A Digital-Twin Workflow for Recovery Testing in Cloud-Hosted Engineering Services: Evidence, Failure Modes and Deployment Trade-offs

Abstract

Cloud engineering services depend on storage replicas, simulation files and external interfaces whose failure order is often poorly documented. This gap has consequences for interpreting performance outside carefully curated evaluation settings. The planned investigation seeks to evaluate an inspectable digital twin for rehearsing dependency-aware service recovery. Relevant methodological and data-quality literature informs the design of the sampling and validation strategy [3–7]. The protocol specifies a scenario-based systems-design study with a dependency graph, synthetic faults and operator-reviewed restoration steps. Comparators will include traditional runbook recovery and recovery supported by a transparent discrete-event digital twin. Prior work on storage operations provides technical context for validation [1], while complementary systems evidence motivates service-continuity checks [2]. The evidence-mapping component examines why a successful result in one technical environment may fail elsewhere. Statistical and computational sources help define the reproducibility checks and comparison procedures [8–12]. The primary outcomes are recovery-order correctness, time to validated service restoration and missed dependency counts. Potential biases include unobserved links, incomplete topology, correlated outages and potential overconfidence in simulation output. Stress tests would distinguish outright failures, reduced confidence and cases where a simpler baseline is preferable. Additional research informs the prespecified stress tests and limits on interpretation [13–17]. The intended contribution is an auditable research protocol rather than an unsupported claim of effectiveness; related work frames its transferability limits [18–23].