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Architectural Evolution and Decomposition Strategies: A Comprehensive Analysis of Microservice Migration, Performance Optimization, And Machine Learning-Assisted Service Boundary Detection

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Dr. Alistair Sterling ¹ ,

⁴ Department of Software Systems Engineering, University of Melbourne, Australia

Abstract

The paradigm shift from monolithic software architectures to microservices represents one of the most significant transitions in modern software engineering. This article provides an extensive investigation into the theoretical and practical dimensions of this transition, focusing on the systemic challenges of decomposition, migration, and performance orchestration. By synthesizing foundational principles from early architectural discourse with contemporary advancements in machine learning-assisted service boundary detection, the research delineates a multidimensional framework for modularizing legacy systems. We explore the granular differences between services, microservices, and nanoservices, while critically evaluating the infrastructure cost-efficiencies of serverless versus container-based deployments. Central to this study is the reconciliation of conflicting requirements between scalability and security, which often emerge during the decomposition phase. The methodology examines workload characterization and interface analysis as primary drivers for service identification, further enhanced by automated performance testing and resilience modeling. Results indicate that while microservices offer superior elasticity and independent deployability, the migration process introduces significant overhead in terms of network latency and operational complexity. This comprehensive analysis concludes with a roadmap for evolutionary architectural transformation, emphasizing the role of automated boundary detection in reducing the cognitive load of system architects.

Keywords

Microservices, Monolithic Architecture, Service Boundary Detection

References

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Architectural Evolution and Decomposition Strategies: A Comprehensive Analysis of Microservice Migration, Performance Optimization, And Machine Learning-Assisted Service Boundary Detection

Abstract

Keywords

References

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