RecCac: Recommendation-Empowered Cooperative Edge Caching for Internet of Things

Abstract

Edge caching is an emerging technology for supporting massive content access in mobile edge networks to address rapidly growing Internet of Things (IoT) services and con⁃ tent applications. However, the edge server is limited with the computation/storage capacity, which causes a low cache hit. Cooperative edge caching jointing neighbor edge servers is re⁃ garded as a promising technique to improve cache hit and reduce congestion of the net⁃ works. Further, recommender systems can provide personalized content services to meet us⁃ er’s requirements in the entertainment-oriented mobile networks. Therefore, we investigate the issue of joint cooperative edge caching and recommender systems to achieve additional cache gains by the soft caching framework. To measure the cache profits, the optimization problem is formulated as a 0–1 Integer Linear Programming (ILP), which is NP-hard. Spe⁃ cifically, the method of processing content requests is defined as server actions, we deter⁃ mine the server actions to maximize the quality of experience (QoE). We propose a cachefriendly heuristic algorithm to solve it. Simulation results demonstrate that the proposed framework has superior performance in improving the QoE.