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Edge caching plays an increasingly important role in boosting user contentretrieval performance while reducing redundant network traffic. Theeffectiveness of caching ultimately hinges on the accuracy of predictingcontent popularity in the near future. However, at the network edge, contentpopularity can be extremely dynamic due to diverse user content retrievalbehaviors and the low-degree of user multiplexing. It's challenging for thetraditional reactive caching systems to keep up with the dynamic contentpopularity patterns. In this paper, we propose a novel Predictive Edge Caching(PEC) system that predicts the future content popularity using fine-grainedlearning models that mine sequential patterns in user content retrievalbehaviors, and opportunistically prefetches contents predicted to be popular inthe near future using idle network bandwidth. Through extensive experimentsdriven by real content retrieval traces, we demonstrate that PEC can adapt tohighly dynamic content popularity, and significantly improve cache hit ratioand reduce user content retrieval latency over the state-of-art cachingpolicies. More broadly, our study demonstrates that edge caching performancecan be boosted by deep mining of user content retrieval behaviors.

Predictive Edge Caching through Deep Mining of Sequential Patterns in User Content Retrievals