Isaac Grosof
Ziv Scully
ABSTRACT
The First-Come First-Served (FCFS) scheduling policy is the most popular scheduling algorithm used in practice. Furthermore, its usage is theoretically validated: for light-tailed job size distributions, FCFS has weakly optimal asymptotic tail of response time. But what if we don't just care about the asymptotic tail? What if we also care about the 99th percentile of response time, or the fraction of jobs that complete in under one second? Is FCFS still best? Outside of the asymptotic regime, only loose bounds on the tail of FCFS are known, and optimality is completely open.
In this paper, we introduce a new policy, Nudge, which is the first policy to provably stochastically improve upon FCFS. We prove that Nudge simultaneously improves upon FCFS at every point along the tail, for light-tailed job size distributions. As a result, Nudge outperforms FCFS for every moment and every percentile of response time. Moreover, Nudge provides a multiplicative improvement over FCFS in the asymptotic tail. This resolves a long-standing open problem by showing that, counter to previous conjecture, FCFS is not strongly asymptotically optimal.
This paper represents an abridged version of [2].
Supplemental Material
- Onno Boxma and Bert Zwart. 2007. Tails in Scheduling. ACM SIGMETRICS Performance Evaluation Review, Vol. 34, 4 (March 2007), 13--20.Google Scholar
Digital Library
- Isaac Grosof, Kunhe Yang, Ziv Scully, and Mor Harchol-Balter. 2021. Nudge: Stochastically Improving upon FCFS. Proc. ACM Meas. Anal. Comput. Syst., Vol. 5, 2 (June 2021), 28 pages.Google ScholarDigital Library
- Ziv Scully, Mor Harchol-Balter, and Alan Scheller-Wolf. 2018. SOAP: One Clean Analysis of All Age-Based Scheduling Policies. Proc. ACM Meas. Anal. Comput. Syst., Vol. 2, 1, Article 16 (April 2018), 30 pages.Google ScholarDigital Library
- Adam Wierman and Bert Zwart. 2012. Is Tail-Optimal Scheduling Possible? Operations Research, Vol. 60, 5 (Oct. 2012), 1249--1257.Google ScholarDigital Library
Index Terms
- Nudge: Stochastically Improving upon FCFS
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