{"title":"Generating realistic wear distributions for SSDs","authors":"Ziyang Jiao, B. Kim","doi":"10.1145/3538643.3539757","DOIUrl":null,"url":null,"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).","PeriodicalId":159120,"journal":{"name":"Proceedings of the 14th ACM Workshop on Hot Topics in Storage and File Systems","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 14th ACM Workshop on Hot Topics in Storage and File Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3538643.3539757","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
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).