Online Algorithms for Weighted Paging with Predictions

TL;DR

This paper studies weighted paging with predictions, introducing the SPRP model that enables a 2-competitive online algorithm, and analyzes how prediction errors affect algorithm performance.

Contribution

It proposes the strong per request prediction (SPRP) model for weighted paging and provides bounds on algorithm performance with varying prediction accuracy.

Findings

SPRP model achieves 2-competitiveness.

Neither lookahead nor next request knowledge alone surpasses lower bounds.

Bounds established for algorithms degrading gracefully with prediction errors.

Abstract

In this paper, we initiate the study of the weighted paging problem with predictions. This continues the recent line of work in online algorithms with predictions, particularly that of Lykouris and Vassilvitski (ICML 2018) and Rohatgi (SODA 2020) on unweighted paging with predictions. We show that unlike unweighted paging, neither a fixed lookahead nor knowledge of the next request for every page is sufficient information for an algorithm to overcome existing lower bounds in weighted paging. However, a combination of the two, which we call the strong per request prediction (SPRP) model, suffices to give a 2-competitive algorithm. We also explore the question of gracefully degrading algorithms with increasing prediction error, and give both upper and lower bounds for a set of natural measures of prediction error.