Architectural Resilience and Systemic Decomposition: A Multi-Dimensional Framework for Microservices, Serverless Paradigms, And Machine Learning-Assisted Modularization
Keywords:
Microservices, Serverless Computing, Machine LearningAbstract
The transition from monolithic architectures to decentralized cloud-native systems represents a fundamental shift in software engineering, characterized by the pursuit of extreme scalability and high availability. This research presents a comprehensive investigation into the mechanisms governing microservice building, serverless adoption, and the modularization of legacy systems. Drawing upon evidence-based software engineering principles and grounded theory, this article synthesizes the current state of high-availability frameworks, structural coupling metrics, and the emerging influence of machine learning in service boundary detection. We explore the evolutionary path from the "big ball of mud" monolith to a dataflow-driven microservices ecosystem, emphasizing the critical role of Conway’s Law in organizational alignment. Furthermore, the study provides a deep conceptualization of technical debt in serverless computing (Function-as-a-Service) and the architectural motivations behind Micro-Frontends. Central to this discourse is the reconciliation of structural coupling with quality attributes defined by ISO/IEC 25010, ensuring that the decomposition process enhances testability and maintainability. By evaluating circuit breaker patterns and the nuances distinguishing Service-Oriented Architecture (SOA) from microservices, this work establishes a publication-ready taxonomy for modern software architects. The results highlight that while machine learning significantly optimizes boundary detection, the human-centric aspects of refactoring and qualitative requirements remain indispensable.
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Copyright (c) 2023 Dr. Julian Sterling (Author)
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