Hard drive failure prediction using Decision Trees

Jing Li, Rebecca J. Stones, Gang Wang, Xiaoguang Liu, Zhongwei Li and Ming Xu

Reliability Engineering and System Safety, 2017, vol. 164, issue C, 55-65

Abstract: This paper proposes two hard drive failure prediction models based on Decision Trees (DTs) and Gradient Boosted Regression Trees (GBRTs) which perform well in prediction performance as well as stability and interpretability. The models are evaluated on a real-world dataset containing 121,698 drives in total. Experimental results show the DT model predicts over 93% of failures at a false alarm rate under 0.01%, and the GBRT model can achieve about 90% failure detection rate without any false alarms. Moreover, the GBRT model evaluates drive health (or fault probability) which provides a quantitative indicator of failure urgency. This enables operators to allocate system resources accordingly for pre-warning migrations while maintaining the quality of user services.

Keywords: Hard drive failure prediction; SMART; Decision Tree; Health degree (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:164:y:2017:i:c:p:55-65

DOI: 10.1016/j.ress.2017.03.004

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