Containerized service placement and resource allocation at edge: A Hybrid Reinforcement Learning approach

Container has already become a default and prevalent solution due to its efficient and easy-to-deploy in edge computing. However, constrained resources in edge nodes may introduce significant deployment costs and increase service response latency in containerized services. Existing studies mainly focus on optimizing container placement strategies, while largely overlooking computational resources allocation. To tackle this problem, we introduce a joint optimization approach for containerized service placement and computational resources allocation from the perspective of image layer sharing. Specifically, we define a profit-driven mixed integer nonlinear programming (MINLP) problem and propose a graph-aware hybrid reinforcement learning (GAHRL) algorithm. By capturing inter-layer sharing dependencies and edge resource distribution, our algorithm optimizes containerized service placement while ensuring efficient computational resources allocation. Extensive experimental results show that the proposed algorithm outperforms other baseline algorithms in maximizing revenue as well as reducing service latency and storage cost.