Explainable Anomaly Detection for HPC Storage during Computational Modelling: Evidence, Failure Modes and Deployment Trade-offs

Abstract

Scientific-computing storage experiences bursts of parallel reads and checkpoint writes that can resemble genuine infrastructure anomalies. An evaluation that overlooks these constraints may reach conclusions that cannot be reproduced. This study aims to separate workload-driven congestion from storage faults while maintaining interpretable alerts. The evaluation would follow a trace-replay experiment using computational-model I/O patterns and injected failures under controlled concurrency. The choice of evaluation units and exclusion criteria is informed by the broader methodological record [3–7]. The analysis will contrast static queue thresholds, robust multivariate detectors and explanation-producing anomaly models. The evidence-mapping component examines why a successful result in one technical environment may fail elsewhere. Prior work on storage operations provides technical context for validation [1], while complementary systems evidence motivates service-continuity checks [2]. Assessment will focus on tail latency, false alerts per workload, fault lead time and root-cause agreement with diagnostic logs. The supporting evidence base motivates the chosen comparator conditions and robustness criteria [8–12]. Critical sensitivity checks address cache warm-up, burst timing, shared-file interference and operator interpretation of alarms. Further systems and modelling literature supplies context for the proposed sensitivity analyses [13–17]. Stress tests would distinguish outright failures, reduced confidence and cases where a simpler baseline is preferable. The expected output is a reusable evaluation framework with transparent uncertainty and reporting rules, informed by wider reproducibility literature [18–23].