Enhancing SSD Reliability Through Explainable AI-Based Failure Prediction Models

Abstract

Solid-state drives (SSDs) have become fundamental storage components in modern computing infrastructures because of their high throughput, low access latency, and suitability for data-intensive workloads. However, SSD reliability remains a significant concern because degradation and failure can emerge from complex interactions among workload characteristics, device states, and accumulated wear. Conventional predictive models may provide useful failure probabilities while offering limited insight into why a drive is classified as high risk. This limitation reduces their practical value in reliability management, where storage administrators require interpretable evidence before initiating preventive maintenance or replacement. This paper proposes an explainable artificial intelligence framework for SSD failure prediction that integrates machine-learning-based classification with local and global explanation mechanisms, particularly LIME and SHAP. The methodological design also considers architectural principles derived from deep residual, densely connected, convolutional, and neural architecture search models. These approaches provide theoretical foundations for constructing robust feature-processing and model-selection components while avoiding unnecessary architectural complexity. The proposed framework emphasizes predictive accuracy, interpretability, feature attribution, and operational usefulness as complementary objectives. The analysis indicates that an effective SSD reliability system should not treat explainability as a post-processing feature alone but incorporate interpretability into the complete prediction pipeline. The study further identifies a trade-off between increasingly sophisticated predictive architectures and the transparency required for operational deployment. The resulting framework provides a research-oriented foundation for developing trustworthy SSD failure prediction systems in which predictions can be accompanied by understandable evidence for decision-making.