Learning a Hybrid Proactive and Reactive Caching Policy in Wireless Edge Under Dynamic Popularity

Caching at wireless edge is a promising way to satisfy the explosively increasing mobile data demands, if future content popularity is known in advance. However, the time-varying nature of content popularity makes the popularity prediction far from perfect, which inevitably degrades the gain from caching. In this paper, we resort to a hybrid proactive and reactive policy to deal with the dynamics of popularity, in particular the hybrid of proactive probabilistic caching policy and least recently used policy, which are appropriate respectively for the contents with low and high dynamic popularity. We divide the contents requested in a region into two classes, where one can be modeled by independent reference model (IRM) and the other can be modeled by shot noise model (SNM). To maximize the total successful offloading ratio achieved by caching the two classes of contents, we optimize the hybrid caching policy including both the cache resource allocation to each class of contents and the probability of caching each IRM content. We find the optimal solution to the problem for general case, and provide a closed-form solution in a special case to gain insights. To provide a viable solution for practical use, we propose a heuristic method to obtain the optimal allocation fraction, and predict the popularity distribution and the allocation fraction using neural networks with historical data. We validate our analytical results by simulation results via synthetic datasets. We evaluate the performance of the proposed hybrid caching policy via synthetic data generated by SNM and two real datasets, and compare it with the proactive policy, the reactive policy and the existing hybrid proactive and reactive policy.