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Edge caching will play a critical role in facilitating the emergingcontent-rich applications. However, it faces many new challenges, inparticular, the highly dynamic content popularity and the heterogeneous cachingconfigurations. In this paper, we propose Cocktail Edge Caching, that tacklesthe dynamic popularity and heterogeneity through ensemble learning. Instead oftrying to find a single dominating caching policy for all the cachingscenarios, we employ an ensemble of constituent caching policies and adaptivelyselect the best-performing policy to control the cache. Towards this goal, wefirst show through formal analysis and experiments that different variations ofthe LFU and LRU policies have complementary performance in different cachingscenarios. We further develop a novel caching algorithm that enhances LFU/LRUwith deep recurrent neural network (LSTM) based time-series analysis. Finally,we develop a deep reinforcement learning agent that adaptively combines basecaching policies according to their virtual hit ratios on parallel virtualcaches. Through extensive experiments driven by real content requests from twolarge video streaming platforms, we demonstrate that CEC not only consistentlyoutperforms all single policies, but also improves the robustness of them. CECcan be well generalized to different caching scenarios with low computationoverheads for deployment.

Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learning