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Equipartitioning versus marginal analysis for parallel job scheduling

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2 Author(s)
B. G. Patrick ; Comput. Sci./Studies, Trent Univ., Peterborough, Ont., Canada ; M. Jack

Given n malleable and nonpreemptable parallel jobs that arrive for execution at time 0, we examine and compare two job scheduling strategies that allocate m identical processors among the n competing jobs. In all cases, n≤m. The first strategy is based on the heuristic paradigm of equipartitioning, and the second is based on the notion of marginal analysis. Equipartitioning uses no a priori information when processor allocations are made to parallel jobs. Marginal analysis, on the other hand, assumes full a priori information in order to maximize processor utility. We compare both strategies with respect to average time-to-completion (system performance) and overall time-to-completion (system efficiency). Using a simple job model characterized by sequential time-to-completion and degree of parallelism, it is demonstrated via simulation that in most cases, the uninformed strategy of equipartitioning outperforms marginal analysis with respect to system performance and without a commensurate degradation in system efficiency.

Published in:

Parallel and Distributed Computing, Applications and Technologies, 2003. PDCAT'2003. Proceedings of the Fourth International Conference on

Date of Conference:

27-29 Aug. 2003