Age of Information Driven Cache Content Update Scheduling for Dynamic Contents in Heterogeneous Networks

Abstract

The recent development in mobile edge computing necessitates caching of dynamic contents, where new versions of contents become available around-the-clock, thus timely update is required to ensure their relevance. The age of information (AoI) is a performance metric that evaluates the freshness of contents. Existing works on AoI-optimization of cache content update algorithms focus on minimizing the long-term average AoI of all cached contents. Sometimes, user requests that need to be served in the future are known in advance and can be stored in user request queues. In this paper, we propose dynamic cache content update scheduling algorithms that exploit the user request queues. We consider a use case, where the trained neural networks (NNs) from deep learning models are being cached in a heterogeneous network (HetNet), as a motivating example. A queue-aware cache content update scheduling algorithm based on constrained Markov decision process (CMDP) is developed to minimize the average AoI of the dynamic contents delivered to the users. By using enforced decomposition technique and deep reinforcement learning, we propose two low-complexity suboptimal scheduling algorithms. Simulation results show that our proposed algorithms outperform the periodic cache content update scheme and reduce the average AoI by up to 30%.