SEH: Size Estimate Hedging for Single-Server Queues

TL;DR

This paper introduces SEH, a heuristic for single-server queue scheduling that improves performance when processing times are estimated rather than known exactly, by dynamically adjusting job priorities based on estimation accuracy.

Contribution

The paper proposes Size Estimate Hedging (SEH), a novel heuristic that enhances SRPT scheduling under uncertain processing time estimates.

Findings

SEH performs well with realistic estimation errors.

Numerical results show improved scheduling performance.

SEH adapts priorities dynamically based on estimation confidence.

Abstract

For a single server system, Shortest Remaining Processing Time (SRPT) is an optimal size-based policy. In this paper, we discuss scheduling a single-server system when exact information about the jobs' processing times is not available. When the SRPT policy uses estimated processing times, the underestimation of large jobs can significantly degrade performance. We propose a simple heuristic, Size Estimate Hedging (SEH), that only uses estimated processing times for scheduling decisions. A job's priority is increased dynamically according to an SRPT rule until it is determined that it is underestimated, at which time the priority is frozen. Numerical results suggest that SEH has desirable performance for estimation error variance that is consistent with what is seen in practice.