Reinforcement Learning-Based Approach for Microservices-Based Application Placement in Edge Environment

Abstract

Edge computing allows for the deployment of applications near end-users, resulting in low-latency real-time applications. The adoption of the microservices architecture in modern applications has made this possible. Microservices architecture describes an application as a collection of separate but interconnected entities that can be built, tested, and deployed individually. Each microservice runs in its own process and exchanges data with others. Instead, edge nodes can independently deploy microservices-based IoT applications. Consistently meeting application service level objectives while also optimizing service placement delay and resource utilization in an edge environment is non-trivial. The present paper introduces a dynamic placement strategy that aims to fulfill application constraints and minimize infrastructure resource usage while ensuring service availability to all end-users of the UE in the edge network.