Disaster Recovery Evidence for Stateful Cloud Services: A Systematic Review of Test Methods and Reported Performance Metrics
Abstract
Disaster recovery claims for stateful cloud services are difficult to compare because studies use inconsistent failure models, recovery definitions, and measurement intervals. We systematically reviewed empirical studies evaluating recovery of databases, storage-backed services, and other stateful cloud workloads. Searches of computing and engineering databases yielded 2,164 records; 112 studies met inclusion criteria after full-text screening. Recovery time was reported in 84% of studies, recovery-point loss in 46%, and end-to-end application consistency in only 29%. Hardware or node failure dominated the literature, whereas logical corruption, dependency failure, and identity-service outages were substantially underrepresented. Only 18% of studies described repeated recovery trials, and 22% provided sufficient configuration detail to support replication. Studies using fault injection and application-level validation produced more complete evidence than those relying on infrastructure health checks alone. Current evidence supports the technical feasibility of rapid recovery under selected conditions but provides limited insight into repeatability across complex production dependencies. Standard reporting should include fault model, workload state, recovery boundary, validation method, trial count, and both recovery-time and recovery-point outcomes.
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