WCSE

Failure Prediction for Temporal Dependency of Hard Drives

Abstract— loud service providers strive to predict hard drives' failure in advance to enhance user confidence in cloud storage resources. We explored the failure property from the self-monitoring, analysis, and reporting technology features of hard drives, finding that the long-term temporal changepoint dependency (LTCD) of hard drive failure creates new reconstruction challenges in failure prediction. The failure prediction for temporal dependency (FPTD) presented in this paper has three characteristics: primary identifying features and changepoint features and enhancing changepoint dependency, all of which make FPTD more sensitive to the failure of hard drives with LTCD. The experimental results show that the five evaluation metrics of FPTD are all above 94% while maintaining a low false alarm rate, among which Accuracy can reach 99.0% and Recall can reach 97.6% on average. In general, the FPTD has higher prediction quality and better stability, and is more suitable for predicting hard drive failures in the long-short temporal

Index Terms— SMART, temporal dependency, changepoint dependency, failure prediction

Xiaojian Li, Liyu Zhu, Cuiping Zhang, Haopeng Yang, Hailan Wang, Jiajia Zhang
School of Computer Science & Information Engineering, Guangxi Normal University, CHINA

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Cite: Xiaojian Li, Liyu Zhu, Cuiping Zhang, Haopeng Yang, Hailan Wang, Jiajia Zhang, "Failure Prediction for Temporal Dependency of Hard Drives," 2021 The 11th International Workshop on Computer Science and Engineering (WCSE 2021), pp. 379-388, Shanghai, China, June 19-21, 2021.