Optimal Cost-Sensitive Microservice Granulation Based on Granular-Ball Computing

Microservice architecture has demonstrated immense advantages in solving the scalability and maintainability problems confronted by traditional monolithic systems. However, existing microservice architectures still encounter the problem of blurred boundaries, which significantly impact the system performance. The primary reason being the lack of unified evaluation indicators and efficient microservice splitting strategies. Specifically, some approaches divide entities like classes subjectively, resulting in inconsistent microservice boundaries, while others utilize objective criteria, but their complexity makes practical implementation challenging. To this end, we formalize the optimal microservice granulation problem, show its NPcompleteness and propose the granulation solution based on granular-ball computing. Our designs encompass a microservicebased information decision system to quantify the structural similarity between entities, a microservice granulation representation method based on granular-ball computing, and an optimal microservice granulation method given fixed budget constraint. Extensive experiments are conducted on open-source microservice projects to showcase the granulation result and the experimental results also show the efficacy and effectiveness.