Learning-Augmented Online Caching: New Upper Bounds
Databases
2025-07-29 v2
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.
Cite
@article{arxiv.2410.01760,
title = {Learning-Augmented Online Caching: New Upper Bounds},
author = {Daniel Skachkov and Denis Ponomaryov and Yuri Dorn and Alexander Demin},
journal= {arXiv preprint arXiv:2410.01760},
year = {2025}
}