2012/10/01 by Adam Wierman, Bert Zwart · 1 citation
Computer Science · Engineering · Business, Management and Accounting · #Optimization and Search Problems #Scheduling and Optimization Algorithms #Advanced Queuing Theory Analysis
paper · doi:10.1287/opre.1120.1086
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.