  With low latency, high throughput and enterprise-grade reliability, SSDs have
become the de-facto choice for storage in the data center. As a result, SSDs
are used in all online data stores in LinkedIn. These apps persist and serve
critical user data and have millisecond latencies. For the hosts serving these
applications, SSD faults are the single largest cause of failure. Frequent SSD
failures result in significant downtime for critical applications. They also
generate a significant downstream RCA (Root Cause Analysis) load for systems
operations teams. A lack of insight into the runtime characteristics of these
drives results in limited ability to provide accurate RCAs for such issues and
hinders the ability to provide credible, long term fixes to such issues. In
this paper we describe the system developed at LinkedIn to facilitate the
real-time monitoring of SSDs and the insights we gained into failure
characteristics. We describe how we used that insight to perform predictive
maintenance and present the resulting reduction of man-hours spent on
maintenance.
