Hybridized Hot Restart via Reinforcement Learning for Microservice Orchestration

The Compute Continuum (CC) represents a set of computing resources residing from remote cloud datacenters to dedicated hardware at the edge of the network. Modern applications based on the composition of several microservices can strongly benefit from tools that realize optimal deployment based on the characteristics of each microservice, the availability of computing resources across the CC, the end-users latency, and pricing perspectives. To realize such goals, there is the need for sophisticated solutions capable of efficiently exploring a large space of potential configurations. In this regard, Reinforcement Learning (RL) and Computational Intelligence (CI) techniques represent valuable approaches. However, one of the critical challenges remains the adaption of these deployments to highly dynamic scenarios such as the CC. When the availability of the computing resources changes, there is the need to re-optimize the deployment efficiently. This calls for solutions with reactive or proactive capabilities to deal with the dynamicity of these ecosystems. The work considers a CC-inspired scenario by exploring hybridization techniques that combine CI and RL to devise a hot restart approach for metaheuristics and assess a new deployment solution efficiently. Preliminary results show the soundness of the proposed hybridization methods in handling severe system changes.