Explainability-Centered Machine Learning for Reliable Solid-State Drive Failure Prediction

Abstract

Solid-state drives (SSDs) are increasingly deployed in computing infrastructures where storage reliability directly influences service continuity, data availability, and maintenance cost. Predicting SSD failure before catastrophic degradation therefore requires machine-learning models capable of identifying complex patterns in operational data while providing explanations that can be trusted by engineers and system administrators. This research develops an explainability-centered conceptual framework for reliable SSD failure prediction by integrating predictive machine learning with local and global interpretability mechanisms, particularly Local Interpretable Model-Agnostic Explanations (LIME) and Shapley-based explanations (SHAP). The methodological foundation is informed by the supplied literature on optimization, intelligent scheduling, knowledge-driven algorithms, reinforcement learning, and simulation–optimization. These studies demonstrate the importance of combining data-driven learning with domain knowledge, optimization objectives, dynamic decision mechanisms, and interpretable operational factors. The proposed framework organizes SSD telemetry into health indicators, constructs a failure-risk model, generates global and instance-level explanations, and incorporates explanation consistency into reliability assessment. Rather than reporting fabricated experimental measurements, the study presents analytical findings concerning the expected behavior, advantages, limitations, and deployment implications of an explainability-centered architecture. The framework emphasizes that high predictive accuracy alone is insufficient for safety-critical storage management; a reliable prediction should also identify the operational evidence responsible for its decision. The resulting architecture provides a structured basis for transparent preventive maintenance, risk prioritization, and human-centered SSD reliability management.