In-Depth Reliability Characterization of NAND Flash based Solid State Drives in High Performance Computing Systems Extended Abstract

NAND flash based solid state drives (SSD) have been widely used in high performance computing (HPC) systems due to their better performance compared with the traditional hard disk drives. However, little is known about the reliability characteristics of SSDs in production systems. Existing works study the statistical distributions of SSD failures in the field. They do not go deep into SSD drives and investigate the unique error types and health dynamics that distinguish SSDs from hard disk drives. In this paper, we explore the SSD-specific SMART (Self-Monitoring, Analysis, and Reporting Technology) attributes to conduct an in-depth analysis of SSD reliability in a production datacenter. Our dataset contains half a million records with more than twenty attributes. We leverage machine learning technologies, specifically data clustering and correlation analysis methods, to discover groups of SSDs which have different health status and the relation among SSD-specific SMART attributes. Our results show that 1) Media wear affects the reliability of SSDs more than any other factors, and 2) SSDs transit from a health group to another which infers the reliability degradation of those drives. To the best of our knowledge, this is the first study investigating SSD-specific SMART data to characterize SSD reliability in a production environment.