| An optimal repartitioning decision policy |
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Winter Simulation Conference
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Proceedings of the 17th conference on Winter simulation
table of contents
San Francisco, California, United States
Pages: 493 - 497
Year of Publication: 1985
ISBN:0-911801-07-3
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Authors
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David M. Nicol
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ICASE, Mail Stop 132C, NASA Langley Research Center, Hampton, VA and Department of Computer Science, Thornton Hall, University of Virginia, Charlottesville, Virginia
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Paul F. Reynolds, Jr.
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Department of Computer Science, Thornton Hall, University of Virginia, Charlottesville, Virginia
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| Bibliometrics |
Downloads (6 Weeks): 2, Downloads (12 Months): 8, Citation Count: 2
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 ABSTRACT
The automated partitioning of simulations for parallel execution is a timely research problem. A simulation's run-time performance depends heavily on the nature of the inputs the simulation responds to. Consequently, a simulation's run-time behavior varies as a function of time. Since a simulation's run-time behavior is generally too complex to analytically predict, partitioning algorithms must be statistically based: they base their partitioning decisions on the simulation's observed behavior. Simulations which are partitioned statistically are vulnerable to radical changes in the run-time dynamics of the simulation. In this paper we discuss a dynamic repartitioning decision policy which detects change in a simulation's run-time behavior and reacts to this change. This decision policy optimally balances the costs and potential benefits of repartitioning a running simulation.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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D. Eager, E. Lazowska and J. Zahorjan, Dynamic Load Sharing in Homogeneous Distributed Systems, Tech Report 84-10-01, University of Washington.
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S. Ross, Stochastic Processes, Wiley and Sons, New York, 1983.
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A. Rapoport, W.E. Stein and GJ. Burkheimer, Response Models for Detection of Change, D. Reidel Publishing Company, Boston, 1979.
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Z. Govindarajulu, Sequential Statistical Procedures, Academic Press, 1975.
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G. Fishman, "Grouping Observations in Digital Simulation", Management Science 24, (1978), 510-521.
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H. Bozedogan, S. Sclove, "Multi-Sample Cluster Analysis Using Akaike's Information Criterion~, Annals of the Institute of Statistical Mathematics 36,1, (1983).
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S. Ross, Applied Probabilit3, Models with Optimization Applications, Holden-Day, S~n Fransico, 1970.
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S. Schmitt, An Elementary Introduction to Bayesian Statistics, Addison-Wesley, 1969.
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D. Nicol and P. Reynolds, "A Statistical Approach to Dynamic Partitioning", Proceedings of the SCS Multi-Conference, San Diego, ~anuary 1985, 53-56,
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