Fresh Caching of Dynamic Contents using Restless Multi-armed Bandits

Abstract

We consider a dynamic content caching problem wherein the contents get updated at a central server, and local copies of a subset of contents are cached at a local cache associated with a Base station (BS). When a content request arrives, based on whether the content is in the local cache, the BS can decide whether to fetch the content from the central server or serve the cached version from the local cache. Fetching a content incurs a fixed fetching cost, and serving the cached version incurs an ageing cost proportional to the age-of-version (AoV) of the content. The BS has only partial information regarding AoVs of the contents. We formulate an optimal content fetching and caching problem to minimize the average cost subject to cache capacity constraints. The problem suffers from the curse of dimensionality and is provably hard to solve. We formulate this problem as a continuous time restless multi-armed bandit process (RMAB), where a single content problem of the corresponding RMAB is a partially observable Markov decision process. We reformulate the single content problem as a semi-Markov decision process, prove indexability, and provide a Whittle index based solution to this problem. Finally, we compare the performance with recent work and show that our proposed policy is optimal via simulations.