Dependency-Aware Orchestration of Storage Provisioning and Cloud Migration: A Reproducible Performance Benchmark
Abstract
Large storage migrations often fail at cutover because infrastructure tasks are scheduled as independent steps even though application, network, replication, and identity components have strict dependencies. We developed a dependency-aware orchestration strategy and benchmarked it against a conventional stage-based runbook in 108 reproducible migration scenarios. Test cases varied dataset size, dependency depth, replication lag, and transient service faults. Primary outcomes were cutover duration, task re-execution, rollback frequency, and service-unavailable time. Dependency-aware orchestration shortened median cutover time by 18.4% and reduced redundant task execution by 36.9%. Rollback was required in 7 of 54 dependency-aware scenarios compared with 16 of 54 conventional scenarios. Gains were most pronounced in scenarios with four or more cross-service dependencies, where service-unavailable time decreased by 29%. The orchestration overhead itself was small, adding less than 2% to pre-cutover planning time. A graph-based dependency model therefore improved both execution efficiency and fault containment, indicating that migration automation should explicitly encode dependency relationships rather than rely on sequential task lists and operator memory.
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