Preemptive weighted completion time scheduling of parallel jobs

  • Uwe Schwiegeishohn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1136)


In this paper we present a new algorithm for the off-line scheduling of parallel and independent jobs on a massively parallel processor system. We also introduce a machine model which is based on existing multiprocessors and accounts for the penalty of preemption. It is shown that the new algorithm achieves a small approximation factor for both weighted completion time and makespan scheduling. To fine tune the algorithm a fairly simple numerical optimization problem is derived. This way different preemption penalties can be considered when determining the approximation factor. Finally, we compare the generated schedule with non-preemptive schedules for the same problem.


Execution Time Completion Time Approximation Factor Preemptive Schedule Total Weighted Completion Time 
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  1. 1.
    X. Deng, N. Gu, T. Brecht, and K. Lu. Preemptive scheduling of parallel jobs on multiprocessors. In Proceedings of the 7 th SIAM Symposium on Discrete Algorithms, January 1996.Google Scholar
  2. 2.
    D.G. Feitelson and L. Rudolph. Parallel job scheduling: Issues and approaches. In D.G. Feitelson and L. Rudolph, editors, Job Scheduling Strategies for Parallel Processing, pages 1–18. Springer-Verlag, Lecture Notes in Computer Science, 1995.Google Scholar
  3. 3.
    M. Garey and R. Graham. Bounds for multiprocessor scheduling with resource constraints. SIAM Journal on Computing, 4(2):187–200, June 1975.CrossRefGoogle Scholar
  4. 4.
    M. Garey and D. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman and Company, 1979.Google Scholar
  5. 5.
    T. Kawaguchi and S. Kyan. Worst case bound of an LRF schedule for the mean weighted flow-time problem. SIAM Journal on Computing, 15(4):1119–1129, November 1986.CrossRefGoogle Scholar
  6. 6.
    R. McNaughton. Scheduling with deadlines and loss functions. Management Science, 6(1):1–12, October 1959.Google Scholar
  7. 7.
    U. Schwiegeishohn, W. Ludwig, J. Wolf, J. Turek, and P. Yu. Smart SMART bounds for weighted response time scheduling. Technical Report RC 19789 (87176), IBM Research Division, July 1994. also to be published in SIAM Journal on Computing.Google Scholar
  8. 8.
    U. Schwiegelshohn, J. Turek, and J. Wolf. Preemptive scheduling of parallel tasks. Technical Report RC 20104 (88932), IBM Research Division, June 1995.Google Scholar
  9. 9.
    W. Smith. Various optimizers for single-stage production. Naval Research Logistics Quarterly, 3:59–66, 1956.Google Scholar
  10. 10.
    J. Turek, W. Ludwig, J. Wolf, L. Fleischer, P. Tiwari, J. Glasgow, U. Schwiegelshohn, and P. Yu. Scheduling parallelizable tasks to minimize average response time. In Proceedings of the 6th Annual Symposium on Parallel Algorithms and Architectures, Cape May, NJ, pages 200–209, June 1994.Google Scholar
  11. 11.
    J. Turek, U. Schwiegelshohn, J. Wolf, and P. Yu. Scheduling parallel tasks to minimize average response times. In Proceedings of the 5 th SIAM Symposium on Discrete Algorithms, pages 112–121, January 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Uwe Schwiegeishohn
    • 1
  1. 1.Computer Engineering InstituteUniversity DortmundDortmundGermany

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