Container Caching Optimization based on Explainable Deep Reinforcement Learning

Serverless edge computing environments use lightweight containers to run different services on a need basis. Container caching at edge nodes is an effective strategy to further reduce the startup latency related to the preparation of container images. However, the capacity limitation of the edge nodes requires an efficient caching strategy that can capture underlying service request patterns. Hence, this paper proposes an EXplainable Reinforcement Learning (XRL)-based container caching strategy to increase the hit rate of cached containers. While a few studies already proposed RL-based caching algorithms, our proposal focuses more on the explainability part of the caching decisions based on a causal graph. The generated explanations from our approach can indicate which caching actions specifically contribute to the increase in the hit rate, which implies the underlying request patterns. Our experiments in a simple network topology demonstrate the validity of the generated explanations.