International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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AI-Optimized Cloud Migration

Author(s) Mr. SRINIVAS PALAPARTHI
Country India
Abstract 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.
Keywords AI-Optimized Cloud Migration, Learning-Driven Migration Planning, Reinforcement Learning for Cloud Migration, Constrained Reinforcement Learning, Automated Migration Wave Planning, Infrastructure as Code Synthesis, Dependency Graph Analysis, Service Selection Optimization, Multi-Cloud Architecture, AWS and Azure Migration, DevOps Automation, Terraform and CDK Automation, Cost-Aware Cloud Optimization, Downtime Reduction, SLA-Aware Migration Planning, Latency-Aware Service Placement
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-01-08
DOI https://doi.org/10.36948/ijfmr.2026.v08i01.64202
Short DOI https://doi.org/hbjmhg
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