Simple Near-Optimal Scheduling for the M/G/1

TL;DR

This paper introduces M-SERPT, a simple scheduling policy that approximates the optimal Gittins policy for minimizing mean response time in M/G/1 queues with unknown job sizes, providing provable near-optimal performance.

Contribution

The paper proposes M-SERPT, a new variant of SERPT, with proven constant-factor bounds on mean response time compared to Gittins, applicable at all loads and for any job size distribution.

Findings

M-SERPT achieves at most 3 times the mean response time of Gittins at load ≤ 8/9.

M-SERPT achieves at most 5 times the mean response time of Gittins at any load.

M-SERPT is the only non-Gittins policy with a constant-factor approximation ratio.

Abstract

We consider the problem of preemptively scheduling jobs to minimize mean response time of an M/G/1 queue. When we know each job's size, the shortest remaining processing time (SRPT) policy is optimal. Unfortunately, in many settings we do not have access to each job's size. Instead, we know only the job size distribution. In this setting the Gittins policy is known to minimize mean response time, but its complex priority structure can be computationally intractable. A much simpler alternative to Gittins is the shortest expected remaining processing time (SERPT) policy. While SERPT is a natural extension of SRPT to unknown job sizes, it is unknown whether or not SERPT is close to optimal for mean response time. We present a new variant of SERPT called monotonic SERPT (M-SERPT) which is as simple as SERPT but has provably near-optimal mean response time at all loads for any job size distribution. Specifically, we prove the mean response time ratio between M-SERPT and Gittins is at most 3 for load $\rho \leq 8/9$ and at most 5 for any load. This makes M-SERPT the only non-Gittins scheduling policy known to have a constant-factor approximation ratio for mean response time.