Automated Versus Operator-Led Rollback During Interrupted Cloud Migration: A Controlled Comparison
Abstract
Interrupted cloud migrations require rapid rollback decisions, but operator-led recovery can be delayed by diagnosis, command sequencing, and incomplete knowledge of partially executed tasks. We compared automated and operator-led rollback across 96 controlled migration interruptions involving network loss, failed validation, replication divergence, and target-service unavailability. Each condition used the same runbook and pre-defined rollback boundary. Automated rollback reduced median recovery time from 31.4 to 18.9 minutes and decreased configuration residue left on the target environment from 13.5% to 4.2%. Operators performed similarly to automation in simple network interruptions but were slower when failures occurred after multiple dependent tasks had completed. Automated rollback failed in three scenarios because preconditions were incorrectly classified, whereas operator teams successfully adapted. Overall service restoration within the target window was 90% for automated rollback and 76% for operator-led rollback. Automation therefore improved speed and consistency, but safe use depended on accurate state detection and explicit stop conditions. Human escalation remained important when failure states were ambiguous or fell outside the modelled rollback path.
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