Generating realistic wear distributions for SSDs

Ziyang Jiao, Bryan S. Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present FF-SSD, a machine learning-based SSD aging framework that generates representative future wear-out states. FF-SSD is accurate (up to 99% similarity), efficient (accelerates simulation time by 2×), and modular (can be integrated with existing simulators and emulators).

Original languageEnglish (US)
Title of host publicationHotStorage 2022 - Proceedings of the 2022 14th ACM Workshop on Hot Topics in Storage and File Systems
PublisherAssociation for Computing Machinery, Inc
Pages65-71
Number of pages7
ISBN (Electronic)9781450393997
DOIs
StatePublished - Jun 27 2022
Event14th ACM Workshop on Hot Topics in Storage and File Systems, HotStorage 2022 - Virtual, Online, United States
Duration: Jun 27 2022Jun 28 2022

Publication series

NameHotStorage 2022 - Proceedings of the 2022 14th ACM Workshop on Hot Topics in Storage and File Systems

Conference

Conference14th ACM Workshop on Hot Topics in Storage and File Systems, HotStorage 2022
Country/TerritoryUnited States
CityVirtual, Online
Period6/27/226/28/22

Keywords

  • machine learning
  • simulation
  • SSD
  • wear-out

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Software

Fingerprint

Dive into the research topics of 'Generating realistic wear distributions for SSDs'. Together they form a unique fingerprint.

Cite this