Microservice Identification Using Multi-Objective Genetic Algorithms

Microservice-based architecture has gained popularity over monolithic architecture during the last few years in both industry and research. This is due to its tremendous advantages (e.g., efficiency, scalability, reliability, etc.). To make use of these advantages, different techniques aiming to migrate monolithic applications to microservices have been proposed. However, these techniques are often based on clustering algorithms that can lead to non-optimal solutions. Also, the identification process often use ad-hoc criteria and tend to overlook several key characteristics of a microservice. Thus, they usually identify microservices that do not match the ones identified by a software architect. To tackle these limitations, we propose in this paper an approach that, on the one hand, takes into account microservice characteristics, and on the other hand, uses well-known metaheuristics in search-based software engineering: the genetic algorithm. The main goal of our approach is to extract microservices from Object-Oriented (OO) monolithic source code by measuring their quality, while considering the semantics of the concept “microservice”. We have experimented with the proposed approach on a collection of Java applications and compared the identified microservices by our approach to those identified by a software architect or domain expert. The conducted experimentation shows the relevance of the identified microservices by our approach.