An Automated Microservice Decomposition Approach for Monolithic Applications Using Evolutionary Clustering

International Journal of Computer Science and Technology | Vol. 2, No. 2, February 2021 | pp. 9–16

Research Article

Title: An Automated Microservice Decomposition Approach for Monolithic Applications Using Evolutionary Clustering

Names of Authors: Liam Walker¹, Olivia Davies², and Ethan Taylor³

Authors’ Affiliations:
¹School of Computer Science, University of Manchester, Manchester, United Kingdom
²Department of Software Engineering, University of Bristol, Bristol, United Kingdom
³Department of Computer Science, University of Edinburgh, Edinburgh, United Kingdom

Abstract: Modern software engineering favors microservice architectures over monolithic structures to improve scalability, maintainability, and continuous deployment velocity. However, migrating legacy monolithic applications to microservices is a labor-intensive, error-prone manual task that requires deep domain knowledge. Automatic decomposition techniques help, but traditional static code analysis often misinterprets runtime structural dependencies, creating highly coupled services. This paper introduces an automated framework for monolithic application migration that combines static metric extraction with runtime business logic tracing. The methodology maps a legacy system as a multi-layered dependency graph where classes represent nodes, and edges model static method calls, database foreign keys, and shared data structures. A multi-objective evolutionary clustering algorithm based on NSGA-II partitions this graph, maximizing internal service cohesion while minimizing inter-service coupling. Empirical evaluations on three open-source monolithic enterprise applications demonstrate that the framework produces microservice boundaries with an average cohesion improvement of 31.4% and a coupling reduction of 27.2% compared to traditional K-means and hierarchical clustering techniques. The resulting service topologies reduce network communication overhead and respect data transactional boundaries. This approach streamlines legacy software modernization workflows by reducing manual architectural redesign efforts.

Keywords: Microservices migration, Software architecture, Monolithic decomposition, Evolutionary clustering, Multi-objective optimization, Static analysis

Manuscript Timeline: Received: November 15, 2020; Revised: December 20, 2020; Accepted: January 14, 2021; Published: February 1, 2021

Citation: Walker, L., Davies, O., & Taylor, E. (2021). An automated microservice decomposition approach for monolithic applications using evolutionary clustering. International Journal of Computer Science and Technology, 2(2), 9–16. DOI: 10.46882/2021/IJCST/000014

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