This work presents (a) Queenie, an application-level tool that can automatically learn 10 internal properties of block-level SSDs, (b) Kelpie, the learning and analysis results of running Queenie on 21 different SSD models from 7 major SSD vendors, and (c) Newt, a set of storage performance optimization examples that use the learned properties. By bringing numerous observations and unique findings, this work exposes substantial improvement spaces for both SSD users and vendors, enlightening possibilities of unleashing more SSD performance potential and highlighting the necessity of further exploring SSD internals.

This article presents the first large-scale field study of NAND-based SSDs in enterprise storage systems (in contrast to drives in distributed data center storage systems). The study is based on a very comprehensive set of field data, covering 1.6 million SSDs of a major storage vendor (NetApp). The drives comprise three different manufacturers, 18 different models, 12 different capacities, and all major flash technologies (SLC, cMLC, eMLC, 3D-TLC). The data allows us to study a large number of factors that were not studied in prior works, including the effect of firmware versions, the reliability of TLC NAND, and the correlations between drives within a RAID system. This article presents our analysis, along with a number of practical implications derived from it.

Fail-slow hardware is an under-studied failure mode. We present a study of 114 reports of fail-slow hardware incidents, collected from large-scale cluster deployments in 14 institutions. We show that all hardware types such as disk, SSD, CPU, memory, and network components can exhibit performance faults. We made several important observations such as faults convert from one form to another, the cascading root causes and impacts can be long, and fail-slow faults can have varying symptoms. From this study, we make suggestions to vendors, operators, and systems designers.