@inproceedings{ac8388b7197e480f879be21b062803a8,
title = "Generating realistic wear distributions for SSDs",
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).",
keywords = "machine learning, simulation, SSD, wear-out",
author = "Ziyang Jiao and Kim, {Bryan S.}",
note = "Funding Information: We thank the anonymous reviewers for their insightful comments and suggestions that help us to improve the quality of this paper. This research was supported, in part, by the National Science Foundation award CNS-2008453. Publisher Copyright: {\textcopyright} 2022 ACM.; 14th ACM Workshop on Hot Topics in Storage and File Systems, HotStorage 2022 ; Conference date: 27-06-2022 Through 28-06-2022",
year = "2022",
month = jun,
day = "27",
doi = "10.1145/3538643.3539757",
language = "English (US)",
series = "HotStorage 2022 - Proceedings of the 2022 14th ACM Workshop on Hot Topics in Storage and File Systems",
publisher = "Association for Computing Machinery, Inc",
pages = "65--71",
booktitle = "HotStorage 2022 - Proceedings of the 2022 14th ACM Workshop on Hot Topics in Storage and File Systems",
}