Reliability Characterization and Failure Prediction of 3D TLC SSDs in Large-Scale Storage Systems
Peng Li, Wei Dang, Congmin Lyu, Min Xie, Quanyang Bao, Xiaofeng Ji, Jianhua Zhou
Abstract
3D triple-level cell (TLC) NAND flash based solid state drive (SSD) is gradually becoming the dominant storage media in large-scale storage systems due to high storage density and low cost-per-bit. It ranks one of the top replaced hardware components in systems and their enormous amount also indirectly increases the failure probability, resulting in irreversible data loss disaster and service unavailability. This paper for the first time investigates system-level 3D TLC SSDs to characterize reliability and sub-health status based on field Self-Monitoring, Analysis and Reporting Technology (SMART) data, and predict impending failure proactively. We explore real-world datasets and derive some findings for each selected attribute in predetermined categories, contributing to the following feature selection and enhancing the interpretability of prediction models. Moreover, various machine learning models are trained to predict failures ahead of time, and experimental results show that random forest model can achieve 0.636 f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score and 0.662 MCC for a 7-day prediction horizon, and 42.5% true positive rate (TPR) with 0.00% false positive rate (FPR). Different time window sizes, training set fractions and ratios of negative to positive are analyzed as well.