Learning-Based Microservice Placement and Migration for Multi-Access Edge Computing

In Multi-Access Edge Computing (MEC), a number of mechanisms exist to determine the optimal placement of monolithic service workflows. For applications designed as microservice workflow architectures, service placement schemes need to be revisited owing to the inherent interdependencies which exist between microservices. The dynamic environment, with stochastic user movement and service invocations, along with a large placement configuration space makes microservice placement in MEC a challenging task. Additionally, owing to user mobility, a placement scheme may need to be recalibrated, triggering service migrations to maintain the advantages offered by MEC. Existing microservice placement and migration schemes consider on-demand strategies. In this work, we take a different route and propose a Reinforcement Learning (RL) based proactive mechanism using a Learning Automata (LA) for microservice placement and migration that on one hand, keeps track of user mobility and resorts to migration when necessary, while on the other hand, keeps track of server residual capacities so that no server is overloaded. We use the San Francisco Taxi dataset to validate our approach. Experimental results show the effectiveness of our approach in comparison to other methods.