Effect of Job Size Characteristics on Job Scheduling Performance

  • Kento Aida
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1911)

Abstract

A workload characteristic on a parallel computer depends on an administration policy or a user community for the computer system. An administrator of a parallel computer system needs to select an appropriate scheduling algorithm that schedules multiple jobs on the computer system efficiently. The goal of the work presented in this paper is to investigate mechanisms how job size characteristics affect job scheduling performance. For this goal, this paper evaluates the performance of job scheduling algorithms under various workload models, each of which has a certain characteristic related to the number of processors requested by a job, and analyzes the mechanism for job size characteristics that affect job scheduling performance significantly in the evaluation. The results showed that: (1) most scheduling algorithms classified into the first-fit scheduling showed best performance and were not affected by job size characteristics, (2) certain job size characteristics affected performance of priority scheduling significantly. The analysis of the results showed that the LJF algorithm, which dispatched the largest job first, would perfectly pack jobs to idle processors at high load, where all jobs requested powerof- two processors and the number of processors on a parallel computer was power-of-two.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    K. Li and K. Cheng. Job Scheduling in a Partitionable Mesh Using a Two-Dimensional Buddy System Partitioning Scheme. IEEE Trans. on Parallel and Distributed Systems, 2(4):413– 422, 1991.CrossRefGoogle Scholar
  2. 2.
    D. A. Lifka. The ANL/IBM SP Scheduling System. In Job Scheduling Strategies for Parallel Processing, Lecture Notes in Computer Science 949, pages 295– 303. Springer-Verlag, 1995.Google Scholar
  3. 3.
    J. S. Skovira, W. Chan, and H. Zhou. The EASY-LoadLev eler API Project. In Job Scheduling Strategies for Parallel Processing, Lecture Notes in Computer Science 1162, pages 41– 47. Springer-Verlag, 1996.Google Scholar
  4. 4.
    P. Krueger, T. Lai, and V. A. Dixit-Radiya. Job Scheduling Is More Important than Processor Allocation for Hypercube Computers. IEEE Trans. on Parallel and Distributed Systems, 5(5):488– 497, 1994.CrossRefGoogle Scholar
  5. 5.
    D. G. Feitelson. Packing Scheme for Gang Scheduling. In Job Scheduling Strategies for Parallel Processing, Lecture Notes in Computer Science 1162, pages 89– 110. Springer-Verlag, 1996.Google Scholar
  6. 6.
    J. Subhlok, T. Gross, and T Suzuoka. Impact of Job Mix on Optimizations for Space Sharing Scheduler. In Proc. of Supercomputing ’96, 1996.Google Scholar
  7. 7.
    A. B. Downey. A parallel workload model and its implications for processor allocation. In Proc. the 6th International Symposium of High Performance Distributed Computing, pages 112– 123, 1997.Google Scholar
  8. 9.
    V. Lo, J. Mache, and K. Windisch. A Comparative Study of Real Workload Traces and Synthetic Workload. In Job Scheduling Strategies for Parallel Processing, Lecture Notes in Computer Science 1459, pages 25– 46. Springer-Verlag, 1998.Google Scholar
  9. 10.
    D. G. Feitelson and L. Rudolph. Toward Convergence in Job Schedulers for Parallel Supercomputers. In Job Scheduling Strategies for Parallel Processing, Lecture Notes in Computer Science 1162, pages 1– 26. Springer-Verlag, 1996.Google Scholar
  10. 11.
    R. Gibbons. A Historical Application Profiler for Use by Parallel Schedulers. In Job Scheduling Strategies for Parallel Processing, Lecture Notes in Computer Science 1291, pages 58– 77. Springer-Verlag, 1997.Google Scholar
  11. 12.
    K. Aida, H. Kasahara, and S. Narita. Job Scheduling Scheme for Pure Space Sharing Among Rigid Jobs. In Job Scheduling Strategies for Parallel Processing, Lecture Notes in Computer Science 1459, pages 98– 121, 1998.Google Scholar
  12. 13.
    H. Franke, J. Jann, J. E. Moreira, P. Pattnaik, and M. A. Jette. An Evaluation of Parallel Job Scheduling for ASCI Blue-Pacific. In Proc. SC99, 1999.Google Scholar
  13. 14.
    E. G. Coffman, M. R. Garey, and D. S. Johnson. Approximation Algorithms for Bin-packing-An Updated Survey. In Algorithm Design for Computer System Design, pages 49– 106. Springer-Verlag, 1984.Google Scholar
  14. 15.
    E. G. Coffman, M. R. Garey, and D. S. Johnson. Bin Packing with Divisible Item Sizes. Journal of Complexity, 3:406– 428, 1987.CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Kento Aida
    • 1
  1. 1.Department of Computational Intelligence and Systems ScienceTokyo Institute of TechnologyYokohama-shiJapan

Personalised recommendations