CacheSack:谷歌数据中心Flash缓存准入优化的理论与经验

IF 2.1 3区 计算机科学 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE ACM Transactions on Storage Pub Date : 2023-01-21 DOI:10.1145/3582014
Tzu-Wei Yang, Seth Pollen, Mustafa Uysal, A. Merchant, H. Wolfmeister, Junaid Khalid
{"title":"CacheSack:谷歌数据中心Flash缓存准入优化的理论与经验","authors":"Tzu-Wei Yang, Seth Pollen, Mustafa Uysal, A. Merchant, H. Wolfmeister, Junaid Khalid","doi":"10.1145/3582014","DOIUrl":null,"url":null,"abstract":"This article describes the algorithm, implementation, and deployment experience of CacheSack, the admission algorithm for Google datacenter flash caches. CacheSack minimizes the dominant costs of Google’s datacenter flash caches: disk IO and flash footprint. CacheSack partitions cache traffic into disjoint categories, analyzes the observed cache benefit of each subset, and formulates a knapsack problem to assign the optimal admission policy to each subset. Prior to this work, Google datacenter flash cache admission policies were optimized manually, with most caches using the Lazy Adaptive Replacement Cache algorithm. Production experiments showed that CacheSack significantly outperforms the prior static admission policies for a 7.7% improvement of the total cost of ownership, as well as significant improvements in disk reads (9.5% reduction) and flash wearout (17.8% reduction).","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":" ","pages":"1 - 24"},"PeriodicalIF":2.1000,"publicationDate":"2023-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CacheSack: Theory and Experience of Google’s Admission Optimization for Datacenter Flash Caches\",\"authors\":\"Tzu-Wei Yang, Seth Pollen, Mustafa Uysal, A. Merchant, H. Wolfmeister, Junaid Khalid\",\"doi\":\"10.1145/3582014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article describes the algorithm, implementation, and deployment experience of CacheSack, the admission algorithm for Google datacenter flash caches. CacheSack minimizes the dominant costs of Google’s datacenter flash caches: disk IO and flash footprint. CacheSack partitions cache traffic into disjoint categories, analyzes the observed cache benefit of each subset, and formulates a knapsack problem to assign the optimal admission policy to each subset. Prior to this work, Google datacenter flash cache admission policies were optimized manually, with most caches using the Lazy Adaptive Replacement Cache algorithm. Production experiments showed that CacheSack significantly outperforms the prior static admission policies for a 7.7% improvement of the total cost of ownership, as well as significant improvements in disk reads (9.5% reduction) and flash wearout (17.8% reduction).\",\"PeriodicalId\":49113,\"journal\":{\"name\":\"ACM Transactions on Storage\",\"volume\":\" \",\"pages\":\"1 - 24\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-01-21\",\"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/10.1145/3582014\",\"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/10.1145/3582014","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

本文描述了CacheSack的算法、实现和部署经验,CacheSack是谷歌数据中心闪存缓存的接纳算法。CacheSack最大限度地减少b谷歌数据中心闪存缓存的主要成本:磁盘IO和闪存占用。CacheSack将缓存流量划分为不相关的类别,分析观察到的每个子集的缓存效益,并制定一个背包问题,为每个子集分配最优的允许策略。在此之前,谷歌数据中心的闪存缓存准入策略是手动优化的,大多数缓存使用Lazy Adaptive Replacement cache算法。生产实验表明,CacheSack显著优于之前的静态准入策略,总拥有成本提高了7.7%,磁盘读取(减少9.5%)和闪存磨损(减少17.8%)方面也有显著改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
CacheSack: Theory and Experience of Google’s Admission Optimization for Datacenter Flash Caches
This article describes the algorithm, implementation, and deployment experience of CacheSack, the admission algorithm for Google datacenter flash caches. CacheSack minimizes the dominant costs of Google’s datacenter flash caches: disk IO and flash footprint. CacheSack partitions cache traffic into disjoint categories, analyzes the observed cache benefit of each subset, and formulates a knapsack problem to assign the optimal admission policy to each subset. Prior to this work, Google datacenter flash cache admission policies were optimized manually, with most caches using the Lazy Adaptive Replacement Cache algorithm. Production experiments showed that CacheSack significantly outperforms the prior static admission policies for a 7.7% improvement of the total cost of ownership, as well as significant improvements in disk reads (9.5% reduction) and flash wearout (17.8% reduction).
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.
期刊最新文献
LVMT: An Efficient Authenticated Storage for Blockchain The Design of Fast Delta Encoding for Delta Compression Based Storage Systems A Memory-Disaggregated Radix Tree Fastmove: A Comprehensive Study of On-Chip DMA and its Demonstration for Accelerating Data Movement in NVM-based Storage Systems FSDedup: Feature-Aware and Selective Deduplication for Improving Performance of Encrypted Non-Volatile Main Memory
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1