Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary

TL;DR

This paper explores online scheduling on heterogeneous machines without knowing exact speeds, proposing algorithms that leverage predictions and machine orderings, with theoretical guarantees and real hardware evaluations.

Contribution

It introduces novel competitive algorithms for speed-oblivious scheduling using predictions and machine orderings, supported by theoretical analysis and empirical testing.

Findings

Competitive algorithms with predictions outperform non-informed methods.

Speed-ordered model enables effective scheduling without exact speeds.

Empirical evaluation on real hardware demonstrates practical viability.

Abstract

We consider online scheduling on unrelated (heterogeneous) machines in a speed-oblivious setting, where an algorithm is unaware of the exact job-dependent processing speeds. We show strong impossibility results for clairvoyant and non-clairvoyant algorithms and overcome them in models inspired by practical settings: (i) we provide competitive learning-augmented algorithms, assuming that (possibly erroneous) predictions on the speeds are given, and (ii) we provide competitive algorithms for the speed-ordered model, where a single global order of machines according to their unknown job-dependent speeds is known. We prove strong theoretical guarantees and evaluate our findings on a representative heterogeneous multi-core processor. These seem to be the first empirical results for scheduling algorithms with predictions that are evaluated in a non-synthetic hardware environment.