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Processor Saving Scheduling Policies for Multiprocessor Systems
February 1998 (vol. 47 no. 2)
pp. 178-189

Abstract—In this paper, processor scheduling policies that "save" processors are introduced and studied. In a multiprogrammed parallel system, a "processor saving" scheduling policy purposefully keeps some of the available processors idle in the presence of work to be done. The conditions under which processor saving policies can be more effective than their greedy counterparts, i.e., policies that never leave processors idle in the presence of work to be done, are examined. Sensitivity analysis is performed with respect to application speedup, system size, coefficient of variation of the applications' execution time, variability in the arrival process, and multiclass workloads. Analytical, simulation, and experimental results show that processor saving policies outperform their greedy counterparts under a variety of system and workload characteristics.

[1] S.-H. Chiang, R.K. Mansharamani, and M.K. Vernon, "Use of Application Characteristics and Limited Preemption for Run-to-Completion Parallel Processor Scheduling Policies," ACM SIGMETRICS, pp. 33-44, 1994.
[2] D.L. Eager, J. Zahorian, and E.D. Lazowska, "Speedup versus Efficiency in Parallel Systems," IEEE Trans. Computers, vol. 38, no. 3, pp. 408-423, Mar. 1989.
[3] K. Dussa, B. Carlson, L.W. Dowdy, and K.-H. Park, "Dynamic Partitioning in a Transputer Environment," ACM SIGMETRICS, pp. 203-213, 1990.
[4] D.G. Feitelson and L. Rudolph, "Distributed Hierarchical Control for Parallel Processing," Computer, vol. 23, no. 5, pp. 65-77, May 1990.
[5] D. Ghosal, G. Serazzi, and S.K. Tripathi, "Processor Working Set and Its Use in Scheduling Multiprocessor Systems," IEEE Trans. Software Eng., vol. 17, no. 5, pp. 443-453, May 1991.
[6] A. Gupta, A. Tucker, and S. Urushibara,“The impact of operating system scheduling policies and synchronization methods on the performance of parallel applications,”inProc. Conf. Measurement and Modeling of Comput. Syst.,1991, pp. 120–132.
[7] Intel Corporation, Paragon OSF/1 User's Guide, 1993.
[8] L. Kleinrock, Queueing Systems, vol. 1. Wiley Interscience, 1975.
[9] S. T. Leutenegger and M. K. Vernon,“The performance of multiprogrammed multiprocessor scheduling policies,”inProc. ACM Sigmetrics Conf., Boulder, CO, 1990, pp. 226–236.
[10] S. Majumdar, "The Performance of Local and Global Scheduling Strategies in Multiprogrammed Parallel Systems," Proc. 11th Ann. Conf. Computers and Comm., pp., 1992.
[11] S. Majumdar, D. L. Eager, and R. B. Bunt,“Scheduling in multiprogrammed parallel systems,”inProc. ACM Sigmetrics Conf., Santa Fe, NM, 1988, pp. 104–113.
[12] MS. Majumdar, D.L. Eager, and R.B. Bunt, "Characterization of Programs for Scheduling in Multiprogrammed Parallel Systems," Performance Evaluation, vol. 13, no. 2, pp. 109-130, 1991.
[13] C. McCann,R. Baswami,, and J. Zahoran,“A dynamic processor allocation policy for multiprogrammed shared-memorymultiprocessors,” ACM Trans. Computer Systems, vol. 11, no. 2, pp. 146-176, 1993.
[14] C. McCann and J. Zahorjan, "Processor Allocation Policies for Message-Passing Parallel Computers," ACM SIGMETRICS, pp. 19-32, 1994.
[15] C. McCann and J. Zahorjan, "Scheduling Memory Constrained Jobs on Distributed Memory Parallel Computers," ACM SIGMETRICS, pp. 208-219, 1995.
[16] V. Naik, S. Setia, and M. Squillante, "Performance Analysis of Job Scheduling Policies in Parallel Supercomputing Environments," Supercomputing, pp. 824-833, 1993.
[17] J. Ousterhout, "Scheduling Techniques for Concurrent Systems," Proc. Third Int'l Conf. Distributed Computing Systems, pp. 22-30, 1982.
[18] K.-H. Park and L.W. Dowdy, "Dynamic Partitioning of Multiprocessor Systems," Int'l J. Parallel Programming, vol. 18, no. 2, pp. 91-120, 1989.
[19] E. Rosti, E. Smirni, L.W. Dowdy, G. Serazzi, and B. Carlson, "Robust Partitioning Policies for Multiprocessor Systems," Performance Evaluation, vol. 19, nos. 2-3, pp. 141-165, 1994.
[20] E. Smirni, "Processor Allocation and Thread Placement Policies in Parallel Multiprocessor Systems," PhD dissertation, Vanderbilt Univ., May 1995.
[21] S.K. Setia, M.S. Squillante, and S.K. Tripathi, "Processor Scheduling in Multiprogrammed, Distributed Memory Parallel Computers," ACM SIGMETRICS, pp. 158-170, 1993.
[22] K. C. Sevcik,“Characterizations of parallelism in applications and their use in scheduling,”inProc. ACM Sigmetrics Conf., Berkeley, 1989, pp. 171–180.
[23] K.C. Sevcik, “Application Scheduling and Processor Allocation in Multiprogrammed Parallel Processing Systems,” Performance Evaluation–An Int'l J., vol. 19, nos. 2-3, pp. 107–140, Mar. 1994.
[24] E. Smirni, E. Rosti, G. Serazzi, L.W. Dowdy, and K.C. Sevcik, "Performance Gains from Leaving Idle Processors in Multiprocessor Systems," Proc. Int'l Conf. Parallel Processing, pp. III.203-III.210, 1995.
[25] A. Tucker and A. Gupta, “Process Control and Scheduling Issues on a Network of Multiprocessors,” Proc. 12th ACM Symp. Operating System Principles, pp. 159–166, Dec. 1989.
[26] J. Zahorjan and C. McCann,“Processor scheduling in shared memory multiprocessors,”inProc. 1990 ACM SIGM Conf. Meas., Model., Comput., Syst., May 1990, pp. 214–225.

Index Terms:
Multiprocessor systems, processor scheduling, processor saving algorithm, work conserving, Markov analysis, performance evaluation.
Emilia Rosti, Evgenia Smirni, Lawrence W. Dowdy, Giuseppe Serazzi, Kenneth C. Sevcik, "Processor Saving Scheduling Policies for Multiprocessor Systems," IEEE Transactions on Computers, vol. 47, no. 2, pp. 178-189, Feb. 1998, doi:10.1109/12.663764
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