Content prefetching method to utilize reinforcement learning-based routing for in-network caching

Abstract

In-network caching is a technology that allows routers in a network to cache contents, enabling users to download the contents from the routers in addition to content servers. As one of routing methods enhancing the performance of in-network caching environments, a multi-armed bandit-based routing (MAB-routing) has been proposed in the past. To further enhance the performance of MAB routing, in this paper, we propose a content prefetching method. In the proposed method, each router periodically retrieves contents according to their popularity, and then notifies its adjacent routers of caching information to efficiently utilize MAB-routing. Through simulation experiments, we show the effectiveness of the proposed method.