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Investigating Global Behavior in Computing Grids

  • Kevin L. Mills
  • Christopher Dabrowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4124)

Abstract

We investigate effects of spoofing attacks on the scheduling and execution of basic application workflows in a moderately loaded grid computing system using a simulation model based on standard specifications. We conduct experiments to first subject this grid to spoofing attacks that reduce resource availability and increase relative load. A reasonable change in client behavior is then introduced to counter the attack, which unexpectedly causes global performance degradation. To understand the resulting global behavior, we adapt multidimensional analyses as a measurement approach for analysis of complex information systems. We use this approach to show that the surprising performance fall-off occurs because the change in client behavior causes a rearrangement of the global job execution schedule in which completion times inadvertently increase. Finally, we argue that viewing distributed resource allocation as a self-organizing process improves understanding of behavior in distributed systems such as computing grids.

Keywords

Service Instance Service Factory Failure Response Open Grid Service Architecture Client Negotiator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    The WS Resource Framework, V1.0. Computer Associates International, Inc., Fujitsu Limited, Hewlett-Packard Development Company, International Business Machines Corporation and The University of Chicago (2004) Google Scholar
  2. 2.
    Foster, I., Kesselman, C., Nick, J., Tuecke, S.: The Physiology of the Grid, An Open Grid Services Architecture for Distributed Systems Integration. Global Grid Forum (June 2002)Google Scholar
  3. 3.
    Frey, J., Tannenbaum, T., Livny, M., Foster, I., Tuecke, S.: Condor-G: A Computation Management Agent for Multi-Institutional Grids. In: Proceedings of the Tenth IEEE International Symposium on High Performance Distributed Computing, San Francisco, August 7-9, pp. 55–67 (2001)Google Scholar
  4. 4.
    Holbrook, M.B.: Adventures in Complexity: An Essay on Dynamic Open Complex Adaptive Systems, Butterfly Effects, Self-Organizing Order, Coevolution, the Ecological Perspective, Fitness Landscapes, Market Spaces, Emergent Beauty at the Edge of Chaos, and All That Jazz. Academy of Marketing Science Review (2003)Google Scholar
  5. 5.
    Web Services Architecture. W3C Working Group Note (February 11, 2004) Google Scholar
  6. 6.
    The Open Grid Services Architecture, Version 1.5. Global Grid Forum (March 10, 2006) Google Scholar
  7. 7.
    Foster, I., et al.: A Globus Primer or, Everything You Wanted To Know About Globus But Were Afraid to Ask, an Early and Incomplete Draft (May 8, 2005)Google Scholar
  8. 8.
    Bak, P.: How Nature Works: the science of self-organized criticality, Copernicus, New York (1996)Google Scholar
  9. 9.
    Legrand, A., Marchal, L., Casanova, H.: Scheduling Distributed Applications: The SimGrid Simulation Framework. In: Proceedings of the third IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2003), Tokyo, May 12-15, pp. 138–145 (2003)Google Scholar
  10. 10.
    Buyya, R., Murshed, M.: GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid Computing. Concurrency and Computation: Practice and Experience 14, 1175–1220 (2002)MATHCrossRefGoogle Scholar
  11. 11.
    Liu, X., Xia, H., Chien, A.: Validating and Scaling the MicroGrid: A Scientific Instrument for Grid Dynamics. Journal of Grid Computing 2(2), 141–161 (2004)CrossRefGoogle Scholar
  12. 12.
    Ernemann, C., Hamscher, V., Yahyapour, R.: Benefits of Global Grid Computing for Job Scheduling. In: Proceedings of the Fifth IEEE International Workshop on Grid Computing (GRID 2004), Pittsburgh, November 8, pp. 374–379 (2004)Google Scholar
  13. 13.
    Wolski, R., Brevik, J., Plank, J., Bryan, T.: Grid Resource Allocation and Control Using Computational Economies. In: Berman, F., Fox, G., Hey, T. (eds.) Grid Computing: Making the Global Infrastructure a Reality, pp. 747–772. Wiley and Sons, New York (2003)Google Scholar
  14. 14.
    Gomoluch, J., Schroeder, M.: Market-based Resource Allocation for Grid Computing: A Model and Simulation. In: Proceedings of the First International Workshop on Middleware for Grid Computing, Rio de Janeiro, June 16-20, pp. 211–218 (2003)Google Scholar
  15. 15.
    Yeo, C.S., Buyya, R.: Service Level Agreement based Allocation of Cluster Resources: Handling Penalty to Enhance Utility. In: Proceedings of the 7th IEEE International Conference on Cluster Computing, Boston, September 27-30 (2005)Google Scholar
  16. 16.
    In, J., Avery, P., Cavanaugh, R., Ranka, S.: Policy Based Scheduling for Simple Quality of Service in Grid Computing. In: Proceedings of the Eighteenth International Parallel and Distributed Processing Symposium (IPDPS 2004), Santa Fe, April 26-30, p. 23 (2004)Google Scholar
  17. 17.
    He, X., Sun, X., Von Laszewski, G.: A QoS Guided Scheduling Algorithm for Grid Computting. Journal of Computer Science and Technology, Special Issue on Grid Computing 18(4), 442–450 (2003)MATHGoogle Scholar
  18. 18.
    Cooper, K., et al.: New Grid Scheduling and Rescheduling Methods in the GrADS Project. In: Proceedings of the Eighteenth International Parallel and Distributed Processing Symposium (IPDPS 2004), Santa Fe, April 26-30, p. 199 (2004)Google Scholar
  19. 19.
    Krothapalli, N., Deshmukh, A.: Dynamic allocation of communicating tasks in computational grids. IIE Transactions 36(11), 1037–1053 (2004)CrossRefGoogle Scholar
  20. 20.
    Chen, H., Maheswaran, M.: Distributed Dynamic Scheduling of Composite Tasks on Grid Computing Systems. In: Proceedings of the Sixteenth International Parallel and Distributed Processing Symposium (IPDPS 2002), Fort Lauderdale, April 15-19 (2002)Google Scholar
  21. 21.
    Subramani, V., Kettimuthu, R., Srinivasan, S., Sadayappan, P.: Distributed Job Scheduling on Computational Grids using Multiple Simultaneous Requests. In: Proceedings of the Eleventh IEEE International Symposium on High Performance Distributed Computing (HPDC-11 2002), Edinburgh, July 24-26, p. 359 (2002)Google Scholar
  22. 22.
    SOAP V1.2 Part 1: Messaging Framework. W3C Recommendation (June 24, 2003)Google Scholar
  23. 23.
    WS Addressing. BEA Systems Inc., International Business Machines Corporation, and Microsoft Corporation, Inc. (March 2004) Google Scholar
  24. 24.
    WS Resource Lifetime, V1.1. Computer Associates International, Inc., Fujitsu Limited, Hewlett-Packard Development Company, International Business Machines Corporation and The University of Chicago (March 2004) Google Scholar
  25. 25.
    Publish-Subscribe Notification for Web Services, V1.0. Akamai Technologies, Computer Associates International, Inc., Fujitsu Limited, Hewlett-Packard Development Company, International Business Machines Corporation, SAP AG, Sonic Software Corporation, Tibco Software Inc. and The University of Chicago (March 2004)Google Scholar
  26. 26.
    WS Services Topics, V1.0. Akamai Technologies, Computer Associates International, Inc., Fujitsu Limited, Hewlett-Packard Development Company, International Business Machines Corporation, SAP AG, Sonic Software Corporation, Tibco Software Inc. and The University of Chicago (March 2004) Google Scholar
  27. 27.
    WS Service Group, V1.0. Computer Associates International Inc., Fujitsu Limited, Hewlett-Packard Development Company, International Business Machines Corporation and The University of Chicago (March 2004) Google Scholar
  28. 28.
    Distributed Resource Management Application API Specification 1.0. Global Grid Forum (June 2004) Google Scholar
  29. 29.
    Web Services Agreement Specification (WS-Agreement). Global Grid Forum (September 2005) Google Scholar
  30. 30.
    Parallel Workloads Archive. The Hebrew University of Jerusalem, http://www.cs.huji.ac.il/labs/parallel/workload/
  31. 31.
    Shan, H., Oliker, L.: Job Superscheduler Architecture and Performance in Computational Grid Environments. In: Proceedings of the 2003 ACM/IEEE Conference on Supercomputing, Phoenix, November 15-21, p. 44 (2003)Google Scholar
  32. 32.
    Mills, K., Dabrowski, C.: Investigating Global Behavior in Computing Grids: the Extended Report. Draft technical report. U.S. National Institute of Standards and Technology (Available from the authors)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kevin L. Mills
    • 1
  • Christopher Dabrowski
    • 1
  1. 1.National Institute of Standards and TechnologyGaithersburgUSA

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