Performance-Telemetry-Based Anomaly Detection for Early Failure Identification in Enterprise Storage Systems

Abstract

Background: Enterprise storage failures rarely emerge as a single binary event. They are often preceded by weak, heterogeneous changes in device-health counters, latency, error rates, queueing behavior, retry activity, media condition, and workload-dependent performance. Static thresholds can identify only a subset of these precursors and frequently fail to generalize across drive models, firmware generations, and deployment environments.

Objective: To synthesize evidence on telemetry-driven anomaly detection and failure prediction in large-scale storage systems and to develop an operational framework for early failure identification using device-health, performance, temporal, and contextual signals.

Methods: A structured evidence synthesis was conducted across peer-reviewed studies of hard-disk-drive (HDD) and solid-state-drive (SSD) reliability, Self-Monitoring, Analysis and Reporting Technology (SMART)-based prediction, anomaly detection, temporal deep learning, feature selection, and large-scale field failure characterization. Studies were prioritized when they used production or real-world storage populations, evaluated low-prevalence failure prediction, or reported operationally relevant metrics such as failure detection rate, false-alarm rate, precision, recall, F1 score, lead time, or generalization across device models.

Results: Field studies show that real storage failures are heterogeneous, temporally correlated, and only partially explained by isolated health attributes. Predictive performance improves when telemetry is treated as a multivariate and temporal signal rather than as independent threshold crossings. Decision-tree, gradient-boosted, recurrent, attention-based, multi-view, semi-supervised, and cost-sensitive methods all demonstrate advantages under particular data conditions. Large production studies further show that device model, wear state, physical location, workload, and correlated node/rack failures materially affect interpretation. The main barriers to enterprise deployment are severe class imbalance, model drift, heterogeneous telemetry semantics, inconsistent labels, false-alarm cost, and the difference between high classification accuracy and actionable early warning.

Conclusion: Performance-telemetry-based anomaly detection is most effective when implemented as a multi-scale, context-aware, closed-loop reliability service rather than a stand-alone classifier. Enterprise systems should combine device-health indicators with performance drift, temporal persistence, topology and workload context, and explicit operational cost functions. Evaluation should emphasize precision-recall behavior, false alarms per device-time, warning lead time, and the proportion of alerts that enable preventive migration or replacement before service impact.