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.

中文翻译：

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轻推：随机改进 FCFS

先来先服务 (FCFS) 调度策略是实践中最流行的调度算法。此外，它的用法在理论上得到了验证：对于轻尾作业大小分布，FCFS 具有弱最优的响应时间渐近尾。但是如果我们不只关心渐近尾呢？如果我们还关心响应时间的第 99 个百分位数，或者在一秒内完成的作业的比例，会怎么样？FCFS 仍然是最好的吗？在渐近机制之外，只有 FCFS 尾部的松散边界是已知的，并且最优性是完全开放的。在本文中，我们引入了一种新策略 Nudge，这是第一个可证明随机改进 FCFS 的策略。我们证明，对于轻尾作业大小分布，Nudge 在尾部的每个点同时改进了 FCFS。因此，在响应时间的每个时刻和每个百分位，Nudge 都优于 FCFS。此外，Nudge 在渐近尾提供了对 FCFS 的乘法改进。这通过表明与之前的猜想相反，FCFS 不是强渐近最优的，从而解决了一个长期存在的开放问题。