Using Static Analysis to Aid Monolith to Microservice System Transformation: Tuning Fuzzy c-Means in a VAE-Based GNN Approach

Transitioning from monolithic systems to cloud-native based on microservice architecture is essential for organizations facing dynamic technological shifts and growing scalability demands. This paper explores a machine learning-driven approach to decompose monolithic systems into microservices, targeting maintainability and modularization. Utilizing static analysis, we extract critical dependency data from the monolith, which guides the configuration of a Variational Autoencoder (VAE) and fuzzy c-means clustering process. This approach enables precise tuning of hyperparameters to optimize the decomposition into highly independent, scalable microservices. Our findings highlight the effectiveness of integrating static analysis with machine learning to enhance the adaptability and efficiency of distributed systems, providing valuable insights into the nuanced impacts of hyperparameter adjustments on system performance. Furthermore, we provide a novel system multi-variant benchmark to the community.