  Edge caching will play a critical role in facilitating the emerging
content-rich applications. However, it faces many new challenges, in
particular, the highly dynamic content popularity and the heterogeneous caching
configurations. In this paper, we propose Cocktail Edge Caching, that tackles
the dynamic popularity and heterogeneity through ensemble learning. Instead of
trying to find a single dominating caching policy for all the caching
scenarios, we employ an ensemble of constituent caching policies and adaptively
select the best-performing policy to control the cache. Towards this goal, we
first show through formal analysis and experiments that different variations of
the LFU and LRU policies have complementary performance in different caching
scenarios. We further develop a novel caching algorithm that enhances LFU/LRU
with deep recurrent neural network (LSTM) based time-series analysis. Finally,
we develop a deep reinforcement learning agent that adaptively combines base
caching policies according to their virtual hit ratios on parallel virtual
caches. Through extensive experiments driven by real content requests from two
large video streaming platforms, we demonstrate that CEC not only consistently
outperforms all single policies, but also improves the robustness of them. CEC
can be well generalized to different caching scenarios with low computation
overheads for deployment.
