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arXiv · 2410.01760

Learning-Augmented Online Caching: New Upper Bounds

Daniel Skachkov·Denis Ponomaryov·Yuri Dorn·Alexander Demin
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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.

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Daniel Skachkov, Denis Ponomaryov, Yuri Dorn, Alexander Demin. 2024-10-02. Learning-Augmented Online Caching: New Upper Bounds. https://arxiv.org/abs/2410.01760

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