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Archit. Code Optim."],"published-print":{"date-parts":[[2024,12,31]]},"abstract":"<jats:p>\n            The NVMe Zoned Namespace (ZNS) is a high-performance interface for flash-based solid-state drives\u00a0(SSDs), which divides the logical address space into fixed-size and sequential-write zones. Meanwhile, ZNS SSDs eliminate in-device garbage collection\u00a0(GC) by shifting the responsibility of GC to the host. However, the host-side GC of ZNS SSDs is not efficient. On the one hand, data migration during GC first moves data to the host buffer and then writes back the transferred data to the new location in the SSD, resulting in an unnecessary end-to-end transfer overhead. On the other hand, due to the pre-configured mapping between zones and blocks, GC incurs a large block-to-block rewrite overhead, i.e., even if most of the data in a block of the victim zone is valid, the valid data will still be rewritten to another block in the target zone. To address these issues, this article proposes a novel ZNS SSD design that features dynamic zone mapping, termed\n            <jats:italic>Brick-ZNS<\/jats:italic>\n            .\n            <jats:italic>Brick-ZNS<\/jats:italic>\n            implements two key functionalities: in-storage data migration and address remapping. New ZNS commands are first designed to realize in-storage data migration to avoid the end-to-end transfer overhead of GC while ensuring performance predictability. Then, a remapping strategy exploiting parallel physical blocks is proposed to reduce the large block-to-block rewrite overhead while ensuring zone-level access parallelism. The basic idea of the strategy is to directly remap the parallel physical blocks with a sufficient amount of valid data in the victim zone to the target zone, hence avoiding the large block-to-block rewrite overhead. Based on a full-stack SSD emulator, the evaluation results show that\n            <jats:italic>Brick-ZNS<\/jats:italic>\n            improves write throughput by 25% and SSD lifetime by 1.41\u00d7.\n          <\/jats:p>","DOI":"10.1145\/3689336","type":"journal-article","created":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T11:25:15Z","timestamp":1724153115000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Optimizing Garbage Collection for ZNS SSDs via In-storage Data Migration and Address Remapping"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4712-7893","authenticated-orcid":false,"given":"Zhenhua","family":"Tan","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China and Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1966-0714","authenticated-orcid":false,"given":"Linbo","family":"Long","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China and Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2090-159X","authenticated-orcid":false,"given":"Jingcheng","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8223-7105","authenticated-orcid":false,"given":"Renping","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2611-2652","authenticated-orcid":false,"given":"Congming","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xiamen University, Xiamen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5278-0406","authenticated-orcid":false,"given":"Kan","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Big Data &amp; Software Engineering, Chongqing University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6951-6296","authenticated-orcid":false,"given":"Yi","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,20]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1","volume-title":"Proceedings of the USENIX Annual Technical Conference (ATC\u201922)","author":"Bergman Shai","year":"2022","unstructured":"Shai Bergman, Niklas Cassel, Matias Bj\u00f8rling, and Mark Silberstein. 2022. 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