure out the optimal checkpoint interval (frequencies). Frequent checkpoints affect to the system performance due to overhead. Infrequent checkpoints may not provide fault tolerance. Especially, there are several studies for minimizing overhead in checkpoint. Compared to the traditional grid computing, a special care should be taken in mobile environment to consider characteristics of mobile devices, especially when mobile devices are utilized as resources for task processing. So, we apply these replication and checkpoint techniques to a SimGrid simulator and show the proper methods for fault tolerance when mobile devices are used as resources in this paper. To do this, we consider two incomplete cases caused by faults in mobile environment. One is the case that a task cannot be completed due to faults, and the other is the case that a task is completed but its outcome cannot be returned to requester due to faults. It is imperative to provide methods to withstand faults in a mobile grid computing.

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Related Works

To withstand faults and provide efficient grid services, it is essential to investigate on task replicas that do not affect the process of other tasks, minimize waste of resources, and improve task execution time with replication. Early studies focused on fault tolerance methods using a fixed number of task duplicates [1,2,3,4]. Recent studies

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shifted to the dynamic decision of task replicas, that is, optimal number of task replica is dynamically determined according to system status [5,6,7]. Kang et al [1] researched on meeting users' QoS requirements and fostering balanced resource consumption by task replication using under-utilized and underpriced resources in computational service market composed of less reliable desktop PCs. They have a point of view that under-utilized and cheap resources can be exploited to build a high quality resource and hence can facilitate balanced resource usage. Katsaros et al [2] suggested installment-based scheduling and task replication to overcome intermittent connectivity in mobile grid. They defined that installment is consecutive fragments of a divisible task. And they used mobile device as a resource. However, the study assumed that every mobile device has the same performance and same network environment. Silva et al [3] proposed a dynamic replication approach, Workqueue with Replication (WQR). The study has not a fixed number of task replication and dynamically replicates tasks to idle resources when there is no other task to schedule. Moreover, replication is not used when system load is high. Dobber et al [4] proposed a method combining dynamic load balancing (DLB) and job replication (JR) to cope with unpredictable and dynamic nature of grid computing. This approach is a scheduling scheme that selects DLB or JR by comparing statistic time of expectation to threshold value set in advance. The study used four job replicas in JR. Limaye et al [5] proposed smart failover approach that is a job-site recovery to proactively handle failures and use backup server to overcome situation where the execution of client’s job is failed due to the failure of primary server. This approach supports transparent recovery by storing job states in local job-manager queue and transferring those states to the backup server. Priya et al [6] proposed task level fault tolerance in grid environment. With the technique, the study claimed that their checkpoint technique achieved the optimal load balance across different grid sites. Katsaros et al [7] considered independent checkpoint activities, proposed a statistical decision-making approach, and defined response time metrics of fault-tolerance performance and effectiveness. Paul J. Darby III et al [8] suggested checkpoint arrangement based on reliability of consumer and provider to maximize recovery probabilities of checkpoint data in mobile grid computing, which concerned about host mobility, dynamicity, less reliable wireless links, frequent disconnections, and variations in mobile environments. A mobile host simply sends its checkpoint data to neighboring mobile host, or saves checkpoint data for neighboring mobile host. There are some comparison studies to investigate the optimal solution for fault tolerance between checkpoint and replication in grid computing. However, studies in mobile grid environment have not been reported to the best of our knowledge. Wu et al [9] compared four different fault tolerance scheduling strategies based on genetic algorithm to improve reliability of grid system. These scheduling strategies are compared in terms of the performance metrics such as makespan, average turnaround time and job failure rate. The study reports that the checkpoint provides the best solution and replication is not suitable due to the overhead. Chtepen et al [10] introduced some heuristics using parameter dynamically based on grid status information and proposed a hybrid scheme based on both checkpoint and replication to improve job throughput against failures. The study reports that the dynamic

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adjustment of checkpoint frequency according to resource stability and remaining job execution time minimizes the checkpoint overhead. It also reports that postponing replication minimizes the replication cost. Moreover, the study reports that the hybrid scheme is the best approach when system information is not provided in advance.

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Task-Fault Tolerance Approaches in Mobile Grid Environment

3.1

Architecture for Campus-Wide Mobile Grid Computing

In this paper, we assume campus-wide mobile grid computing environment similar to [11]. In Fig. 1, Mobile_Network indicates the entire campus that is composed of several Sub_Mobile_Networks. Sub_Mobile_Network indicates small network of each building in campus. MN(Mobile Node; mobile device)s are connected to the Sub_Mobile_Network by AP. There is a Global_Scheduler in a Mobile_Network. There are Local_Schedulers in Sub_Mobile_Networks and there are some Intermediate_Scheduler among Sub_Mobile_Networks. High level scheduler controls low level schedulers and supports load balancing among them. For this environment, we assign Proxies to act as schedulers in networks, so a proxy receives jobs from MNs and delivers outcomes to MNs. Because MNs act both an interface to mobile services and computational resources for task processing, an MN can submit tasks and process the submitted task. Submitted task from user is transmitted to a Local_Scheduler. The Local_Scheduler selects an MN in a Sub_Mobile_Network that including the Local Scheduler to process user’s task. Then the Local_Scheduler allocates the task to the selected MN. If Local_Scheduler has not enough resources, namely MNs, to process the task, the task is transmitted from the Local_Scheduler to an Intermediate_Scheduler or a Global_Scheduler recursively. Reversely, high level scheduler selects low level scheduler to return the task outcome, in turn. Therefore, task outcome is transmitted to the user submitting the task. Because we assume that tasks are divisible and independent in this paper, if big sized task is submitted and single mobile device cannot execute the task, then scheduler can divide the task. Fault includes network disconnection and power-off of mobile device. Further, we regard all types of cases that cause task failures in mobile grid as fault. But, we don’t consider Byzantine failure in this paper. When a fault is occurred, a task cannot complete due to the fault, and user submitting the task cannot receive task outcome even thought the task is completed. 3.2

Status of Mobile Device (MN)

The status of mobile device for processing a job is ‘Available’ or ‘Not Available’. An ‘Available’ status means that an MN can submit, process a job, and receive a job outcome. And a ‘Not Available’ status means that MN can’t be used for resource to provide grid service, because of battery shortage, network disconnection, or other physical faults. We don’t care ‘Not Available’ status in this paper, so we consider only if the MN is available or not for some time duration to process a job.

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Fig. 1. Architecture for Campus-wide Mobile Grid Computing

To select a proper MN for processing a job, a probability that a mobile device is available can be calculated by equation (1). n

Availabili tyij =

 Pers k =1

ijk

Tavail + Tnotavail

(1)

n

where

 Pers k =1

ijk

is the User Persistence(Pers) meaning a time duration during

which the ith MN stays at the jth Sub_Mobile_Network until the MN moves to other Sub_Mobile_Network or network link is down[12]. K is the number of available status. And Tavail and Tnotavail mean a time duration during which a MN can or cannot process a job respectively. 3.3

Task Scheduling Scheme

We apply 3 types of task scheduling schemes for fault tolerance in this paper; No replication and no checkpoint, task with Replication, and task with Checkpoint. The first refers to task execution without replication and checkpoint, the second and the third refer to task execution according to several replication options and checkpoint options. Basic principle in this paper is that a submitted task is processed in the Sub_Mobile_Network that includes the MN first. During a task execution, various situations that task outcome cannot be returned to user can be happened.

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3.3.1 No Replication and No Checkpoint When a task is submitted to a Sub_Mobile_Network, Local_Scheduler in the Sub_Mobile_Network selects an MN among MNs connected to the Sub_Mobile_Network. And the Local_Scheduler allocates the task to the MN to process the task. An MN with the best availability is selected first by equation (1). If there is no MN to process the task in the Sub_Mobile_Network, the request for task processing is transmitted to a high level scheduler. A high level scheduler selects an MN for task processing by using informations of the MN from IS(Information Service). Finally, if there is no MN to process the task in whole network, the task cannot be processed. And if the MN fails due to faults, other MNs can process the task from the beginning. We call this scheduling scheme No-No scheme in section 4. 3.3.2 Replication An original task submitted to a Sub_Mobile_Network can be replicated according to the replication options by a proxy as follows. • option_1 : replicate necessarily • option_2 : replicate when length of the task is long • option_3 : replicate when the size of remaining task is not much • option_4 : replicate when system load in a mobile network is low • option_5 : replicate when the ratio of replicated task in a mobile network is low The default number of replica for a job is one in order to reduce the waste of resources and overhead for replication in whole Mobile Network. System load increases with the number of MN processing a job. If a task is replicated, original task and replicas are executed on different MN simultaneously. Remaining size of the task, system load, and the ratio of replicated task are dynamically changed while the task is executed. So, replicated or not, which is determined dynamically by a proxy. When the faster task between original task and replica is completed, the other task has to be canceled. Fig. 2 shows the algorithm for replication. 3.3.3 Checkpoint An original task can be checkpointed according to the checkpoint options by a proxy as follows. • option_1 : checkpoint necessarily • option_2 : checkpoint when length of task is long • option_3 : checkpoint when the size of remaining task is not much • option_4 : checkpoint when system load in a mobile network is low • option_5 : checkpoint when the ratio of checkpoint task in a mobile network is low

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if (option_1_flag is true) { // replication process create same task. select another available MN to put the task. dispatch the task on the MN. } if (option_2_flag is true) { check task size. if (task is long) { start replication process of option_1 } } if (option_3_flag is true) { check system load. if (system load is low) { start replication process of option_1 } } if (option_4_flag is true) { check the size of remaining task. if (the size of remaining task is not much) { start replication process of option_1 } } if (option_5_flag is true) { check the ratio of replicated task in a mobile network. if (the ratio of replicated task is low) { start replication process of option_1 } } when one task out of the two, original task or replica, is over, kill the other task, and set the completion time by own completion time.

Fig. 2. Algorithm for replication

The default MN number of checkpointing for a job is one, like replication. And remaining size of the task, system load, and the ratio of checkpoint task are dynamically changed while the task is executed. So, checkpointed or not, which is determined dynamically by a proxy. Fig. 3 shows the algorithm for checkpoint. 3.3.4 Example Assume option_1_flag is false and option_2_flag is true, but length of task is short in each replication and checkpoint algorithm. Then a task will not be replicated or checkpointed, and the task is restarted from zero when faults occur.

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Simulation

4.1

Data Analysis

To show the optimal methods for fault tolerance in a mobile grid computing, we analyzed a real-life trace: WLAN trace of Dartmouth campus[13]. The trace is a syslog produced by APs from September 1, 2005 to October 4, 2006[14]. We selected some part of trace as of June 6, 2006 for input of Simulation, which includes 987 APs and 3,367 mobile devices. After analyzing the trace, network information was extracted. The rate of the sessions maintained less than 2 hours is about 80% of all.

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Setup for Simulation

In this paper, we used Java based SimGrid simulator[15,16]. SimGrid is a toolkit that provides core functionalities for the simulation of distributed applications in heterogeneous distributed environments. To consider dynamic characteristics of mobile devices in a mobile grid computing, i.e. unstable and unpredictable characteristics, we setup simulation environment according to the results of real-life trace. And we used MSG of SimGrid that can support a proxy and MN clients. Configurations for simulation are presented in Table 1. The type of a task is a computational intensive task. The number of tasks is classified 10, 100, and 500, namely 3 types. And their length type of a size is categorized as short and long, namely 2 types. We assumed that all MN(mobile devices) has same performance in this paper. They can freely move around the Sub_Mobile_Network or other Mobile_Networks. The number of MN to submit, process, and receive a job is classified 10, 100, 1000, and 4500, namely 4 types. There are 3 types of task scheduling schemes; No replication and no checkpoint, task with Replication, and task with Checkpoint.

check the status of MN executing a task. if (MN fail) { if (option_1_flag is true) { // checkpoint process get the checkpoint information. select another available MN to put the task. restart the task from the checkpoint. } if (option_2_flag is true) { check task size. if (task is long) { start checkpoint process of option_1 } } if (option_3_flag is true) { check system load. if (system load is low) { start checkpoint process of option_1 } } if (option_4_flag is true) { check the size of remaining task. if (the size of remaining task is not much) { start checkpoint process of option_1 } } if (option_5_flag is true) { check the ratio of checkpoint task in a mobile network. if (the ratio of checkpoint task is low) { start checkpoint process of option_1 } } }

Fig. 3. Algorithm for checkpoint

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Table 1. Configurations for simulation •

task

• • •

Mobile devices

• • •

Methods for Task Fault-tolerance

4.3

• • •

type of task : computational intensive the number of tasks : 10, 100, and 200 length for task (task size) : short and long the number of mobile devices : 10, 100, 1000, and 3300 availability : 0 ~ 100% network connectivity : 0 or 1 (0:disconnection 1:connection) whole system load : high and low no replication and no checkpoint (No-No scheme) task with Replication task with Checkpoint

Results of Simulation

Fig. 4 shows the results of No-No scheme, namely No replication and no checkpoint, which is the basis of other scheduling scheme, Replication and Checkpoint. Completion time of tasks, the number of completed tasks, the number of failed tasks, and the number of used hosts are presented in Fig. 4. Y-axis means classification of tasks like 10/10/L that is composed of the number of tasks, the number of hosts, and size of the tasks(Long or Short). In addition, we analyze results of simulation for 4 evaluation metrics, namely average execution time of all tasks, completion rate of tasks, the number of completed tasks, and the utilization rate of resources according to the 5 options of each task scheduling scheme.

Fig. 4. No replication and no checkpoint

● Average Execution Time Fig. 5 and 6 show the average execution time in replication and checkpoint scheme, compared with No-No scheme represented by dotted line. Average execution time in replication scheme is shorter than in checkpoint scheme. It is due to checkpoint overhead that decision of checkpointing is made during processing, unlike replication.

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Fig. 5. Average Execution Time in Replication

Fig. 6. Average Execution Time in Checkpoint

● Completion rate of Tasks Fig. 7 and 8 show the completion rate of tasks in each scheme. In replication scheme, completion rate of tasks is getting higher according to the number of hosts. This means that if there are plenty of hosts, then using replication can increase the completion rate. In checkpoint scheme, the completion rate is mostly higher than in No-No scheme. This means that checkpoint scheme basically increase the probability of job completion to a certain extent.

Fig. 7. Completion Rate of Tasks in Replication

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Fig. 8. Completion Rate of Tasks in Checkpoint

● The number of Completed Tasks Fig. 9 and 10 represent the number of completed tasks in each scheme. The number of completed tasks in checkpoint scheme is higher than in No-No and replication scheme. That’s because a checkpoint scheme deal with the faults of resources more actively

Fig. 9. The number of Completed Tasks in Replication

Fig. 10. The number of Completed Tasks in Checkpoint

● Utilization Rate of Resources Fig. 11 and 12 show the utilization rate of resources in each scheme. There is little difference in two scheme. That seems an extra resource for replication and another resource for checkpoint are the same sense.

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Fig. 11. Utilization Rate of Resources in Replication

Fig. 12. Utilization Rate of Resources in Checkpoint

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Conclusion and Future Work

In this paper, we present fault-tolerance methods for mobile computing environment. We apply replication and checkpoint scheduling scheme to real-life trace data. The results demonstrate that the best solution for fault tolerance in mobile grid computing depends on the situations of the whole network. Average execution time is shorter in replication scheme, and the number of completed tasks is bigger in checkpoint scheme. So if there are plenty of resources in a network and they are comparatively reliable, then replication scheme is the better, else checkpoint scheme is the better. We have a plan to apply the real-life trace to mobile cloud computing environment using GloudSim simulator. And we will conduct provisioning of reliable services based on SLA and QoS in mobile cloud computing.

References 1. Kang, W., Huang, H.H., Grimshaw, A.: A highly available job execution service in computational service market. In: 8th IEEE/ACM International Conference on Grid Computing, September 19-21, pp. 275–282 (2007) 2. Katsaros, K., Polyzos, G.C.: Evaluation of scheduling policies in a Mobile Grid architecture. In: Proc. International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS 2008), Edinburgh, UK (June 2008) 3. Silva, D., Cirne, W., Brasileiro, F.: Trading Cycles for Information: Using Replication to Schedule Bag-of-Tasks Applications on Computational Grids. In: Kosch, H., Böszörményi, L., Hellwagner, H. (eds.) Euro-Par 2003. LNCS, vol. 2790, pp. 169–180. Springer, Heidelberg (2003)

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4. Dobber, M., van der Mei, R., Koole, G.: Dynamic Load Balancing and Job Replication in a Global-Scale Grid Environment: A Comparison. IEEE Transactions on Parallel and Distributed Systems 20(2), 207–218 (2009) 5. Limaye, K., Leangsuksun, C.B., et al.: Job-Site Level Fault Tolerance for Cluster and Grid environments. In: The 2005 IEEE Cluster Computing, Boston, MA, September 27-30 (2005) 6. Baghavathi Priya, S., Prakash, M., Dhawan, K.K.: Fault Tolerance-Genetic Algorithm for Grid Task Scheduling using Check Point. In: Sixth International Conference on Grid and Cooperative Computing, GCC 2007 (2007) 7. Katsaros, P., Angelis, L., Lazos, C.: "Performance and Effectiveness Trade-Off for Checkpointing in Fault-Tolerant Distributed Systems. Concurrency and Computation: Practice and Experience 19(1), 37–63 (2007) 8. Darby III, P.J., Tzeng, N.-F.: Decentralized QoS-Aware Checkpointing Arrangement in Mobile Grid Computing. IEEE Transactions On Mobile Computing 9(8), 1173–1186 (2010) 9. Wu, C.-C., Lai, K.-C., Sun, R.-Y.: GA-Based Job Scheduling Strategies for Fault Tolerant Grid Systems. In: IEEE Asia-Pacific Services Computing Conference (2008) 10. Chtepen, M., Claeys, F.H.A., Dhoedt, B., De Turck, F., Demeester, P., Vanrolleghem, P.A.: Adaptive Task Checkpointing and Replication: Toward Efficient Fault-Tolerant Grids. IEEE Transactions on Parallel and Distributed Systems 20(2), 180–190 (2009) 11. Katsaros, K., Polyzos, G.C.: Optimizing Operation of a Hierarchical Campus-wide Mobile Grid for Intermittent Wireless Connectivity. In: 15th IEEE Workshop on Local & Metropolitan Area Networks, LANMAN 2007, June 10-13, pp. 111–116 (2007) 12. Balazinska, M., Castro, P.: Characterizing Mobility and Network Usage in a CorporateWireless Local-Area Network. In: Proceedings of the First International Conference on Mobile Systems, Applications, and Services (2003) 13. Henderson, T., Kotz, D.: CRAWDAD trace dartmouth/campus/syslog/05_06 (February 8, 2007), http://crawdad.cs.dartmouth.edu 14. Lee, J.H., Choi, S.J., Suh, T., Yu, H.C.: Mobility-aware Balanced Scheduling Algorithm in Mobile Grid Based on Mobile Agent. The Knowledge Engineering Review (2010) (accepted for publication) 15. Buyya, R., Murshed, M.: GridSim: A Toolkit for the Modeling and Simulation of Distributed Resource Management and Scheduling for Grid Computing. J. Concurrency and Computation: Practice and Experience 14, 13–15 (2002) 16. Sulistio, A., Cibej, U., Venugopal, S., Robic, B., Buyya, R.: A toolkit for modelling and simulating data Grids: an extension to GridSim. Concurrency and Computation: Practice & Experience 20(13), 1591–1609 (2008)

Dynamic Arrangement of Control in a Personalized Learning System Based on User Competency Youngseok Lee1, Jungwon Cho2,*, Sungjae Han3, and Byung-Uk Choi4 1

Department of Electronics Computer Engineering, Hanyang University, 17 Haengdang-dong, Sungdong-gu, Seoul, 133-791 South Korea [email protected] 2 Department of Computer Education, Jeju National University, 102 Jejudaehakno, Jeju-si, Jeju-do, 690-756 South Korea [email protected] 3 Department of Electronics Computer Engineering, Hanyang University, 17 Haengdang-dong, Sungdong-gu, Seoul, 133-791 South Korea [email protected] 4 Division of Computer Science & Engineering, Hanyang University, 17 Haengdang-dong, Sungdong-gu, Seoul, 133-791 South Korea [email protected]

Abstract. This paper proposes the e-Space Manager to reconstruct a learning environment and describes the method used to modify components. The proposed system analyzes user competencies, and the results influence which components the users can select. That is, it provides not only for the rearrangement of components but also for the modification of components according to the user’s competencies. To verify the effectiveness of the proposed system, the e-Space Manager was applied to an SQL-based learning system. Users chose the optional learning contents that they wanted. It was confirmed that information about the learner's competency influenced the learning components. A survey of users showed in a higher satisfaction rating for the proposed study system than for the previous system. Using this system, tutors not only could analyze the learners’ abilities precisely based on their competencies but also could recommend study areas and provide a customized environment to each individual. Keywords: Customized Learning, User Competency, e-Learning, Learning Environment, Web 2.0.

1

Introduction

With the advent of Web 2.0 technology has come an increasing interest in the personalization of web sites. In the early days of the web, web sites specified content

*

Corresponding author.

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in a uniform way, delivering essentially the same content to all users according to the web technology of the day. Today, a major concept driving web development is the concept of personalized services [1]. This paper introduces the concept of Web 2.0 to traditional hypertext markup language in a manner that is not possible with traditional rich applications. This concept makes possible a variety of new services that require implementation of a new type of interface, which is the subject of an active area of current research [2]. A larger variety of services is possible with the personalization of sites based on the users’ needs. An example of a typical personalized service is Google's personalized page, which permits the relocation of services such as widgets. However, this relocation is limited to existing components; it requires storing relocation information and does not provide for the creation of new features [3, 4]. Personalized service applied to an e-learning system offers convenience [1]. In this paper, we propose a learning environment using a module that we call e-Space Manager, which provides personalized service, as system users can select components to design their own learning spaces. The learning content as well as an analysis of the user's competency output format, selected by the user, changes the system directly. Thus, the selection of competency elements is user-centered, and the system can take advantage of the respective component to alter the overall system configuration. The learning environment is modified by the user through competency modification and reconfiguration of components. To evaluate the proposed system, learner surveys were conducted. The survey results indicated a high level of student satisfaction, which illustrates the superiority of this new system.

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Review of Pertinent Literature

2.1

Customized Services

Since the advent of the Internet as a marketing tool, customer relations management (CRM or eCRM) software has boosted interest in the "personalization" of web services. Although the concept of personalization has long been a part of traditional offline marketing, the concept re-emerged during the 2000s with respect to web development [5]. Personalized services based on recommendations from the user profile allow modification of the selection of services and user-based customization of services [6, 7]. Web 2.0 permits personalization of a web site from an existing structure according to the changing needs of users, and it enables users to receive desired information quickly, allowing for intensive services to be provided [1, 2]. As shown in Fig. 1, a typical portal site such as Google can provide distinctive and personalized services. These personalized services are based on user recommendations. Recent advances in technology, combined with customization services, permit the continual development of personalized services.

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Fig. 1. Customized service of iGoogle

2.2

Competency

Currently, there is considerable interest in competency-based learning models. Competency is defined as possession of a specific combination of complex knowledge, skills, and attitudes necessary to achieve a specific performance level at a task or job [8]. In competency-based learning, the competencies required are first defined using specific terminology to describe the knowledge, work, and learning outcomes desired. Both competencies and taxonomies, which are key means by which the competencies are classified, are presented in various ways. Fig. 2 illustrates the concept of using a skill gap analysis to target competencies that need to be acquired. The current competence of a learner is determined based on a pre-defined competency model, and the target competencies are derived. Capacity data, reusable competency definitions, and proof of competence can all be categorized by target date or complexity. For example, an estimate of predicted technological advances in the quantitative component can be defined [8, 9].

Fig. 2. Competency-based skill gap analysis

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System Design

In this paper, we propose a learning space, called the e-Space Manager, that allows the user to reconfigure the core engine. A personalized service provides help for the overall system configuration. Through competency modification, the user is able to reconfigure components. 3.1

System Architecture

The proposed system architecture is shown in Fig. 3. Learners access the learning and evaluation modules through the interface module. We use the phrase “learning space representation” of the learning content to refer to the output area of the webpage. A component that is placed in the learning space is called a learning component. A component of the basic study and recommendations is divided into learning components. A basic learning component for a general learning content is used for the purpose of representation. The representation of content can be changed by the user (i.e., is vulnerable to the user). The recommended learning component is the vulnerable content combined with the core functionality of the component. The learning content presented in the learning component is selected from the vulnerable areas according to the learner’s target competencies.

Fig. 3. System architecture

User-selectable configurations are provided in the learning environment. The user's goals, determined through an analysis of the competency model chosen by the learner, are reflected in the components' variable settings, and tasks are performed automatically. The program can generate a page showing the learner's target competency model. Based on the information generated in the existing set of components, changes are made to the program’s settings. When the information is insufficient to update the system, the learning system can use comparators to recommend reorganization.

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Competency Model

The competency model uses SQL as the data manipulation language (DML). A prerequisite course is set to determine a student’s problem-solving abilities. After unpacking all the problems, the student’s answers are compared against the competency model and tokenized. From the results, the evidence records are derived, and the corresponding competency tables are stored in appropriate fields.

Fig. 4. Competency gap analysis process for SQL query

The proposed method first determines the learner’s existing competencies, then determines which competencies need to be acquired, and finally derives the steps necessary to acquire the goal competencies. Data collected from students for this competency record are compared against the competency model. As a result, the learner’s acquired skills and competencies are determined. Goals that the student failed to accomplish indicate the need to acquire essential information or the need to derive competencies. This record is created by performing a comparative analysis against the competency model. The “SELECT” query is used to determine the competencies required in each of the “Grammar” and “Parameter” fields and determines the lack of any item. Fig. 4 shows how SQL queries are used to perform a competency gap analysis. Components that reflect the learner's competence are selected and combined into a single component of the process shown in Fig. 5; weak learners are shown content separately as a new component.

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Fig. 5. Competency gap analysis process for SQL query

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System Evaluation

Fig. 6 shows the competency analysis calculated from the learner's answers to the competency model, and a competency map determined by comparing the learner's screen representation to the competency model. Professors can target students who are identified as lacking certain skills and capacity. After the student has completed the problem-solving section, the records management module, which correlates each item with the student's competency information, can be viewed directly.

Fig. 6. Analysis result of user competency

Fig. 7. Analysis result of survey

“Existing system” is the existing learning system, which consists of standardized content and learning components. “Update system” means to reset the competency fields to reflect the results from the learning components. “Proposed system” means to update the system and create new components. Personalized content is created in a controlled environment, based on the user’s recommendations, content selection, and

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evaluation of content. The system examines which areas the student chooses to study to evaluate the readability of vulnerable areas and to rate the learners' satisfaction. The results of an assessment survey are shown in Fig. 7. The proposed system received higher student satisfaction scores than did the existing system with respect to both learning effectiveness and learning experience.

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Conclusion

In this paper, we have proposed an SQL-Tutor Learning System. At the heart of the system is user-controlled customizability that allows personalized service and maximizes user satisfaction. In this system, users can place a random component of their learning environment into their learning space and have the ability to make it optionally available. Learners are enabled to change settings and reassemble components. This vulnerability of the content to user change makes possible the creation of new learning components. Flexibility is ensured by allowing advances in the learner's competence to continually be reflected in the learning environment.

References 1. Papanikolaou, K.A., Grigoriadou, M.: An Instructional Framework Supporting Personalized Learning on the Web. In: The 3rd IEEE International Conference on Advanced Learning Technologies, pp. 120–124 (2003) 2. Google, iGoogle, http://www.google.com/ig?hl=en 3. Wu, D., Im, I., Tremaine, M., Instone, K., Turoff, M.: A Framework for Classifying Personalization Scheme Used on e-Commerce Websites, System Sciences. In: Proceedings of the 36th Annual Hawaii International Conference (2003) 4. Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., Sarwar, B., Herlocker, J., Riedl, J.: Combining collaborative filtering with personal agents for better recommendations. In: Proceedings of the AAAI 1999 Conference on Artificial Intelligence, Orlando, FL, pp. 439–446 (1999) 5. Thomas, J.P., Thomas, M., Ghinea, G.: Modeling of Web services flow. In: IEEE International Conference on E-Commerce (CEC 2003), pp. 391–398 (June 2003) 6. Papazoglou, M.P., Georgakopoulos, D.: Service-Oriented Computing. CACM 46(10) (2003) 7. Booth, D.: Web Services Architecture W3C Working Group Note 11 (2004), http://www.w3.org/TR/ws-arch/ 8. IMS RDCEO, IMS Reusable Definition of Competency or Educational Objective, http://www.imsglobal.org/competencies/index.html 9. Sayar, A., Pierce, M., Fox, G.: Integrating AJAX Approach into GIS Visualization Web Services. In: IEEE AICT-ICIW 2006, pp. 169–169 (2006)

Design and Implementation of Deduplication Storage Server Using Stride Scheme Ho Min Jung1, , Jin Kim1 , Jin Haeng Cho2 , and Young Woong Ko1 1

Dept. of Computer Engineering, Hallym University, Chuncheon, Korea {chorogyi,jinkim,yuko}@hallym.ac.kr 2 Boston Design Center AMD, MA, USA [email protected]

Abstract. In this paper, we propose a deduplication storage system exploiting stride scheme to minimize storage space and network bandwidth. The key idea of this paper is to provide duplication checking mechanism based on stride scheme of fixed-length block algorithm. Our approach can minimize computation time for detecting duplicated region of blocks while decreasing computation time. We made several experiments to show performance enhancement of the proposed system. In experiments, stride scheme can diminish storage space and efficiently manages duplicated data blocks. Keywords: Stride scheme, Hash, Deduplication, Storage Server.

1

Introduction

Storage administrators constantly strive to deal with increasing proliferation of data, such as, text, audio, video, image, and backup data. In the face of exponentially growing data volumes, redundant data elimination techniques have assumed critical significance in the design of modern storage systems. According to the appeal of a majority of clients who are using storage systems, traditional storage systems typically divide data objects such as files into fixed-sized blocks and then store these blocks. The key goal that due to the inability to efficiently identify those portions of the object that are actually new in the latest update, a large part of existing data must get necessarily rewritten to storage. In particular peer to peer systems, back-ups systems, ftp mirrors, and virtualization system [1] always have high ratio duplication.[2] For instance, in the Linux ftp mirror storage server, there is variety media has been format in a same file that has duplication data blocks more than 50%. In p2p system, there are large size media files with identical content where only title had been changed. 

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology(2011-0002439) and by the Ministry of Education, Science Technology (MEST) and National Research Foundation of Korea(NRF) through the Human Resource Training Project for Regional Innovation.

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Data deduplication is a way to reduce storage space by eliminating redundant data to ensure that only a single instance of the data is stored in a storage medium. If implemented properly, data deduplication can reduce the substantial demand for storage space, thus reducing the cost of the disk. In this paper, we propose a novel deduplication scheme using stride approach. The key idea of this paper is to minimize searching time of duplicated blocks, which can diminish disk I/O and hashing time. The key idea is to adapt slide approach for fixed-length block algorithm, called DBC-SS(Duplicated Block Check with Stride Scheme). We try to improve the performance of fixed-length block algorithm while decreasing computation time. Because the running time of traditional byte shifting approach takes severely long and managing a high-capacity file is difficult in a storage system. The remainder of this paper is organized as follows: Section 2 introduces the related works for deduplication systems. Section 3 describes the design principles of deduplication storage systems and the operation of the deduplication algorithm. Section 4 gives a description of implementation and performance evaluation of the proposed system. Conclusions and future works are given in Section 5.

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Related Work

Rsync[3] is a software application for Unix systems which synchronizes files and directories from one location to another while minimizing data transfer using delta encoding when appropriate. An important feature of Rsync not found in most similar programs/protocols is that the mirroring takes place with only one transmission in each direction. Rsync can copy or display directory contents and copy files, optionally using compression and recursion. Venti[4] is a network storage system that permanently stores data blocks, where 160-bit SHA1 hash used as the address of the data. This enforces a write-once policy since no other data block can be found with the same address. The addresses of multiple writes of the same data are identical. So duplicate data is easily identified and the data block is stored only once. Data blocks cannot be removed, making it ideal for permanent or backup storage. Venti is typically used with Fossil to provide a file system with permanent snapshots. LBFS[5], a network file system designed for low bandwidth networks. LBFS exploits similarities between files or versions of the same file to save bandwidth. It avoids sending data over the network when the same data can already be found in the servers file system or the clients cache. Using this technique, LBFS achieves up to two orders of magnitude reduction in bandwidth utilization on common workloads, compared to traditional network file systems. In IDE[6], modifying the block size of frequent anchor space is too small to resolve the growing hierarchical hash data structures, rarely matrix (sparse matrix) and designing of the block size evenly. In addition to HPs TTTD(Two Thresholds, Two Divisors) algorithm[7] designed to minimize the deviation of the block size of the anchors space and the overhead required for communication between the client and the server has confirmed the decline. DRED system[8][9] can efficiently remove the duplicate data which use delta

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encoding technique in Web pages, and e-mail. Delta encoding is a way through sequence associated with a time of two data sets, the file name, size, and data compression.

3 3.1

Design Principle of Proposed System Overview of Deduplication Stage

Figure 1 shows the general architecture of proposed system. it is made up of the client and the deduplication file server.

Fig. 1. Overall architecture of the proposed system

In file/block hash generation module, it checks duplicated files by comparing file hash and then it process block-level deduplication work. In file deduplication stage, hash data of each file is sent to the server. In server, it checks duplicated file by comparing existing file hashes on a DBMS. The server sends hash list of duplicated files to the client. With this approach, we can prevent duplicated files are transferring to the server. In data deduplication module, block-level data deduplication is processed. The system divides data stream into blocks with chunking function. And then, we can get each data block hash using hash function such as SHA1[10], MD5[11] and SHA256. Generally, a chunking function can be dived into a fixed length chunking method and variable length chunking method. In our work, we adapted a fixed length chunking method because it is much more simple and easy to implement. The chunking size of data block is varying from 4Kbyte to several Mega Byte. In our work, we fixed 4Kbyte chunking size for increasing the performance of data deduplication. By choosing small chunking block, we can increase the possibility of finding duplicated block. Hash retrieval

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also very important because it causes frequent comparison, insert and delete operation. So, we adapted a red-black tree data structure for high performance hash operation. To process file and block deduplication, all the metadata have to be efficiently managed in a database module. The metadata includes file and block information, file and block hash data, file and block location, etc. Moreover, each file is composed of several blocks with and without duplicated blocks. To build file from blocks, we have to carefully manage each block index. 3.2

Previous Deduplication Algorithms

Deduplication schemes are divided into fixed-length blocks, variable-length blocks and delta encoding. Fixed-length blocks let files be partitioned in fixed-sized blocks, and then apply hash functions to create the hash value of the blocks. The main limitation of this approach is data shift. For example when adding a data to a file, all subsequent blocks in the file will be rewritten and are likely to be considered as different from those in the original file. Therefore, it’s difficult to find duplicated blocks in the file, which makes the compression effect less significant. In this approach, smaller blocks will get better deduplication than large one, however, it needs more computation power for deduplication. Fixedlength blocks can be implemented with two approaches. One is duplicated block check with fixed scheme (DBC-FS) and the other is duplicated block check with byte shift (DBC-BS). DBC-FS divides a file into fixed blocks and compare each block with a block in a deduplication server. In this case, if there is data shift, we cannot find duplicated block in a deduplication server. However, DBC-BS compares duplicated block by shifting one byte, therefore this approach can find all the duplicated blocks in a deduplication server. Generally, DBC-BS scheme can detect overall duplicated blocks with the overhead of computation time. In variable-length blocks, each block size is partitioned based on variable approach by anchoring based on their data patterns. This scheme can prevent the data shifting problem of the fixed-size block approach. Delta encoding stores data in the form of differences between sequential data. Lots of backup system adopts this scheme in order to give their users previous versions of the same file from previous backups. This reduces associated costs in the amount of data that has to be stored as differing versions, moreover, those costs in the uploading of each file that has been updated. DRED system use delta encoding approach to implement deduplication service. 3.3

Fixed-Length Block Algorithm Using Stride Scheme

In this paper, we propose a novel fixed-length block algorithm using stride scheme, called DBC-SS. We try to improve the performance of fixed-length block algorithm while decreasing computation time. Because the running time of DBCBS is severely long, managing a high-capacity file is difficult in a storage system. In this work, we mixed DBC-BS approach and DBC-FS. It only search fixed size of region for detecting duplicated blocks, then skip stride size of region. We assume that duplicated data has spatial locality, therefore, if we can find one

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Fig. 2. Conceptual diagram of DBC-SS

duplicated block with DBC-BS then we can find lots of duplicated blocks around that position with DBC-FS approach. Figure 2 shows the conceptual diagram of the proposed system. In our work, we slice each file into fixed blocks and calculate Rabin[12] hash and SHA1 hash. Therefore, each data blocks have two hash data. Rabin hash data is used for fast and simple duplication check for data blocks. However, Rabin hash has high probability of hash collision, so we need another hash data (SHA1) that has no potential for hash collisions. First we check duplicated blocks with Rabin hash by shifting one byte through a chunking. If there is no duplication, DBC-SS skips fixed size of stride region and continue deduplication. Second, if there are duplicated blocks with Rabin hash, we compare the block with SHA1 hash data. If SHA1 hash data is exactly same with the block of deduplication server, then we change duplication check into DBC-FS because this region has high spatial locality. Algorithm 1 shows how DBC-SS works.

4

Performance Evaluation

For experiment, we used Linux Distribution data(CentOS 5.2 i386 package) of CD and DVD image, which contains about 7 GByte of data. We also conducted experiment with several VMware image data which contains Fedora Core 4, Fedora Core 5 Linux system. The system image size of Fedora Core 4 and Fedora Core 5 is 5,235,474,432 bytes and 8,232,566,784, respectively. In this work, we compared computation time and duplication data size for each algorithm (DBCFS, DBC-BS and DBC-SS). All block size in the experiment fixed as 8KByte and the stride size is gradually increased by 8KByte, maximum stride size limit is 56Kbyte. Figure 3 shows deduplication result for Linux distribution data. We can see that DBC-FS method has good performance in running time but shows poor

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Algorithm 1: Fixed-length block algorithm using stride scheme Input: FileStream fd, StrideSize Output: MetaStruct begin offset ← 0; checksize ← 0; fdsize ← legth(fd); while offset < fdsize do if checksize < StrideSize ) then Byte ← ReadByte(fd); checksize ← checksize + 1; if CompareRabinHash( Byte ) > 0 then StopCondition ← 0; repeat Block ← SubString(fd, offset, BlockSize); StopCondition ← CompareSha1( Block ); if StopCondition = 0 then MetaStruct ∪ MS(offset, block); end until StopCondition > 0 ; end else offset ← offset + StrideSize; checksize ← 0; end end return MetaStruct; end

Fig. 3. Performance result for Linux distribution data

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Fig. 4. Performance result for VMware data

performance result for duplication data size. DBC-FS can find 2.6 GByte duplicated data in 2000 seconds. DBC-BS takes very long time (350000 seconds) to complete deduplication but it can find 5.1 GByte of duplicate data. The proposed scheme, DBC-SS, takes about 6000 seconds and finds 4.2 GByte duplicate data. Figure 4 shows performance result for VMware data. It also shows similar result with Linux distribution data. In this result, DBC-SS shows almost same performance on duplication data size.

Fig. 5. Varying stride size for Linux distribution data

Figure 5 shows the result of deduplication for Linux distribution data varying stride size. If we increase the stride size from 8KByte to 56KByte then

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Fig. 6. Varying stride size for VMware Data

deduplication performance is decreased slightly (50Mbyte). The computation time is very high with 8 Kbyte stride up to 2400 seconds but decreased to 1100 seconds when we increased the stride size to 56 Kbyte. In this experiment, we can conclude that broad stride size can be effective for computation time but cause slight performance degrades. Figure 6 shows performance result for VMware data. The running time in VMware data is not efficient compared with Linux distribution data.

5

Conclusion

In this paper, we introduced an algorithm and structure for a deduplication method which can be efficiently used in a storage system. The key points are to exploit stride scheme that supports DBC-FS’s fast and simple approach, and DBC-BS’s deduplication capability. We try to improve the performance of fixedlength block algorithm while decreasing computation time. Because the running time of DBC-BS is severely long, managing a high-capacity file is difficult in a storage system. In this work, we mixed DBC-BS approach and DBC-FS. It only search fixed size of region for detecting duplicated blocks, then skip stride size of region. Experiments result shows that the proposed system can minimize storage space effectively.

References 1. Robin, J.S., Irvine, C.E.: Analysis of the Intel Pentium’s ability to support a secure virtual machine monitor. In: Proceedings of the 9th Conference on USENIX Security Symposium, vol. 9, p. 10. USENIX Association (2000) 2. Cox, L.P., Murray, C.D., Noble, B.D.: Pastiche: Making backup cheap and easy. ACM SIGOPS Operating Systems Review 36, 285–298 (2002)

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3. Tridgell, A.: Efficient algorithms for sorting and synchronization. vol. PhD. Doktorarbeit, Australian National University (1999) 4. Quinlan, S., Dorward, S.: Venti: a new approach to archival storage. In: Proceedings of the 1st USENIX Conference on File and Storage Technologies, pp. 7–7. USENIX Association, Monterey (2002) 5. Muthitacharoen, A., Chen, B., Mazieres, D.: A low-bandwidth network file system. ACM SIGOPS Operating Systems Review 35, 174–187 (2001) 6. Bobbarjung, D.R., Jagannathan, S., Dubnicki, C.: Improving duplicate elimination in storage systems. ACM Transactions on Storage (TOS) 2, 424–448 (2006) 7. Eshghi, K., Tang, H.K.: A framework for analyzing and improving content-based chunking algorithms. Hewlett-Packard Labs Technical Report TR, vol. 30 (2005) 8. Douglis, F., Iyengar, A.: Application-specific delta-encoding via resemblance detection. In: Proceedings of the USENIX Annual Technical Conference, pp. 1–23 (2003) 9. Kulkarni, P., Douglis, F., LaVoie, J., Tracey, J.M.: Redundancy elimination within large collections of files. In: Proceedings of the annual conference on USENIX Annual Technical Conference, p. 5. USENIX Association (2004) 10. Eastlake, D., Jones, P.: US secure hash algorithm 1 (SHA1). RFC 3174 (September 2001) 11. Rivest, R.: RFC 1321: The MD5 message-digest algorithm. Internet activities board 143 (1992) 12. Rabin, M.O.: Fingerprinting by random polynomials. Center for Research in Computing Techn., Aiken Computation Laboratory, Univ. (1981)

Robust Gait Recognition Using Planar Homography and Shape Sequence Descriptor Seungdo Jeong1 , Youngseok Lee1 , Keun-Wang Lee2 , and Jungwon Cho3, 1

Research Institute of Electrical and Computer Engineering, Hanyang University, 17 Haengdang-dong, Sungdong-gu, Seoul 133-791 S. Korea {sdjeong,yslee38}@hanyang.ac.kr 2 Department of Multimedia, Chungwoon University, San 29 Namjang-ri, Hongseong-eup, Hongseong-gun, Chungnam 350-701 S. Korea [email protected] 3 Department of Computer Education, Jeju National University, 102 Jejudaehakno, Jeju-si, Jeju-do 690-756 S. Korea [email protected]

Abstract. In the gait recognition, the dependency to the direction of walking is very serious problem. To reduce this dependency, we propose a view synthesis method based on the planar homography. However, even though the synthesized gait is used, shape information is not enough to recognize individual. Thus, in this paper, we use shape sequence descriptor for recognition, which describes shape information and variation according to motion at a same time. Our experiments show that the proposed method efficiently reduces the dependency to directional variations of gait. Keywords: Gait Recognition, Biometrics, Canonical View, Shape Sequence, View Synthesis.

1

Motivation

In the shape-based gait recognition, the direction of walking influences shape features extracted from gait sequence [1]. That is, difference exists between features obtained from gait which is perpendicular to camera optical axis and ones from oblique-directional gait. To resolve this problem, reconstruction method which synthesizes gaits to the canonical-viewed ones is required [2]. The perfect image synthesis needs exact camera parameters; furthermore, those should be fixed in any environment [3]. However it is great restriction to apply the method to general-purpose recognition system. Therefore, in this paper, we propose view synthesis method through simple operation to minimize shape distortion of gait silhouette caused by the direction of walking. It is impossible to restore shape information without accurate threedimensional reconstruction. Thus, the recognition method using only distorted 

Corresponding author.

T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 484–488, 2011. c Springer-Verlag Berlin Heidelberg 2011 

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(a) perpendicular-directional sequence

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(b) oblique-directional sequence

Fig. 1. Cumulated gait silhouettes according to the direction

shape information degrades the performance seriously. To compensate the degradation, we adopt the feature description algorithm in the gait recognition to consider shape information and variation due to the motion, simultaneously.

2 2.1

Proposed Method View Synthesis Using Planar Homography

Gait sequence has a cue for the direction of walking. Fig. 1 shows the direction that is perpendicular to the camera optical axis and oblique direction, respectively. The cumulated silhouette comprises one plane. From this observation, we adopt the planar homography H which maps specific point Px to the infinite P∞ , where specific point Px is the cross of two lines composed of respective head points and foot points. The planar homography H mapping a point Px to the infinite is represented as equation (2) [4]. P∞ = HPx .

(1)

H = GRT.

(2)

In equation (2), T is the translation matrix which translates center of image x0 to the origin. R is the rotation matrix which rotates px to the point (f, 0, 1)T lying on the x-axis. G maps (f, 0, 1)T onto the infinite point (f, 0, 0)T . However, if H is used to synthesize all of silhouettes without any modification, distortion of body in synthesized silhouette increases as gap between silhouette and the center of image increases. To compensate the distortion, the center of transformation should vary according to the position of each silhouette.

Fig. 2. The center of transformation

Fig. 2 shows the proposed center of transformation, where V L denotes vanishing line which is parallel to the x-axis and goes through the cross point px .

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As shown in the figure, the center of transformation of each silhouette is the cross point between V L and center line of silhouette. As a result, R becomes identity matrix I because y coordinate value of px is identical to the one of the center of transformation. To be coincided scale of all silhouettes, we insert scale factor s = hn /h to G as equation (3), where h and hn are real height and normalized height of silhouette, respectively. Therefore, the proposed planar homography Hp is represented as equation (4), where Tp translate each center of transform to the origin. ⎡ ⎤ s 00 Gp = ⎣ 0 s 0 ⎦ . (3) − f1 0 1 Hp = Gp ITp . 2.2

(4)

Shape Sequence Descriptor

Even though oblique-directional gait is synthesized to canonical-viewed one, there are some distortions in shape information, thus, it causes the degradation in recognition performance. To improve the performance, we describe gait feature using the Shape Sequence Descriptor(SSD) which represents variation caused by the motion as well as shape itself [5]. The point of the SSD is the Angulr Radial Transform(ART) [6]. nm-th coefficients of the ART Fnm is computed as equation (5). Vnm denotes basis function computed as equation (6), where Am is angular component and Rn is radial component. ART coefficient is complex because angular component is complex. Degrees of the ART coefficients n and m which are recommended in the MPEG7 standard are 3 and 12, respectively, thus, dimension of ART is 36 [6]. However, in this paper, we use n = 4 and m = 9 because radial component is more dominant to discriminate gait silhouette.  2π  1 ∗ Fnm =< Vnm (ρ, θ), f (ρ, θ) >= Vnm (ρ, θ)f (ρ, θ)ρdρdθ. (5) 0

0

Vnm (ρ, θ) = Am (θ)Rn (ρ) 1 Am (θ) = 2π exp(jmθ) 1 n=0 Rn (ρ) = { 2 cos(πnρ) n=  0

(6)

The SSD is created from the matrix which is a set of the ART coefficients arranged with time axis. Thus, starting points of silhouette sequences of respective individual should be coincided with themselves to preserve consistent pattern. For this, we set the starting point at frame of silhouette with the maximum stride length, and the following 30 frames comprise the shape sequence for respective description of each gait. The shape sequence formed with 30 frames is not same comparing to the one of other people due to the different speed of walking. However, it could be also respective characteristic of individual, thus, is reflected to

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Fig. 3. Matrix of the ART coefficients for shape sequence

the description automatically. Fig. 3 shows 3-tuple of 36 × 30 matrix composed of the ART coefficients for shape sequence of single gait. The final step in the SSD is selection of the spatial frequency contents about the ART coefficient matrix in the frequency domain. For this, this paper uses the 2D FFT algorithm. The low frequency components represent the outline of content while the high frequency components represent the detail of content. In the case of this paper, the DC component is useless to separate individual because it denotes average silhouette which might be almost same regardless each person. The high frequency components have high possibility to be influenced by noises due to imperfect segmentation for the silhouette, light condition and so on. Thus, we use only top 5 lines in frequency component except the DC and L1 distance is used the similarity measure.

3

Experimental Results

To evaluate the performance of the proposed method, we perform experiment with gait sequences of 16 people. Gait sequences for 16 people are obtained through three times at different days with different view point. Gait sequences for database are with direction almost perpendicular to the camera optical axis. One set for the test is almost same direction comparing to one for database, the other set is oblique-directional gait sequences. We compare experimental results using the proposed method and using simple normalization method. Table 1 presents the recognition rate for all experiments. Table 1. Experimental results in the recognition rate Direction Perpendicular Oblique

The proposed method 93.75% 68.75%

Normalization 87.5% 43.75%

As shown in the table using the perpendicular-directional gait sequence, the proposed method failed to recognize for only one person. Even though, the method using simple normalization missed only two people. It denotes that the

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shape sequence descriptor is useful to apply the shape-based gait recognition. The reason about a little improvement is because the direction of gait is not exact perpendicular but almost perpendicular to the camera optical axis, thus, the proposed method compensates such a little variation. In the case of experiments using the oblique-directional gait, recognition rate based on the normalization method dropped seriously down with over 40%. It denotes that the direction of walking influences bed effect to the recognition, seriously. However, the proposed method decreases such dependency, thus, outperforms the one using normalization by 25%.

4

Conclusion

This paper has proposed the gait recognition method based on planar homography and shape sequence descriptor. The dependency to the direction of walking is very serious problem in the shape-based gait recognition. To reduce the dependency, this paper have proposed view synthesis method based on the planar homography which not only transform the oblique-directional gait to canonical-viewed one but also normalize the size of silhouette with simple operation regardless the position. Even though the synthesized silhouette is used, distortion related to shape information for the gait might give the bed effect in recognition. To compensate that, this paper has also proposed the recognition method based on shape sequence descriptor which is able to describe shape information and variation caused by the motion, simultaneously. From experiment with 16 people, we have proof experimentally that the SSD is effectively used for the shape-based gait recognition. Furthermore, it has noticed that the proposed method has efficiently reduced the dependency to directional variation of gait.

References 1. Lee, L., Grimson, W.E.L.: Gait Appearance for Recognition. In: Proc. ECCV Workshop, Biometric Authentication, pp. 143–154 (2002) 2. BenAbdelkader, C., Cutler, R.: View-invariant Estimation of Height and Stride for Gait Recognition. In: Proc. ECCV Workshop on Biometric Authentication, pp. 155– 167 (2002) 3. RoyChowdhury, A.K., Kale, A., Chellappa, R.: Video Synthesis of Arbitrary Views for Approximately Planar Scenes. In: Proc. IEEE Conf. on Acoustics, Speech and Signal Processing, vol. 3, pp. 497–500 (2003) 4. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press (2000) 5. Choi, M.S.: An Efficient Representation and Matching of a Moving Object in Silhouette, Ph.D. Thesis, Hanyang University (2004) 6. Bober, M.: Mpeg-7 Visual Shape Descriptors. IEEE Trans. Circuits System for Video Technology 11, 716–719 (2001)

A Real-Time Distributed Architecture for RFID Push Service in Large-Scale EPCglobal Networks Min Sung Kang1 and Do-Hyeun Kim2,* 1

Dept. of Research, Gaunuri Co., Ltd., Jeju, Republic of Korea [email protected] 2 Dept. of Computer Engineering, Jeju National University, Jeju, Republic of Korea [email protected]

Abstract. Recently, a large-scale EPCglobal network rapidly increasing, the vision of a large-scale EPCglobal network is close to becoming a reality. However, there is neither a research for efficient accessing EPCIS(EPC Information Service) repository and distributing to the business application. In this paper, we propose a real-time architecture of effective accessing EPCIS repository based on RFID push services in distributed large-scale EPCglobal network. Proposed architecture provides the real-time monitoring and transferring EPC data to the business application when happen the event. Keywords: EPCglobal, Distributed architecture, RFID.

1

Introduction

Radio Frequency Identification (RFID) is a technology that can be used for the efficient tracking of materials as they move though the supply chain[1]. RFID is expected to significantly increase the amount of data available to decision makers who are in charge of steering the supply chain. Current research on RFID among other things focuses on efficient distributed system architectures for making contextualized RFID data usable for decision support software. EPCglobal, an international industry consortium, has provided a specification for the Electronic Product Code (EPC) which can be used to uniquely identify single products. The consortium has also initiated the standardization of infrastructure components that are supposed to work together in order to collect, filter, store, and retrieve EPC related data in a supply chain context[2]. Traditional databases support one-time queries over stored data, and stream processing engines focus on processing push-based data continuously. However, there is not a research related efficient accessing EPCIS repository and distributing to the business application. This paper proposes a real-time architecture of effective accessing EPCIS repository and distributing to the business application for business application services in large-scale EPCglobal network [3].

*

Corresponding author.

T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 489–495, 2011. © Springer-Verlag Berlin Heidelberg 2011

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The rest of this paper is structured as follows. In Section 2, we detail the related work of the EPCglobal network. In Section 3, we describe our proposed architecture and show how our design addresses. Finally we conclude in Section 4.

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Related Works

The EPCglobal Network is a network providing a shared view of the disposition of EPC-bearing objects between EPCglobal subscribers, within a relevant business context. In the EPCglobal Network, product data is distributed to several EPCISes (EPC Information Services) via movement of the product. The ONS (Object Naming Service) and the EPCIS DS (EPCIS Discovery Service) are used to identify the distributed data for tracing the product [3].

Fig. 1. EPCglobal architecture framework

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The EPCglobal architecture framework can be seen in figure 1. The standards can be divided into three parts; identity, capture and exchange. The identity portion contains standards for the identification of tags and the translation of tag data. The capture portion contains the standards for filtering and collection of the tag data. The exchange portion contains standards for storing and sharing collected and filtered EPC product data, and also has the main role for the tracing of products. We will therefore focus on EPCIS, EPCISDS and ONS in this paper. The EPCglobal architecture framework is a collection of interrelated standards for hardware, software and data interfaces, together with core services that are operated by EPCglobal and its delegates, all in service for a common goal of enhancing the supply chain through the use of EPCs. Figure 1 shows the role and the interface of each part of the EPCglobal standards.

Fig. 2. Use of the EPC in EPCglobal architecture framework

The role of EPCIS in the EPCglobal Network is to provide a repository for EPC event and master data. Event data refers to data arising when business processes are carried out and master data is additional data providing the necessary context for interpretation of the event data [3]. Due to the role of EPCIS in the EPCglobal Network, querying to EPCISes is essential for obtaining EPC data. EPCIS Standard has been defined by EPCglobal. This contains EPCIS interface specification, event data schemes and master data. However, it lacks specifications for the master data attributes and the master data management interface [4-5].

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The EPCglobal Architecture Framework includes software standards at various levels of abstraction, from low-level interfaces to RFID reader devices all the way up to the business application level. The different forms of the EPC specified in the EPC Tag Data Standard are intended for use at different levels within the EPCglobal architecture framework. The primary representation of an Electronic Product Code is as an Internet Uniform Resource Identifier (URI) called the Pure Identity EPC URI. The EPC memory bank of a Gen 2 RFID Tag contains the EPC plus additional “control information” that is used to guide the process of data capture from RFID tags. The EPC memory bank of a Gen 2 RFID Tag actually contains a compressed encoding of the EPC and additional “control information” in a compact binary form. Use of the EPC in EPCglobal Architecture Framework can be seen in figure 2[2].

3

A Proposed Real-Time Distribution Architecture for RFID Push Services

RFID applications are numerous and far reaching. The most interesting and widely used applications include those for supply chain management, security, and the tracking of important objects and personnel. In supply chain management, RFID tags are used to track products throughout the supply chain—from supplier delivery, to warehouse stock and point of sale. New applications target tracking from checkout through customer billing. A central database records product movement, which manufacturers or retailers can later query for location, delivery confirmation, or theft prevention. Security and personal identification applications are a major and broad application of RFID. A common use of RFID is in identification cards to control

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building access. Many organizations use RFID tags embedded in ID cards, which are readable at a building entrance. Some schools are requiring children to wear tagembedded bracelets or wrist bands while on school grounds, to monitor attendance and to locate lost children. We propose real-time distribution architecture for supporting push services of RFID applications in figure 3. The architecture consist of the business application interface, the real-time EPC data distributor, EPCIS query interface, and EPCIS monitoring part. The role of EPCIS monitoring part is checking using trigger method and forwarding by EPCIS query interface when we add and modify and delete EPC data in EPC repository. EPCIS query interface supports to deliver EPCIS events. The role of the real-time EPC data distributor receives and handles the EPC event data, and transmits these to necessary business applications.

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The real-time EPC data distributor setups to control for connection of database. And this gets the table information back of database to support user interface. This distributor is consisted of the user interface part, distributed data process part; EPCIS query interface, and business application interface. Also, the distributed data process has the data communication module, XML parser module, data queue management module, control module, data filter module, repository management module, thread pool, configuration management module. XML parser module is analyzed the received XML data and filtering. And there is Database administration module that

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connects to database and sets necessary table information and Forwarding module. The control module supports the distributed processing and buffeting with thread pool. The forwarding service setup module supports to make lists of business applications the defined IP address, port number, and transmission interface. The role of data filter is to select the transferred data for each business application. The proposed real-time EPC data distributor architecture can be shown in figure 4. [Configuration Part]

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The figure 5 show a data flow in the real-time EPC data distributor for push services. Then data communication module receives from EPCIS repository using Web services and stores the message queue for buffering. XML parser disassembles XML EPC data received from EPCIS. And data filter classifies EPC data using configuration setup information. Also data queue management module supports temporary to storage for transmitting to each business application using many message queues.

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Conclusions

In this paper, we propose a real-time distributed architecture for RFID push services in large-scale EPCglobal network. This architecture supports to distribute more quick, saleable, and effective. Our approach has two benefits for existing business application based on RFID: (a) it is possible a real-time push services based on RFID due to the centralized distributor, (b) it reduce the processing and storage overhead due to distributed processing and storage architecture. It is a main research area of distribution of EPC data in business application based on RFID. Yet, many RFID issues are still unsolved and require more efforts from industrial practitioners. Acknowledgments. This work was supported by the Industrial Strategic Technology Development Program funded by the Ministry of Knowledge Economy (MKE, Korea). [10038653, Development of Semantic based Open USN Service Platform]. This research was supported by the project of regional technology innovation of the Ministry of Knowledge Economy (MKE, Korea).

References 1. Niederman, F., Mathieu, R.G., Morley, R., Kwon, I.-W.: Examining RFID Applications in Supply Chain Management. Journal of Communications of the ACM 50(7) (2007) 2. EPCglobal.: EPCglobal Architectural Framework v. 1.4, http://www.epcglobalinc.org/standards/architecture/ architectarc_1_4-framework-20101215.pdf 3. EPCglobal: EPCIS Standard v. 1.0.1, http://www.epcglobalinc.org/standards/epcis/ epcis_1_0_1-standard-20070921.pdf 4. EPCglobal.: EPC Information Services (EPCIS), Standard v. 1.0.1., http://www.epcglobalinc.org/standards/epcis/ epcis_1_0_1-standard-20070921.pdf 5. Weinstein, R.: RFID: a technical overview and its application to the enterprise. IT Professional 7(3) (2005)

Heuristic Scheduling Strategies for Linear-Dependent and Independent Jobs on Heterogeneous Grids Min-Yi Tsai, Ping-Fang Chiang, Yen-Jan Chang, and Wei-Jen Wang* Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan [email protected]

Abstract. Grid computing came into being an active research area because of the advances in wide-area network technologies and the low cost of computing resources. One motivation of grid computing is to aggregate the power of distributed resources and integrate the resources into a unified platform. To minimize the total completion time of the submitted computing jobs to a grid platform, people employ various scheduling algorithms to dispatch the jobs to the resources. However, it has been proved that the optimal scheduling algorithm is NP-hard. Therefore, many people turn to use heuristic approaches for grid scheduling. In this paper, we introduce ten common scheduling heuristics to schedule a combination of job-chains (linear-dependent jobs) and independent jobs on a heterogeneous environment. We implemented these methods on a grid simulator to evaluate their performance under different circumstances. The results of scheduling job-chains and independent jobs on a heterogeneous environment are quite different from previous studies, and we provide our explanations for the differences. We also propose a hybrid method based on our observation, and the simulation results show that it has good performance in terns of makespan. Keywords: Job scheduling, Grid computing, Heuristics, Job dependency.

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Introduction

With the development of science and technology, the computing power of a CPU has become more powerful; moreover the number of cores in the CPU keeps increasing in the past decade as well. Grid computing technology [1] can be used to integrate these distributed resources into a more powerful, unified system. By coordinating the scheduler, a grid system automatically and efficiently finds appropriate computing nodes. Then, it can assign and transfer jobs that are waiting for execution to available nodes. In a typical grid system, a scheduler may schedule dependent jobs and independent jobs. A set of independent job can be executed without any constraints, while a series of dependent jobs must follow some order to complete those jobs.

*

Corresponding author.

T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 496–505, 2011. © Springer-Verlag Berlin Heidelberg 2011

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According to the dependency relationship, dependent jobs can form job-chains (linear-dependent jobs) or DAG (directed-acyclic-graphical). According to the above considerations, a great deal of algorithms and mechanisms for job scheduling had been proposed in the past [2-6]. Job scheduling on parallel and distributed computing systems is a hard problem. Under multi-processor systems, a typical job scheduling problem is proved to be NP-complete [7]. As a result, many heuristics has been proposed in the past decades to improve efficiency of the job schedule problems. These heuristics may focus on different considerations of situations and constraints. For example, some heuristics, such as the longest-job-first heuristic, have good completion time (makespan) while scheduling job-chains [4] on a heterogeneous environment. Sahu et al. [6] found out that the min-max method has the best makespan while scheduling independent jobs on a homogeneous environment. To minimize the makespan of the system, this paper proposes a hybrid scheduling method that achieves good makespan under most circumstances. The proposed scheduling method combines the max-min method and the longest-job-first method to schedule a combination of job-chains and independent jobs. This Hybrid method can reduce the constraints that single heuristic may face under some specific situations. Therefore, the proposed method can adapt to heterogeneous environments while scheduling a set of jobs. We have implemented several heuristic scheduling methods on grid simulator, namely GridSim [8], conducted experiments to evaluate the performance of several commonly-used heuristics, and compared them with the proposed hybrid method using a set of job-chains and independent jobs on a heterogeneous environment. The rest of the paper is organized as follows. Section 2 describes the background of this study and some related work. Section 3 shows the simulation results of ten heuristic scheduling methods on a heterogeneous environment to schedule job-chains and independent jobs. Section 4 describes the proposed hybrid method and Section 5 provides the experimental results. Finally, Section 6 gives concluding remarks.

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Background

This section will briefly introduce the concept of the objective of grid scheduling, and the related work of this research. 2.1

Scheduling Objectives

The essence of grid scheduling can be formulated into an optimization problem involving several objective functions, such as makespan (completion time), flowtime, resource utilization, matching proximity, and algorithm computation time [6]. Among those objectives, makespan is the most popular objectives to optimize, and this paper focuses on the problem of makespan optimization on a heterogeneous environment. Makespan represents the time from the first executing job to the last finished job. Let Cj represent the makespan of 1 ≤ j ≤ n. Then, the makespan optimization problem is to minimize the following equation:

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Cmax = MAX {Cj | 1 ≤ j ≤ n} 2.2

(1)

Heuristic Methods for Grid Scheduling

Many heuristic scheduling algorithms [4-6] have been employed to improve the performance of grid scheduling. The subsection will describe some common heuristic algorithms. Note that most heuristic methods are originally proposed for independentjob-scheduling. Those heuristic strategies must be modified to support job-chain scheduling. • Minimum Completion Time (MCT): The MCT algorithm randomly chooses a job that is ready for execution, and computes the finish time of the job on each machine. The job is then assigned to the machine with the smallest finish time. • Min-Min: The min-min algorithm is based on the MCT algorithm. The MCT algorithm considers one job at a time while min-min considers all unscheduled jobs. The purpose of the min-min algorithm is to assign as many jobs as possible to the fastest machine. Thus, it can minimize the overall makespan. • Max-Min: The max-min algorithm is a variation of min-min. The min-min algorithm selects the job with the minimum completion time. However, max-min chooses the job with the largest minimum-completion-time. The goal of max-min algorithm is to reduce the cost of executing the job with long minimum completion time. • Min-Max: The min-max heuristic calculates the minimum completion time and the minimum execution time (MET) for every unassigned job, and then selects the job with the largest ratio of MCT to MET. • Longest-Job-First (LJF): The goal of the longest-job-first method is to reduce the total makespan by preventing the job with longest execution time from being the late-time bottleneck. To achieve this goal, the longest-job-first method schedules the longest job to the earliest machine that can process the job. If many machines are available at the same time, the method always picks the fastest machine. • Shortest-Job-First (SJF): The shortest-job-first method schedules the shortest job to the earliest available machine that can process the job. If many machines are available at the same time, the method always picks the fastest machine. • Opportunistic-Load-Balancing (OLB): The method chooses the earliest idle machine, and randomly picks an unassigned job for execution. • Suffrage: Suffrage for a job is the difference between second-best minimum completion time and the best minimum completion time. The job with the maximum suffrage value is assigned to the machine that gives the best minimum completion time. • Work Queue (WQ): The method randomly selects a job and assigns it to the machine with the minimum workload. • Average-Execution-Time (AET): The method is dedicated for job-chains. It assigns an entire job-chain to a machine for execution. It calculates the averagejob-size of each job-chain, and then uses LJF to schedule the job-chain that has the largest average-job-size.

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Related Work on Analysis of Heuristic Scheduling Methods

Many researchers have been conducted many experiments and simulations to evaluate the performance of scheduling heuristics. Braun et al. [5] evaluated eleven heuristic methods for independent-job-scheduling on a heterogeneous environment. They found that the genetic-based algorithm, min-min. and duplex (a hybrid of min-min and min-max) are among the best scheduling strategies. Lin et al. [4] pointed out that, to schedule job-chains (linear-dependent jobs) on a heterogeneous environment, longest-job-first is the best strategies among average-execution-time, and shortestjob-first. Sahu et al. [6] evaluated twelve heuristic methods for independent-job scheduling on a homogeneous environment. They indicated that, in the category of makespan comparison, min-min, min-max, suffrage, and RC (Relative Cost that considers both the load balancing of machines and the execution time of jobs) are among the best. The results also showed that min-max is the best among the min-min, min-max, suffrage, and RC.

3

Preliminary Analysis for Ten Scheduling Heuristics

In this section, we will show the simulation results for the ten scheduling heuristics described in Subsection 2.2. We implemented these scheduling heuristics on a grid simulator, namely GridSim [8, 11] to evaluate their performance. We devised several cases for scheduling job-chains and independent jobs on a heterogeneous environment, simulated those cases, and compared the results of using different scheduling strategies in those cases. 3.1

Supporting Scheduling for Job-Chains

Nine of the ten scheduling heuristics described in Subsection 2.2 are not able to schedule job-chains directly, and only the AET strategy can schedule both job-chains and independent jobs. To support the ability to schedule job-chains in the remaining nine scheduling heuristics, we enforce dependency checking in each of them. The concept of the procedure is shown as follows: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

let k be a small constant value for each unscheduled job J that is ready to be executed calculate the cost of scheduling J to a machine M using the heuristic strategy find the best pair of J and M using the heuristic strategy if J has a preceding job I then if I.finish_time > J.start_time then J.start_time = I.finish_time + k set J to be executed at J.start_time in M update the earliest available time of M goto Line 2 until all jobs are scheduled

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Parameter Total amount of jobs Job size Total amount of nodes Computing power of a node Times of simulation

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Value 512 Random( 1~999999 ) million instructions 10 Random (1000, 2000, …, 10000) MIPS 100

Preliminary Simulation Results on Heterogeneous Environments

We have designed three simulation cases for scheduling independent jobs and jobchains in a heterogeneous environment. The goal is to find the best scheduling strategy among the ten scheduling heuristics under different circumstances. The parameters of our simulations are shown in Table 1. The total amount of jobs is fixed at 512, and each job has a random size. The job size represents the number of instructions to be executed, and it ranges from 1 to 999999 million instructions. The jobs are scheduled on a heterogeneous environment consisting of 16 nodes. Each computing node has random processing power. The computing power of a grid node is set to 1000n MPIS, where n ranges from 1 to 10. To obtain the result of a particular simulation case, we simulate it a hundred times and take the average of the simulated makespan values as the result. The first simulation case (Case 1) is to schedule 512 independent jobs on a cluster consisting of heterogeneous computing nodes. Figure 1 shows the makespan values of different heuristic strategies. The simulation result indicates that LJF and AET are the best for scheduling independent jobs on a heterogeneous environment; min-min, SJF, and WQ are the relatively bad scheduling strategies. Note that AET has the same behavior as LJF while scheduling independent jobs. The max-min strategy also works well in this simulation case since its behavior is similar to the LJF strategy. The result shows that the longest-job-first strategy, used by LJF, AET, and maxmin, works well for scheduling independent jobs on a heterogeneous environment. On the contrary, min-min and SJF schedule the shortest job to the earliest available machine. Thus, they may increase the chance of running bigger jobs on slower machines, which results in a larger makespan. The WQ always allocates jobs to the machine with the minimum workload and doesn’t consider the completion time for each job on every machine, So, a slow machine has a higher chance to execute a large job and greatly increases the makespan. The second simulation case (Case 2) is to schedule 64 job-chains, each of which consists of eight jobs, and the jobs are executed on a heterogeneous environment. According to Figure 2, when jobs are linear-dependent, LJF no longer performs well; the max-min strategy and the suffrage strategy become the best among the ten heuristics. Note that max-min does not always schedule the longest job first. It prefers the job that has the largest minimal-completion-time on the heterogeneous environment. The suffrage strategy has similar behavior to the max-min strategy since it utilizes the information of the largest minimal-completion-time of a job. Therefore,

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its performance is relatively good in Case 2. The min-max strategy and the min-m min strategy do not work welll in this case. The result of Case 2 indicates that, whhile scheduling job-chains in thee heterogeneous environment, a scheduling method shoould first consider the job with th he largest minimal-completion-time. The third simulation case (Case 3) is to schedule a set of job-chains onn a heterogeneous environmentt. Each job-chain consists of a normally distributed randdom number of jobs, given the average to be 3 and the standard deviation to be 1. T The simulation result is shown n in Figure 3. The simulation result of Case 3 show ws a similar result to Case 2. Th hat is, max-min and suffrage are the better choices, whhile min-min and min-max are the t worse choices.

Fig. 1. Case 1: th he simulation result of scheduling independent jobs

Fig. 2. Case 2: the result of o scheduling job-chains, each of which consists of eight jobs

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Fig. 3. Case 3: the result off scheduling job-chains, each of which consists of a norm mally distributed random number of jobs j

The results of Cases 1-3 3 show that a heterogeneous environment favors the L LJFbased strategies, especially the max-min strategy. This finding is consistent with Lin et al.’s study [4]. On the contrary, c a homogeneous environment favors the SJF-baased strategies, especially the min-min m strategy [5]. Our simulation results, which are not provided the paper, also co onfirm the results by Sahu et al [6]. However, the studyy by Braun et al. [5] is quite diffferent from our observation since they found that min-m min is a better solution. Our ex xplanation is that, the simulation models are different and thus the simulations producced different results. The simulation model in Braun et aal.’s study assumes task hetero ogeneity, making the execution time of a particular job almost unrelated to the com mputing power of a machine. For example, the executtion time on Machine 1 for Job b A and Job B can be 25137.5 and 399562.1 respectively, while the execution time on n Machine 2 can be 30802.6 and 58987.9 respectively. T This assumption favors SJF-baased strategies since longer minimum completion tiime usually implies wasting of time. t

4

The Proposed Hybrid H Method

Base on the simulation resu ults described in Section 3, we can conclude that LJF is the best while scheduling indeependent jobs on a heterogeneous environment, and m maxmin is the best while sched duling job-chains. Thus, we propose a new hybrid methhod using these two heuristic methods. m The strategy is simple. First, the proposed methhod examines whether a job is linear-dependent or not. Second, this hybrid method uuses LJF to schedule independent jobs and the uses max-min to schedule job-chains. T The algorithm can be expressed as follows: 1. 2. 3. 4. 5.

let k be a small constaant value for each unscheduled d independent job J that is ready to be executed use LJF to find thee best pair of J and M set J to be executed in nM update the earliest avaailable time of M

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6. 7. 8. 9. 10. 11. 12. 13. 14. 15.

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goto line 2 until all in ndependent jobs are scheduled for each unscheduled d dependent job J that is ready to be executed calculate the cost of o scheduling J to a machine M using max-min find the best pair of o J and M if J has a precedin ng job I then if I.finish_time > J.start_time then J.start_time = I.finish_time + k set J to be executed at J.start_time in M update the earliest avaailable time of M goto line 7 until all jo obs are scheduled

Preliminary Exp perimental Results

We used the same configurration described in subsection 3.2 for simulations. We hhave designed several schedulin ng scenarios to evaluate the performance of the propoosed hybrid method.

Fig. 4. Case 4: the simulation result of normal distributed job chain length with hybrid heuriistic

Fig. 5. Case 5: the t simulation result of scheduling 20% job-chains

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Fig. 6. Case 6: the t simulation result of scheduling 50% job-chains

Fig. 7. Case 7: the t simulation result of scheduling 80% job-chains

First, we use the settin ng of Case 3 (each job-chain has a normally distribuuted length) to compare the pro oposed hybrid method with other heuristics. The propoosed hybrid method has the best makespan, as shown in Figure 4. It can save up to 2.5% % of the makespan compared with w the second best, suffrage. We also designed thhree different simulation scenarrios, Cases 5~7 to compare the proposed hybrid methhod with other heuristic methods. Case 5 is to schedule 20% of job-chains and 80% % of independent jobs, Case 6 is to schedule 50% of job-chains and 50% of independdent jobs, and Case 7 is to sched dule 80% of job-chains and 20% of independent jobs. E Each job-chain consists of five jobs in Cases 5~7. The simulation results are shownn in Figures 5~7 respectively. The T results indicated that the proposed hybrid method is a relatively better strategy. However, H the gain of using the proposed hybrid methodd is limited.

6

Conclusions

This paper used GridSim to t simulate and to demonstrate the results of ten comm mon heuristics for scheduling lin near-dependent jobs (job-chains) and independent jobs oon a

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heterogeneous environment. From the simulation results, we figured out that LJF is the fastest method for scheduling independent jobs, and max-min is the best method for scheduling job-chains. The observation is different from previous studies that are conducted on homogeneous environments [6] or heterogeneous environments [5] to schedule independent jobs. We provided our explanations for the difference. We also proposed a hybrid heuristic method using LJF and max-min. The simulation results showed that the hybrid method demonstrates good performance values under most circumstances. When a job-chain has a random number of jobs or has a normally distributed number of jobs, the proposed hybrid method can still save up to 3% of makespan compared to the other ten scheduling method. Acknowledgments. This work was partially supported by the Nation Science Council of Republic of China under Grant No. 100-2218-E-008-005-. We are grateful to the National Center for High-performance Computing, Taiwan, for computer time and facilities.

References 1. Foster, I., Kesselman, C.: The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers Inc., San Francisco (2003) 2. Yu, J., Buyya, R.: A Taxonomy of Workflow Management Systems for GridComputing. Journal of Grid Computing 3(3), 171–200 (2005) 3. Du, J., Leung, J.Y.-T., Young, G.H.: Scheduling chain-structured tasks to minimize makespan and mean flow time. Information and Computation 92, 219–236 (1991) 4. Lin, P.-Y., Liu, P.: Job Scheduling Techniques for Distributed Systems with Temporal Constraints. In: Bellavista, P., Chang, R.-S., Chao, H.-C., Lin, S.-F., Sloot, P.M.A. (eds.) GPC 2010. LNCS, vol. 6104, pp. 280–289. Springer, Heidelberg (2010) 5. Braun, T.D., Siegel, H.J., Beck, N., Bölöni, L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Hensgen, B.Y.D., Freund, R.F.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib.Comput. 61(6), 810–837 (2001) 6. Sahu, R., Chaturvedi, A.: Many-Objective Comparison of Twelve Grid Scheduling Heuristics. International Journal of Computer Applications 13(6), 9–17 (2011) 7. Ullman, J.D.: NP-complete scheduling problems. Journal of Computer and System Sciences 10, 384–393 (1975) 8. GridSim, http://www.cloudbus.org/gridsim/ 9. Al-ali, R.J., Amin, K., Laszewski, G., Rana, O.F., Walker, D.W., Hategan, M., Zaluzec, N.: Analysis and provision of QoS for distributed grid applications. Journal of Grid Computing 2(2), 163–182 (2004) 10. Krauter, K., Buyya, R., Maheswaran, M.: A taxonomy and survey of grid resourcemanagement systems. Software Practice and Experience 32, 135–164 (2002) 11. Sulistio, A., Cibej, U., Venugopal, S., Robic, B., Buyya, R.: A toolkit for modelling and simulating data Grids: an extension to GridSim. Concurr. Comput.: Pract. Exper. 20, 1591–1609 (2008)

Fusion Context Model Based on User Scenario for Smart Service Svetlana Kim1, HagYoung Kim2, and YongIk Yoon1 1

Department of Multimedia Science, Sookmyung Women’s University, Chungpa-dong 2ga, Younsan-Gu, 140-742, Seoul, Korea 2 Electronics and Telecommunications Ressearch Institute(ETRI), 218 Gajeong-ro, Yuseong-gu 305-700, Daejeon, Korea [email protected], [email protected], [email protected]

Abstract. Today, smart phones are used continuously and repeatedly in daily life. In addition, smart phones can be equipped with various sensors. These features take advantage of the functions of the smart phone. The information collected is made to the scenario depending on time, location, action based on the Fusion process. This paper is proposes a service recommendation model based on user scenario using fusion context-awareness. The scenarios can help predict a user’s situation and provide the services in advance. Also, content categories as well as the content type are determined depending on the scenario. The scenario is a method for providing the best service as well as a basis for the user’s situation. Using this method, proposing a new service model with context –awareness is the goal of this paper. Keywords: Context-Awareness, Smart phone, Scenario, User Behavior, Smart Service.

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Introduction

The service model for users has changed from hardware to software-centric to usercentric, due to the development and diffusion of various wireless devices including smart phones. In addition, free internet access is also possible in a situation with 3rd generation mobile networks (3G) and Wi-Fi, in addition to this availability in 4th generation mobile networks (4G). These services are currently not provided and not required at a fixed location. The users always have a smart phone for their overall daily life and lifetime. We can use this position for searching information and / or services according to their needs; smart phones without restrictions on time and place. The service providers are able to prepare and offer a variety of services to target smart phones and their users. Furthermore, customized services including user’s location, time and preferences are tailored to the user’s context and are increasingly getting more attention. Sensing data coming from various sensors mounted on a smart phone is possible. This application is able to run data within the smart phone and is very useful information for appropriate situational awareness. In addition, the web browsing is accessing information based on identifying the main tasks of the user, preferences and IP connections in particular are useful in identifying the location. At this point, the correct and accurate recognition of the obtained user’s situation is very important. T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 506–514, 2011. © Springer-Verlag Berlin Heidelberg 2011

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Fusion elements are making a collection of all relevant information of the user. The information includes the location, user’s action and the user’s areas of interest. The fusion of data is a useful collection of continual user patterns in the form of relevant data. The whole process is undertaken intelligently and automatically, so users are able to use the required services in the relevant situations and events. In this paper, through of fusing the variety of collected user information and pattern analysis, we generate contextual scenarios. Scenarios consist of elements such as user’s patterns, time, location, place and information action. Action information determines the content type selection for the appropriate user’s situation and needs. Services that are provided to users can be optimized into a kind of service and content type based on Scenarios.

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2.1

Context-Awareness Technology

Context awareness technology is finding the optimal solution by input information and user’s information provided at the user’s device interface and sensors. Future, user interface (UI) has the intelligence to implement the required technology, like context awareness technology. In particularly, the technology is recognized as an important technology that affects all fields and industries for a ubiquitous computing environment (health, education, disaster relief, shopping etc.). This technology has also been introduced as being one of four trends / technologies leading 2010 in the ‘Gartner forecast 2010’. In addition, the smart phone is a device that can be possible to exact contextawareness that is an indispensable technology for providing high quality service to smart phone users on either side. So the study of context awareness in smart phones is being extensively conducted by many parties [1][2][3]. In this paper, a custom service model is proposed by gathering information from smart phones and identifying potential patterns and creating scenarios. 2.2

Status of Mobile Context-Awareness

Studies in user situations are actively progressing in many methods and fields. In a study of Service-oriented architecture (SOA) published in 2009 [4] low-level context data was extracted by applying an emphasis on studying events for a long period of time. The SOA model is in order to analyze and reinforce some solid statistics on many or any actions on collected information. Based on SOA, the overall, broad perspective on the study of the user is available and presented here. In addition, the study was performed by using context-awareness by extracting sub context using the built-in sensor in the mobile device and then extracting a higher level of context from a logic inference engine, a logical proposition is proposed and used. In this study, data was collected and processed according to the user’s schedule and timeline using a built-in GPS and Bluetooth mobile sensor. Context Aware Browser (CAB) of SMDC Lab at the University of Udine R&D [5] shows appropriate web content based on using a built-in sensor data from the smart phone. However, CAB provides the content from understanding on the user’s

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location, so an argument is not available for what the user is performing or doing at each particular location. SenSay is a context-aware model mobile prototype being researched at the Carnegie Mellon University. In this study SenSay recognizes situations of users using input data from around the phone and user, automatically changing the reception mode of the mobile phone depending on the state and situation of the user. But SenSay considers only the time as a factor and cannot apply context-awareness precisely [6-9]. The current studies presented above are not enough to provide sufficient reflection and analysis for the user’s living patterns and situation. This is because most of these studies are limited to time and location. To ensure proper application of this paper, we created scenarios that focused on the time and location but also more importantly identified a regular pattern to the user and use it effectively.

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Fusion Context Information Model

The Context Information is by all means information on the user’s situation taken from the smart device. Ordinarily all the collected information is sorted and stored depending on a flow of time in a chronological order. The time stamp is a reference point for all of the analysis, processing and convergence on all information. In addition, the location information is the typical user’s context, so location-based research is an area that received the primary focus of research. The Fusion context that is proposed in this paper also focuses on time and location, but it is the concept of combining a variety of other information that is its focus. This other information can consist of web access, IP address, and application category. Fig 1 shows the user information acquisition model.

Fig. 1. Collection information architecture

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Context information is collected throughout the three stages. The user situation in the architecture of the whole system is the most important state and all of the user’s information exists in this main step. The Context Monitoring (CM) monitors the various stages of data continuously, and this acquires the sources of information for collecting and processing. The CM can obtain time, place, environment and weather information with sensors on a smart phone. Using GPS to extract time and place is not difficult. Also, this is able to obtain temperature, humidity, illumination and weather information. The smart phone has benefits of internet access which allows it to install and run various applications within the phone using its capabilities. This feature allows for immediate action from the user and of the user and is suitable to identify the user’s preferences. For this reason the application types collected provide data such as media type, run time, the number of executions on smart phone and internet sites. The information about the using, existing device is also very important as well as the environment and preference. Services should be provided according to the device specifications. These specifications such as screen size, quality, storage capacity, features of hardware devices are required to be checked as a final stage in order to provide personalized services. Additionally, device information about Bluetooth or other information connected to the network is expected to be useful for further N-screen services. This entire user’s information will always be in constant change, the changing situation needs to be updated constantly in Context Monitoring. Finally, information obtained through continual observation is processed and stored in the database in the Context Collection step. The information collected is processed by filter combinations according to needs and is classified into four categories. After this step is generated, renewal information for creating scenarios. “Fusion” means to combine similar categories in the first place. Thus, the user information will then be fused with accurate and quality by being classified and matched according to the salient features and relevant patterns (see Fig 2.)

Fig. 2. Fusion context

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Analysis of actions, that represents the movement, depending on the flow at the time including stopping, walking or running, taking a bus or a subway. In combination with the environmental data such as current location including brightness, noise level, weather etc. The frequently used application, web accessed sites, SMS and phone reception history can determine user preferences that the user often wants and needs as well as the kind of services required. This data variety has a great influence on the subject matter, variety and type for further recommendations. Additional services can be provided with the distribution through using physical data such as device hardware, internet access, IP and whether or not it is connected to other devices So that the stored data is basis for creating scenarios for providing personalized service. The Fig 3 shows the details of belonging to each category of information that is stored in any entry. According to following classification the situation is determine then scenario will be created.

Fig. 3. Information of Service Category

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Fusion Context Model Based on Scenario

4.1

Process of Scenario

Information collected does not have any rules or method. It is firstly not easy to identify the user’s situation. The collected information of the user is then stored in the

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specified order and categories. In proposing scenarios (S) the user’s location and time is used to record the collected information (see Fig. 4). To create scenarios a number of factors are used the Location (L) Time (T) Actions (A) based on the user’s collected data. The user’s actions(A) includes four kinds of typical behavior: stop/wait, walk, run and possible transportation by car, bus or subway. Depending on the place and time through the sensor information can determine the user’s behavior. The Table 2 and the Fig 5 shows a simple example scenario.

Fig. 4. Service Building Algorithm Table 1. Scenario Time T1 = morning T2 = morning T3 = morning

Location L1 = home L2 = street L3 = street

Action A1 = wait A2 = wait A3 = walk

Fig. 5. Example of scenario

Scenario S1 S2 S3

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The scenario S1 is an example of the waiting action at home in the morning. Example S3 shows a scenario of walking on the street in the morning. This scenario is not a common scenario generated every time; it is representative of the exact behavior of the information collected during a certain period. The point of this scenario depends on the location, time of the user and an ability to know in advance the expected users behavior. Time, place and action information have more detailed subcategories. But in this paper, we propose the primary sub categories. The final step is creating scenarios that are identified in the action; this allows for the offering of information on the best content on the service models and provides better services. The form of content rating and symbols from users for example device information can be provided using the most up to date and typical scenarios. The user does not need to research and select information themselves because this system recognizes the recommendation automatically. 4.2

Service Scenario Model

To provide a behavior service to the user uses “Fusion” information. The Fusion information describes and extracts information of various multimedia materials; the fusion contents. These contents have the same information with one scenario and stored for quick and efficient access when needed. In this section we introduce how to utilize “Fusion” information in a scenario. In the [Table 2] shows a predicted service depending on the scenario. In S1, the time is in the morning, the place, home and situation of the user is stationary. The expected situation here is that the user is eating or drinking coffee for breakfast. Table 2. Scenario Scenario

Kind of Content

Type

S1

Schedule, news, breakfast, sport, music, weather inform Schedule, news, sport, music. Movie, traffic info, bus station, metro Schedule, news, sport, music. Movie, traffic info, bus station, metro

Audio , Smartphone image, text Video, audio, Smartphone graphic image, text Audio, text Smartphone

S2

S3

Device

Connected State TV, Tablet PC null

null

The content can be provided in the morning, news, and weather or breakfast recommendation. The users use the main application or service information available on the basis of the existing user preference; which is prepared in advance, whom is

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able to enjoy the service. In addition, the scenario S1 can be modified according to the user behavior and determined content type. In the case of the motion situation the video content is not appropriate, it is hard to use. Therefore, the video content is provided to the user in only wait/stop situations. In scenario S3, the user is walking in the street. In S3, there is provided more necessary information in the street; street advertising, bus arrival times. Considering the walking scenario the content is recommended as a simple text or voice.

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Conclusion

The proposed scenario and the resulting service recommendation models are expected to give much greater satisfaction to the users due in part to receiving data and information quickly and conveniently in each individual’s busy and hectic lives and lifestyles. In this paper the process of collecting information on the user and how to create scenarios are introduced. The user can be provided with a fitted content type dependent on user situation and through adding action information to the scenarios. To satisfy operator position and interests in providing appropriate services and relevant user position content. The service is applied through using created scenarios in order to easily understand user situations and for performing context-awareness efficiently and effectively. In future research, we have a plan to study the detail conditions and concrete service domain information, method and analysis. Acknowledgment. This work was supported by the Ministry of Knowledge Economy through GLORY project and the (R00044941) Business for Cooperative R&D between Industry, Academy, and Research Institute funded Korea Small and Medium Business Administration in 2011.

References 1. Siewiorek, D., Smailagic, A., Furukawa, J., Moraveji, N., Reiger, K., Shaffer, J.: SenSay: A Context-Aware Mobile Phone. In: Proc. of IEEE International Symposium on Wearable Computers (ISWC) (2003) 2. Zheng, P., Nio, L.M.: The rise of the smart phone. IEEE Distributed Systems Online 7(3) (2010) 3. Raento, M., Oulasvirta, A., Petit, R., Toivonen, H.: ContextPhone - A prototyping platform for context-aware mobile applications. IEEE Pervasive Computing, 51–59 (2005) 4. Yu, L., et al.: Applying Context-awareness to Service-oriented Architecture. In: Proceedings of IEEE International E-Business Engineering Conf (ICEBE 2009), pp. 397– 402 (2009) 5. http://smdc.uniud.it/smdc/en/projects/cab 6. Korpipaa, P., Mantyjarvi, J., Kela, J., Keranen, H., Malm, E.-J.: Managing context information in mobile devices. IEEE Pervasive Computing 2, 42–51 (2003) ISSN 15361268

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7. Cheung, G., Tan, W., Yoshimura, T.: Double feedback streaming agent for real-time delivery of media over 3G wireless networks. IEEE Trans. Multimedia, Special Issue on Streaming Media (April 2004) 8. Yoon, Y., Kim, S.: Mobile Cloud Computing Technology. NIPA National IT Industry Promotion Agency 1439, 28–39 (2010) 9. Yoon, Y., Kim, S.: Mobile OS. Communication of the Korea Institute of Information Science and Engineers 28/6(253), 79–87 (2010)

A Data Processing Framework for Cloud Environment Based on Hadoop and Grid Middleware* Hyukho Kim1, Woongsup Kim2, Kyoungmook Lee2, and Yangwoo Kim2 1 R&D Innovation Center, SAIT, Maetan-3dong, Yeongtong-gu, Suwon, 416 – South Korea 2 Dept. of Information and Communication Engineering, Dongguk University, Seoul, 100-715 – South Korea [email protected], {woongsup,mooky8099,ywkim}@dongguk.edu

Abstract. Owing to performance improvement of mobile devices, number of mobile applications and their variety has increased exponentially in recent years. However, many of these mobile applications are not executed alone and need server-side Internet services which require computing functions such as processing, networking, and storage. The server-side Internet services are usually provided using computing resources at Cloud data center because mobile applications are rapidly increasing in number and they tend to be more and more complex in nature. In addition, the conventional data managing framework, like 3-tier architecture, face additional problems such as heterogeneous external data to import and the vast amount of data to process. In this paper, we propose a data processing framework for mobile applications based on OGSA-DAI for heterogeneous external data import and MapReduce for large data processing. We designed and implemented a data connector based on OGSA-DAI middleware which can access and integrate heterogeneous data in a distributed environment, supporting various data management functions. And then we deployed a data processing framework (we call this data connector) into a Cloud system for mobile applications. We also used MapReduce programming model for data connector. Finally, we conducted various experiments and showed that our proposed framework can be used to access heterogeneous external data and to process large data with negligible or no system overhead. Keywords: Cloud computing, hadoop, HDFS, mapreduce, grid computing, Globus, OGSA-DAI.

1

Introduction

Owing to performance improvement of mobile devices, number of mobile applications and their variety has increased exponentially in recent years. However, many of these applications are not executed alone and need server-side Internet *

This research was supported by the MKE, Korea, under the ITRC(Information Technology Research Center) support program supervised by the NIPA(NIPA-2011-C1090-1101-0008). This work was supported by the Dongguk University Research Fund of 2011.

T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 515–524, 2011. © Springer-Verlag Berlin Heidelberg 2011

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services which require computing functions such as processing, networking, and storage. Due to limited processing capacity and small screen of mobile devices, mobile computing works like an old client/server model with mobile clients and remote servers with server-side Internet services. The server-side Internet services are usually provided using Cloud computing resources at Cloud data center because mobile applications are rapidly increasing in number and they tend to be more and more complex in nature. In addition, the conventional data processing methods like 3tier architecture face additional problems such as managing heterogeneous external data to import/export and process the vast amount of data. Cloud computing is a new style of web based computing [1], whereby shared resources, software, and information are provided on demand to personal computers, Smartphones [2], and other similar devices. In cloud computing, scalable resources are provided dynamically through virtualization technology [3]. This allows service providers and users to adjust their computing capacity depending on how much is needed at a given time or for a given task. Typically the implementation of cloud service is composed of storage devices clustered by network, distributed file systems, a set of applications domains, and a middleware supporting programmable service interfaces. And hence cloud computing requires effective cooperation among multiple devices, application domains, and middleware in order to provide scalable and virtualized services, and the performance is heavily affected by various factors such as the amount of data exchanged, response time of applications and middleware, the status of network traffic, and the effectiveness of cooperation among client, middleware and application services. For example, Facebook provides various plug-in based services, such as user login analysis service that analyzes user access and usage patterns. These kinds of services gathered and analyze Facebook’s data, and then posted the analyzed results to Facebook clients. So Cloud systems require storage servers holding user data, network infrastructure enabling user data exchange, applications supporting database access and usage information analysis, and a persistent access point where client applications interact with. Existing cloud implementations have shown that the level of performance, in terms of throughput and predictability, does not satisfy user expectations when there is high volume of data exchange or heavy network traffic. The read and write throughput is small and highly variable causing significant fluctuation in client performance [4][5]. And they also do not guarantee the acceptable response time when various types of data are used [6]. In this paper, we propose a method to reduce the data processing time and hence enable guaranteed QoS for the Cloud system. We designed and implemented a data processing framework (we call this data connector) extending OGSA-DAI middleware [3] which can access, import and integrate heterogeneous data in a distributed environment. And we deployed our data connector into a Cloud system for external application usage. We used HDFS [7][8] in Hadoop [9] framework to managing distributed file system. To provide programmable service interface, we adopted MapReduce programming model [10][11][12] which is applied for processing distributed large volume of data.

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Our data processing framework aims at integrating and managing heterogeneous data sources such as database and distributed file systems using Hadoop and grid based middleware OGSA-DAI. Through our proposed framework, files in user file system are located first using single query, then transferred to local storage in the Cloud data center, and finally uploaded to HDFS. We used HDFS for the primary file system and MapReduce for complex data analysis and processing tasks. Our data process framework is based on OGSA-DAI, and we extended and customized OGSADAI to support various file management functions like data ‘search’, ‘transfer’, and ‘upload’ because current OGSA-DAI implementation focuses on simple unified data access only to heterogeneous database sources not to heterogeneous distributed file system. Therefore our approach will provide more diverse data resource management functions using simple unified query that is possible in OGSA-DAI middleware. This paper is organized as follows. Chapter 2 introduces background technologies, such as OGSA-DAI, MapReduce, and HDFS, which we used to implement our data processing framework. We also mentioned the limitations in current OGSA-DAI and HDFS and improvement we obtained. In chapter 3, we briefly describe our system architecture and our primary strategy to implement data connector. Chapter 4 illustrates our experimental results to show our approach has benefits in handling large volume of data while our approach does not give significant overhead to existing approach.

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Background Technologies

Hadoop [9] consists of two major components - Hadoop's Distributed File System (HDFS) [7][8] and MapReduce framework [9][10][11]. These two components working together allow for Hadoop to promise ease of programming and high reliability. MapReduce is a software framework for easily writing applications which process vast amounts of data in-parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner. A job submitted to MapReduce usually splits the input data-set into independent chunks which are processed by the map tasks in a parallel. Then the framework sorts the outputs of the maps, which are then inputs to the reduce tasks. (Fig. 1)

Fig. 1. Map Reduce Data Flow [9]

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The HDFS is modeled very closely on the Google file system [13]. The approach to this file system assumes that failure in a large scale computing environment is commonplace, rather than a unique event. HDFS uses a three way replication scheme to ensure that the files stored are always intact in three separate places across a Hadoop cluster. Fig. 2 shows a user application accessing the file system in HDFS.

Fig. 2. HDFS architecture [9]

OGSA-DAI (Open Grid Service Architecture – Data Access and Integration) is developed as an extensible framework accessed via web services that executes datacentric workflows involving heterogeneous data resources for the purposes of data access, integration, transformation and delivery within a grid and is intended as a toolkit for building higher-level application-specific data services. OGSA-DAI provides three types of Grid services: data resource registration and discovery, Grid proxies for data resources, and data access and manipulation [3]. Using OGSA-DAI middleware implementation has a benefit that only one single query is needed to access multiple various data resources and only one single result is returned as a query. However, the usage of function is currently limited to some data resource types. Moreover, data management functions are mainly focused on data access rather than data transfer, so that there are limitations to cover diverse client application needs.

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System Design

3.1

System Architecture

In this section, we present the overall system architecture which is composed of job scheduler, allocator, MapReduce-based (MR-based) data processing service, OGSADAI data processing components and interfaces. As you can see Fig. 3, we used HDFS as a primary distributed file system. And we implemented data connector (noted three ‘C’ in Fig 3.) for the access and loading of

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data which is located in the external data of HDFS. The data resource connector can use all of data resources which are supported by OGSA-DAI middleware, such as, RDBMS (DB2, MySQL, Oracle, PostgreSQL, SQL Server), XMLDB, eXist, and user file systems.

Fig. 3. System architecture

We used two kinds of the data handling strategies: one is Load-and-Copy method. This strategy loads the external data and then, makes a copy of the loaded data to HDFS. The other is Load-Only method. It loads the external data and then, the loaded data is deleted right after data processing is completed. In the Load-and-Copy method, it has the disadvantage that it takes a long time to load and create replication data into HDFS. However, if same data is re-used frequently, it has the advantage that it can analyze faster than first processing time due to HDFS replication mechanism. On the other hand, the Load-Only has the advantage for faster analysis time when new data is used as the method does not require time for data replication. It, however, has the disadvantage that it is slower than the Load-and-Copy method when there are frequent data reuses. The performance comparison of two data handing methodologies is shown in Chapter 4. Job Scheduler, Allocator, and MR (MapReduce)-based Data Analysis Service are running based on HDFS. Job Scheduler uses FIFO scheduling algorithm. Allocator is responsible for checking if the system uses the external data, allocates tasks to task nodes depends on which data is used. Finally, MR-based Data Analysis Service is responsible for carrying out data processing service. 3.2

System Configuration

We used 5 nodes for our experiments. Each computing node has two 3.00 GHz CPUs, 1GB Memory, 120 GB local disk space, and connected to the others by Ethernet. We used only a 1Gbps Ethernet for the network communication. Every node uses Fedora Linux Core 8. The home directory is shared by NFS, and each node has own local disk. For implementation, we used Apache Hadoop 0.20, Grid middleware – Globus

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Toolkit [14][15], and OGSA-DAI component [16] for system configuration. We also designed and implemented Data Analysis Service based on Android platform for measuring of data processing performance. It is running on mobile devices. And we used additional tools for experiments such as Apache Ant 1.6.5, J2SDK 1.6, and Apache Log4j-1.2.8.

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Evaluation

4.1

Analysis of Data Connector Performance

In this paper, we used HDFS as basic file system. We conducted experimental comparison of the two data handling strategies for the external data handling. The external data is located in user file system which is supported by OGSA-DAI. We used the same size and type of the data which is located in HDFS and OGSA-DAI supported file system. And we measured the analysis time by increasing the data size.

Fig. 4. The performance results of two data connectors

The performance result of two data handling strategies implementing data connector is shown in Fig. 4. We compared Load-and-Copy data connector with Load-Only data connector. And job requests are sent by mobile device. So we measured the request time (Req. time) to send jobs to MR-based Data Analysis Service. Both of two data connectors do not heavily affect the data analyzing time because the Req. time takes quite a short duration. And the analysis time of Load-Only connector is a little faster than Load-and-Copy connector. However as you can see graphs in Fig 4, performance differences of two data processing strategies are quite small. Therefore we integrated Load-and-Copy strategies in our framework and used it in all the rest experiments. We also conducted additional experiments for comparison of file download time with our data connector, OGSA-DAI, and HDFS.

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Fig. 5. The analysis results of file download time

HDFS supports various functions like file upload function (put) and file download function (get), list function (ls), etc. HDFS shows the best performance because it was designed to be optimized the data I/O operations. OGSA-DAI middleware, however, performed the worst because it focuses to access and integrate the heterogeneous data resources. And OGSA-DAI middleware doesn’t work alone. It deploys into Globus Toolkit (GT) or Axis/Tomcat container and then, it works on the container. In other words, OGSA-DAI takes a long time because it has additional processes like property parsing of Data resource, service location and service behavior confirmation, etc. So data downloading of OGSAI-DAI middleware had poor performance. Lastly, Data Connector is worse than HDFS because it was designed and implemented with OGSA-DAI libraries for access and loading the data which is located in the remote file systems. But it is better than OGSA-DAI middleware because it was implemented to be optimized the transmission function by removing unnecessary parsing processes. 4.2

Analysis of MapReduce and Multi-thread with Data in HDFS

This experiment is aimed at comparing the performance of typical data analysis using multi threading (thread-based analysis) and MR-based data analysis with data in HDFS only. And we do not include data request time in the result because we believe data request time is quite small considering total data analysis time and does not significantly affect the comparison results.

Fig. 6. The experiment results with data in HDFS

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We conducted experiments for performance analysis of those two methodologies with a varying data size. As you can see in Fig. 6, two methods have similar experimental results. MR-based data analysis, however, is better than thread-based data analysis, though the performance difference is small. That is, when MR-based analysis method applied to mobile Cloud system, it means that MR-based data analysis can provide good performance. 4.3

Analysis of MapReduce and Multi-thread with Data in Data Resource

This experiment is designed to compare the performance of thread-based data analysis and MR-based data analysis with data under OGSA-DAI supported file system. The experiment results are shown in Fig. 7. The performance of two methods is almost same because two methods used the data which is located in the external file system. These results imply that the overhead to load and analyze the data using Data Grid middleware is quite small.

Fig. 7. The experiment results with data in OGSA-DAI supported file system

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Analysis of MapReduce and Multi-thread with Data in HDFS and Data Resource

We conducted experiments with data which is located in HDFS and OGSA-DAI supported file system. The experiment results are shown in Fig. 8. The performance difference of two methods is not significant when we use small size of data. Therefore, we figure out that there is no noteworthy operational overhead though both HDFS and OGSA-DAI supported file system are used together. We, however, noticed significant performance difference when large volume of data is used. As a result, MR-based data analysis shows better performance than the typical thread-based method, when large volume of data is used and exchanged in distributed file system.

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Fig. 8. The experiment results with data in HDFS and OGSA-DAI combined file system

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Conclusions

In paper, we proposed the method for reducing of data processing overhead in Cloud systems. We also proposed application method extending Grid middleware for accessing and importing the large volume of external data. So we implemented Data process framework and then, deployed it into Cloud system. For experimental purpose, we made a test bed utilizing existing HDFS and OGSA-DAI frameworks supporting distributed file management and conducted comparative experiments to measure the effectiveness of our proposed method. From the experiment results, we conclude that MapReduce-based data processing method shows better performance than the existing method, especially when we use large volume of external data. And the overhead of using Grid middleware was quite small even though our approach uses both HDFS and OGSA-DAI supported file system together, and it does not affect overall the system performance. As a result, we present that Grid middleware can be applied to Cloud environment to improve data processing performance, and using with Hadoop framework together considering Grid middleware does not make significant overhead for the performance of MapReduce-based data connector.

References 1. Kovachev., D., Renzel., D., Klamma, R., Cao, Y.: Mobile community cloud computing: emerges and evolves. In: Proc. 1st Intl. Workshop on Mobile Cloud Computing (MDM 2010), pp. 393–395 (2010) 2. Smartphone, Wikipedia, http://en.wikipedia.org/wiki/Smartphone/ 3. Marinelli, E.: Hyrax: cloud computing on mobile devices using MapReduce. Master thesis, Carnegie Mellon University (2009) 4. Wang, G., Ng, T.: The impact of virtualization on network performance of amazon ec2 data center. In: Proc.of INFOCOMM, pp. 1–9 (2010) 5. Wang, J., Varman, P., Xie, C.: Avoiding performance fluctuation in cloud storage. In: Proc., of HiPC (2010)

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6. Brito, M., Kakugawa, F., Sato, L., Correa, P.: An Architecture for Integrating Databases with Replication Support Based on the OGSA-DAI Middleware. In: Proc. of International Conference on Computational Science and Engineering (2009) 7. Jiang., L., Li., B., Song, M.: THE optimization of HDFS based on small files. In: Proc. of 3rd IEEE International Conference on Broadband Network and Multimedia Technology, pp. 912–915 (2010) 8. Mackey, G., Sehrish, S., Wang, J.: Improving metadata management for small files in HDFS. In: Proc. of IEEE International Conference on Cluster Computing, pp. 1–4 (2009) 9. Hadoop, A.: http://hadoop.apache.org/ 10. Huang, L., Wang, X.-W., Zhai, Y.-D., Yang, B.: Extraction of User Profile Based on the Hadoop Framework. In: Proc. of IEEE Conf. on Wireless Communications, Networking and Mobile Computing, pp. 1–6 (2009) 11. Gunarathne, T., Wu, T.-L., Qiu, J., Fox, G.: MapReduce in the Clouds for Science. In: Proc. of IEEE Conf. on Second International Conference, pp. 565–572 (2010) 12. Mackey, G., Sehrish, S., Bent, J., Lopez, J., Habib, S., Wang, J.: Introducing map-reduce to high end computing. In: Proc. of Petascale Data Storage Workshop, pp. 1–6 (2008) 13. Dean, J.: Experiences with mapreduce, an abstraction for large-scale computation. In: Proc. of the 15th International Conference on Parallel Architectures and Compilation Techniques, New York, pp. 1–1 (2006) 14. Montero., R.S., Huedo., E., Llorente, I.M.: Dynamic deployment of custom execution environments in Grids. In: 2nd International Conference on Advanced Engineering Computing and Applications in Sciences, pp. 33–38 (2008) 15. Foster, I., Kesselman, C., Tuecke, S.: The Anatomy of the Grid: Enabling Scalable Virtual Organizations. International Journal of High Performance Computing Applications 15(3), 200–222 (2001) 16. Grant, A., Antonioletti, M., Hume, A.C., Krause, A., Dobrzelecki, B., Jackson, M.J., Parsons, M., Atkinson, M.P., Theocharopoulos, E.: OGSA-DAI: Middleware for Data Integration: Selected Applications. In: Proc. of IEEE Fourth International Conference on eScience, pp. 343–343 (2008)

CloudTSS: A TagSNP Selection Approach on Cloud Computing Che-Lun Hung1, Yaw-Ling Lin2, Guan-Jie Hua2, and Yu-Chen Hu3 1

Dept. of Computer Science & Communication Engineering, Providence University, 200 Chung Chi Rd., Taichung 43301, Republic of China, Taiwan [email protected] 2 Dept. of Computer Science & Information Engineering, Providence University, 200 Chung Chi Rd., Taichung 43301, Republic of China, Taiwan [email protected], [email protected] 3 Dept. of Computer Science & Information Management, Providence University, 200 Chung Chi Rd., Taichung 43301, Republic of China, Taiwan [email protected]

Abstract. SNPs are fundamental roles for various applications including medical diagnostic, phylogenies and drug design. They provide the highestresolution genetic fingerprint for identifying disease associations and human features. Genetic variants that are near each other tend to be inherited together; these regions of linked variants are known as haplotypes. Recently, genetics researches revealed that SNPs within certain haplotype blocks induce only a few distinct common haplotypes in the majority of the population. The existence of haplotype block structure has serious implications for associationbased methods for the mapping of disease genes. This paper proposes a parallel haplotype block partition and SNPs selection method under a diversity function by using the Hadoop MapReduce framework. The experiment shows that the proposed MapReduce-paralleled combinatorial algorithm performs well on the real-world data obtained in from the HapMap data set; the computation efficiency can be significantly improved proportional to the number of processors being used. Keywords: Hadoop, MapReduce, SNPs, Haplotype, cloud computing.

1

Introduction

Genome-wide association studies based on linkage disequilibrium (LD) offer a promising approach to detect genetic variation responsible for common human diseases. Single nucleotide polymorphisms (SNPs) are promising markers for disease association researches because of their high abundance along the human genome, the low mutation rate and accessibility to high-throughput genotyping. A SNP refers to the existence of two specific nucleotides at a single locus in a population. Haplotype can be defined as an asset of SNPs on a single chromosome that are associated and inherited as a unit. Recently, haplotype analysis has been successfully applied to identify DNA variations that ae relevant to several common and complex diseases T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 525–534, 2011. © Springer-Verlag Berlin Heidelberg 2011

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[1-6]. Many studies suggest that human genome maybe arranged into block structure, in which SNPs are relevant and only a small number of SNPs are sufficient to obtain most of haplotype structures, called tagSNP [7-13]. Several approaches have been suggested for defining block structure from which some are more commonly used. Four main criteria for haplotype block partitioning are based on haplotype diversity, LD, four gamete test and information complexity. In diversity-based methods [9, 14, 15], a block is defined as a region, in which a certain percentage of haplotypes are common haplotypes, haplotypes which are represented more than certain percent in the population. In LD-based methods [8, 16], a block is defined as a region with high pair-wise LD with in block and low pair-wise LD between blocks. In methods based on four gamete test [17, 18], a block is defined as a recombination-free region of consecutive SNPs. However, existing approaches that address these issues are generally complicated and computationally intensive. Hadoop [19] is a software framework intended to support data-intensive distributed applications. It is able to process petabytes of data with thousands of nodes. Hadoop supports MapReduce programming model [20] for writing applications that process large data set in parallel on Cloud Computing environment. The advantage of MapReduce is that it allows for distributed computing of the map and reduction operations. Each map operation is independent of the others and all maps can perform the tasks in parallel. In practice, the total number of the maps is limited by the data source and/or the number of CPUs near that data. Similarly, a set of reducers can perform the reduce operations. All outputs of the map operation which share the same key are presented to the same reducer, at the same time. In addition, one of the important benefits to use Hadoop to develop the applications is due to its high degree of fault tolerance. Even when running jobs on a large cluster where individual nodes or network components may experience high rates of failure, Hadoop can guide jobs toward a successful completion. Many applications of bioinformatics are often computation-consuming; sometimes it needs weeks or months to complete the jobs. The traditional parallel models, such as MPI, OpenMP and Multi-thread, are not suitable to such applications, where a fault occurred in some nodes leads the entire application into total failure. In these situations, the Hadoop platform is considered as a much better solution for these real-world applications. Recently, Hadoop has been applied in various domains in bioinformatics [21, 22]. In this paper, we propose parallel diversity-based haplotype block selection algorithms on Hadoop MapReduce framework. In particular, the map-stage is utilized to calculate the diversity of each block, while the reduce-stage is utilized to locate the blocks. As these experimental results indicate that these proposed algorithms are significantly faster than the corresponding sequential algorithms as the number of Map operations increased.

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Method and Materials

Haplotype Block Partitioning. A SNP (Single Nucleotide Polymorphisms) is defined as a position in a chromosome where each one of two (or more) specific nucleotides is observed in at least 10% of the population [9]. The nucleotides involved in a SNP are called alleles. A SNP is said biallelic if it has only two different alleles. Almost all SNP are biallelic and we will consider exclusively biallelic SNP in this paper.

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Diversity Function. Abstractly, the input to the haplotype blocking problem consists of m haplotype vectors. Each position in a vector is associated with a site of interest on the chromosome. Usually, the position in the haplotype vector has a value of 0 if it is the major allele or 1 if minor allele. Let the haplotype matrix A be an m×n matrix of m observations over n markers (sites). We refer to the jth allele of observation i by Aij. For simplicity, we assume that Aij ∈ {0, 1}. A block, or marker interval, [j, k] = {j, j + 1, …, k} is defined by two marker indices 1 ≤ j ≤ k ≤ n. A segmentation is a set of non-overlapping non-empty marker intervals. A segmentation is full if the union of the intervals is [1, n]. The data matrix limited to interval [j, k] is denoted by M(j, k); the values of the i-th observation are denoted by M(i, j, k), a binary string of length k － j + 1. Figure 1 illustrates an example of a 7×11 haplotype matrix. Given an interval [j, k], a diversity function, δ : [j, k] → (j, k) ∈ R is an evaluation function measuring the diversity of the submatrix M(j, k). We say an interval [j’, k’] is a subinterval of [j, k], written [j’, k’] ⊂ [j, k], if j ≤ j’ and k’ ≤ k. Note that δ-function is a monotonic non-decreasing function from [1..n, 1..n] to the unit real interval [0, 1]; that is, 0 ≤ ( j’, k’) ≤ (j, k) ≤ 1 whenever [j’, k’] ⊂ [j, k]. Given an input set of n haplotype vectors, a solution to the Haplotype Block Selection (HBS) Problem is a segmentation of marker intervals, revealing these nonoverlapped haplotype blocks of interest in the chromosome. In our previous work [23], we have proposed several efficient algorithms related to selecting interesting haplotype blocks under different evaluation (diversity) functions that generalizes many previous results in the literatures [8, 9, 12, 13, 16, 17, 18, 24, 25, 26]. In this paper, we focus on finding common haplotypes. Common Haplotypes. Two haplotypes are said to be compatible if the alleles are identical at all loci for which there are no missing data; otherwise the two haplotypes are said to be incompatible. As in Patil et al., [9] we define the ambiguous haplotypes as those haplotypes compatible with at least two haplotypes that are themselves incompatible. It should be noted that when there are no missing data, all of the haplotypes are unambiguous. We define the common haplotypes as those haplotypes that are represented more than once in a block. The haplotypes are called singleton if they are not compatible with any others.

Fig. 1. A haplotype matrix B and its corresponding submatrix M(8, 11)

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We are mainly interested in the common haplotypes. Therefore we require that, in the final block partition, a significant fraction of the haplotypes in each block is common haplotypes. Patil et al. [9] require that at least α = 70%, 80%, and 90%, respectively, of the unambiguous haplotypes appear more than once. The parameter, α, is also referred to as the coverage of common haplotypes in a block. Ambiguous haplotypes are not included in calculating percent coverage. The coverage of block B can be mathematically formulated as a form of diversity: δ S ( B) = 1 −

U U = C S

(1)

Here U denotes the number of unambiguous haplotypes, C denotes the number of common haplotypes, and S denotes the number of singleton haplotypes. For example, the submatrix M(8, 11) of Fig. 1 can be viewed as a sample S = {0001, 0011, 0100, 1000, 1011}. It follows that δ(S) = δ(M(8, 11)) =1 4/7 = 3/7.

－

TagSNPs Selection. For each block, we want to minimize the number of SNPs that uniquely distinguish at least 80% (the α parameter) of the unambiguous haplotypes in the block. Those SNPs can be interpreted as a signature of the haplotype block partition. They are referred to as tagSNPs that are able to capture most of the haplotype diversity, and therefore, could potentially capture most of the information for association between a trait and the marker loci [28]. Our strategy for selecting the tagSNPs in haplotype blocks is as the following. First, the common haplotypes are grouped into k distinct patterns in each block. After the missing data are assigned, as explained in the next subsection, we decide the least number of groups needed such that haplotypes in these groups contain at least 80% ( α ) of the unambiguous haplotypes in the block. Finally, we select a loci set which consists of the minimum number of SNPs on the haplotypes such that each pattern can be uniquely distinguish. Exhaustive searching methods are used very efficiently here since the number of tagSNPs needed for each block is usually modest in the situation. The exhaustive searching algorithm enumerates next γ-combination in lexicographic order to generate the next candidate tagSNP loci set until each pattern can be uniquely distinguish. 2.1

Hadoop MapReduce Framework

Hadoop is a software framework for coordinating computing nodes to process distributed data in parallel. Hadoop adopts the map/reduce parallel programming model, to develop parallel computing applications. The standard map/reduce mechanism has been applied in many successful Cloud computing service providers, such as Yahoo, Amazon EC2, IBM, Google and so on. An application developed by Map/Reduce is composed of Map stage and Reduce stage (optionally). Input data will be split into smaller chunks corresponding to the number of Maps. Output of Map stage has the format of pairs. Output from all Map nodes, pairs, are classified by key before being distributed to Reduce stage. Reduce stage combines value by key. Output of Reduce stage are pairs where each key is unique.

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Hadoop cluster includes a single master and multiple slave nodes. The master node consists of a jobtracker, tasktracker, namenode, and datanode. A slave node, as computing node, consists of a datanode and tasktracker. The jobtracker is the service within Hadoop that farms out Map/Reduce tasks to specific nodes in the cluster, ideally the nodes that have the data, or at least are in the same rack. A tasktracker is a node in the cluster that accepts tasks; Map, Reduce and Shuffle operations from a jobtracker. Hadoop Distributed File System (HDFS) is the primary file system used by Hadoop framework. Each input file is split into data blocks that are distributed on datanodes. Hadoop also creates multiple replicas of data blocks and distributes them on datanodes throughout a cluster to enable reliable, extremely rapid computations. The namenode serves as both a directory namespace manager and a node metadata manager for the HDFS. There is a single namenode running in HDFS architecture. 2.2

Haplotype Block Partitioning and Selection on Map/Reduce Framework

Figure 2 illustrates the MapReduce framework for the block partitioning and selection scheme. Assume that the number of map operations is N and the pattern length is L, the input N×L haplotype matrix is split into L/N chunks. Each map calculates the diversity scores of each block within the chunk where the map operationis responsible. Thus the output pairs for each Map are pairs. The mapi calculates diversity scores of blacks {δ(i N/L, i N/L), δ(i N/L, i N/L+1), …, δ(i N/L+N/L, i N/L+N/L)}. Therefore, each map has (N/L)2 diversity scores. Reduce stage performs haplotype block selection algorithm. In our algorithm, just one reduce operation is needed in the reduce stage. Since the selection is a linear time algorithm, it is not necessary to perform the computation in parallel. The haplotype block selection algorithm is described in the previous section 2.1. The reduce operation finds the longest block by merging blocks with the interesting diversity scores.

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Fig. 2. Haplotype block partitioning and selection on MapReduce framework

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3

Experiment

3.1

Experimental Environment and Data Source

All of the experiments were performed on two IBM blade servers within our Cloud Computation laboratory. Each server is equipped with two Quad-Core Intel Xeon 2.26GHz CPU, 24G RAM, and 296G hard disk running under the operation system Ubuntu version 10.4 with Hadoop version 0.2 MapReduce platform. Under the current system environment, we control the server execution processes by up to 8 map operations and 8 reduce operations and the total number of the map/reduce operations are up to 16 respectively. The SNP haplotype data sources are gathered from the International HapMap Project (http://hapmap.ncbi.nlm.nih.gov/), which is a multi-country effort in order to identify and catalog genetic similarities and differences in human beings. This project collects many genetic sequences of different individuals. We downloaded the sequence data (Chromosome 1) from the HapMap3 Genome Browser release #2African ancestry in Southwest USA (ASW). ASW includes 136 Chromosome 1 (chr 1) sequences (patterns) and the length of SNP is 116,416. These sequences are treated the input data for our experiments. In the experiments, we applied formula (1) to as calculate diversity scores of blocks. 3.2

Experimental Results

To assess the performance of the proposed Hadoop MapReduce algorithm, we compare the computational time between various sequence data and various number of map/reduce operations. The sequential algorithm [23] has been proved as an efficient algorithm than other algorithms. Two factors, number of patterns and the length of patterns, affect the performance of sequential algorithm and the proposed algorithm. Patil et al [9] proposed that the haplotype block can be found within 300bp and 500bp. Therefore, the block size can be 300bp and 500bp. The diversity scores are calculated according to their corresponding block sizes; these scores are {δ(1, 1), δ(1, 2), …, δ(1, 500), δ(2, 2), …, δ(2, 501), δ(3, 3), …, δ(L, L)}. Figures 3 and 4 illustrate the comparisons between sequential algorithm and our proposed algorithm under the MapReduce framework. In Fig. 3 and Fig.4, the block sizes are 300bp and 500bp, respectively. In Fig. 3 and Fig. 4, it is observed that computational time increases corresponding to number of pattern and sequence length. The computational time for our algorithm with block size 300bp is less than that with block size 500bp. More patterns and longer sequence length lead to higher computational cost. These experimental results are corresponding to the algorithm analysis in previous section. The experimental results reveal that the computational time is effectively reduced when more map operations are deployed. Two and four map operations almost improve the computation time by factors of two and four times accordingly, comparing to the original sequential algorithm, respectively. Moderate enhancements between 8 and 16 map operations are observed in all experiments, since the size of data set split by 8 is similar to that by 16. Figure 5 illustrates the computation efficiency can be effectively improved proportional to the number of processors being used.

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Fig. 3. Performance comparison between sequential haplotype block selection and MapReduce haplotype block selection with block size 300bp

Fig. 4. Performance comparison between sequential haplotype block selection and MapReduce haplotype block selection with block size 500bp

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Fig. 5. Speed up comparisons for MapReduce haplotype block selection over equential haplotype block selection. (a) illustrats the speed up with block size 300bp. (b) illustrats the speed up with block size with 500 bp.

4

Conclusions

Studying on SNP and haplotype blocks can assist biomedical researchers to detect inherited diseases and contribute to classify the race of human and researching on species evolution. The traditional methods for detecting haplotype blocks are based on dynamic programming approach. With the abundance of bioinformatic data that are all too common these days, the time-consuming traditional sequential methods require imminent assistance of the emerging parallel processing methodology. Here in this paper we discuss how we develop the parallelized frame works improving our original dynamic programming algorithms, based on Hadoop map/reduce framework. The haplotype block partitioning copes with the problem of finding the minimum number of representative SNPs required to account for most of the haplotype block quality in each block. Due to the fault tolerance of Hadoop, the jobs are just re-submitted to other nodes if the node is failure. This property is useful for analyzing large amount of sequence data since the job will not be stopped by node’s fail. The experimental results show that the proposed algorithm can decrease the computational cost significantly. In this paper, we compared the performance between various sequence lengths and pattern numbers. We also compared the performance between different block sizes. In the future, we will apply more diversity functions to the parallel algorithms to provide more perspectives for biologists to analyze these SNP data and investigate the relations of haplotype block selection between various block sizes and diversity measurements.

References 1. Bonnen, P.E., Wang, P.J., Kimmel, M., Chakraborty, R., Nelson, D.L.: Haplotype and linkage disequilibrium architecture for human cancer-associated genes. Genome Res. 12, 1846–1853 (2002) 2. Gray, I.C., Campbell, D.A., Spurr, N.K.: Single nucleotide polymorphisms as tools in human genetics. Hum. Mol. Genet. 9, 2403–2408 (2000)

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3. Indap, A.R., Marth, G.T., Struble, C.A., Tonellato, P.J., Olivier, M.: Analysis of concordance of different haplotype block partitioning algorithms haplotype tagging for the identification of common disease genes. BMC Bioinformatics 6, 303 (2005) 4. Mas, A., Blanco, E., Monux, G., Urcelay, E., Serrano, F.J., de la Concha, E.G., Martinez, A.: DRB1-TNF-alpha-TNF-beta haplotype is strongly associated with severe aortoiliac occlusive disease, a clinical form of atherosclerosis. Hum. Immunol. 66, 1062–1067 (2005) 5. Nowotny, P., Kwon, J.M., Goate, A.M.: SNP analysis to dissect human traits. Curr. Opinion Neurobiol. 11, 637–641 (2001) 6. Reif, A., Herterich, S., Strobel, A., Ehlis, A.C., Saur, D., Jacob, C.P., Wienker, T., Topner, T., Fritzen, S., Walter, U., Schmitt, A., Fallgatter, A.J., Lesch, K.P.: A neuronal nitri coxide synthase (NOS-I) haplotype associated with schizo-phrenia modifies prefront alcortex function. Mol. Psychiatry 11, 286–300 (2006) 7. Daly, M.J., Rioux, J.D., Schaffner, S.F., Hudson, T.J., Lander, E.S.: High-resolution haplotype structure in the human genome. Nat. Genet. 29, 229–232 (2001) 8. Gabriel, S.B., Schaffner, S.F., Nguyen, H., Moore, J.M., Roy, J., Blumenstiel, B., Higgins, J., DeFelice, M., Lochner, A., Faggart, M., Liu-Cordero, S.N., Rotimi, C., Adeyemo, A., Cooper, R., Ward, R., Lander, E.S., Daly, M.J., Altshuler, D.: The structure of haplotype blocks in the human genome. Science 296, 2225–2229 (2002) 9. Patil, N., Berno, A.J., Hinds, D.A., Barrett, W.A., Doshi, J.M., Hacker, C.R., Kautzer, C.R., Lee, D.H., Marjoribanks, C., McDonough, D.P., Nguyen, B.T.N., Norris, M.C., Sheehan, J.B., Shen, N.P., Stern, D., Stokowski, R.P., Thomas, D.J., Trulson, M.O., Vyas, K.R., Frazer, K.A., Fodor, S.P.A., Cox, D.R.: Blocks of limited haplotype diversity revealed by high-resolution scanning of human chromo- some 21. Science 294, 1719–1723 (2001) 10. Dawson, E., Abecasis, G.R., Bumpstead, S., Chen, Y., Hunt, S., Beare, D.M., Pabial, J., Dibling, T., Tinsley, E., Kirby, S.: First-generation linkage disequilibrium map of human chromosome 22. Nature 418, 544–548 (2002) 11. Mahdevar, G., Zahiri, J., Sadeghi, M., Nowzari-Dalini, A., Ahrabian, H.: Tag SNP selection via a genetic algorithm. J. Biomed. Inf. (2010), doi:10.1016/j.jbi.2010.05.011 12. Zhang, K., Calabrese, P., Nordborg, M., Sun, F.: Haplotype block structure and its applications to association studies: power and study designs. Am. J. Hum. Genet. 71, 1386–1394 (2002) 13. Wall, J.D., Pritchard, J.K.: Assessing the performance of the haplotype block model of linkage disequilibrium. Am. J. Hum. Genet. 73, 502–515 (2003) 14. Johnson, G.C.L., Esposito, L., Barratt, B.J., Smith, A.N., Heward, J., Di Genova, G., Ueda, H., Cordell, H.J., Eaves, I.A., Dudbridge, F., Twells, R.C.J., Payne, F., Hughes, W., Nutland, S., Stevens, H., Carr, P., Tuomilehto-Wolf, E., Tuomilehto, J., Gough, S.C.L., Clayton, D.G., Todd, J.A.: Haplotype tagging for the identification of common disease genes. Nat. Genet. 29, 233–237 (2001) 15. Zahirib, J., Mahdevar, G., Nowzari-dalini, A., Ahrabian, H., Sadeghic, M.: A novel efficient dynamic programming algorithm for haplotype block partitioning. J. Theor. Biol. 267, 164–170 (2010) 16. Greenspan, G., Geiger, D.: High density linkage disequilibrium mapping using models of haplotype block variation. Bioinformatics 20, i137 (2004) 17. Wang, N., Akey, J.M., Zhang, K., Chakraborty, R., Jin, L.: Distribution of recombination crossovers and the origin of haplotype blocks: the interplay of population history, recombination, and mutation. Am. J. Hum. Genet. 71, 1227–1234 (2002)

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The Study on Spatial Reuse Efficiency of Network Nodes Using Advanced Power Control Algorithm* Seung-dae Lee Electronic Engineering, Namseoul Univ., 21 Maeju-ri, Seonghwan-eup, Seobuk-gu, Cheonan-city, Choongnam, Korea [email protected]

Abstract. An algorithm to minimize power consumption while keeping network throughput such as distributed power control, PCM(power control MAC), and F-PCF(fragmentation based PCM) was suggested to reduce power consumption through power control up to the present. A potential spatial for communication of surrounding nodes was generated as such power control method was planned to make optimal transmit power in order to minimize power consumption. But, potential spatial for communication generated through power control had not yet been utilized appropriately as an algorithm focused on power consumption. This study suggested an algorithm that advanced lots of network throughput by using potential spatial for communication generated by power control. Keywords: Network node, PCM, Ad Hoc, MANET, Power control.

1

Introduction

There had actively been study on delay reduction, bandwidth fair distribution, power control and improvement of network throughput in the MAC layer of an ad-hoc network [1]. Especially, the study on power control related to an ad-hoc equipment using limited energy and network throughput had been regarded as the main area of concern. Multi-hop information transmission from a network source area to a destination with optimal power through power control has advantage of reducing transmit power required in proportion to the 5th power of the square of propagation distance [2],[3]. In other words, the whole lifespan of a network can be extended as optimal transmit power is made to be consumed through a multi-hop instead of consuming transmit power with one node high. There had been study on an algorithm that did not exert effect on network performance, reducing unnecessary power consumption as a power control algorithm suggested before now controlled transmit power of a source node with minimum optimal transmit power that a node of the next hop was able to receive. A MAC protocol of a mobile ad-hoc network based on wireless LAN limits communication interference as it transmits communication of surrounding nodes that *

Funding for this paper was provided by Namseoul university.

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influence transmission and reception with the RTS/CTS/DATA/ACK maximum transmit output to avoid transmission collision between nodes through physical carrier sense and to solve problems between hidden nodes and exposed nodes through virtual carrier sense. Especially, even if a protocol is planned considering only a communication radius in the majority of cases when a MAC layer protocol is planned, a career sense area becomes twice the size of a communication radius because delay threshold about a carrier sensing threshold is set up as double as shown in the figure 1 [4].

Fig. 1. Carrier Detection Threshold and Delay Threshold [8]

Therefore, interference between much more nodes occurs not by a real carrier sensing threshold area, but by a defer threshold area in multi-hop communication and loss in network performance to that extent is brought about. It means that transmission error can occur as the president signal works as interference in case of locating within a defer threshold area of transmit node even if surrounding node is outside the radius of a carrier sensing threshold.

Fig. 2. Power control and carrier sensing zone

The section that interference explained earlier exists is as shown in the figure 2. The line source based on node A shows the communication radius, a carrier detect threshold of A and the dotted line source drawn at the same center is a defer threshold area that the maximum signal of node A can operate as interference. Node C can receive power more than a carrier detect threshold and node D receives defer threshold power lower than a carrier threshold. However, as this signal has power

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more than defer threshold, node D defers media approach. Collision is blocked as collision window value was back-off and media approach is made delayed during time defined by EIFS when EIFS (Extended Inter-Frame Space) value is defined to transmit defer threshold for this defer in IEEE 802.11 wireless LAN. The use of optimal transmission and reception power through power control algorithm can enable the improvement of performance of network as it makes area that spatial reuse is possible by reducing a carrier spatial area explained earlier and interference areas of nodes. This study attempted to suggest an algorithm for reuse of power control spatial that improved network performance as communication can be possible at the same time without interference between node in communication and surrounding nodes by using a spatial reuse area calculated after a real carrier detect area was reduced with optimal transmission and reception power through a power control algorithm. This study was organized as follows. Chapter two, mentioned power control technique used in the existing wireless ad-hoc network and chapter three explained a power control algorithm for spatial reuse suggested in this study. Chapter 4 conducted simulation evaluation to judge the suitability of an algorithm proposed in this study and chapter 5 stated study findings.

2

Power Control Techniques in a Wireless Ad-Hoc Network

The transmitting power Pd was made to be estimated by a transmitter side for physical carrier detect between nodes in a system following RTS/CTS/DATA/ACK mechanism basically used in IEEE 802.11 wireless LAN as a transmitter side transmitted RTS with the maximum transmit power Pmax and a receiver side that received this with Pr power decided power level Pd that was able to keep desirable signal to noise ratio considering receiving power Pr and the present channel condition to contain it in CTS and to transmit with Pmax power. A node at a transmitter side uses transmit power of Pd on transmitting data. The reason that CTS is transmitted with Pmax is to solve collision between nodes through physical carrier detect. However, interference can occur in a transmission and reception node as power asymmetry with transmission and reception power is caused when a node that exists in a defer threshold area that the EIFS period is done transmits RTS with power of Pmax on transmitting data with transmit power of Pd. Distributed power control is a method that transmissible power level is divided into 10 levels and power level that can reduce power consumption is selected as each node is made to manages the optimal transmit power level with surrounding nodes with a table. However, interference due to power asymmetry can be caused by a node that exists in a defer threshold area on transmitting link data like the basic power control [6]. In case of PCM, the possibility of interference caused by power asymmetry is decreased as data is periodically transmitted into a basic power control method with transmit power of Pmax to solve an asymmetry problem of transmit power shown in basic and distributed power control. This cycle is a smaller value than EIFS and nodes that existed in a defer threshold area keep backing off for communication time through a EIFS value [7]. It becomes a main cause that lowers network efficiency in a topology with high network node density as it stops nodal communication within a

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carrier detect area, caused by communication with static power even if power loss can be decreased as data is transmitted and received by decreasing transmit power as much as Pd in Pmax. F-PCM is an algorithm to solve a problem that brings about interference in a node at the receiver side as nodes that exist in a carrier detect area at the receiver side do not detect corresponding signals and then transmits RTS with Pmax because data is periodically transmitted with transmit power Pmax on transmitting data in PCM. This algorithm is divided into the size of data that can be transmitted at the shorter time than a EIFS period. As ACK is transmitted to each with the maximum transmit power Pmax, it makes nodes that exist in a carrier detect area at a receiver side back-off [8]. However, it is necessary to expand EIFS as much as a overhead is enlarged and transmit time can be covered because this algorithm divides data into many pieces to transmit if using EIFS value. Even if this algorithm makes nodes that exist in all the carrier detect areas not interfere communication of a transmission and reception node, much more power is consumed than PCM. Nodes within a carrier detect area can have a problem that specific nodes do not approach media for a long time due to periodic use of the maximum transmit power like PCM even if the channel is empty according to result of a carrier detect.

3

Power Control Algorithm for Space Reuse

No concern had been paid to network capacity loss of nodes caused by interference of surrounding nodes as a power control algorithm had been planned based on efficiency of power consumption on a mobile ad-ho network up to the present. However, use of optimal transmission and reception power through a power control algorithm reduces a carrier detect area and interference spatial of surrounding nodes to enable to make a spatial re-usable area. His chapter explained an algorithm that was able to improve network performance through spatial reuse in power control. The number of nodes that receive interference becomes smaller because a carrier detect area becomes smaller that mush in case that optimal transmit power Pd is acquired to transmit and receive through exchange of RTS/CTS. Surrounding nodes that are not interfered when real data is transmitted comes to maintain the resting state as the existing power control algorithm delays surrounding nodes using the maximum transmit power periodically to avoid interference caused by a problem of power asymmetry between surrounding nodes and transmission/reception nodes. A suggested algorithm suggested a plan that had a surrounding node enable to use a potential reuse spatial generated by power control as a surrounding node solves an asymmetry problem of power not to interfere communication of nodes at the transmitter- receiver side. A potential space on a network came to be consumed as much as a range area of the figure 3 as node interference occurred in a ( 4π + 3 3 / 2)( Pmax`s range ) 2 m 2 area related to a carrier detect area of Pmax even if a real carrier detect area was (4π + 3 3 / 2)( Pd `s range ) 2 m 2 when it was communicated with Pd's transmit power on data transmission as shown in the figure 3, as a node at the transmitter side transmitted RTS with transmit power Pmax and the receiver side that receives this transmitted with Pmax's power after deciding power level Pd to contain it in CTS.

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Fig. 3. Ideal carrier sensing zone

In an algorithm for spatial reuse based power control using potential spatial on this network, node A and B transmitted SRS(Spatial Reuse to Send) including length of data that would be exchanged and information of optimal power level Pd after a transmit node A in the figure 4 transmitted RTS and a receiver side that received this with Pr's power decided the power level Pd that was able to keep a desirable signal to noise ratio, considering a channel condition to transmit with CTS. Edge node C and D in a carrier detect threshold area that received this transmitted RPT that included power information of PSR (Pmax/2) and information about data length that would be exchanged of node A and B after an EIFS period as optimal power PSR (Pmax/2) in a spatial reuse area calculate based on Pd. Node E and F that do not exist in a carrier detect threshold and received RPT was able to improve performance on the whole network through spatial reuse as it exchanged information through transmission/reception power of PSR and RTS/CTS/ DATA /ACK for a data exchange period of node A and B. PSR had to be smaller than Pmax/2 to avoid interference of transmission/reception node A and B so that s surrounding node communicates without interference through spatial reuse. A node that receives RPT through PSR transmit power was able to reuse 95% spatial of ( 4π + 3 3 / 2)( Pmax`s range − Pd `s range) 2 m 2 as shown in the figure 5 as it communicated with transmission/reception power of PSR.

4

Simulation Performance Evaluation

A simulation for performance evaluation used NS-2 allinone-2.29. A parameter set up on simulation was set up as the value to calculate propagation distance at a two-ray model from frequency to loss factor as shown in the table 1. The received threshold(Rx Threshold) shows threshold received power to detect a idle state of a channel and the edge threshold is threshold received power to distinguish the edge within a communication radius from a suggested algorithm. The set-up of the present simulation was able to obtain expected effect only when the more proper difference had to be made the smaller a node density became even if it was set-up without any big difference with received threshold.

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Fig. 4. SR-PCM algorithm

Fig. 5. Spatial reuse zone

A directional antenna was used for an antenna and transmission speed was set up as 2Mbps. For the transmit power level, transmit power was divided into 10 levels to be used to have a transmit radius from the minimum 25m to the maximum 250m based on the value in the table 1. A simulation compared increased data throughput with PCM to evaluate performance as two nodes located at the 1/2 distance of a carrier detect area added a CBR traffic to surrounding nodes that existed at a grid in a carrier detect area generated while exchanging data.

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Table 1. Simulation Parameter Items Frequency Propagation Tx Ant. Gain Rx Ant. Gain Loss factor Rx threshold CS threshold Traffic Routing protocol Data size

Value 5.8GHz Two-ray Model Omni-Antenna 1 1 1 1.295999e-9W CBR/UDP DSR 210 bytes

(a) CBR traffic 2

(b) CBR traffic 3

(c) CBR traffic 4

(d) CBR traffic 5

Fig. 6. Throughput of CBR Traffic

Result measured as two nodes added more than two CBR traffics in a carrier detect area communicating shows that the whole data throughput was improved as shown in the figure 6. However, it appeared that the initial delay was had at the early of transmission compared with an existing PCM according to the additional treatment process for spatial reuse. The fact that data throughput was not increased at the multiple as much as a CBR traffic occurred due to packet loss transmitted through ARP message, routing table update, and multi-hop. The figure 7 shows result about improvement limit of throughput and CBR traffic crossing two existing nodes. There was little increase effect of throughput from 6 traffics in this scenario as shown in the figure 7(a). This shows that a carrier detect area that spatial reuse was available was almost used. This showed limitation that was able to bring limited performance improvement even if a node density was high and there were many traffics that intended to transmit. However, it was identified that performance improvement was brought through a multi-hop in a spatial reuse area even in CBR traffic of a surrounding node crossing an existing node like (b).

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(a) CBR traffic 5,6

(b) Cross CBR traffic

Fig. 7. Throughput of CBR Traffic

5

Conclusion

There had been lots of studies on efficient multi-hop transmission and an power control algorithm to reduce power loss in an ad-hoc network. This power control algorithm reduces a carrier detect area dn interference areas of surrounding nodes to make a spatial reusable area and data throughput on the whole network can be improved by using this space. This study introduced a power control algorithm that spatial reuse was available and identified performance improvement through simulation as spatial reuse power calculated base on power of a transmission and reception node was transmitted to surrounding nodes. As a result, throughput per 01. second of network was improved for 11.1% to the maximum 51% according to the traffic distribution. A power control algorithm for spatial reuse suggested in this study conducted evaluation focusing on principles of an algorithm based on a ideal case. Further study would be needed on route set-up of routing class for multi-hop of nodes that existed within a spatial reuse area and a limited algorithm as well as on solution related to a part assumed to evaluate principles of a further algorithm and an algorithm to select the outermost transfer mode to suggest a more improved algorithm.

References 1. Siva Ram Mupthy, C., Manoi, B.S.: Ad Hoc Wireless Network Architectures and protocols. Prentice Hall (2004) 2. Abramson, N.: The ALOHA System Another Alternative for Computer Communications. In: Proc. AFIPS Fall Joint Computer Conference, pp. 281–285 (1970) 3. Santi, P.: Topology Control in Wireless Ad Hoc and Sensor Networks. John Wiley& Sons (2005) 4. Kamerman, A., Monteban, L.: WaveLan-II: A High-Performance Wireless LAN for Unlicensed Band. Bell Labs Technical Journal 2(3) (1997) 5. Wireless, L.A.N.: Medium Access Control (MAC) and Physical Layer (PHY) Specifications. IEEE Standard 802.11 (2007)

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6. Agarwal, S., Krishnamurthy, S., Katz, R.H., Dao, S.K.: Distributed Power Control in Ad Hoc Wireless Networks. In: Proc. IEEE PIMRC, pp. 56–66 (2001) 7. Jung, E.-S., Vaidya, N.H.: A Power Control MAC Protocol for Ad Hoc Networks. Springer Science Business Media Wireless Network (2005) 8. Kim, D.: F-PCM: A Fragmentation Based Power Control MAC Protocol for IEEE 802.11 Mobile Ad Hoc Network. Wiley Inter-Science, Wireless Communications and Mobile Computing 6, 727–739 (2006)

Agents for Cloud Resource Allocation: An Amazon EC2 Case Study J. Octavio Gutierrez-Garcia and Kwang Mong Sim Gwangju Institute of Science and Technology, Gwangju 500-712, Republic of Korea [email protected], [email protected]

Abstract. Infrastructure-as-a-service consumers are presented with numerous Cloud providers with a wide variety of resources. However, consumers are faced with providers that may offer (even similar) resources at different hourly cost rates, and also that no single provider may have matching resource capabilities to fulfill a highly heterogeneous set of requirements. This work proposes an agent-based approach endowed with the well-known contract net protocol for allocating heterogeneous resources from multiple Cloud providers while selecting the most economical resources. The contributions of this paper are: (i) devising an agent-based architecture for resource allocation in multiCloud environments, and (ii) implementing the agent-based Cloud resource allocation mechanism in commercial Clouds using Amazon EC2 as a case study. The Amazon EC2 case study shows that agents can autonomously select and allocate heterogeneous resources from multiple Cloud providers while dynamically sampling resources’ allocation cost for selecting the most economical resources. Keywords: agent-based Cloud computing, Cloud computing, multi-agent systems, resource allocation.

1

Introduction

Infrastructure-as-a-service consumers are offered a wide diversity of Cloud resources from multiple, distributed Cloud providers (e.g., Amazon EC2 [3], GoGrid [9], and RackSpace [11]) supplied at different hourly cost rates. Moreover, similar Cloud resources may be priced differently by each Cloud provider. Furthermore, Cloud consumers may request heterogeneous sets of Cloud resources that may not be available in a single Cloud provider. Thus, autonomously carrying out resource allocation from multiple and self-interested Cloud providers while sampling hourly cost rates associated to Cloud resources is necessary to provide efficient (i.e., with low allocation costs) resource allocation services to consumers in a dynamic manner. This stresses the need for the agent paradigm. Agents are autonomous problem solvers that can act and collaborate flexibly and self-interestedly among each other. In this paper, agents represent Cloud participants (consumers, providers, and brokers), which use the contract net protocol (CNP) [13] to establish service contracts with Cloud providers while sampling hourly cost rates. The CNP is used as the T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 544–553, 2011. © Springer-Verlag Berlin Heidelberg 2011

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foundation for an agent-based Cloud resource allocation mechanism to autonomously allocate Cloud resources in multi-Cloud environments. Finally, an agent-based system prototype was devised and implemented in which a case study using Amazon EC2 as Cloud provider was carried out. The significance and novelty of this paper is that, to the best of the authors’ knowledge, it is the earliest work in applying, developing, and deploying an agentbased approach to allocate Cloud resources from multiple and commercial Cloud providers (e.g., Amazon EC2) in an autonomous manner. The contributions of this work are as follows. (i) Devising an agent-based Cloud architecture for resource allocation in multi-Cloud environments (Section 2), and (ii) implementing and deploying the agent-based resource allocation mechanism in commercial Clouds using Amazon EC2 as a case study (Section 3). In addition, Section 4 includes a related work comparison, and Section 5 presents conclusion and future work.

2

An Agent-Based Cloud Resource Allocation Architecture

The components of the agent-based architecture that supports resource allocation in multi-Cloud environments (Fig. 1) are as follows:

Fig. 1. Agent-based Cloud resource allocation architecture

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1) A Cloud resource ontology (Fig. 2) is a formal specification of Cloud resources. Cloud resources are defined by their functional and non-functional capabilities as well as their location (URI address). Non-functional capabilities are (i) storage capacity, (ii) memory, (iii) architecture type, and (iv) processing capacity. Functional capabilities are (i) pre-installed operative system, and () pre-installed applications, e.g., database servers.

Fig. 2. Cloud resource ontology

2) A Cloud directory is a listing of Cloud participants either brokers or service providers and their capabilities. The capability of brokers is stated as providing resource allocation services to Cloud consumers. The capabilities of service providers are defined by the Cloud resource types they offer. The Cloud directory is handled by a system agent (see Section 3 for details) to which (i) service provider agents and broker agents register their services, and (ii) consumer agents and broker agents contact to request for broker and service provider agents’ addresses, respectively. 3) Consumer agents (CAs) act on behalf of Cloud consumers and are in charge of submitting resource allocation requests composed of a set of Cloud requirements. To do this, CAs adopt the initiator (manager) role of the CNP consisting of (i) sending a call-for-proposals (resource allocation request) to q broker agents (contractors). Then, from the p (p ≤ q) broker agents’ proposals, CAs select the best (e.g., cheapest) proposal and send an accept message to the broker agent with the winning bid, and reject messages to the remaining broker agents. 4) Broker agents (BAs) provide Cloud resource allocation services to CAs. To do this, BAs adopt the participant (contractor) role of the CNP consisting of (i) receiving resource allocation requests (call-for-proposals) from CAs, and (ii) replying with proposals based on the current hourly cost rates associated to the Cloud resources. If a BA is selected, the BA allocates the Cloud resources from service providers listed in the Cloud directory with matching resource capabilities. To do this, BAs adopt the initiator role of the CNP with service provider agents as participants. BAs’ call-forproposals are composed of single Cloud requirements. In doing so, CAs’ allocation requests consisting of multiple Cloud requirements are handled by BAs as independent and parallel resource allocations from the best (cheapest) service providers for each Cloud requirement. 5) Service provider agents (SPAs) offer and supply Cloud resources to BAs by adopting the participant role of the CNP with BAs as initiators. SPAs’ proposals consist of allocation costs based on the Cloud resource types. In addition, SPAs

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handle Cloud resource provisioning by requesting (de)allocations of Cloud resources to resource agents. 6) Resource agents (RAs) (de)allocate and monitor Cloud resources by using specific vendor APIs (usually web services), e.g., Amazon AWS APIs [1]. RAs are described by the Cloud resource type they can allocate. RAs’ functions are: (i) receiving allocation requests from SPAs, (ii) creating private/public keys to access Cloud resources, (iii) extracting and decrypting passwords to access Cloud resources, (iv) extracting public IP addresses from recently allocated Cloud resources, and (v) forwarding Cloud resources’ access information to SPAs. An agent-based Cloud resource allocation scenario (Fig. 3) is as follows. A CA retrieves BAs’ addresses from the Cloud directory, and then the CA sends a call-forproposals (consisting of a set of p Cloud consumer requirements) to a set of BAs. The BAs reply with proposals containing the overall allocation cost. Afterwards, the CA sends an accept-proposal message to the selected BA and reject-proposal messages to the remaining BAs. Subsequently, the selected BA adopts p CNP executions with SPAs (listed in the Cloud directory) as participants in a parallel manner, i.e., the

Fig. 3. Cloud resource allocation interaction protocol

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selected BA adopts a CNP execution for each Cloud resource requested by the CA. The SPAs (selected by the BA) send allocation requests to appropriate RAs (i.e., RAs matching the Cloud consumer requirements). Finally, the RAs extract the public IP addresses and passwords from the recently allocated Cloud resources, and forward the results to their SPAs that forward the information to the BA, which hand over the information to the CA.

3

Agent-Based Cloud Resource Allocation in Amazon EC2

A case study using Amazon EC2 was carried out for which an agent-based system prototype was implemented using (i) the java agent development framework (JADE) [6], (ii) Bouncy Castle Crypto APIs [7] (for encrypting and decrypting Cloud resources’ passwords), and (iii) Amazon SDK for Java [5]. 3.1

Amazon EC2 Cloud Ontology

The Cloud resource ontology (Fig. 2) was provided with Cloud resource definitions based on (i) Amazon instance types and (ii) Amazon machine images (AMIs), for a total of 58 different Cloud resource definitions that resulted from the valid combinations (i.e., not all the AMIs can be run on a given instance type) between instance types and AMIs (Table 1). Table 1. Amazon instance types and Amazon machine images Amazon instance types (i) t1.micro, (ii) m1.small, (iii) m1.large, (iv) m1.xlarge, (v) m2.xlarge, (vi) m2.2xlarge, (vii) m2.4xlarge, (viii) c1.medium, (ix) c1.xlarge, (x) cc1.4xlarge, and (xi) cg1.4xlarge.

3.2

Amazon machine images (i) Basic 32-bit Amazon Linux, (ii) Basic 64-bit Amazon Linux, (iii) Red Hat Enterprise Linux 6.1 32 bit, (iv) Red Hat Enterprise Linux 6.1 64 bit, (v) SUSE Linux Enterprise Server 11 64-bit, (vi) SUSE Linux Enterprise Server 11 32bit, (vii) Microsoft Windows Server 2008 Base, (viii) Microsoft Windows Server 2008 R2 Base, (ix) Microsoft Windows Server 2008 R2 with SQL Server Express and IIS, (x) Microsoft Windows Server 2008 R2 with SQL Server Standard, (xi) Cluster Instances Amazon Linux, and (xii) Cluster Instances HVM SUSE Linux Enterprise 11.

Cloud Participants and Distributed Cloud Environment

The agents involved in the case study were 1 RA, 5 BAs, 5 SPAs, and 2500 RAs. Each agent, either CAs, BAs, or SPAs was deployed on a different JADE agent container (see Fig. 1 and Fig. 4), i.e., an instance of a JADE runtime environment. In addition, since RAs do not interact among themselves, all the RAs were deployed on a single container (Container-1 in Fig. 4). In doing so, SPAs had to contact RAs located at a remote location, and an unnecessary large number of containers was avoided in the system prototype. The Cloud resource type of the RAs was randomly

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selected from the available 58 Cloud resource types (Table 1). Moreover, all the RAs were randomly assigned to the SPAs to simulate a scenario with highly heterogeneous Cloud providers. All the agent containers must be and were registered in a main JADE container that manages and supports the agent-based platform by (i) handling asynchronous message passing communication through Java RMI and IIOP, (ii) starting and killing agents, and (iii) providing services such as: a directory facilitator agent (Cloud directory), a sniffer agent, a remote management agent, etc., see [6] for details of JADE.

Fig. 4. JADE sniffer agent showing an agent-based Cloud resource allocation scenario

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Fig. 5. AWS management console – Key pairs option

Fig. 6. AWS management console – My instances option

As shown in Fig. 4, agent-based platform CloudMAS had: (i) 1 Main-Container including a remote monitoring agent (RMA@CloudMAS), an agent management system (ams@CloudMAS), a directory facilitator (df@CloudMAS), i.e., a Cloud directory, and a sniffer agent (for illustrative purposes); and (ii) 12 basic containers (from Container-1 to Container-12). Container-1 included all the RAs. The SPAs and BAs were included in agent containers ranging from Container-2 to Container-11, one agent for each container. Finally, the CA was included in Container-12. Since all the agent containers were deployed on the same host, each container was provided with a different network port to simulate a fully distributed environment. 3.3

Cloud Resource Allocation Scenario

The CA was provided with a Cloud resource allocation request composed of 6 Cloud resources: 4 m1.small instances with an AMI ami-8c1fece5 (Basic 32-bit Amazon Linux AMI 2011.02.1 Beta) and 2 m1.large instances with an AMI ami-8e1fece7 (Basic 64-bit Amazon Linux AMI 2011.02.1 Beta).

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Fig. 7. Console output for agent-based resource allocations using Amazon SDK

The CA submitted the allocation request to the 5 BAs by using the CNP. The selected BA executed 6 CNP (one for each Cloud resource to be allocated) with the 5 SPAs in a parallel manner. Finally, the selected SPAs requested the Cloud resource allocations to their RAs. Fig. 4 shows an extract of the messages exchanged among all the agents to carry out the Cloud resource allocation request, which was fulfilled by agent BA4 (a BA selected by consumer CA1). The messages received by agent BA4 (Fig. 4) came from the SPAs bidding for allocating a Cloud resource and/or providing data to access the recently allocated Cloud resources, e.g., the messages exchanged between agents SPA1 and BA4, see Fig. 4. In addition, as soon as allocation data (public IP address and password to access a given Cloud resource) was received, broker BA4 forwarded the data to consumer CA1, as shown in the bottom of Fig. 4. The interleaving of messages received by agent BA4 from all the SPAs (see Fig. 4) is the result of the parallel execution of CNPs for allocating Cloud resources. 3.4

Technical Aspects to Handle Cloud Resource Allocation in Amazon EC2

The RAs were provided with (i) Amazon EC2 API tools to handle Cloud resource allocations, and (ii) Amazon AWS security credentials to access Amazon EC2. It should be noted that although the RAs shared the same security credentials (i.e., all the RAs accessed Amazon EC2 using the same Amazon AWS account), sharing the credentials had no advantageous effects on the agent-based Cloud resource allocation approach.

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When the RAs received the SPAs’ requests to allocate Cloud resources, the RAs created new RSA key pairs to access Amazon EC2 (Fig. 5). The key pairs were automatically named based on the identifiers of the RAs that allocated the Cloud resources, e.g., newKeyRA2462 (see the left side of Fig. 5). Right afterwards, the RAs proceeded to allocate the Cloud resources (Fig. 6) corresponding to the CA’s initial allocation request (consisting of 6 Cloud resources, see Section 3.3). The console output of the agent-based system (Fig. 7) corresponding to the CA’s allocation request shows: (i) JADE initialization messages displaying agent containers’ addresses and names, (ii) self-generated output messages derived from the creation of key pairs by using Amazon SDK, and (iii) self-generated output messages derived from the Amazon instance allocations by using Amazon SDK. In general, the self-generated output messages contained the following information: timestamp, key pair name, AWS access key, type of instance allocated, etc., see Fig. 7 for details. Since Amazon instances take some time to be fully functional (i.e., to start) and the delay time may vary due to the size of AMIs, number of instances to be allocated, among other factors [2], the RAs were continuously checking (every 200 s) whether Amazon instances were up and running by retrieving the console output of the recently allocated instances as indication of the start of the instances. Once the RAs detected an output in the instances’ console, the RAs proceeded to extract the public IP addresses and passwords (only possible when the instances are up and running), which were forwarded to their corresponding SPAs.

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Related Work

Resource allocation mechanisms have been widely investigated (see [12]). However, little attention has been directed to (i) Cloud resource allocation in multi-Cloud environments, and (ii) to actual implementations of autonomous Cloud resource allocation mechanisms. Whereas current Cloud management systems (see [8], [10], and [14]) may allocate Cloud resources from different Clouds to execute consumers’ applications, no explicit consideration of autonomous Cloud resource selection based on fees associated to Cloud resources has been made. In contrast, this present work uses both the agent paradigm and the CNP to (i) sample Cloud resources’ hourly cost rates, and (ii) allocate Cloud resources in multi-Cloud environments in an autonomous and dynamic manner. In addition, the proposed agent-based Cloud resource allocation mechanism is fully distributed, in contrast to centralized allocation mechanisms (see [4]) that require a central control entity (allocator) that commonly becomes a system bottleneck.

5

Conclusion and Future Work

The contributions of this paper are as follows. (i) Devising the earliest (to the best of the authors’ knowledge) agent-based Cloud architecture for resource allocation in multiCloud environments, and (ii) implementing and deploying the agent-based resource allocation mechanism in commercial Clouds using Amazon EC2 as a case study.

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In this work, autonomous agents equipped with the CNP to (i) dynamically sample hourly cost rates and (ii) support cost-based Cloud resource allocation among selfinterested Cloud participants were used to deal with Cloud resource allocation in multi-Cloud environments. By using the agent paradigm, Cloud consumers can efficiently (i.e., with the lowest allocation costs) allocate heterogeneous sets of Cloud resources from multiple, distributed Cloud providers in a dynamic and autonomous manner as shown in the Amazon EC2 case study. Since this work provides the foundations for a general-purpose agent-based multiCloud platform by providing an infrastructure-as-a-service solution (allocated from multiple Cloud providers) to Cloud consumers, future research directions include: (i) adding agent capabilities to schedule and execute both workflows and bag-of-tasks applications in multi-Cloud environments, and (ii) implementing access to more commercial Cloud providers, such as: GoGrid [9] and RackSpace [11]. Acknowledgments. This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MEST) (KRF-2009-220-D00092) and the DASAN International Faculty Fund (project code: 140316).

References 1. 2. 3. 4. 5. 6.

7. 8.

9. 10.

11. 12. 13. 14.

Amazon EC2 API Tools, http://aws.amazon.com/developertools/351 Amazon EC2 FAQs, http://aws.amazon.com/ec2/faqs Amazon Elastic Compute Cloud (Amazon EC2), http://aws.amazon.com/ec2 Asmild, M., Paradi, J.C., Pastor, J.T.: Centralized Resource Allocation BCC Models. Omega 37(1), 40–49 (2009) AWS SDK for Java – A Java Library for Amazon S3, Amazon EC2, and More, http://aws.amazon.com/sdkforjava Bellifemine, F., Poggi, A., Rimassa, G.: JADE - A FIPA-Compliant Agent Framework. In: 4th International Conference and Exhibition on the Practical Application of Intelligent Agents and Multi-Agents, pp. 97–108 (1999) Bouncy Castle Crypto APIs, http://www.bouncycastle.org Buyya, R., Pandey, S., Vecchiola, C.: Cloudbus Toolkit for Market-Oriented Cloud Computing. In: Jaatun, M.G., Zhao, G., Rong, C. (eds.) Cloud Computing. LNCS, vol. 5931, pp. 24–44. Springer, Heidelberg (2009) GoGrid, http://www.gogrid.com Lee, K., Paton, N.W., Sakellariou, R., Deelman, E., Fernandes, A.A.A., Metha, G.: Adaptive Workflow Processing and Execution in Pegasus. Concurr. Comput.: Pract. Exper. 21(16), 1965–1981 (2009) RackSpace, http://www.rackspace.com Sim, K.M.: A Survey of Bargaining Models for Grid Resource Allocation. SIGecom Exch. 5(5), 22–32 (2006) Smith, R.G.: The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver. IEEE Trans. Comput. 29(12), 1104–1113 (1980) Yang, Y., Liu, K., Chen, J., Lignier, J., Jin, H.: Peer-to-peer Based Grid Workflow Runtime Environment of SwinDeW-G. In: 3rd IEEE International Conference on e-Science and Grid Computing, pp. 51–58. IEEE Computer Society, Washington (2007)

An Offline Quality Management Strategy to Improve Online Quality of Service for Next Generation Networks Calduwel Newton Pitchai1,*, Arockiam Lawrence2,**, and Gang-soo Lee3,*** 1

Computer Science, Bishop Heber College (Autonomous) Computer Science, St.Joseph’s College (Autonomous) Tiruchirappalli, India 3 Dept. of Computer Engineering, 133 Ojeong-dong Daedeok-gu Daejeon, Korea [email protected]

2

Abstract. The Next Generation Networks (NGN) create various challenges to deal with network management operations, like fault management, service restoration, performance management, traffic management, data gathering and analyses, and routine network tests. As the network is growing exponentially, the amount of data that has to be stored to improve Quality of Service (QoS) are also high. In other words, data about the past history has to be stored effectively in order to deal with above challenges. If there is no effective strategy to overcome these challenges then the NGN or the upcoming networks could not perform well. This will result in poor QoS. The word ‘Offline’ refers already stored data or past data. The word ‘Online’ refers the QoS for the current requirements. That is, a strategy is proposed to improve current QoS requirements by analyzing the past data. The objectives of this paper are to appropriately monitor or manage the above operations, effectively gather and analyze the voluminous of data, efficiently identify the reason behind the problem and successfully take remedial measures to improve QoS. Ultimately, the outcome of this paper will improve the performance of the above network management operations. Keywords: QoS, Next Generation Networks, Traffic, Network Management, Offline, Online.

1

Introduction

The upcoming networks are witnessing a drive to the convergence of wired and wireless networks. The challenges in the converged networks are increasing day by day. Improving or ensuring Quality of Service (QoS) is a great challenge for the upcoming networks as they grow rapidly. QoS is the ability to deliver network services according to the parameters specified in a service level agreement. Quality is *

Assistant Professor. Associate Professor. *** Professor. **

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characterized by service availability, delay, jitter, and throughput and packet loss ratio. At a network resource level, QoS refers to a set of capabilities that allow a service provider to prioritize traffic, control bandwidth, and network latency [1]. Frequently, quality is referred as customer satisfaction, first class, good quality rather than quantity, etc. The term QoS has become a very dominant and attractive notion in many aspects of our daily life. Since communication networks become a very essential part of our life, many efforts have been made towards improving quality of service in order to achieve more and more customer satisfaction which leads to strong loyalty and therefore more profit to the service providers and to achieve global efficiency in resource utilization. Moreover, recent advances in high speed networking technology have created opportunities for the development of multimedia applications. These applications integrate several media such as text, graphics, audio, video, image, etc. By having algorithms for QoS may not guarantee the requirements, because some of the nodes will violate the service requirements. In other words, flow must be monitored and appropriate measures must be taken in order to ensure the Quality of Service. Ultimately, ensuring the QoS will improve the performance of the network. QoS management can be done in two ways. They are, QoS Assurance Strategy (QAS) and QoS Control Strategy (QCS). The QAS is used to prevent QoS problem. It will give 100% guarantee for the QoS. The QCS is used to control the QoS problem. It is a kind of detection and correction method. In other words, identify the QoS problem(s) in the previous services and then correct them to improve future QoS. In all the cases, QoS problem cannot be prevented. Hence, there is a great need for the QCS. This paper concentrates on the second method. The rest of the paper is organized as follows. Section 2 emphasizes on the motivations behind this paper. Section 3 describes the strategy to improve QoS in the future. Section 4 shows the analysis and results of our strategy. Section 5 focuses on the conclusion and future works. Finally, references are listed.

2

Motivation

QoS management is an important discipline that should be addressed to deliver good services and can be considered by two different viewpoints. One is the infrastructure and another one is the techniques and tools for service delivery and management. Service Quality Management (SQM) is a general term to address QoS management under the second viewpoint [2]. SQM functions can be classified in three categories: collection and consolidation; inference and control; and presentation [3]. Collection and consolidation functions interact with external systems to obtain the necessary data. Inference and control functions are the core of SQM. These are used to derive action plan to improve the QoS in the future. Presentation functions construct service level reports. In a connection-oriented network, all the packets belonging to a flow follow the same route; in a connectionless network, they may follow different routes. There is a great need to guarantee Quality of Service through network and protocol design [4]. QoS routing offers serious challenges even for today’s Internet [5]. Different service types (e.g., voice, live video, and document transfer) have significantly

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different objectives for delay, bandwidth, and packet loss. Determining the QoS capability of candidate links is not simple for such scenarios; for multicast services, the difficulties are even larger. More than one QoS constraint often make the QoS routing problem NP complete [6]. Suboptimal algorithms such as sequential filtering are often used, especially for large networks, where an optimal path based on a single primary metric (e.g., bandwidth) is elected first, and a subset of them are eliminated by optimizing over the secondary metric (e.g., delay), and so on, until all the metrics have been taken into account. A random selection is made if there is still more than one choice after considering the network throughput as the last metric. As long as the QoS constraints are satisfied, the same route is used for all packets in the flow. Once a route has been selected for a specific flow, the necessary resources, (bandwidth, buffer space in routers, etc.) must be reserved for the flow. These resources will not available to other flows until the end of this flow. Consequently, the amount of remaining network resources available to accommodate the QoS requests of other flows will have to be recalculated and propagated to all other pertinent nodes as part of the topology update information. Analysis of data from number of sources clearly shows that the internet traffic has been almost doubling every year since 1997 [7]. As of now, the Internet has only supported best effort service; best effort in the sense that it will do its best to transport the user packets to their intended destination, although without any guarantee [8]. IP-based networks were never designed for realtime traffic, yet QoS support in such networks is needed to accommodate both global use and the more demanding applications now emerging [9]. There are various schemes used to enhance QoS [10]. The above discussions elucidates that QoS problem is a serious issue that needs effective strategy to provide good or better services. This issue can not be prevented in all the situations. So, a strategy is very much needed to control QoS problem. In other words, previous flow characteristics must be analyzed, reason for the QoS problem must be identified and necessary steps must be taken to reduce or eliminate QoS problem in the future.

3

The Strategy: QCS

The QCS refers the Quality of Service Control Strategy. The upcoming and future networks expect very high QoS requirements and it continues to grow. Unless, there is a proper monitoring and measuring mechanism to analyze the past flows, the future networks will create complex problems. So an effective strategy to serve the future QoS requirements is very much needed. The needs of each flow can be characterized by four primary parameters: reliability, delay, jitter and bandwidth [4]. These parameters are considered as binary attributes. If expected requirement is met, and then it is 1, otherwise, it is 0. For example, if E-mail application utilized high reliability, low delay, low jitter and low bandwidth, then the corresponding values will be 1,1,1 and 1 [11, 12]. In other words, the QoS is met for E-mail application. If it has utilized low reliability, then the value will be 0. Similarly, In Videoconferencing, only the

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Table 1. Actual QoS requirements

Application E-mail File Transfer Web Access Remote login Audio Video Telephony Video Conference

Reliability 1 1 1 0 1 1 1 1

Delay

Jitter

1 1 1 0 1 1 1 0

1 1 1 0 1 1 1 0

Band width 1 1 0 0 1 0 1 0

Status 1 1 0 0 1 0 1 0

reliability is high and all other parameters are low. It shows the poor QoS. Table 1 shows the actual performance of various applications such as E-mail, File transfer, etc. The last column ‘Status’ shows the final outcome of the QoS. To monitor and measure QoS effectively, the ‘Status’ column has been included. It shows the final outcome of the data transfer that is either QoS met or not met. If it has a value 1 then QoS is met. Otherwise, QoS is not met. The Status column is computed by doing Logical AND with the values of all the parameters such as Reliability, Delay, Jitter and Bandwidth. Table 1 reveals that 50% of the applications received QoS and the remaining 50% has not received QoS. This strategy is definitely effective because all the values are in binary. The ‘Status’ column is needed to identify whether QoS is met or not. This column can be used to monitor all the flows and measure the status of QoS and take remedial steps to improve the QoS. This separates the applications that enjoy the QoS from those that starve QoS. As the number data transfers are extremely high in today’s network and exponentially growing, an effective strategy to monitor and measure the flow is needed. This strategy will ensure future QoS requirements to be met.

4

Analysis and Results

This section uses the QoS management functions to analyze QCS strategy and shows the results using graph. As mentioned in section 2, QoS management functions can be classified in three categories. First, the collection and consolidation functions get the data from different nodes after the data transfer is over and consolidates the data in the master table. This table can be stored in a server or gateway. Table 3 shows the outcome of first function. Second, the inference and control functions deduct, what applications could not receive QoS, what parameters are responsible for the QoS problem. The problem deductions can be done in two ways depending on the requirements. They are application-wise deduction and parameter-wise deduction. Apart from this, it is also possible to deduct the exact reason(s) for the QoS problem. For example, if reliability is the problem then the reason(s) may be an error deduction or error correction

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mechanism or a cable problem. Now, it possible to control the QoS problem as the exact reason(s) has been deducted. Third, the presentation functions deal with service level reports. They contain the reason (s) behind the problem and the necessary steps to be taken to reduce or eliminate the problem. The value of the ‘Status’ column is ‘1’ then there is no QoS problem. Otherwise, there is QoS problem. Table 2 shows the sample data for the application-wise deduction (E-mail). The value of third data transfer in Table 2 is 0,1,1,1. The value ‘0’ indicates that the third data transfer is affected by the reliability. Table 2. Application-wise Deduction ( E-mail Application )

Reliability 1 1 0 0 1 1 1 1

Delay

Jitter

1 1 1 0 1 1 1 0

1 1 1 0 1 1 1 0

Band width 1 1 1 0 1 0 1 0

Status 1 1 0 0 1 0 1 0

Table 3 shows sample data for parameter-wise deduction (Bandwidth). The value of first data transfer in Table 3 is 1,1,1,0,1,1,1,1. Here, the value ‘0’ indicates that the remote login application could not receive the required bandwidth. Wherever the value is zero, it indicates there is a QoS problem in the corresponding attribute (i.e. either application or parameter).

Remote Login

Audio

Video

1 1 1 0 1 1 1 0

0 1 0 0 1 0 1 0

1 1 0 1 1 0 1 1

1 1 1 1 1 0 1 0

1 1 0 1 1 1 1 0

Status

Web Access

1 1 1 0 1 1 1 0

Video Conference

File Transfer

1 1 1 0 1 1 1 1

Telephony

E-mail

Table 3. Parameter-wise Deduction ( Bandwidth )

1 1 0 1 1 0 1 1

0 1 0 0 1 0 1 0

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Performance (High / Low)

E-mail Application

0

1

2

3

4

5

6

7

8

9

No. of Data Transfe r

Fig. 1. QoS Status of E-mail Application

Perform an ce (H igh / L ow )

Bandwidth

E-mail

File Transfer

Web Access

Remote Login

Audio

Video

Telephony

Video Conf.

Fig. 2. QoS Status of Bandwidth for all Applications

Fig. 1 shows the QoS status of E-mail application. It shows for four data transfers the QoS is met and for the remaining it is not met. Fig. 2 shows the QoS status of bandwidth for all the applications. It shows only three applications (File Transfer, Audio, Telephony) have received QoS with respect to bandwidth. QoS for the remaining five applications are affected by bandwidth. It is also possible to store the status of a particular application for a particular parameter, instead of a particular application for all parameters (Table 2) and a particular parameter for all applications (Table 3). Figure 1 clearly shows the data transfers that had QoS problem. They are

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data transfer 3, 4, 6, and 8. The data transfer 3 had a QoS problem due to reliability. This problem is due to error deduction or error correction mechanism or a cable problem. So, measures can be taken to check the above mechanisms or a cable. Similarly, the reasons for the other data transfers (4, 6 & 8) can also be identified using Table 2.

5

Conclusion and Future Works

As the upcoming networks are unpredictably expanding day by day, the QoS assurance can not be given. So, the Quality of Service Control Strategy (QCS) is very much needed. The QCS uses the three major functions such as collection and consolidation; inference and control; and presentation. Section 3 describes these functions with the sample data. The analysis and the results are shown in Section 4. This paper proposed a strategy that contributes to appropriately monitor or manage the above operations, effectively gather and analyze the voluminous amount of data, efficiently identify the reason behind the problem and successfully take remedial measures to improve QoS. The following are the advantages of our strategy. They are, 1. QCS is easy to implement. 2. All the values are in binary which speeds up computation and needs less memory. 3. Only one column (i.e. Status) is enough to check whether QoS is met or not. It also helps to identify which parameter(s) is / are responsible for QoS problem. Though this paper has many advantages, it also has few weaknesses as well. The number of tables used may require more spaces and if more parameters and more applications are used care must be taken to reduce them. In future, this strategy could be implemented to experience the above advantages. Acknowledgments. This work was supported by the Security Engineering Research Center, granted by the Korea Ministry of Knowledge Economy.

References 1. Westerinen, A., Schnizlein, J., Strassner, J., Scherling, M., Quinn, B., Herzog, S., Huynh, A., Carlson, M., Perry, J., Waldbusser, S.: Terminology for Policy-Based Management. IETF RFC 3198 (2001) 2. Menawda, S., Rich, R., Hawley, D.: Service Quality Management: An Emerging Source of Competitive Advantage. The Yankee Group (2002) 3. Penna, M.C., Wandresen, R.R.: On-line Techniques for Service Quality Management. In: Proceedings of the First International Conference on Quality of Service in Heterogeneous Wired/Wireless Networks. IEEE CS (2004) 4. Tanenbaum, A.S.: Computer Networks, 4th edn., pp. 397–417. PHI (2005)

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5. Crawley, E., et al.: A Framework for QoS-Based Routing in the Internet. RFC 2386 (1998), http://www.ietf.org/rfc/rfc.2384.txt 6. Chen, S.: Routing Support For Providing Guaranteed End-To-End Quality-Of-Service. Ph.D. thesis, Univ. of IL at Urbana-Champaign (1999), http://cairo.cs.uiuc.edu/papers/SCthesis.ps 7. Gupta, G.K.: Introduction to Data Mining with Case Studies, pp. 218–244. PHI (2006) 8. Chakrabarti, S., Mishra, A.: QoS Issues in Ad Hoc Wireless Networks. IEEE Communications Magazine, 142–148 (2001) 9. Bhatti, S.N., Crowcroft, J.: QoS-Sensitive Flows: Issues in IP Packet Handling. IEEE Internet Computing, 48–57 (2000) 10. Calduwel Newton, P., Arockiam, L.: Route Optimization Mechanisms for Internet Applications in Mobile Networks: A Survey. International Journal of Future Generation Communication and Networking 3(3), 57–69 (2010) 11. Calduwel Newton, P.: A Contemporary Technique to Guarantee Quality of Service (QoS) for Heterogeneous Data Traffic. In: Proceedings of the International Conference on Information Security and Assurance. IEEE CS, pp. 210–213 (2008) 12. Calduwel Newton, P., Arockiam, L.: A Novel Prediction Technique to Improve Quality of Service (QoS) for Heterogeneous Data Traffic. Journal of Intelligent Manufacturing (2009)

Hegemonic Power and Technology Advancement O.F. Bahrisch and Jin-Suk Kim Namseoul University, Department of International Management {Bahrisch,mktjskim}@gmail.com

Abstract. During the past 600 years the world has seen changing hegemonic powers, situations in which one country dominates the world economically, intellectually and militarily. Since 1945 this attribution is undoubtedly true for the USA. A hegemonic change always occurred after a period of global war. Is the current ‘global war of terror’ such a global war that might cause a new hegemon to emerge? This paper summarizes the existing literature on the debate over the emergence of a new hegemon and analyses two possible scenarios a) the USA stay the dominating power, b) a new hegemon arises and one of the aspirants of the hegemonic throne, take over the leadership position.

1

Theory of Hegemony

In order to understand the present day hegemonic situation in helps to draw on the field of history. Modern European history (the period after the Middle Ages beginning at the end of the 15th century) has seen four full hegemonic cycles so far, with the fifth cycle still in progress. The first superpower in modern history was Portugal. Growing out of the middle ages, a period of decline for almost one thousand years, Portugal was a the first nation that spearheaded the Age of Discovery and dominated the world from about 1494 on. Portuguese explores, like Vasco da Gama, opened trade routes to Africa and India and established trading posts protected by a strong navy. Portuguese dominance lasted until 1580, when The Netherlands took over the hegemonic throne. The Netherlands profited form mass immigration of highly skilled craftsmen and rich merchants. Protestants from Bruges, Ghent and Antwerp (main trading ports at that time) where forced out after Spain conquered the cities, and settled in Amsterdam. From Portugal and Spain arrived Sephardi Jews that fled from religious prosecution. A highly skilled workforce and a strong financial endowment combined with a cheap source of energy (the windmill) and efficient transportation (canals) brought the Dutch Golden Age. The sawmill enabled dutch shipbuilders to construct new ships in unprecedented numbers that where used for trading and military use. The purpose of the dutch navy was mainly to protect shipping lanes and securing uninterrupted trade. An impressive example of the dutch naval might was given in 1607, when the Dutch navy attacked the Spanish fleet in Gibraltar wiping it out within four hours. The Dutch opened up Japan for trade, secured trade with the

T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 562–572, 2011. © Springer-Verlag Berlin Heidelberg 2011

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English colonies in North America, and dominated trade between European countries. Dutch domination lasted for about 110 years until 1690. A serious of wars and internal disputes weakened Holland to the advantage of two of its neighbors: France and England. France aimed to control Continental Europe while England focused on control of the seas. Holland was not strong enough to keep its colonies in North America and lost New Amsterdam (which is called New York from 1667 on) to England. In relative safety, protected by the English Channel, England grew in the slipstream of Holland's colonial efforts into the British Empire with control over India and North America. Until about 1792 Britain was successfully defending its position as the hegemon. In 1776 the Thirteen Colonies in North America declared its independence and drew Britain in the costly American War of Independence. The power of Britain in decline, France was able to regain its might and started to control the continent. Driven by an ambitious emperor, Napoleon, and his Grande Armée France was able to influence the power of balance to its advantage. But Britain was not finished. Fueled by the Industrial Revolution, Britain became the first industrialized country and was able to maintain its fleet to control its shipping lanes and most of its colonies. Between 1792 and 1914 Britain shared the status of a superpower with France. World War I and II changed the power balance a fifth time and left the US as the only aspirant of the hegemonic throne with an intact economy. As the Omaha WorldHerald aptly noted in 1986 "There were 20 million Russians dead, Europe was flattened, Asia was flattened. The reason why we were so fantastic as exporters for 30 years was because there wasn't anybody else. It's like having the Chicago Bears play against a Brownie troop and then being proud of the score we've run up."(Gauger, 1986, p. 43). 1.1

Realist School

The study of hegemony has produced four main theoretical directions that can be divided in 2 schools of thought. The first school of thought is the Realist School with its two theoretical approaches to hegemony: the Theory of Hegemonic Stability and the Power Transition Theory. The intellectual roots of the Realist School reaching back in the 16th century with the seminal book of Niccolo Machiavelli "The Prince". In the first sentence of the first chapter Machiavelli unveils the ultimate source of power: the state (Machiavelli, 1961, p. 8). Following that logic there can be no actor above the state and states are in free competition with each other with no referee defining or enforcing any rules. Therefore, from a Realist perspective the world is made up of antagonists (states) with the ultimate goal to enhance their position, if necessary by any means, under the condition of national survival. Therefore the focus of analysis of the Realist School is politics, especially politics between states that can be enforced be a strong military. The main idea of Hegemonic Stability Theory is that the best possible condition for economic prosperity, peace and stability only exists when it is enforced by a strong hegemon. One single dominant state dictates the rules of the game and punishes deviators. One reason for the superiority of a system with one strong a hegemon over

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a system with numerous weak but equal countries is the successful provision of a solution for the public good problem (see Kindleberger, 1986 p. 205). This gametheoretic approach states that order in an international system can only be provided at certain cost. In an system with many small but weak states, no state would contribute to this cost. Free-riding would be the rational pattern of behavior. Without a strong hegemon no country could be excluded from using an orderly system and no country could be forced to contribute to this order. If everybody is using and nobody maintains, the system will ultimately collapse and fail. For example, international trade depends much on sea transportation. If international shipping lanes would be threatened by pirates (like off the Somali coast nowadays) a strong navy is required to secure sea bound trade. Every trading nation would have an advantage of pirate-free waters but no country would be willing to bear the cost to send its own navy. Only a strong hegemon could enforce order and distribute the costs. Interestingly, three of the main schoolers of Hegemonic Stability Theory argued that the U.S. lost its hegemonic status at the end of the 1960s (Kindleberger, 1981), in the mid 1970s(Gilpin, 1975), or are a declining hegemonic position since the 1970s (Webb & Krasner, 1989). Power Transition Theory, initiated by A.F.K. Organski (Organski, 1958), deals, unlike the name might suggest, less with hegemonic succession but "... is mainly concerned with the explanation of the onset international conflict" (Clark, 2011, p. 14). Power Transition Theory models the international system of states into a set of hierarchies with five degrees of power (Kugler & Organski, 1989, p. 174). The five degrees of power distinguish a dominant nation, great powers, middle powers, small powers and colonies. The first place and most powerful country is the dominant nation. Below the dominant nation are the great powers. The great powers are not a match for the dominant nation in any point of time during its reign but might do so in the future during a period of hegemonic change. The next dominant power will be among the great powers. Middle powers and small powers are accordingly down in this hierarchy. Colonies, the last place in this order, have disappeared and have no real corresponding country today. The important point in the Power Transition Theory is the level of dissatisfaction of states in different hierarchy levels. As lower in the hierarchy a country is, as higher is level of dissatisfaction with the present situation. The only country that is free from dissatisfaction is the dominating country; all other countries would like to see a change of the present situation and would opt for a different hegemon, preferably themselves. As lower in the hierarchy a country is as more it wishes to initiate change, but unfortunately, countries of a low place in this hierarchy have the least means to do so. Transition Theory provides a good tool to judge the stability of a given system. Instability is only likely when two potential rivals grow into the same size (Kugler & Organski, 1989, p. 175): "As a dissatisfied great nation approaches parity by growing in power more rapidly than the dominant nation, instability increases and so does the probability of conflict. The closure of the power gap engenders fear on the part of the leaders in the dominant nation that the challenger will (1) surpass the dominant country, (2) become increasingly unwilling to accept a subordinate position in the international order, and

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(3) challenge the leadership and rules of the international order. And this, in fact, is very likely what the challenger will do. Thus, power transition argues that competition for dominance in the international order is finally joined when the dissatisfied party anticipates greater benefits and privileges if a conflict is successfully waged than if the current status quo is preserved". It should be noted that Power-of-Balance Theory (Morgenthau, 1978) suggests a contrary outcome: only if power is balanced between at least two nations a stable system is possible. 1.2

Systemic School

The second school of thought is the Systemic School. The Systemic approach to the study of hegemony takes off with one important insight of the second half of the 20th century. That is that states might be the main actors on the world stage but the part of the director takes someone else: organizations. Therefore scholars of the Systemic School take their seat far from the stage in order to see as much as possible. They see the whole world as one system and they recognize that politics might trigger events of hegemonic importance but the driving force is the economy. The Systemic School "... renders political-military power merely a subordinate extension of economic power, and its use of a global unit of analysis - the capitalist world economy-relegates national, state actors to a backup role." (McCormick, 1990, p. 127). Carrying the spirit of the systemic approach, Long Cycle Theory came to popularity in the mid 80's by the works of George Modelski and George Thompson. One of their important insights is that the international system and most of the events in it (like war) move in recurring cycles (Modelski, 1987). Each cycle passes through four distinct phases (Modelski & Thompson, 1989, p. 24): 1. Macrodecision - a period of war in which the question for leadership is settled, 2. Implementation - a period with a dominating nation (global leader) that implements major new programs, 3. Agenda setting - a period in which questions about the legitimacy of the global leader its agenda are raised, 4. Coalitioning - a period of de-concentration in which leadership reaches a low point and challengers recognize opportunities to spring into action. The idea that the international system moves in recurring cycles parallels Nikolai Kondratieff's suggestion, that economies move in cycles, or waves, of about 50 years (Kondratieff & Stolper, 1935). This theory was developed further by Jospeph Schumpeter, who suggested that the trigger for a new cycle to begin is a 'leading sector' (Schwartz, 1994). A leading sector is a cluster of innovation that creates new products with high demand and new and cheaper forms of energy. Sectors at the periphery of that leading sector would profit from the boom, developing innovative new products and lifting the whole economy. Following the finding that the international system as well as global economic development move in cycles it comes to mind that both types of cycles might be

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correlated. Combining Carlo Cipolla (1970, p. 5) and Robert Gilpin's argumentation (Gilpin, 1981, p. 156) it might be suggested the following causal chain: 1) A nation that ascends the hegemonic throne provides a higher standard of living for its people due to superior access to raw materials, trade surplus and the economic fruits of the exploitation of Schumpetererian leading sectors. 2) Nations at the rim of the hegemon display tendencies of emulation and threaten the high standard of living of the leading nation. 3) The Hegemon is forced to allocate more recourses from the private sector to the public sector to maintain a political and military superiority in fending off emulating nations. 4) Protection costs of the hegemon rise due to an internal high standard of living. 5) Allies of the hegemon tend to take free rider positions and benefit form measures that are necessary for the hegemon to keep its superiority. 6) The equilibrium of the costs to maintain a leading position is reached: costs increase exponentially compared to benefits. 7) Snowball effect sets in and more and more resources are allocated away from the efficient private sector toward military spending, which increases the (relatively inefficient) public consumption. 8) The gap of superiority decreases until the reigning hegemon is too weak to defend its position. Following that, long economic waves and the long cycles of the international system should be highly correlated. (Rosecrance, 1987), studying this relationship, found only a rather week correlation. He concluded that only "every other Kondratieff upswing is associated with Modelski's global war phase" (Modelski's initial phase in each cycle) (p.291). Consistency is fully missing from the 1970s on, when economic development and war periods are completely out of step (Rosecrance, 1987, p. 291). Similar to Long Cycle Theory, the focus of the World-Systems Theory is the global system. Nation states are part of the world system but not center of the analysis. The primary representative in the advancement of the World-Systems Theory is Immanuel Wallerstein. McCormick (1990, p. 126) gives a splendid summary of Wallerstein's World-Systems Theroy: "... Wallerstein posited the existence since 1500 of a world system or capitalist world economy. Its chief actors have been transnational business organizations and operations, driven by the premise that capitalism functioned most efficiently and thus most profitably when its universe of options was sufficiently large and fluid for capital, goods, services, and people to move from one place to another to secure greater returns, even if that place was both distant and foreign. Its chief process was the spatial expansion of European capitalism at the expense of autarkic empires in Russia, China, and the Near East, as well as the subsistence, village minisystems of Eastern Europe, Ireland, the Americas, Africa, and Asia. Its chief consequence was a system of three successive zones, each performing a specialized function in a complex, hierarchical, international division of labor. Core countries (the First World) monopolize high-tech, high-profit enterprises. The periphery (the Third World)

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specializes in primary production of agricultural commodities and raw materials: "hewers of wood and carriers of water." In between, the semiperiphery (the Second World) performs inter- mediate functions of transport, local capital mobilization, and less complex, less profitable forms of manufacturing". It is important to note that Wallerstein's analysis starts off from an sociological standpoint ("social or societal behavior" (Wallerstein, 1979, p. 665)), less form an economical perspective (Wallerstein, 1998, p. 103). Wallerstein approaches the topic through an interpretation of perception of a society (Wallerstein uses the term "Weltanschauung" (1979, p. 665)). Human greed has lead to "the capitalist world system..." which "...has been well established now for some 400-500 years. It covers the globe" (Wallerstein, 1992, p. 616). "Virtually everyone tends to see capitalism as the system in which humans seek to transform (or "conquer") nature in an eternally expansive way and to amass ever larger residues of this expansion. Whether it is David Landes speaking of "Prometheus unbound" or Carlyle deploring the "cash nexus" or Marx analyzing the search for an endless accumulation of capital or Keynes's references to the "animal spirits" of (Schumpeter's) entrepreneurs or, as we have already seen, the description of the achievement of intensive growth as a "miracle," the phenomenon that is being observed takes the form of a hyper-bolic curve that knows no social limit" (Wallerstein, 1992, p. 567). Nations are in Wallerstein's analysis only a vehicle for businesses to achieve their goals for growth (access to markets, technology, labor, raw materials and so forth).

2

Hegemonic Change

The aforementioned four theoretical approaches to hegemonic power, despite belonging to different schools of thought and very different foci, identify one common trigger that sets in motion hegemonic change: extended military conflict. Gilpin refers to it as 'hegemonic war' (1988), Modelski/Morgan named it 'global war' (1985). "Throughout the history of the modern international state system, ascending powers have always challenged the position of the dominant (hegemonic) power in the international system—and these challenges have usually culminated in war (Layne, 2008, p. 16). A global or hegemonic war does not necessarily mean one single war, but a period of warfare with the participation of major powers. World War I and II have been such a period of warfare which, and that is throughout the the different theoretical school undisputed, lifted the United States on the hegemonic throne. Modelski/Morgan (1985) estimate that World War II consumed possibly one-third of the total world product between 1939 and 1945. They further quote Rasler and Thompson (1983) and Thompson and Zuk (1982), which analyzed statistical time series since 1694 and found that all earlier periods of global warfare were marked by extraordinary abrupt and permanent discontinuities in public debt levels of the hegemon and other participating nations. Additionally they found evidence for

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significant changes of price levels and economic growth. Interstate wars occurring outside a period of global warfare did not show similar changes. In order to determine whether the current hegemonic cycle has reached its end and a new cycle starts, it is necessary to identify the current period of war as a global war. To qualify as a global war two measures must be met: first, engagement of the major powers and, second, extreme and permanent changes of public debt levels. The first measure is probably met. The current period of war is named Global War on Terror (manly fought in Afghanistan since 2001 and in Iraq since 2003) and involves with varying intensity all major nations. As Long Cycle Theory suggests, the major burden is carried by the current hegemon, the US, while the other participating nations rather reluctantly show willingness to shoulder arms and move to the hegemon's aid in the War on Terror. Each of the participating nations, but the US, have a strong incentive to free ride and the recent withdrawal of allies from the battlefield (Great Britain from Iraq in 2009) shows that the allies are attempting to shift costs to the hegemon. The second measure, permanent changes of debt levels, is more difficult it estimate. Although the US is currently running an nominal record level of public debt, it does not appear to be historically outstanding. The War on Terror is costly but not yet costly enough for lasting debt level changes. Figure 1 shows the ratio of US government spending in relation to the GDP. The current level of government spending is still dwarfed be the level of public spending during the years 1943-1945, the entry of the US into World War II - the last part of the a period of global warfare. Other core countries (or First World countries, according to Wallerstein) do not yet show signs of permanent changes of their debt levels. It might be argued that Japan, with public debt about twice of its GDP - on the rise since the late 1990s - would qualify for signaling the end of an hegemonic cycle. But Japan still appears to be an isolated case. The question whether we reached the end of the current hegemonic cycle is difficult to answer, like all predictions of the future. But it is possible to discuss the possible developments with the highest likelihood to become reality. Therefore are in the following three scenarios discussed are most likely to theorize the future hegemonic development: the US stays the hegemonic power, a new hegemon arises, or the hegemonic concept becomes obsolete.

Fig. 1. US government spending in percent of gross domestic product (1929-2010) (Government consumption expenditures and gross investment/Gross Domestic Product). (own calculation).

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Scenario 1: USA continue to be Number One Despite the currently popular prediction of the decline of United States, consultation of the present facts might suggest that the US, nevertheless remains in the hegmonic seat. During past hegemonic cycle changes have other core countries (World-System Theory), nations at the rim (Long-Cycle Theory) or great nation (Hegemonic Stability Theory) challenged the hegemon by attempting to match his military power (e.g. German-British naval arms race until 1914). The US defense spending had 2009 an unparalleled share of the entire world defense spending of 43 percent (SIPRI, 2009, 349). But even with an defense budget of USD 661bn, this sum only accounts for 4.3 percent of the US GDP. That makes the US a military superpower "on the cheap" (Kennedy, 2002, p. 13). The Stockholm International Peace Research Institute estimates second largest defense spender after the US as China with about USD 100 bn. Especially, when paying attention to the US naval power it becomes clear that for the next two decades there is no close rival to fear. An aircraft carrier battle group is probably the strongest military instrument a nation may posses. It is deployable with great flexibility and enormous firepower. The US maintain all of the 11 aircraft carrier of the larges class (supercarrier). As Kennedy (2002, p. 8) puts it: "No equivalent of power to a US carrier task force exists in the world; the few UK, French and Indian carriers are minuscule by comparison, the Russian ones rusting away." Military power alone does not keep a hegemon on the throne. Considerable political power is necessary as well in order to stay in control. In 1999, Samuel Huntington aptly summarized the overwhelming political ability to bend others to its will, but it is still valid today (p. 39): "In the past few years the United States has, among other things, attempted or been perceived as attempting more or less unilaterally to do the following: pressure other countries to adopt American values and practices regarding human rights and democracy; prevent other countries from acquiring military capabilities that could counter American conventional superiority; enforce American law extraterritorially in other societies; grade countries according to their adherence to American standards on human rights, drugs, terrorism, nuclear proliferation, missile proliferation, and now religious freedom; apply sanctions against countries that do not meet American standards on these issues; promote American corporate interests under the slogans of free trade and open markets; shape World Bank and International Monetary Fund policies to serve those same corporate interests; intervene in local conflicts in which it has relatively little direct interest; bludgeon other countries to adopt economic policies and social policies that will benefit American economic interests; promote American arms sales abroad while attempting to prevent comparable sales by other countries; force out one U.N. secretary-general and dictate the appointment of his successor; expand nato initially to include Poland, Hungary, and the Czech Republic and no one else; undertake military action against Iraq and later maintain harsh economic sanctions against the regime; and categorize certain countries as "rogue states," excluding them from global institutions because they refuse to kowtow to American wishes".

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This impressive account is only true for the US it is difficult to imagine that there could be any nation that could come close to it in the two decades to come. Besides its superiority in military and political matters, the U.S. is also still the world’s economic powerhouse. The total output of the U.S. economy amounts to about one fifth of the entire world economy. Scenario 2: A New Hegemon Arises Despite of the apparently strong looking hegemon there are certain indicators that might be interpreted as signs of decline. Militarily the U.S. reached its limit. Engaging in two costly wars (Iraq and Afghanistan) left the U.S. with little room to be involved in a third war without risking to be overstretched. Economically has the aftermath of the financial cries 2008 left its mark in the shape of the highest level of public debt since World War II. That limits the monetary and fiscal policy of the government and casts the dark shadow of inflation over the U.S. economy. Fergueson (2010) found that increasing debt is responsible for the decline of most empires. The US might be no exception. Reinhart and Rogoff (2010), analyzing the relation of public debt, economic growth and inflation with a huge dataset reaching back until 1790, found a level of debt that exceeds 90% of the annual GDP has a negative impact on economic growth. They stated that: "above the threshold of 90 percent, median growth rates fall by one percent, and average growth falls considerably more" (p. 2). That might be interpreted that the current fiscal policy has a high possibility of being unsustainable. Currently a large share of US public debt is financed by China. If for any reason, China reduces its level of holdings in US government bonds, other financiers have to be found. Very likely is that US households will have to step into the breach, crowding out capital that would have otherwise flown into more productive private projects. Especially for future growth perspectives it is decisive whether money is used for the government's debt service or if it would finance innovations. A further point is that a strong military and economy might be of less importance in the future. It seems that the ability to establish networks (military, science, diplomacy and economy) might be much more contributing to strong influence. The US have lost some of this ability while other nations have gained. Probably the most discussed heir to the hegemonic throne is China. Its growing interest in raw material rich Africa, its growing military spending and its high economic growth rates lifted China to the position of the first possible successor. Tkacik (2007) estimated that the Chinese defense spending is actually way higher than the reported one. He assumes that in PPP China is spending as much as the U.S.

3

Hegemony and Technology

In May 1890 Theodore Roosevelt red “The Influence of Sea Power Upon History, 1660-1793” by Alfred Thayer Mahan (Morris, 2001, p. 433), a new book that probably not only influenced the future President of the United States, but also Kaiser Wilhelm II and Japan before World War II at aiming to build not only a strong navy

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but a navy with fierce striking power that leaves competing nations far behind. Mahan (2004) suggested that a nations security and international greatness depended on the navy, the technological superiority of ships and weaponry, and the concentration of various controlling spots that would be essential to global commerce. Mahan argued that striking with a superior force against certain pressure points could paralyze whole regions more effectively than any army. The power of great forces on land would be left useless compared with the concentrated use of a technologically advanced navy. Mahan correctly identified a source of ‘greatness’ but delivered a less correct explanation. The concentration of a society’s recourses on the development of certain innovative military instruments does not necessarily lead to a hegemonic position. It is rather being first in creating a cluster of technologies in leading sectors with spillover effects to the whole economy that put a nation on the hegemonic throne (Schumpeter, 1939, 1942). The basic idea is that innovation changes one sector into a leading sector, a cluster of innovation that creates new products with high demand and new and cheaper forms of energy and innovation. Sectors at the periphery would profit from the boom as well and might generate another key innovation that starts the cycle anew (Schwartz, 1994, p. 69). A hegemonic nation is therefore always a technological hegemon as well.

References Cipolla, C.M.: The economic decline of empires. Methuen, London (1970) Clark, I.A.N.: China and the United States: a succession of hegemonies? International Affairs 87(1), 13–28 (2011) Ferguson, N.: Complexity and Collapse - Empires on the Edge of Chaos. Foreign Affairs (89), 18–32 (2010) Gauger, A.J.: U.S. Business must Revolutionize, vol. 43. Omaha World-Herald, Omaha (1986) Gilpin, R.: U.S. power and the multinational corporation: the political economy of foreign direct investment. Basic Books, New York (1975) Gilpin, R.: War and change in world politics. Cambridge University Press, New York (1981) Gilpin, R.: The Theory of Hegemonic War. Journal of Interdisciplinary History 18(4), 591–613 (1988) Huntington, S.P.: The Lonely Superpower. Foreign Affairs 78(2), 35–49 (1999) Kennedy, P.: The Greatest Superpower ever. New Perspectives Quarterly 19(3 (Winter)), 8–18 (2002) Kindleberger, C.P.: Dominance and Leadership in the International Economy: Exploitation, Public Goods, and Free Rides. International Studies Quarterly 25(2), 242–254 (1981) Kindleberger, C.P.: The world in depression, pp. 1929–1939. (Rev. and enl. ed.)University of California Press, Berkeley (1986) Kondratieff, N.D., Stolper, W.F.: The Long Waves in Economic Life. The Review of Economics and Statistics 17(6), 105–115 (1935) Kugler, J., Organski, A.F.K.: The power transition: A retrospective and prospective evaluation. In: Midlarsky, M.I. (ed.) Handbook of war studies, pp. 171–194. Unwin Hyman, Boston (1989) Layne, C.: China’s Challenge to US Hegemony. Current History 107(705), 13–18 (2008) Machiavelli, N.: The Prince (G. Bull, Trans.). Penguin Books, Harmondsworth (1961)

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Mahan, A.T.: The influence of sea power upon history, pp. 1660–1783. Barnes & Noble Books, New York (2004) McCormick, T.J.: World Systems. The Journal of American History 77(1), 125–132 (1990) Modelski, G.: Long cycles in world politics. University of Washington Press, Seattle (1987) Modelski, G., Morgan, P.M.: Understanding Global War. The Journal of Conflict Resolution 29(3), 391–417 (1985) Modelski, G., Thompson, W.R.: Long Cycles and Global War. In: Midlarsky, M.I. (ed.) Handbook of war studies, pp. 23–54. Unwin Hyman, Boston (1989) Morgenthau, H.J.: Politics among nations: the struggle for power and peace, 5th edn. Knopf, New York (1978) Morris, E.: The rise of Theodore Roosevelt. Random House, New York (2001) Organski, A.F.K.: World politics. Knopf, New York (1958) Rasler, K.A., Thompson, W.R.: Global Wars, Public Debts, and the Long Cycle. World Politics 35(4), 489–516 (1983) Reinhart, C., Rogoff, K.: Dept and Growth Revisited. Munich Personal RePEc Archive, Munich (2010) Rosecrance, R.: Long Cycle Theory and International Relations. International Organization 41(2), 283–301 (1987) Schwartz, H.M.: States versus markets: history, geography, and the development of the international political economy. St. Martin’s Press, New York (1994) Thompson, W.R., Zuk, L.G.: War, Inflation, and the Kondratieff Long Wave. Journal of Conflict Resolution 26(4), 621–644 (1982) Tkacik, J.J.: A Chinese Military Superpower? The Heritage Foundation. Asian Studies Center, The Heritage Foundation, Washington (2007) Wallerstein, I.: Kondratieff up or Kondratieff Down? Review (Fernand Braudel Center) 2(4), 663–673 (1979) Wallerstein, I.: The West, Capitalism, and the Modern World-System. Review (Fernand Braudel Center) 15(4), 561–619 (1992) Wallerstein, I.: The Rise and Future Demise of World-Systems Analysis. Review (Fernand Braudel Center) 21(1), 103–112 (1998) Webb, M.C., Krasner, S.D.: Hegemonic Stability Theory: An Empirical Assessment. Review of International Studies 15(2), 183–198 (1989)

Adaptive Hand Color Extraction Using Wave Motion Detection Kyungboo Jung1, Il-Moek Kim1, and Byung-Uk Choi2 1

Dept. of Electronics and Computer Eng., Hanyang University Division of Computer Science & Eng., Hanyang University {kbjung,imkim,buchoi}@hanyang.ac.kr

2

Abstract. Correct hand segmentation in image sequences is the most important aspect of hand-gesture interfaces. Existing hand-segmentation methods may be classified into two general approaches. One uses predefined skin color and the other uses a learning process. The skin color-learning method gives good results but its excessive processing time precludes its use in real time. In the predefined color approach, automatically modifying the predefined color when the environment changes would increase convenience and efficiency. Identifying a change in the environment usually requires user intervention. We propose a method by which the user can easily modify the predefined skin color any time in any environment simply by waving his/her hand in front of the camera. The hand interface system detects the waving hand region and efficiently extracts the hand color for use in hand-gesture interfaces.

1

Introduction

The development of hand-gesture interfaces has attracted recent interest in many areas. Such interfaces have two well-known benefits. First, they require only a few devices to detect and recognize hand gestures. Second, they are intuitive for humans to use. Hardenberg and Bérard [1] introduced several applications using gestures: virtual painting on a wall, controlling presentations, and positioning virtual items. These applications help the user control a system without any physical contact so they can concentrate on the task at hand rather than the actual control process. O’Hagan and Zelinsky [2] and Quek et al. [3] described controlling a cursor on the screen using hand gestures, one of the simplest applications. Mistry and Maes [4] described an application in which simple hand gestures can be used to take a photograph. A user can examine the details of virtual objects using hand gestures instead of a keyboard to rotate and translate objects on the screen [5]. One of the simplest and best methods of hand detection used in many applications is thresholding by skin color. This approach is relatively easy to implement and is quite fast. Skin color can be described in many color spaces. Because the HSV color space is more efficient for hand segmentation than RGB or any other color space [6], we use the HSV color threshold information to segment hands. A method of learning the hand color is necessary for a stable hand-recognition system because the hand color depends on the illumination. Once the skin color is detected, the hand interface T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 573–580, 2011. © Springer-Verlag Berlin Heidelberg 2011

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system segments the hand and extracts skin color from the detected hand region for use in the next frame [5]. However, the learning process is very complex and time consuming. Another approach is to train the system about the hand color before the user actually uses the hand interface. The user simply positions his/her hand at some specific location in the scene so the system can extract the skin color and process it before the system is used [6]. Even though the predefined skin color can be modified by the user whenever the environment changes, this is a complicated interruption. We propose a method that uses the skin color extracted from the user’s simple hand motion in video images. When the user realizes that the hand interface system is no longer able to detect hands, he/she simply makes a gesture that can be detected easily in any environmental condition. The proposed method makes modifying the predefined skin color easy and efficient under any conditions. In the following sections, we briefly describe an overview of our method. Section 2 presents waving hand-detection and robust color-extraction methods. Section 3 describes the results of hand segmentation and fingertip estimation using our method. Section 4 presents our conclusions.

Fig. 1. Flowchart of proposed method

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Proposed Method

In this section, we describe our approach for obtaining hand color semi-automatically. 2.1

Flowchart of Proposed Method

The proposed method of setting the hand color consists of two steps. The first step includes detection of the hand-waving motion and necessary preprocessing. We use the difference between two successive images to detect motion in image sequences. A residual image is created before the estimation phase for detecting successive finger movements. This residual image is a blend of the current differentiated image and the previous residual image. We can estimate and detect hand-waving gestures using these differentiated images. The second step is color extraction. The hand-waving estimation results in a color extraction circle. Hand color is extracted and accumulated until the number of clustered color samples reaches a specific threshold. Figure 1 presents a flowchart of our method. 2.2

What Is Waving Hand?

We cannot recognize hands without initial information about the skin color. To provide this, the user simply waves his/her hand in front of the camera. The arrow in Figure 2 shows how a waving hand motion causes finger movement. The figure also shows how the hand region below the finger motion can be described. The hand region contains much information about hand color in that specific environment. The red circle in the figure represents the hand region and the red point represents the center point of the hand.

Fig. 2. Waving hand gesture and hand region

2.3

Residual Image

Analyzing the difference between two successive frames is an easy way to detect a moving object in image sequences. However, the method must deal with the constant unexpected background motion due to normal human movement with portable interface devices such as head-mounted cameras. To remove this unexpected motion

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in the image sequences, we estimate camera movement between two successive frames and calculate a transform matrix. The motion can be reduced by warping the previous image, making it possible to distinguish between large object movements and small camera movements. We use the global motion estimation technique to calculate the transform matrix. We use a residual image to detect stable movements such as the fingers of a waving hand in the image sequences. A residual image is a blend of the previous residual image and the current difference image calculated as shown in Eq. (1): R

1

r R

rF .

(1)

where and are the current and previous residual images, respectively; is the current difference image; and is the blend ratio. Figure 3 shows how residual images are generated; panels (a), (c), and (e) show the original images in the sequence, and panels (b), (d), and (f) show the residual images. If the movement to extract hand color is very stable in the image sequences and shaped in the residual image at time , the movement region is estimated in the residual image. Finally, the hand color is extracted from the appropriate region.

Fig. 3. Difference images are blended top to bottom to form Rt. Camera images (a), (c), and (e) are at times t0, t1, and t2, respectively, while the corresponding residual images are in (b), (d), and (f).

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577

Estimation of Waving Hand

A waving hand can be estimated using Eq. (2), which is a formula known as kernel density estimation (KDE) [7]: D x

∑N

N

K

R x .

(2)

is the estimated function, which is a probability density function of the where is a residual image; is the number of waving hand; is the kernel function; pixels in the image; and is the width of the kernel. The rough two-dimensional function denotes how much movement occurred in the scene during the time in question. The maximum point in the density function is the most active movement positions in the scene. The center point of the function is formulated using Eq. (3): µ

max D x .

(3)

where µ denotes the center point of the moving object. The standard deviation σ is calculated to determine the radius of the color extraction circle using Eq. (4): σ

N

∑

x

µ

T

x

µ D x

/

.

(4)

Figure 4 shows a residual image and the result of waving hand estimation. Figure 4(a) shows a residual image for estimation and Figure 4(b) shows the estimated density function. The circle indicates the most probable region for the waving hand to exist. We experimented with several types of kernel such as uniform kernel, Gaussian kernel, and the difference between two Gaussian kernels. We determined experimentally that the difference between two Gaussian kernels defined in Eq. (5) is suitable for detecting only waving hand movement:

Fig. 4. (a) Residual (b) the result of waving hand estimation

x

N

e

e

.

(5)

where K is the kernel, N is the number of pixels in the kernel, and σ is the standard deviation of a Gaussian kernel.

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Keyframe Selection and Color Extraction

In extracting hand color from image sequences, frames that exhibit a stable central point with a certain size of extraction circle are selected as keyframes. These have the correct hand color extraction circle. The colors extracted from the circle have the highest probability of being hand colors. To determine which frames are keyframes, we use the probabilistic model defined by Eq. (6): ∑

P S

C .

(6)

where P is an accumulation histogram and S is a random value that is true if frame t is chosen to extract the color or false if the frame is not chosen. The C is 1 if frame t meets several conditions for extracting hand color; otherwise it is 0. Eq. (7) is used to determine whether frame t is suitable for color extraction: 1, C 0,

, ,|

|

∆,

∆

.

(7)

otherwise

If the estimated waving hand value at central point D µ is greater than the threshold d, the central point has not moved more than µ∆ , and the standard derivation is and σ and has not changed more than σ∆ , then this between specific sizes σ frame t has the correct extraction circle positioned inside the hand in the image. The color information inside the circle is used in the next steps. We use the HSV color space because of its convenient characteristics. We take the colors in the circle to be the hand color. However, the circle may also contain a small proportion of nonhand colors such as the background. Therefore, colors are clustered in the HSV color space. The group with the most samples represents the hand color. For greater reliability and correct results, the colors extracted from only one frame are not sufficient. The colors extracted in each keyframe are accumulated as one color histogram. This makes the ratio of hand color increase dramatically in the accumulation process so that the clustering result is reliable. In each accumulation process, one color group that represents hand color has a significantly higher number of samples than any other group. The accumulation step can be terminated by checking the ratio of hand color to the others.

3

Experiments

To segment hands, we use the region-growing method that grows segmented regions by checking for similar colors among the seed pixels. Figure 5 shows the results of segmentation using our method along with the generated histogram, where panel (b) shows the segmented hand and detected fingers by applying the result of our method to the original video image in panel (a). Panels (c) and (d) show samples extracted from video images and the full HSV histogram that we used, respectively.

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Fig. 5. Hand segmentation result using the color that extracted by our method and generated histogram. (a) Original video image (b) Segmented image (c) HSV color histogram with extracted color samples. (d) A full HSV color histogram.

4

Conclusions

We proposed a method that chooses a hand color by extracting a color from the densest hand-waving movement region for use in hand-gesture interfaces in various environments. Our method includes using the difference between two Gaussian kernels for KDE. With hand-waving detection, color extraction occurs only when the user waves his/her hand in front of the camera. This results in robust and efficient hand color extraction that is suitable for use in hand-gesture interfaces in any environment.

References 1. Hardenberg, C., Bérard, F.: Bare-hand human-computer interaction. In: Proceedings of the 2001 workshop on Perceptive user interfaces. ACM International Conference Proceeding Series, vol. 15, pp. 1–8 (2001) 2. O‘Hagan, R., Zelinsky, A.: Finger Track – A Robust and Real Time Gesture Interface. In: Australian Joint Conference on Artificial Intelligence, pp. 475–484 (1997) 3. Quek, F., Mysliwiee, T., Zhao, M.: Finger mouse: A freehand pointing interface. In: International Workshop on Automatic Face and Gesture-Recognition, pp. 372–377 (1995) 4. Mistry, P., Maes, P.: SixthSense – A Wearable Gestural Interface. In: The Proceedings of SIGGRAPH, Asia (2009)

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5. Lee, T., Hollerer, F.: Handy AR: Markerless Inspection of Augmented Reality Objects Using Fingertip Tracking. Wearable Computers. In: 11th IEEE International Symposium on In Wearable Computers, pp. 83–90 (2007) 6. Xiaoming, Y., Dong, G., Ming, X.: Hand image segmentation using color and RCE neural network. Robotics and Autonomous Systems 34(4), 235–250 (2001) 7. Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Stat. 33, 1065–1076 (1962)

A Dynamic Resource Allocation Model for Virtual Machine Management on Cloud* Chao-Tung Yang**, Hsiang-Yao Cheng, and Kuan-Lung Huang Department of Computer Science, Tunghai University, Taichung 40704, Taiwan [email protected], {hsycheng,peter760504}@gmail.com Abstract. It is current issue to achieve the goal of management multiple virtualization platforms and multiple virtual machine migrations across physical machines without disruption. We discuss that ensure load balance when multiple virtual machines run on multiple physical machines. In this paper, we present a system which is implementation of optimization with Dynamic Resource Allocation (DRA) dealing with virtualization machines on physical machines. And practice DRA method in this system. The results confirmed that the virtual machine which loading becomes too high, it will automatically migrated to another low loading physical machine without service interrupting. And let total physical machine loading reaching balance. Keywords: Virtualization, VM Migration, Dynamic Resource Allocation, Cloud Computing.

1

Introduction

A virtual machine was originally defined by Popek and Goldberg as ―an efficient, isolated duplicate of a real machine [1]. Virtual machine operates in a virtual platform can be considered an independent operating system. In addition, due to the independence of this individual, the gust operating system can operate from the original virtual platform, and to maintain the operation of the original schedule. And this move action, generally referred to as Migration [2-23, 30-38]. The migration is dynamic: if a move action, cause the system to pause time is extremely small enough, it cannot be aware of user using the system services. Setting up virtual machine cluster environment on physical machine can provide stable service, but this environment often includes unpredictable workloads. Currently most systems of virtual machine are loading balanced statically, as the systems where the load changes dynamically over time run it is inevitable that some physical hosts with higher load, so for throughput and response time of a system to be maximized it is necessary for load to be distributed to each part of the system in proportion to their computing/IO capacity [3, 20, 24-27]. *

This work is supported in part by the National Science Council, Taiwan R.O.C., under grants no. NSC 100-2218-E-029-001 and NSC 100-2218-E-029-004. ** Corresponding author. T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 581–590, 2011. © Springer-Verlag Berlin Heidelberg 2011

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In this paper, we develop an adaptive resource control system on a virtual machine cluster that dynamically adjusts the resource shares to individual tiers in order to meet DRA (Dynamic Resource Allocation) goals. There are three efforts in this paper: (1) supporting DRA mechanism; (2) implementation OpenNebula management tool on a web-based interface; and (3) efficiently isolating the cluster workloads.

2

Virtualization

Virtualization is simply the logical separation of the request for some service from the physical resources that actually provide that service. In practical terms, virtualization provides the ability to run applications, operating systems, or system services in a logically distinct system environment that is independent of a specific physical computer system. Obviously, all of these have to be running on a certain computer system at any given time, but virtualization provides a level of logical abstraction that liberates applications, system services, and even the operating system that supports them from being tied to a specific piece of hardware. Virtualization, focusing on logical operating environments rather than physical ones, makes applications, services, and instances of an operating system portable across different physical computer systems. Virtualization can execute applications under many operating systems, manage IT more efficiently, and allot resources of computing with other computers [1, 4-7, 9, 11, 12, 28].

3

Related Work

Recently, the dramatic performance improvements in hypervisor technologies have made it possible to experiment with virtual machines (VM) as basic building blocks for flexible computational platforms. Many research efforts have been introduced to reduce the overhead of the networking in virtualized environments. Data transfer between server nodes frequently occurs in parallel and distributed computing systems, the high overhead of networking may induce significant performance loss in the overall system. Jae-Wan Jang [19] use virtualized parallel and distributed computing systems are rapidly becoming the mainstream due to the significant benefit of high energyefficiency and low management cost. Processing network operations in a virtual machine incurs a lot of overhead from the arbitration of network devices between virtual machines, inherently by the nature of the virtualized architecture. Wang L et al.[19] propose a new methodology for Grid computing – to use virtual machines as computing resources and provide Virtual Distributed Environments (VDE) for Grid users. Paul Willmann [36] presents hardware and software mechanisms to enable concurrent direct network access by operating systems running within a virtual machine monitor. It is declared that employing virtual environment for Grid computing can bring various advantages, for instance, computing environment customization, QoS

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guarantee and easy management. A light weight Grid middleware, Grid Virtualization Engine, is developed accordingly to provide functions of building virtual environment for Grids. VMware DRS [20] achieves an on-demand resource scheduling scheme for virtual machine cluster via migrating virtual machines among physical machines. In our scheme, the two measures are used simultaneously while reallocating resource of virtual machines within same physical machine is the first choice to get higher efficiency. Additionally, R. S. Montero [21] proposes a performance model to characterize these variable capacity (elastic) cluster environments. The model can be used to dynamically dimension the cluster using cloud resources, according to a fixed budget, or to estimate the cost of completing a given workload in a target time. This paper focuses VMs running on physical machines and use DRA technology to Implementation a virtualization environment of HPC.

4

System Implementation

The purpose of our proposed Dynamic Resource Allocation (DRA) is to reach the best balance between each physical machine. To avoid computing resources centralized on some specify physical machines, how to balance the resources becomes the most important issue. To achieve the maximum efficiency the resource must be evenly distributed. DRA manages resources allocation to a set of virtual machines running on a cluster hosts to the goal of fair and effective use of resources. Virtual machine placement and migration recommendations serve to enforce resource-based service level agreements, user-specified constraints, and loading balance maintenance across the cluster as workloads change, as shown in Figure 1. Load balancing seeks to improve the performance of a distributed system by allocating the workload amongst a set of cooperating hosts. Such system may attempt to ensure the workload on each host is within a small tolerance of the workload on all other physical hosts, or may attempt to avoid congestion of individual servers. Load balancing can be either centralized or distributed [29]. The OpenNebula core orchestrates three different management areas: image and storage technologies (that is, virtual appliance tools or distributed file systems) for preparing disk images for VMs, the network fabric (such as Dynamic Host Configuration Protocol [DHCP] servers, firewalls, or switches) for providing VMs with a virtual network environment, and the underlying hypervisors for creating and controlling VMs. The core performs specific storage, network, or virtualization operations through pluggable drivers. Thus, OpenNebula isn’t tied to any specific environment, providing a uniform management layer regardless of the underlying infrastructure. Besides managing individual VMs’ life cycle, we also designed the core to support services deployment; such services typically include a set of interrelated components (for example, a Web server and database back end) requiring several VMs. Thus, we

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can treat a group of related VMs as a first-class entity in OpenNebula. Besides managing the VMs as a unit, the core also handles the delivery of context information (such as the Web server’s IP address, digital certificates, and software licenses) to the VMs [35].

Fig. 1. Global load balancing algorithm concept

A separate scheduler component makes VM placement decisions. More specifically, the scheduler has access to information on all requests OpenNebula receives and, based on these requests, keeps track of current and future allocations, creating and updating a resource schedule and sending the appropriate deployment commands to the OpenNebula core. The OpenNebula default scheduler provides a rank scheduling policy that places VMs on physical resources according to a ranking algorithm that the administrator can configure. It relies on real-time data from both the running VMs and available physical resources. Figure 2 shows the system perspective. According to the previous works we build a cluster system with OpenNebula and also provide a web interface to manage virtual machines and physical machine. Our cluster system was built up with four homogeneous computers; the hardware of these computers is equipped with Intel i7 CPU 2.8 GHz, four gigabytes memory, 500 gigabytes disk, Debian operating system, and the network connected to a gigabit switch. The Dynamic Resource Allocation is an efficient approach to increasing availability of host machine. However, at present open source virtual machine management software merely provide a web interface for user managing virtual machine. Such as Eucalyptus [37] cannot accomplish load balance. When a part of virtual machines load increasing, it will affect all virtual machine on the same host machine. Our Dynamic Resource Allocation algorithm can overcome this obstacle, and improve host machine performance. Dynamic Resource Allocation works by continuously monitoring all virtual machines resource usage to determine which virtual machine have to migrate to another host machine. The goal is to make all host machine CPU and memory loading identically.

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Fig. 2. Our system architecture

The Dynamic Resource Allocation process is as follows. Assuming j host machines are in this pool. Every host machine loading ideal ratio is α=1⁄j. And i virtual machines are not running load balancing in these host machine. Each virtual machine resource usage is defined as “VMi Ratio” ሺܸ‫݁ݐܽܿ݋݈݈ܽܯܣܴ݅ܯܸ כ ݁ݏݑܷܲܥ݅ܯ‬ሻΤσ௡௜ୀଵሺܸ‫݁ݐܽܿ݋݈݈ܽܯܣܴ݅ܯܸ כ ݁ݏݑܷܲܥ݅ܯ‬ሻ(1)

Where VMi Ratio denotes virtual machine resource usage percentages in all allocate CPU and memory physical resource. When VMi CPUuse increases, VMi Radio is also increase too. In the next step, virtual machines resource usage ratio has been added up on different host machine. Each host machine current resource usage is defined “HOSTj Ratio”, is: ∑

(2)

Where HOSTj Rate must compare with ideal ratio α. When HOSTj Rate is bigger than α, it presents this host loading is too high, must migrate virtual machine to another host machine, and it also determine migrate source host. At the first, decide which host machine to be the migrated source host. Such as: max (HOST_(j Rate)-α), and decide which host machine to be the migrated destination host, such as: min (HOST_(j Rate)-α). And at last, the migrated virtual machine is defined VMk migrate. Such as: (3) The algorithm performs some calculations for monitoring physical resource information. It is follows: [Initialization] Defined virtual machine amount i and host machine amount j

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Calculate ideal ratio α=1⁄j, virtual machine resource ratio VMi Rate, host machine resource ratio HOSTj Rate do determine migrate source host machine m max (HOST_(j Rate)-α) determine migrate destination host machine n min (HOST_(j Rate)-α) determine migrated virtual machine on HOSTj VMk migrate migrate virtual machine VMk of host m to host n while (VMk migrate value = 0)

5

Experimental Results

We focus on resource utilization of computing under DRA model. Therefore, we also used HPCC software to verify that DRA has a good performance and utilization on virtualization cluster. HPCC is an abbreviation of High Performance Computing Challenge, the HPC Challenge Benchmark is a set of benchmarks targeting to test multiple attributes that can contribute substantially to the real-world performance of HPC systems, co-sponsored by the DARPA High Productivity Computing Systems program, the United States Department of Energy and the National Science Foundation [37]. First of all, we focus on resource utilization of computing under DRA model. Therefore, we used HPCC [11, 16, 17, 20, 39-42] software to verify that DRA has a good performance and utilization on virtualization cluster. HPCC is an abbreviation of High Performance Computing Challenge, the HPC Challenge Benchmark is a set of benchmarks targeting to test multiple attributes that can contribute substantially to the real-world performance of HPC systems [31]. Figure 3 is shown HPCC computing time. The horizontal axis represented HPCC problem size and the vertical axis represented HPCC computing time. We noticed that while HPCC problem size growing up, the difference of HPCC computing finished time when DRA function enable or not will be more obviously. In this experiment, we run HPCC programs onto six virtual machines and calculate HPCC performance on these six virtual machines. It caused virtual machines cluster CPU usage jumped and affect HOST machine CPU usage relatively. When DRA function disable, virtual machines located on same HOST machine and proceeding HPCC computing simultaneously. It caused virtual machines snatch at physical resource each other. When DRA function enable, it will detect all host machine resource usage was balancing or not, therefore, virtual machines on same HOST machine were migrated to others automatically. Figure 4 is also shown DRA function effectiveness. The vertical axis represented virtual machine floating point calculation performance. With DRA function enabled will obtain good performance. It also proved our thesis is workable under this circumstance. In Figure 4, it shows better performance when virtual machines centralized on the same host than on distributed hosts. Because HPCC performance computing on virtual machines cluster transfer computing data to each virtual machine, so these virtual machines deliver message to each other by the host virtual switch. But we observed that when problem size reach 6000, DRA enabled virtual machines

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distributed to different hosts, the HPCC performance is better than DRA disabled virtual machines. Because problem size is too big, so virtual machines cluster on the single host cannot afford the computation.

DRA Disabled DRA enabled 2500

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2000 1500 1000 500 0 1000

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Fig. 3. Execution time for running HPCC on VM

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3000

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Fig. 4. The performance of Floating Point per second

Figure 4 is shown HPCC computing time. The horizontal axis represented HPCC problem size and the vertical axis represented HPCC computing time. We noticed that while HPCC problem size growing up, the difference of HPCC computing finished time when DRA function enable or not will be more obviously. Figure 5 is also shown DRA function effectiveness. The vertical axis represented virtual machine floating point calculation performance. With DRA function enabled will obtain good performance. It also proved our thesis is workable under this circumstance.

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Fig g. 5. The CPU usages of host machines

6

Conclusions

In this work we have pressented an optimization with dynamic resource allocattion model for clusters that allo ows a flexible management of these computing platforrms by: (1) supporting DRA mechanism; m (2) implementation OpenNebula managem ment tool on web-based interfacee; and (3) efficiently isolating the cluster workloads. Mooreover, this architecture is able a to transparently grow the cluster's capacity usingg an external cluster provider. Although A there is another Open Source virtualization proj oject like, Eucalyptus. But it is difficult to reach DRA goal, because lack life migrattion oose the OpenNebula solutions to hit our goal in this papper. function. Therefore, we cho Based on this model it is straightforward to plan the capacity of the cluster to, for o complete a given workload. We envision the use of thhese instance, meet a deadline to kinds of models by addition nal components to dynamically change the cluster capaccity according to a given budg get, performance policy or in conjunction with a run and queue wait time prediction service. Finally, the architecture presented in this workk is compatible with the use of physical resources. These resources can be divided eveenly by the mechanism.

References 1. Goldberg, R.P., Popek, G.J.: G Formal requirements for virtualizable third generation arrchitectures (1974) 2. Clark, C., et al.: Live migration of virtual machines. Presented at the Proceedings off the 2nd Conference on Symp posium on Networked Systems Design & Implementation, vool. 2 (2005)

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3. Yi, Z., Wenlong, H.: Adaptive Distributed Load Balancing Algorithm Based on Live Migration of Virtual Machines in Cloud. Presented at the INC, Fifth International Joint Conference on IMS and IDC, NCM 2009 (2009) 4. Hagen, W.V.: Professional Xen Virtualization (2008) 5. Uhlig, R., Neiger, G., Rodgers, D., Santoni, A.L., Martins, F.C.M., Anderson, A.V., Bennett, S.M., Kagi, A., Leung, F.H., Smith, L.: Intel virtualization technology. Computer 38, 48–56 (2005) 6. Whitaker, A., et al.: Rethinking the design of virtual machine monitors. Computer 38, 57– 62 (2005) 7. Rosenblum, M., Garfinkel, T.: Virtual machine monitors: current technology and future trends. Computer 38, 39–47 (2005) 8. Kivity, A., et al.: kvm: the Linux virtual machine monitor. In: OLS 2007: Proceedings of the Linux Symposium, Ottawa, Ontario, Canada, pp. 225–230 (2007) 9. Andrew Whitaker, M.S., Gribble, S.D.: Denali: Lightweight Virtual Machines for Distributed and Networked Applications. Technical Report 02, 10 (2002) 10. Barham, P., et al.: Xen and the art of virtualization. Presented at the Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, Bolton Landing, NY, USA (2003) 11. Lowe, S.: Mastering VMware vSphere 4, pp. 26–41 (2009) 12. Williams, K.B.D.E., Garcia, J.R., Rosen, R.: Virtualization with Xen Including XenEnterprise, XenServer, and XenExpress, pp. 23–117 (2007) 13. Srodawa, R.J., Bates, L.A.: An efficient virtual machine implementation. Presented at the Proceedings of the Workshop on Virtual Computer Systems, Cambridge, Massachusetts, United States (1973) 14. Xu Hui, M. H., Wang, X., Wang, Z.: Study on the dynamic model of leaf area of Eucalyptus camaldulensis. Yunnan Forestry Science and Technology, 20–22 (2000) 15. Nurmi, D., et al.: The Eucalyptus Open-Source Cloud-Computing System. In: 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGRID 2009, pp. 124–131 (2009) 16. Sempolinski, P., Thain, D.: A Comparison and Critique of Eucalyptus, OpenNebula and Nimbus. In: 2010 IEEE Second International Conference on Cloud Computing Technology and Science (CloudCom), pp. 417–426 (2010) 17. Dejan, F.D., Miloji, S., Paindaveine, Y., Wheeler, R., Zhou, S.: Process migration. ACM Comput. Surv. 32, 241–299 (2000) 18. Hansen, J.G., Jul, E.: Self-migration of operating systems. Presented at the Proceedings of the 11th Workshop on ACM SIGOPS European Workshop, Leuven, Belgium (2004) 19. Yang, C.-T., Tseng, C.-H., Chou, K.-Y., Tsaur, S.-C.: Design and Implementation of a Virtualized Cluster Computing Environment on Xen. In: Presented at the The second International Conference on High Performance Computing and Applications, HPCA (2009) 20. Resource Management with VMware DRS 21. Seo, E., Jang, J.-W., Jo, H., Kim, J.-S.: A low-overhead networking mechanism for virtualized high-performance computing systems. The Journal of Supercomputing (2010) 22. Willmann, P., et al.: Concurrent Direct Network Access for Virtual Machine Monitors. In: IEEE 13th International Symposium on High Performance Computer Architecture, HPCA 2007, pp. 306–317 (2007) 23. Sotomayor, B., et al.: Virtual Infrastructure Management in Private and Hybrid Clouds. IEEE Internet Computing 13, 14–22 (2009) 24. Greschler, D., Mangan, T.: Networking lessons in delivering ’Software as a Service’: part I. Int. J. Netw. Manag. 12, 317–321 (2002)

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25. Greschler, D., Mangan, T.: Networking lessons in delivering ’Software as a Service’: part II. Int. J. Netw. Manag. 12, 339–345 (2002) 26. Hine, J.H., Bubendorfer, K.: A Compositional Classification For Load-Balancing Algorithms (1998) 27. Somani, G., Chaudhary, S.: Load Balancing in Xen Virtual Machine Monitor. In: Ranka, S., Banerjee, A., Biswas, K.K., Dua, S., Mishra, P., Moona, R., Poon, S.-H., Wang, C.-L. (eds.) IC3 2010. CCIS, vol. 95, pp. 62–70. Springer, Heidelberg (2010) 28. Smith, J.E., Ravi, N.: The architecture of virtual machines. Computer 38, 32–38 (2005) 29. Amazon Web Services home page, http://aws.amazon.com/ 30. Enomalism elastic computing infrastructure, http://www.enomaly.com/ 31. Salesforce Customer Relationships Management (CRM) system, http://www.salesforce.com/ 32. Logical Volume Manager, http://linuxconfig.org/Linux_lvm_-_Logical_Volume_Manager 33. Meth, K.Z., Satran, J.: Features of the iSCSI protocol. IEEE Communications Magazine 41, 72–75 (2003) 34. Anderson, E., et al.: Hippodrome: Running Circles Around Storage Administration. Presented at the Proceedings of the 1st USENIX Conference on File and Storage Technologies, Monterey, CA (2002) 35. OpenNebula, http://www.opennebula.org 36. Shafer, J., Willmann, P., Carr, D., Menon, A., Rixner, S., Cox, A.L., Zwaenepoel, W.: Concurrent Direct Network Access for Virtual Machine Monitors. In: The Second International Conference on High Performance Computing and Applications, HPCA (2007) 37. Eucalyptus, http://open.eucalyptus.com 38. Borja Sotomayor, R.S.M., Llorente, I.M., Foster, I.: Virtual Infrastructure Management in Private and Hybrid Clouds. IEEE Internet Computing 13 (2009) 39. Apache JMeter, http://jakarta.apache.org 40. You, J., et al.: JMeter-based aging simulation of computing system. In: 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE), pp. 282–285 (2010) 41. HPCC, http://icl.cs.utk.edu/hpcc/ 42. Browne, S., et al.: The National HPCC Software Exchange. IEEE Computational Science & Engineering 2, 62–69 (1995)

Design of an Iterative Learning Controller of Nonlinear Dynamic Systems with Time-Varying In Ho Ryu1, Hun Oh2, and Hyun Seob Cho3 1

Dept. of IT Applied System Engineering, Chonbuk National University #664-14, 1-ga, Duck-jin Dong Duck-jin Ku, Jeonju, 561-756, Rep. of Korea 2 Dept. of Electrical Electronic and Information, WonKwang University Engineering #416, Jinbuk-dong, Deokjin-gu, Jeonju-si, Jeonbuk, Rep. of Korea 3 Dept. of Electronic Engineering, ChungWoon University #San29, Namjang-ri, Hongseong-eup Hongseong-gun, Chungnam, Rep. of Korea [email protected]

Abstract. Connectionist networks, also called neural networks, have been broadly applied to solve many different problems since McCulloch and Pitts had shown mathematically their information processing ability in 1943. In this thesis, we present a genetic neuro-control scheme for nonlinear systems. Our method is different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its training.

1

Introduction

Control refers to a task which is to apply appropriate inputs to a plant so that the plant performs in a desirable way. In practical, many control problems suffer from difficulty due to system nonlinearity, uncertainty, and dynamic property. To cope with this difficulty regarding system dynamics and its environment, the controller has to estimate the unknown information during its operation. When the information pertaining to the unknown features of the plant or its environment is gained, if a control system has an ability to improve its performance in the future based on the obtained past experience, it is called a learning control system. The need for learning capability of control system has made several ways for new control techniques, and neuro-control technique is one of them. Some experimental methodologies for bench marking of algorithms have been utilized by neural network and machine learning technologies. One of most popular problems is an inverted pendulum problem(also known as pole balancing problem). Michie and Chambers attempted the problem using their boxes paradigm, later improved by Barto et al.'s ASE/ACE controller. Anderson has applied neural networks to the problem. Jervis applied their controllers to real inverted pendulum. We also applied our method to the problem. Genetic algorithms do not produce complete neural networks, it just utilized for optimization of networks, and then the optimized network is trained by using suitable learning T.-h. Kim et al. (Eds.): GDC 2011, CCIS 261, pp. 591–596, 2011. © Springer-Verlag Berlin Heidelberg 2011

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algorithm. We applied reinforcement learning, a powerful machine learning mechanism, particularly Q-learning, to genetically optimized networks.

2

Systems and Control

2.1

Nonlinear Systems

In a conventional way, nonlinear control problems have been solved by using linearization technique. However, it provides a method which is valid for only a limited range of operation. An nth-order continuous-time system has the following general form:

(2.1)

Similarly, an nth-order discrete-time system has the following form:

(2.2)

for k=0, 1, 2, ... . Those can be expressed in the vector form (2.3) (2.4) The systems above depend upon both the state x and time, k or t. These systems are, in general, time varying. 2.2

Dynamics of Neural Networks

The activation function or transfer function, denoted by F[⋅], maps the unbounded junction value to a bounded output of neuron, and defines the activation level of node. There are three main classes of activation functions which have been developed and used: Binary: The output is hardlimited to binary [0, 1] or bipolar [-1, 1] values.

3

Genetic Algorithms

Genetic algorithms are a highly parallel mathematical algorithms that transform a set (population) of mathematical objects(typically fixed -length binary character strings), each with an associated fitness value, into a new set (population) of mathematical

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objects. The transformation performed by GAs consists of naturally occurring genetic operations and the Darwinism of reproduction and survival of the fittest. 3.1

Genetic Reinforcement Learning Control

In the inverted pendulum problem, each real-valued string in the population is decoded to form a network with five input units, five hidden units, and one output units. The network is fully connected and this network configuration is same as that used by Anderson with the AHC algorithm. Since there are 35 links in the network, each string used by the genetic search includes 35+1 real values concatenated together. Before any input is applied to the network, the four state variables are normalized between 0 and 1. A bias unit fixed at 0.5 is also used as a fifth input to the net; a weight from the bias unit to a hidden node(or output node) in effect changes the threshold behavior of that node. The action of the neural network for a particular set of inputs is determined from the activation of the output unit. Learning is stopped when a network was found that was able to maintain the system without generating a failure signal for 120,000 time steps. One potential problem with such a simple evaluation criterion is that a favorable or unfavorable start state may bias the fitness ranking of an individual net. In other words, the evaluation function is noisy. We would like to assign a fitness value to a string based on its ability to perform across all possible start states.

Fig. 1. Genetic reinforcement learning control system

4

Simulation Results

Figure 2 and Figure 3 shows results for an AHC network and a genetically trained network using a failure signal occurring at 12 degrees during learning. These plots illustrate the tracking control behavior over time. During training, the output is determined probabilistically depending on the activation of the output unit. During testing, the action applied to the system is obtained by deterministically thresholding the activation value of the output unit. If the activation value is greater than 0, then

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output 1 and push right; if it is less than or equal to 0, then output -1 and push left. If the pole is vertical and the cart is centered and the velocities are 0, then all state variables will have the normalized value 0. When the system is started in an ideal state, then a successfully trained network will maintain the state variables close to the 0 level. It is not possible to balance the pole and avoid the track terminals from all possible, a perfectly trained network should drive all state variables back to the 0 level representing the ideal state of the system. In Figure 2 and 3, the cart is at the far right end of the track with the pole learning 32degrees to the left; the 12-degree failure signal is not suitable for these tests. The cart velocity and pole velocity are initialized 0. This initial state constitutes a position from which it is difficult for the system to recover. Both the AHC network and the genetically trained network used to produce these graphs are the best networks obtained for the 12-degree problem. In the case of Figure 3, the genetically trained network gets all of the input variables into tolerable ranges fairly quickly, whereas the AHC network takes longer. The AHC network quickly damps pole velocity and reduces oscillation in the pole position, however at that time, the cart almost crashes into the opposite end of the track. The genetically trained network handles problems with starting pole angles beyond 32 degrees, but the AHC network does not. Figure 4 and 5 show results for an AHC network and a genetically trained network using a failure signal at 36 degrees during learning. These plots indicate that both AHC network and the genetically trained network exploit similar information to determine the output activation levels and that they employ similar control strategies. The networks trained at 36 degrees proved to be more similar across a wider range of start states, but as the difficulty of the initial start states is increased the AHC networks fail sooner than the genetically trained networks. In these plots, the system is started with cart in the same far right position and the pole learning 35 degrees to the left. Cart velocity and pole velocity are initially 0. These plots make it evident that both networks track pole velocity by varying the magnitude of the output value. The correlation between pole velocity and the output activation is not as discernible in the first 50 to 100 time steps because the system is recovering from a difficult initial situation; correlation between the pole velocity and the output activation is much more pronounced as the networks begin to bring the system under control. Also notable is that cart velocity and pole velocity tend to be negatively correlated. Given the input definitions used in our experiments, cart velocity and pole velocity have a similar, but opposite relationship.

Fig. 2. Control results by AHC network for 12°problem

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Fig. 3. Control results by genetically optimized network for 12°problem

Fig. 4. Control results by AHC network for 36°problem

Fig. 5. Control results by genetically optimized network for 36°problem

5

Conclusions

In this thesis, we showed genetic algorithms can be used for optimizing neural network topology and connection weights. In addition, we presented the optimized neural network was good for solving nonlinear control problem. The performance of the proposed system was confirmed by applying it to the inverted-pendulum control problem. In our genetic approach, updates to the action network occur only after one or more failures: learning is not continuous. Another difference is that the genetic approach used in the experiments will assign an equal evaluation to two networks that

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avoid failure for an equal number of time steps. However the AHC algorithm evaluates networks by the trajectory of states by that are experienced. The evaluation associated with any two networks would differ when the AHC algorithm is used, favoring the network that drives the cart-pole through more highly valued states. The restriction of the search to highly valued states may also explain why the performance of AHC networks did not improve when the stricter stopping criterion was used. The genetic algorithm, because it ranks each network based on performance, is able to ignore those cases where the pole cannot be balanced; only the successful cases will obtain the chase to engage in genetic reproduction. For the AHC evaluation network, however, the preponderance of failures may cause all states to overpredict failure. This problem can be corrected either by selectively sampling the space to extract a better balance of success and failure information or by tuning the AHC algorithm to place more emphasis on positive results and less on failure. Overall, proposed method can be used for nonlinear control problems. Improvement for more nonlinear and complicated problems is the future work.

References [1] Franklin, G.F., David Powell, J., Emami-Naeini, A.: Feedback Control of Dynamic Systems, 3rd edn. Addison-Wesley (1994) [2] Ogata, K.: Modern Control Engineering, 2nd edn. Prentice Hall (1990) [3] Hocking, L.M.: Optimal Control: An Introduction to the Theory with Applications. Oxford University Press (1991) [4] Fu, L.: Neural Networks in Computer Intelligence. McGraw-Hill (1994) [5] Hecht-Nielsen, R.: Neurocomputing. Addison-Wesley (1990) [6] Haykin, S.: Neural Networks. Macmillan (1994) [7] Kolk, W.R., Lerman, R.A.: Nonlinear System Dynamics. Van Nostrand Reinhold (1992) [8] Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)

Adaptive Controller Design of the Nonlinear Dynamic Systems with a Neural Networks Compensator Hyun Seob Cho Dept. of Electronic Engineering, ChungWoon University #San29, Namjang-ri, Hongseong-eup Hongseong-gun, Chungnam, Rep. of Korea [email protected]

Abstract. Dynamic Neural Unit (DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our method is different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its training. Keywords: DNU-Control, Genetic Algorithm, Nonlinear System.

1

Introduction

The conventional design methods of a control system often require the construction of a mathematical model describing the dynamic behavior of the plant to be controlled. When such a mathematical model is difficult to obtain due to uncertainty or complexity of systems, these conventional techniques based on a mathematical model are not well suited for dealing with. Artifical neural network teachiques have been suggested for identification and control of nonlinear plants for which conventional techniques of control do not give satisfactory performance, such as the accuracy in matching the behavior of the physical system. A good method of applying neural networks for control must have the following properties: 1. It must be applicable to nonlinear plants, since there are already good methods of control for linear plants. 2. It should not put too much restriction on the type of nonlinearity that it can handle. 3. It is preferable to have an Unsupervised Learning method for the neural network because the desired output form of a system for a given input may be known, but the input form of a plant that produces that desired output is not generally known. Unsupervised Training can avoid identification of the plant or its inverse model, which is generally not easy to obtain. 4. The system should be stable at least for the class of inputs it has been trained for.

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5. In most cases open loop performance of a plant can be observed and a approximate controller can be devised for that. It would be desirable if we could put as much knowledge as possible in the design of this controller and only leave the extra fine tuning to the neural network controller. According to the above requirements, a direct auxiliary controller for nonlinear plants using neural network is presented.

2

Controller Design

The controller presented here is composed of an approximate controller and a neural network auxiliary controller. The structure of controller is shown in Figure 1. The approximate controller gives the approximate performance and the neural network auxiliary controller is used for the purpose of further fine tuning. The approximate controller can be a PID or any other conventional controller. It can be designed by using the known dynamics of nonlinear plant. The neural network employed in this scheme is an Radial Basis Function Network (RBFN). It produces the complementary signal to further reduce the error e between output y and the reference input r. The structure of RBFN is showed in Figure 2. It is a network with two layers. A hidden layer of radial basis neurons and an output layer of linear neurons. A common choice for the basis function is a Gaussion given by the equation:

 x − ci Gi ( x) = exp −  2σ 2 

2

 , i = 1,2,...m  

(1)

Where Cirepresents the center of the basis function and denotes its width. The norm |||| in equation can be expressed by Euclidean distance. The weights and biases of each neuron in the hidden layer define the position and width of a radial basis function. Each linear output neuron forms a weighted sum of these radial basis functions. With the correct weight and bias values for each layer, and enough hidden neurons, a RBFN can fit any function with any desired accuracy. The advantage of the RBFN is its rapid learning, generality and simplicity. RBFN finds the input to output map using local approximators. It can be trained faster than BP and have none of BP's training problems such as saturation and local minima.

Fig. 1. The structure of the controller

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In the RBFN training stage we first observe the performance of the system with the approximate controller for a certain period of time and measure the range of error between the output of the plant and desired output. Then we divide this error span into certain sections and for each section we perform a perturbation test: We increased the input to the plant by e whenever total square error between the output of the plant and desired output falls within a specified region. If this change of the input results in a lower value of the total square error, we modify the output weight of the neural network controller to work accordingly. This action is continued for all sections and the whole process is repeated until no modification can reduce the error. Taking an overlapping Gaussian activation function for kernel units supposedly provides a smoother response and better generalization. but in our case the amount of interference was so high and we obtained a better performance with non-overlapping regions. Nevertheless, smoothness of the output can be enhanced by dividing the correction for each section by modifying the cost function used for training from J=?e2 to J=?(enew2+k(eneweold)2) for k