Making Disk Failure Predictions SMARTer!

Authors: 

Sidi Lu and Bing Luo, Wayne State University; Tirthak Patel, Northeastern University; Yongtao Yao, Wayne State University; Devesh Tiwari, Northeastern University; Weisong Shi, Wayne State University

Abstract: 

Disk drives are one of the most commonly replaced hardware components and continue to pose challenges for accurate failure prediction. In this work, we present analysis and findings from one of the largest disk failure prediction studies covering a total of 380,000 hard drives over a period of two months across 64 sites of a large leading data center operator. Our proposed machine learning based models predict disk failures with 0.95 F-measure and 0.95 Matthews correlation coefficient (MCC) for 10-days prediction horizon on average.

FAST '20 Open Access Sponsored by NetApp

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BibTeX
@inproceedings {246172,
author = {Sidi Lu and Bing Luo and Tirthak Patel and Yongtao Yao and Devesh Tiwari and Weisong Shi},
title = {Making Disk Failure Predictions SMARTer!},
booktitle = {18th {USENIX} Conference on File and Storage Technologies ({FAST} 20)},
year = {2020},
isbn = {978-1-939133-12-0},
address = {Santa Clara, CA},
pages = {151--167},
url = {https://www.usenix.org/conference/fast20/presentation/lu},
publisher = {{USENIX} Association},
month = feb,
}

Presentation Video