Random-forest-based failure prediction for hard disk drives

Jing Shen, Jian Wan, Se-Jung Lim and Lifeng Yu

International Journal of Distributed Sensor Networks, 2018, vol. 14, issue 11, 1550147718806480

Abstract: Failure prediction for hard disk drives is a typical and effective approach to improve the reliability of storage systems. In a large-scale data center environment, the various brands and models of drives serve diverse applications with different input/output workload patterns, and non-ignorable differences exist in each type of drive failures, which make this mechanism much challenging. Although many efforts are devoted to this mechanism, the accuracy still needs to be improved. In this article, we propose a failure prediction method for hard disk drives based on a part-voting random forest, which differentiates prediction of failures in a coarse-grained manner. We conduct groups of validation experiments on two real-world datasets, which contain the SMART data of 64,193 drives. The experimental results show that our proposed method can achieve a better prediction accuracy than state-of-the-art methods.

Keywords: Failure prediction; random forest; clustering algorithm; hard disk drives (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:14:y:2018:i:11:p:1550147718806480

DOI: 10.1177/1550147718806480

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