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Many cloud applications are migrated from the monolithic model to amicroservices framework in which hundreds of loosely-coupled microservices runconcurrently, with significant benefits in terms of scalability, rapiddevelopment, modularity, and isolation. However, dependencies amongmicroservices with uneven execution time may result in longer queues, idleresources, or Quality-of-Service (QoS) violations.  In this paper we introduce Reclaimer, a deep reinforcement learning modelthat adapts to runtime changes in the number and behavior of microservices inorder to minimize CPU core allocation while meeting QoS requirements. Whenevaluated with two benchmark microservice-based applications, Reclaimer reducesthe mean CPU core allocation by 38.4% to 74.4% relative to theindustry-standard scaling solution, and by 27.5% to 58.1% relative to a currentstate-of-the art method.

Reclaimer: A Reinforcement Learning Approach to Dynamic Resource Allocation for Cloud Microservices