论文类型：期刊论文
发表刊物：Design, Automation and Test in Europe Conference -DATE 2023
收录刊物：SCI
所属单位：华中科技大学
学科门类：工学
一级学科：电子科学与技术
文献类型：M
关键字：SSD, multidimensional features, failure prediction, machine learning, system availability
DOI码：10.23919/DATE56975.2023.10137082
摘要：As SSD failures seriously lead to data loss and service interruption, proactive failure prediction is often used to improve system availability. However, the unidimensional SMART-based prediction models hardly predict all drive failures. Some other features applied in data centers and enterprise storage systems are not readily available in consumer storage systems (CSS). To further analyze related failures in production SSD-based CSS, we study nearly 2.3 million SSDs from 12 drive models based on a dataset of SMART logs, trouble tickets, and error logs. We discover that SMART, Firmware Version, WindowsEvent, and BlueScreenof Death (SFWB) are closely related to SSD failures. We further propose a multidimensional-based failure prediction approach (MFPA), which is portable in algorithms, SSD vendors, and PC manufacturers. Experiments on the datasets show that SFWB-based MFPA achieves a high true positive rate (98.18%) and low false positive rate (0.56%), which is 4% higher and 86% lower than the SMART-based model. It is robust and can con-tinuously predict for 2–3 months without iteration, substantially improving the system availability.