Conformal Monitoring of Storage-Failure Predictors during Workload Drift: A Cross-Site Validation Agenda

Abstract

Predictive maintenance systems often report confident failure probabilities even after the underlying workload distribution changes. An evaluation that overlooks these constraints may reach conclusions that cannot be reproduced. The planned investigation seeks to quantify when conformal uncertainty monitoring can warn of deteriorating failure-prediction reliability. Relevant methodological and data-quality literature informs the design of the sampling and validation strategy [3–7]. The protocol specifies a chronologically blocked simulation that calibrates uncertainty on historical storage telemetry and tests later hardware cohorts. Comparators will include fixed-score alarms, probability-calibrated models and rolling conformal warning policies. Prior work on storage operations provides technical context for validation [1], while complementary systems evidence motivates service-continuity checks [2]. The cross-site extension holds out an entire institution or deployment location rather than relying only on random test splits. Statistical and computational sources help define the reproducibility checks and comparison procedures [8–12]. The primary outcomes are empirical coverage, alert lead time, false-positive workload and stability under distribution shift. Potential biases include serial dependence, rare-event labelling, replacement hardware and uncertainty during outages. Between-site heterogeneity would be examined through prespecified subgroup estimates and transparent domain-shift diagnostics. 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].