Automated Microservice Identification in Legacy Systems with Functional and Non-Functional Metrics

Abstract

Since microservice has merged as a promising architectural style with advantages in maintainability, scalability, evolvability, etc., increasing companies choose to restructure their legacy monolithic software systems as the microservice architecture. However, it is quite a challenge to properly partitioning the systems into suitable parts as microservices. Most approaches perform microservices identification from a function-splitting perspective and with sufficient legacy software artifacts. That may be not realistic in industrial practices and possibly results in generating unexpected microservices. To address this, we proposed an automated microservice identification (AMI) approach that extracts microservices from the execution and performance logs without providing documentation, models or source codes, while taking both functional and non-functional metrics into considerations. Our work firstly collects logs from the executable legacy system. Then, controller objects (COs) are identified as the key objects to converge strongly related subordinate objects (SOs). Subsequently, the relation between each pair of CO and SO is evaluated by a relation matrix from both the functional and non-functional perspective. We ultimately cluster classes(objects) into the microservices by optimizing the multi-objective of high-cohesion-low-coupling and load balance. The usefulness of the proposed approach is illustrated by applying to a case study.