Towards Intelligent Adaptive Edge Caching Using Deep Reinforcement Learning

The tremendous expansion of edge data traffic poses great challenges to network bandwidth and service responsiveness for mobile computing. Edge caching has emerged as a promising method to alleviate these issues by storing a portion of data at the network edge. However, existing caching approaches suffer from either poor caching efficiency with low content-hit ratio or unintelligence of caching policies lacking self-adjustability. In this article, we propose ICE, a novel Intelligent Edge Caching scheme using a deep reinforcement learning (DRL) method to capture specific valuable information from the requested data. With the benefit of our proposed popularity model based on Newton's law of cooling, ICE fully takes into account the popularity of the contents to be cached and leverages the formulated Markov decision model to decide whether or not the contents should be cached. Moreover, to further improve the caching efficiency, we propose a novel distributed multi-node caching framework, named DCCC, assisted by a multi-tiered caching hierarchy. Comprehensive experiments show that the single-node ICE scheme greatly improves the cache hit rate and contents exchanging time in comparison with both DRL-based and legacy approaches, and our distributed multi-node caching scheme DCCC further significantly improves the overall utilization of caching space.