USENIX ATC '24 and OSDI '24

Yunfei Gu and Chentao Wu, Shanghai Jiao Tong University; Xubin He, Temple University

Solid State Drives (SSDs) based on flash technology are extensively employed as high-performance storage solutions in supercomputing data centers. However, SSD failures are frequent in these environments, resulting in significant performance issues. To ensure the reliability and accessibility of HPC storage systems, it is crucial to predict failures in advance, enabling timely preventive measures. Although many failure prediction methods focus on improving SMART attributes and system telemetry logs, their predictive efficacy is constrained due to the limited capacity of these logs to directly elucidate the root causes of SSD failures at the device level. In this paper, we revisit the underlying causes of SSD failures and first utilize the device-level flash wear characteristics of SSDs as a critical indicator instead of solely relying on SMRAT data. We propose a novel Aging-Aware Pseudo Twin Network (APTN) based SSD failure prediction approach, exploiting both SMART and device-level NAND flash wear characteristics, to effectively forecast SSD failures. In practice, we also adapt APTN to the online learning framework. Our evaluation results demonstrate that APTN improves the F1-score by 51.2% and TPR by 40.1% on average compared to the existing schemes. This highlights the potential of leveraging device-level wear characteristics in conjunction with SMART attributes for more accurate and reliable SSD failure prediction.

https://www.usenix.org/conference/atc24/presentation/gu-yunfei