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Distributed and Parallel Computing

6th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP, Melbourne, Australia, October 2-3, 2005. Proceedings

  • Michael Hobbs
  • Andrzej M. Goscinski
  • Wanlei Zhou
Conference proceedings ICA3PP 2005

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3719)

Table of contents

  1. Front Matter
  2. Fengjung Liu, Chu-sing Yang
    Pages 1-10
  3. Stefan Frenz, Renaud Lottiaux, Michael Schoettner, Christine Morin, Ralph Goeckelmann, Peter Schulthess
    Pages 23-33
  4. Ying Zhao, Wanlei Zhou, Yingying Zhang, E. J. Lanham, Jiumei Huang
    Pages 34-39
  5. Chao-Tung Yang, I-Hsien Yang, Kuan-Ching Li, Ching-Hsien Hsu
    Pages 40-49
  6. Khin Mi Mi Aung, Kiejin Park, Jong Sou Park
    Pages 73-82
  7. Ching-Hsien Hsu, Guan-Hao Lin, Kuan-Ching Li, Chao-Tung Yang
    Pages 83-92
  8. Sang Boem Lim, Geoffrey Fox, Ali Kaplan, Shrideep Pallickara, Marlon Pierce
    Pages 93-102
  9. Muzhou Xiong, Hai Jin, Song Wu
    Pages 103-111
  10. Yin Li, Fanyuan Ma, Liang Zhang
    Pages 112-117
  11. Eric Dines, Andrzej Goscinski
    Pages 125-131
  12. Christophe Cérin, Michel Koskas
    Pages 132-138
  13. YanPing Yang, QingPing Tan, Yong Xiao
    Pages 139-148
  14. Michael Messig, Andrzej Goscinski
    Pages 149-158
  15. Chen Yanping, Li Zengzhi, Jin Qinxue, Wang Chuang
    Pages 159-164
  16. J. H. Abawajy
    Pages 165-173
  17. Bao-Yin Zhang, Ze-Yao Mo, Guang-Wen Yang, Wei-Min Zheng
    Pages 174-183
  18. E. Ilavarasan, P. Thambidurai, R. Mahilmannan
    Pages 193-203
  19. Beatriz Otero, José M. Cela, Rosa M. Badia, Jesús Labarta
    Pages 214-224
  20. DongWoo Lee, R. S. Ramakrishna
    Pages 225-231
  21. Dan Feng, Lingfang Zeng
    Pages 240-245
  22. Jin Yang, Jiannong Cao, Weigang Wu, Cheng-Zhong Xu
    Pages 246-256
  23. Grace J. Hwang, Joseph M. Arul, Eric Lin, Chung-Yun Hung
    Pages 257-266
  24. Haifang Zhou, Yu Tang, Xuejun Yang, Hengzhu Liu
    Pages 267-276
  25. M. Fazeli, H. Sarbazi-Azad, R. Farivar
    Pages 287-292
  26. Shiming Xu, Wenguang Chen, Weimin Zheng, Tao Wang, Yimin Zhang
    Pages 293-300
  27. Kuan-Ching Li, Hsiang-Yao Cheng, Chao-Tung Yang, Ching-Hsien Hsu, Hsiao-Hsi Wang, Chia-Wen Hsu et al.
    Pages 315-325
  28. Sena Seneviratne, David Levy
    Pages 336-344
  29. Zhan Tao, Zhou Xingshe, Liao Zhigang, Chen Yan
    Pages 345-350
  30. Liang Peng, Melvin Koh, Jie Song, Simon See
    Pages 351-356
  31. Wei Shi, Yang Xiang, Wanlei Zhou
    Pages 357-362
  32. Dan Wu, Dongyang Long, Changji Wang, Zhanpeng Guan
    Pages 370-375
  33. C. Izu, J. Miguel-Alonso, J. A. Gregorio
    Pages 396-405

About these proceedings

Introduction

There are many applications that require parallel and distributed processing to allow complicated engineering, business and research problems to be solved in a reasonable time. Parallel and distributed processing is able to improve company profit, lower costs of design, production, and deployment of new technologies, and create better business environments. The major lesson learned by car and aircraft engineers, drug manufacturers, genome researchers and other specialist is that a computer system is a very powerful tool that is able to help them solving even more complicated problems. That has led computing specialists to new computer system architecture and exploiting parallel computers, clusters of clusters, and distributed systems in the form of grids. There are also institutions that do not have so complicated problems but would like to improve profit, lower costs of design and production by using parallel and distributed processing on clusters. In general to achieve these goals, parallel and distributed processing must become the computing mainstream. This implies a need for new architectures of parallel and distributed systems, new system management facilities, and new application algorithms. This also implies a need for better understanding of grids and clusters, and in particular their operating systems, scheduling algorithms, load balancing, heterogeneity, transparency, application deployment, which is of the most critical importance for their development and taking them by industry and business.

Keywords

DJ Monitor Multithreading Performance Simulation algorithm algorithms cluster computing grid computing high performance computing load computing mobile computing network computing operating system security

Editors and affiliations

  • Michael Hobbs
    • 1
  • Andrzej M. Goscinski
    • 2
  • Wanlei Zhou
    • 3
  1. 1.School of Information Technology, GeelongDeakin UniversityAustralia
  2. 2.School of Engineering and Information TechnologyDeakin UniversityGeelong
  3. 3.Deakin UniversityMelbourneAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/11564621
  • Copyright Information Springer-Verlag Berlin Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-29235-7
  • Online ISBN 978-3-540-32071-5
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site