Regret-Optimal Learning for Minimizing Edge Caching Service Costs

Abstract

Edge caching has been widely implemented to efficiently serve data requests from end users. Numerous edge caching policies have been proposed to adaptively update cache content based on various statistics including data popularities and miss costs. Nevertheless, these policies typically assume that the miss cost for each data item is known, which is not true in real systems. A promising approach would be to use online learning to estimate these unknown miss costs. However, existing techniques cannot be directly applied, because the caching problem has additional cache capacity and cache update constraints that are not covered in traditional learning settings. In this work, we resolve these issues by developing a novel edge caching policy that learns uncertainty miss costs efficiently, and is shown to be asymptotically optimal. We first derive an asymptotic lower bound on the achievable regret. We then design a Kullback-Leibler lower confidence bound (KL-LCB) based edge caching policy, which adaptively learns the random miss costs by following the “optimism in the face of uncertainty” principle. By employing a novel analysis that accounts for the new constraints and the dynamics of the setting, we prove that the regret of the proposed policy matches the regret lower bound, thus showing asymptotic optimality. Further, via numerical experiments we demonstrate the performance improvements of our policy over natural benchmarks.