Non-clairvoyant Scheduling with Partial Predictions

TL;DR

This paper studies non-clairvoyant scheduling with limited predictions, providing theoretical bounds and algorithms that balance robustness, consistency, and smoothness when only partial job size predictions are available.

Contribution

It introduces a learning-augmented approach for scheduling with partial predictions, establishing bounds and a tradeoff analysis for scenarios with limited prediction access.

Findings

Near-optimal bounds for perfect predictions

A new learning-augmented algorithm with robustness and consistency

Tradeoff between consistency and smoothness in partial prediction scenarios

Abstract

The non-clairvoyant scheduling problem has gained new interest within learning-augmented algorithms, where the decision-maker is equipped with predictions without any quality guarantees. In practical settings, access to predictions may be reduced to specific instances, due to cost or data limitations. Our investigation focuses on scenarios where predictions for only $B$ job sizes out of $n$ are available to the algorithm. We first establish near-optimal lower bounds and algorithms in the case of perfect predictions. Subsequently, we present a learning-augmented algorithm satisfying the robustness, consistency, and smoothness criteria, and revealing a novel tradeoff between consistency and smoothness inherent in the scenario with a restricted number of predictions.