Optimistic No-regret Algorithms for Discrete Caching

Abstract

We take a systematic look at the problem of storing whole files in a cache with limited capacity in the context of optimistic learning, where the caching policy has access to a prediction oracle. The successive file requests are assumed to be generated by an adversary, and no assumption is made on the accuracy of the oracle. We provide a universal lower bound for prediction-assisted online caching and proceed to design a suite of policies with a range of performance-complexity trade-offs. All proposed policies offer sublinear regret bounds commensurate with the accuracy of the oracle. In this pursuit, we design, to the best of our knowledge, the first optimistic Follow-the-Perturbed leader policy, which generalizes beyond the caching problem. We also study the problem of caching files with different sizes and the bipartite network caching problem.