Load Balancing with Duration Predictions

TL;DR

This paper explores load balancing on unrelated machines with duration predictions, proposing algorithms that adapt to prediction accuracy and establishing bounds on their performance.

Contribution

It introduces algorithms that leverage duration predictions for dynamic load balancing, bridging the gap between clairvoyant and unknown settings.

Findings

Algorithms' performance depends smoothly on prediction accuracy.

Lower bounds are established for algorithms with inaccurate predictions.

Proposed methods outperform classical algorithms when predictions are reasonably accurate.

Abstract

We study the classic fully dynamic load balancing problem on unrelated machines where jobs arrive and depart over time and the goal is minimizing the maximum load, or more generally the l_p-norm of the load vector. Previous work either studied the clairvoyant setting in which exact durations are known to the algorithm, or the unknown duration setting in which no information on the duration is given to the algorithm. For the clairvoyant setting algorithms with polylogarithmic competitive ratios were designed, while for the unknown duration setting strong lower bounds exist and only polynomial competitive factors are possible. We bridge this gap by studying a more realistic model in which some estimate/prediction of the duration is available to the algorithm. We observe that directly incorporating predictions into classical load balancing algorithms designed for the clairvoyant setting can lead to a notable decline in performance. We design better algorithms whose performance depends smoothly on the accuracy of the available prediction. We also prove lower bounds on the competitiveness of algorithms that use such inaccurate predictions.