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extended-abstract

Selective scrubbing based on algorithmic randomness

Published: 06 June 2022 Publication History

Abstract

Disk scrubbing is a background process to fix read errors by reading the disks. However, scrubbing the entire storage array can significantly increase the system load and degrade system performance when there is high incoming IO. Deciding "which disk to scrub" complemented with "when to scrub" can significantly improve the data centre's overall reliability and power saving. We present a solution on an open-source SMART dataset that performs selective scrubbing and designs a scrub frequency based on the scrub cycle. The method leverages an algorithmic randomness framework to quantify the health of the concerned drives and ranks them for selective scrubbing.

References

[1]
Rahul Deo Vishwakarma. Bing Liu. 2022. Managing storage device scrubbing. https://patentimages.storage.googleapis.com/d4/da/3f/e9c833ae55ad04/US20220019564A1.pdf. [Online; accessed 11-March-2022].
[2]
Open Source SMART dataset. 2013. Smart Dataset from Nankai University and Baidu, Inc. https://pan.baidu.com/share/link?shareid=189977&uk=4278294944/. [Online; accessed 04-March-2022].
[3]
Jinha Hwang. 2022. Source Code for Selective scrubbing based on algorithmic randomness. https://github.com/jinzzup/systor2022-disk-scrubbing/. [Online; accessed 11-March-2022].
[4]
Guanying Wang, Ali Raza Butt, Chris Gniady, and Virginia Tech. 2008. On the Impact of Disk Scrubbing on Energy Savings. In HotPower.

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cover image ACM Conferences
SYSTOR '22: Proceedings of the 15th ACM International Conference on Systems and Storage
June 2022
163 pages
ISBN:9781450393805
DOI:10.1145/3534056
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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  • Technion: Israel Institute of Technology
  • USENIX Assoc: USENIX Assoc

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2022

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Author Tags

  1. conformal prediction
  2. selective scrubbing

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  • Extended-abstract

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SYSTOR '22
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SYSTOR '22 Paper Acceptance Rate 12 of 41 submissions, 29%;
Overall Acceptance Rate 108 of 323 submissions, 33%

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