Is Tail-Optimal Scheduling Possible?

Authors:
Adam Wierman;Bert Zwart
Affiliations:
Department of Computer Science, California Institute of Technology, Pasadena, California 91125;VU University Amsterdam, Eurandom, Georgia Institute of Technology, and CWI Amsterdam, 1098 XG Amsterdam, The Netherlands
Venue:
Operations Research
Year:
2012

Citing 17
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Abstract

This paper focuses on the competitive analysis of scheduling disciplines in a large deviations setting. Although there are policies that are known to optimize the sojourn time tail under a large class of heavy-tailed job sizes e.g., processor sharing and shortest remaining processing time and there are policies known to optimize the sojourn time tail in the case of light-tailed job sizes e.g., first come first served, no policies are known that can optimize the sojourn time tail across both light-and heavy-tailed job size distributions. We prove that no such work-conserving, nonanticipatory, nonlearning policy exists, and thus that a policy must learn or know the job size distribution in order to optimize the sojourn time tail.