基于DM-SMR和HM-SMR磁盘深度优化的通用smr感知缓存框架

IF 2.1 3区 计算机科学 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Diansen Sun, Ruixiong Tan, Yunpeng Chai
{"title":"基于DM-SMR和HM-SMR磁盘深度优化的通用smr感知缓存框架","authors":"Diansen Sun, Ruixiong Tan, Yunpeng Chai","doi":"https://dl.acm.org/doi/10.1145/3588442","DOIUrl":null,"url":null,"abstract":"<p>To satisfy the enormous storage capacities required for big data, data centers have been adopting high-density shingled magnetic recording (SMR) disks. However, the weak fine-grained random write performance of SMR disks caused by their inherent write amplification and unbalanced read–write performance poses a severe challenge. Many studies have proposed solid-state drive (SSD) cache systems to address this issue. However, existing cache algorithms, such as the least recently used (LRU) algorithm, which is used to optimize cache popularity, and the MOST algorithm, which is used to optimize the write amplification factor, cannot exploit the full performance of the proposed cache systems because of their inappropriate optimization objectives. This article proposes a new SMR-aware cache framework called SAC+ to improve SMR-based hybrid storage. SAC+ integrates the two dominant types of SMR drives—namely, drive-managed and host-managed SMR drives—and provides a universal framework implementation. In addition, SAC+ integrally combines the drive characteristics to optimize I/O performance. The results of evaluations conducted using real-world traces indicate that SAC+ reduces the I/O time by 36–93% compared with state-of-the-art algorithms.</p>","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":"2011 2","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Universal SMR-aware Cache Framework with Deep Optimization for DM-SMR and HM-SMR Disks\",\"authors\":\"Diansen Sun, Ruixiong Tan, Yunpeng Chai\",\"doi\":\"https://dl.acm.org/doi/10.1145/3588442\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>To satisfy the enormous storage capacities required for big data, data centers have been adopting high-density shingled magnetic recording (SMR) disks. However, the weak fine-grained random write performance of SMR disks caused by their inherent write amplification and unbalanced read–write performance poses a severe challenge. Many studies have proposed solid-state drive (SSD) cache systems to address this issue. However, existing cache algorithms, such as the least recently used (LRU) algorithm, which is used to optimize cache popularity, and the MOST algorithm, which is used to optimize the write amplification factor, cannot exploit the full performance of the proposed cache systems because of their inappropriate optimization objectives. This article proposes a new SMR-aware cache framework called SAC+ to improve SMR-based hybrid storage. SAC+ integrates the two dominant types of SMR drives—namely, drive-managed and host-managed SMR drives—and provides a universal framework implementation. In addition, SAC+ integrally combines the drive characteristics to optimize I/O performance. The results of evaluations conducted using real-world traces indicate that SAC+ reduces the I/O time by 36–93% compared with state-of-the-art algorithms.</p>\",\"PeriodicalId\":49113,\"journal\":{\"name\":\"ACM Transactions on Storage\",\"volume\":\"2011 2\",\"pages\":\"\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Storage\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/https://dl.acm.org/doi/10.1145/3588442\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Storage","FirstCategoryId":"94","ListUrlMain":"https://doi.org/https://dl.acm.org/doi/10.1145/3588442","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0

摘要

为了满足大数据的巨大存储容量需求,数据中心开始采用高密度瓦式磁记录(SMR)磁盘。然而,由于SMR磁盘固有的写放大和读写性能不平衡,导致其细粒度随机写性能较弱,这对SMR磁盘的性能提出了严峻的挑战。许多研究建议使用固态硬盘(SSD)缓存系统来解决这个问题。然而,现有的缓存算法,如用于优化缓存流行度的最近最少使用(LRU)算法和用于优化写放大因子的MOST算法,由于其优化目标不合适,无法充分利用所提出的缓存系统的性能。本文提出了一种新的smr感知缓存框架SAC+,以改进基于smr的混合存储。SAC+集成了两种主要类型的SMR驱动器——即驱动器管理的和主机管理的SMR驱动器——并提供了一个通用的框架实现。此外,SAC+集成了驱动器特性,以优化I/O性能。使用实际跟踪进行的评估结果表明,与最先进的算法相比,SAC+将I/O时间减少了36-93%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Universal SMR-aware Cache Framework with Deep Optimization for DM-SMR and HM-SMR Disks

To satisfy the enormous storage capacities required for big data, data centers have been adopting high-density shingled magnetic recording (SMR) disks. However, the weak fine-grained random write performance of SMR disks caused by their inherent write amplification and unbalanced read–write performance poses a severe challenge. Many studies have proposed solid-state drive (SSD) cache systems to address this issue. However, existing cache algorithms, such as the least recently used (LRU) algorithm, which is used to optimize cache popularity, and the MOST algorithm, which is used to optimize the write amplification factor, cannot exploit the full performance of the proposed cache systems because of their inappropriate optimization objectives. This article proposes a new SMR-aware cache framework called SAC+ to improve SMR-based hybrid storage. SAC+ integrates the two dominant types of SMR drives—namely, drive-managed and host-managed SMR drives—and provides a universal framework implementation. In addition, SAC+ integrally combines the drive characteristics to optimize I/O performance. The results of evaluations conducted using real-world traces indicate that SAC+ reduces the I/O time by 36–93% compared with state-of-the-art algorithms.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
ACM Transactions on Storage
ACM Transactions on Storage COMPUTER SCIENCE, HARDWARE & ARCHITECTURE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
4.20
自引率
5.90%
发文量
33
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Storage (TOS) is a new journal with an intent to publish original archival papers in the area of storage and closely related disciplines. Articles that appear in TOS will tend either to present new techniques and concepts or to report novel experiences and experiments with practical systems. Storage is a broad and multidisciplinary area that comprises of network protocols, resource management, data backup, replication, recovery, devices, security, and theory of data coding, densities, and low-power. Potential synergies among these fields are expected to open up new research directions.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信