A Novel Iterative Machine Learning-Driven Framework for Reliable and Adaptive Cloud Data Migration

Abstract

Cloud data migration has become an essential requirement for modern enterprises due to the rapid growth of cloud computing and data-intensive applications. However, achieving reliable and efficient migration remains challenging because of heterogeneous data types, fluctuating network bandwidth, diverse migration tools, and the need to preserve data consistency throughout the migration process. Most existing approaches evaluate migration tools independently, assume static network conditions, and provide limited support for adaptive optimization and verifiable migration outcomes. This paper presents AMF-CloudForge, a unified machine learning-driven framework that integrates migration state analysis, intelligent scheduling, consistency preservation, and real-time adaptive management into a single end-to-end architecture. The proposed framework begins with Polytype Migration State Encoding (PMSE), which performs comprehensive micro-benchmarking across different data types, file sizes, and network conditions to generate Migration State Tensors and Tool Efficiency Scores that accurately characterize migration behavior. These learned representations are then utilized by the Topology-Aware Diffusion Scheduler (TADS) to optimize chunking, routing, and migration tool selection by considering dynamic network topology, bandwidth variations, and transfer costs. To ensure data integrity and consistency, the Causal Delta with Erasure-coded Commitment (CDEC) module combines causal dependency tracking, adaptive erasure coding, and cryptographic quorum verification to guarantee reliable and verifiable data commits without data loss or metadata inconsistencies. Furthermore, the Uncertainty-Quantified Twin Supervisor (UQTS) continuously monitors the migration process through a probabilistic digital twin, enabling risk-aware scheduling adjustments and real-time Service Level Objective (SLO) verification under changing system conditions.Together, these components form a fully automated migration framework capable of policy-driven execution and machine-verifiable migration reporting. Experimental evaluation on heterogeneous cloud workloads demonstrates that the proposed framework reduces overall migration makespan by 35–45%, sustains effective bandwidth utilization above 85%, and limits unrecoverable data loss to approximately 10⁻¹¹ per 10 TB of migrated data. By integrating machine learning-based performance modeling with adaptive scheduling, cryptographic consistency verification, and intelligent runtime supervision, the proposed framework transforms cloud data migration from a static, tool-centric process into a reliable, adaptive, and continuously optimized cloud service.