Scrub Unleveling: Achieving High Data Reliability at Low Scrubbing Cost
Tianming Jiang1,a, Ping Huang2 and Ke Zhou1,b
1Huazhong University of Science and Technology, Wuhan, China
ajiangtianming@hust.edu.cn
bk.zhou@hust.edu.cn
2Temple University, Philadelphia, USA
templestorager@temple.edu
ABSTRACT
Nowadays, proactive error prediction, using machine learning methods, has been proposed to improve storage system reliability by increasing the scrubbing rate for drives with higher error rates. Unfortunately, the majority of works incur non-trivial scrubbing cost and ignore the periodic characteristic of scrubbing. In this paper, we aim to make the prediction guided scrubbing more suitable for practical use. In particular, we design a scrub unleveling technique that enforces a lower rate scrubbing to healthy disks and a higher rate scrubbing to disks subject to latent sector errors (LSEs). Moreover, a voting-based method is introduced to ensure prediction accuracy. Experimental results on a real-world field dataset have demonstrated that our proposed approach can achieve lower scrubbing cost together with higher data reliability than traditional fixed-rate scrubbing methods. Compared with the state-of-the-art, our method can achieve the same level of Mean-Time-To-Detection (MTTD) with almost 32% less scrubbing.
