{"title":"为ssd生成真实的磨损分布","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":"{\"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}","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}
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).
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