AIOPS Prediction for Hard Drive Failures Based on Stacking Ensemble Model

Abstract

This paper mainly shares a successful Artificial Intelligence for IT Operations (AIOps) solution we have built for a cloud storage array to deal with unbalanced hard disk failure data and predict disk failure. Firstly, we preprocessed the unbalanced disk data to filter out irrelevant raw data. Based on SMART (Self-Monitoring, Analysis, and Reporting Technology) attributes, we extracted 14 preliminary attributes as training features for disk failure prediction. Secondly, we used a feature extraction library called Tsfresh and 16 extraction methods to regenerate more than 1500 features. To accelerate machine learning process, we used the Benjamini Yekutieli procedure with a significance test to select the most relevant features. Since a single predictive model no longer performs sufficiently well on the unbalanced dataset, we finally input the prediction results calculated by three algorithms(XGBoost classification, LSTM classification, and XGBoost regression) as new features input of a stacking ensemble learning model that can generate more stable and accurate prediction results. The experimental results showed that the proposed stacking ensemble learning model can accurately predict the disk failure and necessity of disk replacement 0 to 14 days, 14 to 42 days and more days in advance.