  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.
