A Microservices Identification Method Based on Spectral Clustering for Industrial Legacy Systems

TL;DR

This paper introduces an automated spectral clustering-based method for decomposing industrial legacy systems into microservices, improving efficiency and objectivity over manual approaches.

Contribution

It presents a novel spectral graph theory-based approach for microservice identification that reduces reliance on domain experts and enhances decomposition performance.

Findings

Outperforms state-of-the-art methods in key metrics

Requires less manual intervention and domain expertise

Effective in extracting microservice candidates from legacy systems

Abstract

The advent of Industrial Internet of Things (IIoT) has imposed more stringent requirements on industrial software in terms of communication delay, scalability, and maintainability. Microservice architecture (MSA), a novel software architecture that has emerged from cloud computing and DevOps, presents itself as the most promising solution due to its independently deployable and loosely coupled nature. Currently, practitioners are inclined to migrate industrial legacy systems to MSA, despite numerous challenges it presents. In this paper, we propose an automated microservice decomposition method for extracting microservice candidates based on spectral graph theory to address the problems associated with manual extraction, which is time-consuming, labor intensive, and highly subjective. The method is divided into three steps. Firstly, static and dynamic analysis tools are employed to extract dependency information of the legacy system. Subsequently, information is transformed into a graph structure that captures inter-class structure and performance relationships in legacy systems. Finally, graph-based clustering algorithm is utilized to identify potential microservice candidates that conform to the principles of high cohesion and low coupling. Comparative experiments with state of-the-art methods demonstrate the significant advantages of our proposed method in terms of performance metrics. Moreover, Practice show that our method can yield favorable results even without the involvement of domain experts.