An approach for microservices-oriented migration based on business information and GNN

Abstract

Microservices are popular because they have the advantages of loose coupling, low cohesion, and small and autonomous compared to monolithic systems. And when it comes to cloud deployment, it also has a natural advantage. As a result, more practitioners today choose to refactor monolithic applications into one or more microservices, each of which contains a set of partitions composed of components that point to some specific function of the original monolith, so that the entire software system can be represented by a graph, each component can be regarded as a node, and the dependencies between components can be regarded as edges between nodes. In recent years, there has been an approach to using graph neural networks (GNN) to help migrate from monoliths to microservices. However, due to the differences in the research field, some developers rely heavily on the source code of the monolithic system as an important basis for migration, but in the software field, the business information of the project also has a strong symbol for different microservices. Therefore, we will use GNN to comprehensively migrate microservices from the perspectives of business information and source code in the project. The findings indicate that our methodology is superior in efficiency compared to the migration of single features extracted solely from source code.