AI-Driven Intelligent Framework for Zero-Downtime Migration of Distributed On-Premises Systems to AWS Cloud

Abstract

Enterprise organizations are migrating mission-critical distributed systems from on-premises data centers to cloud platforms in pursuit of elastic scalability, reduced infrastructure overhead, and improved operational resilience. Zero-downtime migration of large-scale distributed enterprise applications, however, remains a persistent engineering challenge, since production environments supporting financial, healthcare, or e-commerce workloads cannot tolerate meaningful service interruption. Existing approaches typically address Infrastructure-as-Code provisioning, continuous delivery orchestration, and operational monitoring as separate, loosely coupled concerns, leaving deployment validation during migration only weakly informed by the telemetry these systems already produce. This paper proposes an AI-driven intelligent migration framework that closes this gap by integrating Infrastructure-as-Code provisioning, event-driven continuous delivery orchestration, full-stack enterprise observability, and machine-learning-based deployment health scoring into a single closed-loop architecture for migrating distributed on-premises systems to Amazon Web Services (AWS). The framework couples a manifest-driven orchestration engine with a four-layer observability platform and a deployment intelligence layer that evaluates infrastructure, application, and historical release telemetry to recommend automatic promotion, manual review, or rollback through an interpretable, three-tier Deployment Health Score policy. As a rigorous mathematical baseline and architectural feasibility verification prior to physical enterprise pilot deployment, the framework’s interactive mechanics, state-space sensitivity, and multi-model algorithmic thresholds are evaluated using a high-fidelity, standalone algorithmic simulation modeling an 80-microservice topology. This synthetic evaluation serves as a structural validation phase to verify algorithmic thresholds under simulated real-world degradation modes prior to physical multi-tenant deployment. When subjected to simulated real-world degradation modes, including linear database connection pool exhaustion, the closed-loop multi-model architecture suppresses stochastic background noise and achieves a 56.1% optimization in Mean Time to Detect (MTTD) over traditional multivariate static threshold monitoring within the boundaries of the simulated environment.