AI-Enhanced Modularization And Migration Strategies For Legacy Enterprise Systems In Cloud-Native Environments
Keywords:
Legacy System Modernization, Machine Learning, Service Boundary Detection, MicroservicesAbstract
The accelerating adoption of cloud-native architectures and microservices has fundamentally reshaped enterprise software engineering, emphasizing scalability, resilience, and modularity. Legacy systems, however, present significant challenges for organizations seeking to migrate to cloud infrastructures while retaining business continuity and minimizing technical debt. This study explores the integration of machine learning-assisted service boundary detection and advanced modularization techniques for legacy system transformation. By leveraging Hebbar's (2022) framework for automated service delineation, this research evaluates methodological approaches for identifying cohesive service units within monolithic applications and assesses their subsequent deployment within microservices-based cloud architectures. The investigation incorporates an extensive review of contemporary cloud migration strategies, high-availability deployment models, and digital transformation case studies in financial and e-commerce domains. Theoretical underpinnings from systems architecture and software engineering literature are synthesized to propose a comprehensive model that integrates automated boundary detection, risk-aware migration planning, and iterative service decomposition. Furthermore, this research evaluates trade-offs in scalability, security, and operational continuity, emphasizing the implications for enterprise-level digital transformation initiatives. Key contributions include a conceptual framework linking machine learning-driven modularization with practical deployment strategies, critical discussion of architectural patterns, and actionable insights for infrastructure modernization. The study concludes by identifying persistent research gaps, particularly regarding automated validation of service cohesion, adaptive scaling policies, and cross-organizational standardization, providing avenues for future exploration.
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Copyright (c) 2025 Dr. Hugo T. Belmont (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
