Distributed Learning-Based Cache Replacement in Collaborative Edge Networks

In this letter, distributed content caching is considered in a collaborative edge caching system where a central infostation broadcasts information about the content migration to all edge nodes. Each edge node is equipped with a small base station for fetching the requested contents from its neighbouring edge nodes and with a storage unit for caching the contents. To achieve efficient content caching and collaboration, an online decision-making problem of maximizing the cache-hit-ratio whilst ensuring the end users’ quality-of-experience (QoE) is formulated. Furthermore, it is assumed that the content popularity knowledge is not available in advance and has to be leaned regularly over time in an online manner. To this end, we propose a distributed online content-popularity leaning algorithm based on Thompson sampling for updating the cache storage units in real-time. Simulation results demonstrate that the proposed algorithm outperforms the benchmarks in terms of the cache-hit-ratio and QoE in the long run.