Paper
:
SSD Failures in the Field: Symptoms, Causes, and Prediction Models
Event Type
Paper
Registration Categories
TP
Tags
Best Practices
Energy
Exascale
Machine Learning
Memory
Modelling
Networks
Performance
Power
Reliability
Reproducibility
TimeThursday, 21 November 20191:30pm - 2pm
Location405-406-407
DescriptionIn recent years, solid state drives (SSDs) have become a staple of high-performance data centers for their speed and energy efficiency. In this work, we study the failure characteristics of 30,000 drives from a Google data center spanning six years. We characterize the workload conditions that lead to failures and illustrate that their root causes differ from common expectation but remain difficult to discern. Particularly, we study failure incidents that result in manual intervention from the repair process. 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.
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