Contextual Learning for Content Caching With Unknown Time-Varying Popularity Profiles via Incremental Clustering
Xingchi Liu, Mahsa Derakhshani, Sangarapillai Lambotharan · IEEE Transactions on Communications · 2021
With the rapid development of social networks and high-quality video sharing services, the demand for delivering large quantity and high quality contents under stringent end-to-end delay requirement is increasing. To meet this demand, we study the content caching problem modelled as a Markov decision process in the network edge server when the popularity profiles are unknown and time-varying. In order to adapt to the changing trends of content popularity, a context-aware popularity learning algorithm is proposed. We prove that the learning error of this scheme is sublinear in the number of requests. In light of the learned popularities, a reinforcement learning-based caching scheme is designed on top of the state-action-reward-state-action algorithm with a function approximation. A reactive caching algorithm is also proposed to reduce the complexity. The time complexities of both the caching schemes are studied to demonstrate their feasibility in real time systems and a theoretical analysis is performed to prove that the cache hit rate of the reactive caching algorithm asymptotically converges to the optimal cache hit rate. Finally the simulations are presented to demonstrate the superiority of the proposed algorithms.