The Life and Death of SSDs and HDDs: Similarities, Differences, and Prediction Models

TL;DR

This study compares HDDs and SSDs in data centers, analyzing failure causes over six years, and develops machine learning models that accurately predict failures and reveal underlying root causes based on workload and device data.

Contribution

It provides a comprehensive comparative analysis of HDD and SSD failures using large-scale field data and introduces effective machine learning models for failure prediction and root cause analysis.

Findings

HDD failures are linked to head positioning time rather than age.

SSD failures show high infant mortality rates.

Machine learning models achieve high recall and low false positives in failure prediction.

Abstract

Data center downtime typically centers around IT equipment failure. Storage devices are the most frequently failing components in data centers. We present a comparative study of hard disk drives (HDDs) and solid state drives (SSDs) that constitute the typical storage in data centers. Using a six-year field data of 100,000 HDDs of different models from the same manufacturer from the BackBlaze dataset and a six-year field data of 30,000 SSDs of three models from a Google data center, we characterize the workload conditions that lead to failures and illustrate that their root causes differ from common expectation but remain difficult to discern. For the case of HDDs we observe that young and old drives do not present many differences in their failures. Instead, failures may be distinguished by discriminating drives based on the time spent for head positioning. For SSDs, we observe high levels of infant mortality and characterize the differences between infant and non-infant failures. We develop several machine learning failure prediction models that are shown to be surprisingly accurate, achieving high recall and low false positive rates. These models are used beyond simple prediction as they aid us to untangle the complex interaction of workload characteristics that lead to failures and identify failure root causes from monitored symptoms.