Online Caching with no Regret: Optimistic Learning via Recommendations. (arXiv:2204.09345v2 [cs.NI] UPDATED)

The design of effective online caching policies is an increasingly important
problem for content distribution networks, online social networks and edge
computing services, among other areas. This paper proposes a new algorithmic
toolbox for tackling this problem through the lens of \emph{optimistic} online
learning. We build upon the Follow-the-Regularized-Leader (FTRL) framework,
which is developed further here to include predictions for the file requests,
and we design online caching algorithms for bipartite networks with
pre-reserved or dynamic storage subject to time-average …

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