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The advent of Industrial Internet of Things (IIoT) has imposed more stringentrequirements on industrial software in terms of communication delay,scalability, and maintainability. Microservice architecture (MSA), a novelsoftware architecture that has emerged from cloud computing and DevOps,presents itself as the most promising solution due to its independentlydeployable and loosely coupled nature. Currently, practitioners are inclined tomigrate industrial legacy systems to MSA, despite numerous challenges itpresents. In this paper, we propose an automated microservice decompositionmethod for extracting microservice candidates based on spectral graph theory toaddress the problems associated with manual extraction, which istime-consuming, labor intensive, and highly subjective. The method is dividedinto three steps. Firstly, static and dynamic analysis tools are employed toextract dependency information of the legacy system. Subsequently, informationis transformed into a graph structure that captures inter-class structure andperformance relationships in legacy systems. Finally, graph-based clusteringalgorithm is utilized to identify potential microservice candidates thatconform to the principles of high cohesion and low coupling. Comparativeexperiments with state of-the-art methods demonstrate the significantadvantages of our proposed method in terms of performance metrics. Moreover,Practice show that our method can yield favorable results even without theinvolvement of domain experts.

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