The impact of job arrival patterns on parallel scheduling

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Abstract
In this paper we present an initial analysis of the job arrival patterns from a real parallel computing system and we develop a class of traffic models to characterize these arrival patterns. Our analysis of the job arrival data illustrates traffic patterns that exhibit heavy-tail behavior and other characteristics which are quite different from the arrival processes used in previous studies of parallel scheduling. We then investigate the impact of these arrival traffic patterns on the performance of parallel space-sharing scheduling strategies.

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