AI-Optimized Cloud Migration

Cloud migration initiatives frequently fail to deliver expected reliability and performance improvements due to unresolved application-level bottlenecks that surface after deployment to elastic cloud environments. Existing migration approaches emphasize infrastructure readiness while deferring performance remediation, leading to instability, excessive latency, and costly post-migration tuning. This paper presents an automated, pre-migration system for bottleneck discovery and minimal refactor planning. The proposed system combines static code analysis, runtime telemetry, dependency graph modeling, and historical incident data to identify dominant performance constraints and generate a ranked, minimal refactor plan prior to cloud migration. Unlike traditional approaches that advocate large-scale architectural rewrites, the system focuses on targeted, low-impact interventions that maximize stability and scalability while minimizing refactor effort and migration risk. Experimental evaluation on representative enterprise workloads demonstrates significant reductions in post-migration incidents, tail latency, and rollback frequency, validating the effectiveness of pre-migration bottleneck remediation.