Learning-Augmented Online Caching: New Upper Bounds

TL;DR

This paper improves bounds on online caching algorithms using predictions, demonstrating near-optimal competitive ratios through a combination of algorithms and establishing fundamental lower bounds.

Contribution

It introduces improved upper bounds for learning-augmented caching algorithms and proves a lower bound, showing the optimality of a combined approach.

Findings

Enhanced competitive ratio bounds for learning-augmented caching algorithms.

Proved a lower bound on the competitive ratio for any randomized algorithm.

Combined algorithms achieve near-optimal competitive ratios.

Abstract

We address the problem of learning-augmented online caching in the scenario when each request is accompanied by a prediction of the next occurrence of the requested page. We improve currently known bounds on the competitive ratio of the BlindOracle algorithm, which evicts a page predicted to be requested last. We also prove a lower bound on the competitive ratio of any randomized algorithm and show that a combination of the BlindOracle with the Marker algorithm achieves a competitive ratio that is optimal up to some constant.