GGRME: A GGNN-based Graph Reconstruction Method for Microservice Extraction
Ying Cao Xu, Ying Li, Suxiang Wu, Linghao Li, Xinzhou Zhu, Meng Xi, Jianwei Yin · 2025
Driven by the flexibility, reliability, and scalability of microservice architecture, an increasing number of enterprises are decomposing monolithic applications into microservices. However, existing deep learning-based decomposition methods rely heavily on partition number selection, which, if unreasonable, can lead to frequent microservice communication and reduced performance. Moreover, manual partition suggestions not only decrease automation but also fail to adapt to rapid business iteration, and existing methods inadequately capture the relationship characteristics between application classes. To address these issues, this paper proposes a novel graph-based partitioning technique, GGRME. It constructs a system dependency graph through static, dynamic, and semantic analysis, and then employs a self-supervised gated graph neural network combined with cross-supervised optimization for community detection to automatically generate microservice decomposition results. Experiments demonstrate that GGRME outperforms benchmark methods in 65 % of tests, yielding superior microservice decomposition performance.