R&D: Selective Scrubbing Based on Algorithmic Randomness

ACM Digital Library has published, in SYSTOR ’22: Proceedings of the 15th ACM International Conference on Systems and Storage, an article written by Rahul Vishwakarma, California State University Long Beach, CA, USA, Bing Liu, Dell Technologies, Beijing, China, Peter Gatsby, and Jinha Hwang, California State University Long Beach, CA, USA.

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.“