A Predictive–Prescriptive Framework for HPC Storage Maintenance via Explainable Artificial Intelligence

As High-Performance Computing (HPC) architectures evolve towards the Exascale, storage infrastructure reliability has emerged as a critical operational challenge, with traditional reactive and static preventive maintenance strategies proving increasingly insufficient. This study addresses this gap by proposing a comprehensive methodological framework for the transition from predictive to predictive-prescriptive maintenance in large-scale storage environments. By integrating the CRISP-DM industry standard with a multi-layered eXplainable Artificial Intelligence (XAI) suite, we develop a system capable of isolating hardware degradation signals amidst massive volumes of routine telemetry. To validate our approach, we leveraged a publicly available disk failure dataset to evaluate multiple Machine Learning configurations, addressing the challenge of severe class imbalance through optimized oversampling and Gradient Boosting algorithms. The methodology employs global and local XAI techniques, including Permutation Feature Importance, SHAP, and surrogate decision trees, to translate probabilistic risk assessments into auditable hardware engineering rules. Our results demonstrate that this hybridization of robust predictive modeling with multi-layered explainability provides a transparent, evidence-based decision support system. Ultimately, we conclude that converting opaque risk predictions into technical justifications enables infrastructure managers to optimize hardware lifecycle management and minimize system downtime in mission-critical environments, establishing a viable pathway toward more resilient and auditable storage management.