Solid-State Drive Failure Prediction Using Anomaly Detection

Solid-State Drives (SSDs) enabled the implementation of real-time cloud services, with a primary focus on high performance and high availability. SSD failure prediction can improve overall system availability by preventing data loss and service interruption. SSDs employ a built-in SMART (Self-Monitoring, Analysis, and Reporting Technology) system to predict failures when certain operating parameters exceed predefined thresholds. Such univariate SMART-based models can predict a limited set of drive failures. Research in SSD failure prediction is focused on multivariate models, which can exploit the complex interactions between SMART attributes that lead to drive failure in order to detect a much larger set of failures. This paper presents an anomaly detection model, based on the Mahalanobis distance measure, which is used for the failure prediction of SSD drives. The model is able to rank the features according to their influence on failure prediction by using a forward feature selection algorithm. The proposed model is tested on a publicly available Alibaba SSD dataset, where the six highest-ranked SMART features were identified. Using this subset of SMART features, our model was able to detect 64% of failures with 81% accuracy while keeping a high precision of 96%.