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Average-case performance analysis and validation of online scheduling of independent parallel tasks

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1 Author(s)
Keqin Li ; Dept. of Comput. Sci., State Univ. of New York, New Paltz, NY, USA

Summary form only given. We analyze the average-case performance of an online scheduling algorithm for independent parallel tasks. We develop a method to calculate an analytical asymptotic average-case performance bound for arbitrary probability distribution of task sizes. In particular, we show that when task sizes are uniformly distributed in the range [1..C], an asymptotic average-case performance bound of M-(3-(1+1/C)C+1)C-1 can be achieved, where M is the number of processors. We also present extensive numerical and simulation data to demonstrate the accuracy of our analytical bound.

Published in:

Parallel and Distributed Processing Symposium, 2004. Proceedings. 18th International

Date of Conference:

26-30 April 2004