Reinforcement Learning for Caching with Space-Time Popularity Dynamics
This work addresses caching problems in networks for improving data traffic management, but it appears incremental as it applies reinforcement learning to a known bottleneck in caching policy design.
The paper tackles the challenge of designing effective caching policies for next-generation networks by addressing dynamic space-time content popularity and limited storage. It presents a reinforcement learning approach that achieves near-optimal performance in both single-node and networked settings, as demonstrated through numerical tests showing merits over standard policies.
With the tremendous growth of data traffic over wired and wireless networks along with the increasing number of rich-media applications, caching is envisioned to play a critical role in next-generation networks. To intelligently prefetch and store contents, a cache node should be able to learn what and when to cache. Considering the geographical and temporal content popularity dynamics, the limited available storage at cache nodes, as well as the interactive in uence of caching decisions in networked caching settings, developing effective caching policies is practically challenging. In response to these challenges, this chapter presents a versatile reinforcement learning based approach for near-optimal caching policy design, in both single-node and network caching settings under dynamic space-time popularities. The herein presented policies are complemented using a set of numerical tests, which showcase the merits of the presented approach relative to several standard caching policies.