Enterprise Disk Drive Scrubbing Based on Mondrian Conformal Predictors
Rahul Vishwakarma Jinha Hwang Soundouss Messoudi Ava Hedayatipour
Disk scrubbing is a process aimed at resolving read errors on disks by
reading data from the disk. However, scrubbing the entire storage array at once
can adversely impact system performance, particularly during periods of high
input/output operations. Additionally, the continuous reading of data from
disks when scrubbing can result in wear and tear, especially on larger capacity
disks, due to the significant time and energy consumption involved. To address
these issues, we propose a selective disk scrubbing method that enhances the
overall reliability and power efficiency in data centers. Our method employs a
Machine Learning model based on Mondrian Conformal prediction to identify
specific disks for scrubbing, by proactively predicting the health status of
each disk in the storage pool, forecasting n-days in advance, and using an
open-source dataset. For disks predicted as non-healthy, we mark them for
replacement without further action. For healthy drives, we create a set and
quantify their relative health across the entire storage pool based on the
predictor's confidence. This enables us to prioritize selective scrubbing for
drives with established scrubbing frequency based on the scrub cycle. The
method we propose provides an efficient and dependable solution for managing
enterprise disk drives. By scrubbing just 22.7% of the total storage disks, we
can achieve optimized energy consumption and reduce the carbon footprint of the
data center.