理解仓库级数据中心服务中的数据压缩

Geonhwa Jeong, Bikash Sharma, Nick Terrell, A. Dhanotia, Zhiwei Zhao, Niket Agarwal, A. Kejariwal, T. Krishna
{"title":"理解仓库级数据中心服务中的数据压缩","authors":"Geonhwa Jeong, Bikash Sharma, Nick Terrell, A. Dhanotia, Zhiwei Zhao, Niket Agarwal, A. Kejariwal, T. Krishna","doi":"10.1109/ISPASS55109.2022.00028","DOIUrl":null,"url":null,"abstract":"Data compression has emerged as a promising technique to alleviate the memory, storage, and network cost with some associated compute overheads in warehouse-scale datacenter services. Despite being one of the most important components of the overall datacenter taxes, there has not been a comprehensive characterization of compression usage in data center workloads. In this work, we first provide a holistic characterization of compression as used by various warehouse-scale datacenter services at a global social media provider (Meta). Next, we deep dive into a few representative use cases of compression in the production environment and characterize compression usage of services while running live traffic.","PeriodicalId":115391,"journal":{"name":"2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)","volume":"37 4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Understanding Data Compression in Warehouse-Scale Datacenter Services\",\"authors\":\"Geonhwa Jeong, Bikash Sharma, Nick Terrell, A. Dhanotia, Zhiwei Zhao, Niket Agarwal, A. Kejariwal, T. Krishna\",\"doi\":\"10.1109/ISPASS55109.2022.00028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data compression has emerged as a promising technique to alleviate the memory, storage, and network cost with some associated compute overheads in warehouse-scale datacenter services. Despite being one of the most important components of the overall datacenter taxes, there has not been a comprehensive characterization of compression usage in data center workloads. In this work, we first provide a holistic characterization of compression as used by various warehouse-scale datacenter services at a global social media provider (Meta). Next, we deep dive into a few representative use cases of compression in the production environment and characterize compression usage of services while running live traffic.\",\"PeriodicalId\":115391,\"journal\":{\"name\":\"2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)\",\"volume\":\"37 4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISPASS55109.2022.00028\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPASS55109.2022.00028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

数据压缩已经成为一种很有前途的技术,可以在仓库规模的数据中心服务中减轻内存、存储和网络成本以及一些相关的计算开销。尽管压缩是整个数据中心税中最重要的组成部分之一,但目前还没有对数据中心工作负载中的压缩使用情况进行全面的描述。在这项工作中,我们首先提供了全球社交媒体提供商(Meta)的各种仓库规模数据中心服务使用的压缩的整体特征。接下来,我们将深入研究生产环境中一些具有代表性的压缩用例,并描述在运行实时流量时服务的压缩使用情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding Data Compression in Warehouse-Scale Datacenter Services
Data compression has emerged as a promising technique to alleviate the memory, storage, and network cost with some associated compute overheads in warehouse-scale datacenter services. Despite being one of the most important components of the overall datacenter taxes, there has not been a comprehensive characterization of compression usage in data center workloads. In this work, we first provide a holistic characterization of compression as used by various warehouse-scale datacenter services at a global social media provider (Meta). Next, we deep dive into a few representative use cases of compression in the production environment and characterize compression usage of services while running live traffic.
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
自引率
0.00%
发文量
0
×
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:604180095
Book学术官方微信