GRID 2002: Grid Computing — GRID 2002 pp 219-231 | Cite as

Enhanced Algorithms for Multi-site Scheduling

  • Carsten Ernemann
  • Volker Hamscher
  • Achim Streit
  • Ramin Yahyapour
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2536)

Abstract

This paper discusses two approaches to enhance multi-site scheduling for grid environments. First the potential improvements of multi-site scheduling by applying constraints for the job fragmentation are presented. Subsequently, an adaptive multi-site scheduling algorithm is pointed out and evaluated. The adaptive multi-site scheduling uses a simple decision rule whether to use or not to use multi-site scheduling. To this end, several machine configurations have been simulated with different parallel job workloads which were extracted from real traces. The adaptive system improves the scheduling results in terms of a short average response time significantly.

Keywords

Resource Consumption Single Machine Grid Environment Schedule Process Free Resource 
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.
    M. Brune, J. Gehring, A. Keller, and A. Reinefeld. Managing clusters of geographically distributed high-performance computers. Concurrency-Practice and Experience, 11(15):887–911, 1999.CrossRefGoogle Scholar
  2. 2.
    S. J. Chapin, D. Katramatos, J. Karpovich, and A. Grimshaw. Resource management in Legion. Future Generation Computer Systems, 15(5–6):583–594, 1999.CrossRefGoogle Scholar
  3. 3.
    A. B. Downey and D. G. Feitelson. The Elusive Goal ofWorkload Characterization. Technical report, Hebrew University, Jerusalem, March 1999.Google Scholar
  4. 4.
    C. Ernemann, V. Hamscher, U. Schwiegelshohn, A. Streit, and R. Yahyapour. On Advantages of Grid Computing for Parallel Job Scheduling. In Proceedings of the 2nd IEEE International Symposium on Cluster Computing and the Grid (CC-GRID 2002), pages 39–46, 2002.Google Scholar
  5. 5.
    C. Ernemann, V. Hamscher, A. Streit, and R. Yahyapour. On Effects of Machine Configurations on Parallel Job Scheduling in Computational Grids. In International Conference on Architecture of Computing Systems, (ARCS 2002), pages 169–179. VDE-Verlag, April 2002.Google Scholar
  6. 6.
    D. G. Feitelson, L. Rudolph, U. Schwiegelshohn, and K. C. Sevcik. Theory and Practice in Parallel Job Scheduling. Lecture Notes in Computer Science, 1291:1–34, 1997.Google Scholar
  7. 7.
    D.G. Feitelson. A Survey of Scheduling in Multiprogrammed Parallel Systems. Research report rc 19790 (87657), IBM T.J. Watson ResearchCen ter, Yorktown Heights, NY, February 1995.Google Scholar
  8. 8.
    D.G. Feitelson. Packing Schemes for Gang Scheduling. Lecture Notes in Computer Science, 1162:89–101, 1996.Google Scholar
  9. 9.
    D.G. Feitelson and L. Rudolph. Parallel job scheduling: Issues and approaches. In IPPS’95 Workshop: Job Scheduling Strategies for Parallel Processing, pages 1–18. Springer Verlag, Lecture Notes in Computer Science LNCS 949, 1995., 1995.Google Scholar
  10. 10.
    D.G. Feitelson and A.M. Weil. Utilization and Predictabillity in Scheduling the IBM SP2 with Backfilling. In Proceedings of IPPS/SPDP 1998, IEEE Computer Society, pages 542–546, 1998.Google Scholar
  11. 11.
    I. Foster and C. Kesselman. Globus: A Metacomputing Infrastructure Toolkit. The International Journal of Supercomputer Applications and High Performance Computing, 11(2):115–128, Summer 1997.CrossRefGoogle Scholar
  12. 12.
    I. Foster and C. Kesselman, editors. The GRID: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, 1998.Google Scholar
  13. 13.
    R. Gibbons. A Historical Application Profiler for Use by Parallel Schedulers. In Job Scheduling Strategies for Parallel Processing, pages 58–77. Springer, Berlin, Lecture Notes in Computer Science LNCS 1291, 1997.Google Scholar
  14. 15.
    V. Hamscher, U. Schwiegelshohn, A. Streit, and R. Yahyapour. Evaluation of Job-Scheduling Strategies for Grid Computing. Lecture Notes in Computer Science, 1971:191–202, 2000.Google Scholar
  15. 16.
    S. Hotovy. Workload Evolution on the Cornell Theory Center IBM SP2. In D.G. Feitelson and L. Rudolph, editors, IPPS’96 Workshop: Job Scheduling Strategies for Parallel Processing, pages 27–40. Springer-Verlag, Lecture Notes in Computer Science LNCS 1162, 1996.Google Scholar
  16. 17.
    D. A. Lifka. The ANL/IBM SP Scheduling System. In D. G. Feitelson and L. Rudolph, editors, IPPS’95 Workshop: Job Scheduling Strategies for Parallel Processing, pages 295–303. Springer, Berlin, Lecture Notes in Computer Science LNCS 949, 1995.Google Scholar
  17. 18.
    U. Schwiegelshohn and R. Yahyapour. Analysis of First-Come-First-Serve Parallel Job Scheduling. In Proceedings of the 9 th SIAM Symposium on Discrete Algorithms, pages 629–638, January 1998.Google Scholar
  18. 19.
    U. Schwiegelshohn and R. Yahyapour. Fairness in parallel job scheduling. Journal of Scheduling, 3(5):297–320. John Wiley, 2000.MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Carsten Ernemann
    • 1
  • Volker Hamscher
    • 1
  • Achim Streit
    • 2
  • Ramin Yahyapour
    • 1
  1. 1.Computer Engineering InstituteUniversity of DortmundDortmundGermany
  2. 2.Paderborn Center for Parallel ComputingUniversity of PaderbornPaderbornGermany

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