Analysis of non-work-conserving processor partitioning policies

  • E. Rosti
  • E. Smirni
  • G. Serazzi
  • L. W. Dowdy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 949)


In multiprocessor systems, a reasonable goal of the scheduler is to keep all processors as busy as possible. One technique for doing this is to allocate all available processors to the jobs waiting for service. Techniques which allocate all available processors are known as work-conserving policies. In this paper, non-work-conserving policies are examined. These policies keep some number of processors idle (i.e., unallocated) even when there are parallel jobs that are waiting for service. Such non-work-conserving policies set aside idle processors for anticipated new job arrivals or for unexpected system behavior. Two classes of non-work-conserving space-sharing policies are examined. One policy class keeps a certain percentage of the processors free. The other policy class makes an allocation decision based on previously observed system behavior. Two non-work-conserving policies, each selected from the two classes, are evaluated against their work-conserving counterparts. It is demonstrated that non-work-conserving policies can be particularly useful when the workload or the system behavior are irregular. Variability in the workload behavior including bursty arrivals, a high coefficient of variation in the workload execution time, unstable systems with processor failures are among the situations where non-work-conserving policies improve performance.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [AMV93]
    R. Agrawal, R.K. Mansharamani, M.K. Vernon, “Response time bounds for parallel processor allocation policies,” Technical Report # 1152, Computer Science Dept., Univeristy of Wisconsin, Madison, WI, June 1993.Google Scholar
  2. [CMV94]
    S.-H. Chiang, R.K. Mansharamani, M.K. Vernon, “Use of application characteristics and limited preemption for run-to-completion parallel processor scheduling policies,” Proc. ACM SIGMETRICS, 1994, pp. 33–44.Google Scholar
  3. [EZL89]
    D.L. Eager, J. Zahorjan, E.D. Lazowska, “Speedup versus efficiency in parallel systems,” IEEE Trans. on Computers, Vol 38(3), March 1989, pp. 408–423.Google Scholar
  4. [DCDP90]
    K. Dussa, B.M. Carlson, L.W. Dowdy, K.-H. Park, “Dynamic partitioning in a transputer environment,” Proc. ACM SIGMETRICS, 1990, pp. 203–213.Google Scholar
  5. [FR90]
    D.G. Feitelson, L. Rudolph, “Distributed hierarchical control for parallel processing,” IEEE Computer, Vol 23(5), May 1990, pp. 65–77.Google Scholar
  6. [GST91]
    D. Ghosal, G. Serazzi, S.K. Tripathi, “Processor working set and its use in scheduling multiprocessor systems,” IEEE Trans. on Software Engineering, Vol 17(5), May 1991, pp. 443–453.Google Scholar
  7. [GTU91]
    A. Gupta, A. Tucker, S. Urushibara, “The impact of operating system scheduling policies and synchronization methods on the performance of parallel applications,” Proc. ACM SIGMETRICS, 1991, pp. 120–132.Google Scholar
  8. [Int93]
    Intel Corporation, Paragon OSF/1 User's Guide, 1993.Google Scholar
  9. [Klei75]
    L. Kleinrock, Queueing Systems, Vol 1, Wiley Interscience, 1975.Google Scholar
  10. [LV90]
    S.T. Leutenegger, M.K. Vernon, “The performance of multiprogrammed multiprocessor scheduling policies,” Proc. ACM SIGMETRICS, 1990, pp. 226–236.Google Scholar
  11. [MEB88]
    S. Majumdar, D.L. Eager, R.B. Bunt, “Scheduling in multiprogrammed parallel systems,” Proc. ACM SIGMETRICS, 1988, pp. 104–113.Google Scholar
  12. [MEB91]
    S. Majumdar, D.L. Eager, R. B. Bunt, “Characterization of programs for scheduling in multiprogrammed parallel systems,” Performance Evaluation, Vol 13(2), 1991, pp. 109–130.Google Scholar
  13. [MVZ93]
    C. McCann, R. Vaswani, J. Zahorjan, “A dynamic processor allocation policy for multiprogrammed shared memory multiprocessors,” ACM Trans. on Computer Systems, Vol 11(2), February 1993, pp. 146–178.Google Scholar
  14. [MZ94]
    C. McCann, J. Zahorjan, “Processor allocation policies for message-passing parallel computers,” Proc. ACM SIGMETRICS, 1994, pp. 19–32.Google Scholar
  15. [Oust82]
    J. Ousterhout, “Scheduling techniques for concurrent systems,” Proc. 3rd International Conference on Distributed Computing Systems, 1982, pp. 22–30.Google Scholar
  16. [PD89]
    K.-H. Park, L.W. Dowdy, “Dynamic partitioning of multiprocessor systems,” International Journal of Parallel Programming, Vol 18(2), 1989, pp. 91–120.Google Scholar
  17. [RSDSC94]
    E. Rosti, E. Smirni, L.W. Dowdy, G. Serazzi, B.M. Carlson, “Robust partitioning policies for multiprocessor systems,” Performance Evaluation, Vol 19(2–3), March 1994, pp. 141–165.Google Scholar
  18. [SST93]
    S.K. Setia, M.S. Squillante, S.K. Tripathi, “Processor scheduling in multiprogrammed, distributed memory parallel computers,” Proc. ACM SIGMETRICS, 1993, pp. 158–170.Google Scholar
  19. [Sev89]
    K.C. Sevcik, “Characterization of parallelism in applications and their use in scheduling,” Proc. ACM SIGMETRICS, 1989, pp. 171–180.Google Scholar
  20. [Sev94]
    K.C. Sevcik, “Application scheduling and processor allocation in multiprogrammed multiprocessors,” Performance Evaluation, Vol 19(2–3), March 1994, pp. 107–140.Google Scholar
  21. [SRDS93]
    E. Smirni, E. Rosti, L.W. Dowdy, G. Serazzi, “Evaluation of multiprocessor allocation policies,” Tech. Report, Computer Science Dept., Vanderbilt University, Nashville, TN, August 1993.Google Scholar
  22. [SRSDS95]
    E. Smirni, E. Rosti, G. Serazzi, L.W. Dowdy, K.C. Sevcik, “Performance gains from leaving idle processors in multiprocessor systems,” to appear in International Conference on Parallel Processing.Google Scholar
  23. [TG89]
    A. Tucker, A. Gupta, “Process control and scheduling issues for multiprogrammed shared-memory multiprocessors,” Proc. of the 12th ACM Symposium on Operating Systems Principles, 1989, pp. 159–166.Google Scholar
  24. [ZM90]
    J. Zahorjan, C. McCann, “Processor scheduling in shared memory multiprocessors,” Proc. ACM SIGMETRICS, 1990, pp. 214–225.Google Scholar
  25. [ZB91]
    S. Zhou, T. Brecht, “Processor pool-based scheduling for large-scale NUMA multiprocessors,” Proc. ACM SIGMETRICS, 1991, pp. 133–142.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • E. Rosti
    • 1
  • E. Smirni
    • 2
  • G. Serazzi
    • 3
  • L. W. Dowdy
    • 2
  1. 1.Dipartimento di Scienze dell'InformazioneUniversità di MilanoItaly
  2. 2.Department of Computer ScienceVanderibilt UniveristyNashvilleUSA
  3. 3.Dipartimento di Elettronica e InformazionePolitecnico di MilanoItaly

Personalised recommendations