Adaptive Evidence-Graph Consistency Assurance for Cloud Database Migration

Abstract

Cloud database migration is no longer a one-time bulk transfer problem; it is a distributed consistency-control problem involving change data capture, dual writes, schema evolution, network partitions, and operational cutover risk. The original challenge of keeping source and target databases synchronized becomes more complex when cloud-native services, streaming telemetry, and heterogeneous storage engines interact during migration. This paper presents a computer-science treatment of the problem through an Adaptive Evidence-Graph Consistency Assurance Model (AECAM). AECAM represents migration as a time-indexed evidence graph that unifies source records, target records, transaction logs, schema mappings, replication lag, conflict events, validation checksums, and operational signals. The model combines deterministic integrity checks with graph-temporal anomaly detection, retrieval-assisted evidence comparison, and constrained large-model reasoning for explainable triage. We formalize consistency risk, define a drift-aware migration control policy, and evaluate the framework through scenario-based migration workloads covering bulk load, online replication, schema transformation, and controlled cutover. The analysis shows that evidence-graph validation improves observability, narrows conflict localization, and provides stronger auditability than isolated checksum or replication-lag monitoring. The study contributes a structured method for preserving transactional integrity during cloud database migration while remaining compatible with modern model-assisted observability and governance practices.