R&D: Fantastic SSD Internals and How to Learn and Use Them

ACM Digital Library has published, in SYSTOR ’22: Proceedings of the 15th ACM International Conference on Systems and Storage, an article written by Nanqinqin Li, University of Chicago and Princeton University, Mingzhe Hao, University of Chicago, Huaicheng Li, University of Chicago and Carnegie Mellon University, Xing Lin, Tim Emami, Netapp, Inc., and Haryadi S. Gunawi, University of Chicago.

Abstract: “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.“