A Digital-Twin Workflow for Recovery Testing in Cloud-Hosted Engineering Services: A Reproducibility-Centred Evaluation

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. This study aims to evaluate an inspectable digital twin for rehearsing dependency-aware service recovery. The evaluation would follow a scenario-based systems-design study with a dependency graph, synthetic faults and operator-reviewed restoration steps. The choice of evaluation units and exclusion criteria is informed by the broader methodological record [3–7]. The analysis will contrast traditional runbook recovery and recovery supported by a transparent discrete-event digital twin. The reproducibility extension records dataset versions, model settings, exclusion rules and provenance before any held-out evaluation. Prior work on storage operations provides technical context for validation [1], while complementary systems evidence motivates service-continuity checks [2]. Assessment will focus on recovery-order correctness, time to validated service restoration and missed dependency counts. The supporting evidence base motivates the chosen comparator conditions and robustness criteria [8–12]. Critical sensitivity checks address unobserved links, incomplete topology, correlated outages and potential overconfidence in simulation output. Further systems and modelling literature supplies context for the proposed sensitivity analyses [13–17]. An independent replication exercise would repeat the main analysis from the documented artefacts and quantify disagreements. The expected output is a reusable evaluation framework with transparent uncertainty and reporting rules, informed by wider reproducibility literature [18–23].