Scrub Unleveling: Achieving High Data Reliability at Low Scrubbing Cost

Nowadays, proactive error prediction, using ma-chine 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.