@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 = "SSD, machine learning, simulation, wear-out",
author = "Ziyang Jiao and Kim, {Bryan S.}",
note = "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",
}