Mono2MS: Deep Fusion of Multi-Source Features for Partitioning Monolith into Microservices
G. Y. Chen, Chenzihan Li, Shmuel S. Tyszberowicz, Zhiming Liu, Bo Liu · 2024
Microservice architecture is favoured for its significant scalability, independent evolution, and advantages in performance elasticity. Partitioning a monolith into microservices has become a pivotal issue in software architecture refactoring. Concurrently, assessing the quality of such partitioning also presents a significant challenge. To address this problem, we propose a solution that (1) proposes a method for extracting and representing the multi-source features such as semantics, functionality, and performance of monolithic systems; (2) designs a deep fusion graph clustering model for partitioning a monolith into microservices intelligently; and (3) establishes a comprehensive set of assessment metrics to quantify the quality of the partitioning suggestion. We conducted experiments and analyses on five benchmark projects. By comparing our approach with six other methods, we have demonstrated the advantages of our methodology. Furthermore, ablating different modules has validated the effectiveness of our proposed monolith features analysis and deep fusion graph clustering model.