Data Analysis of a Google Data Center

P. Minet, É. Renault, I. Khoufi, S. Boumerdassi
{"title":"Data Analysis of a Google Data Center","authors":"P. Minet, É. Renault, I. Khoufi, S. Boumerdassi","doi":"10.1109/CCGRID.2018.00049","DOIUrl":null,"url":null,"abstract":"Data collected from an operational Google data center during 29 days represent a very rich and very useful source of information for understanding the main features of a data center. In this paper, we highlight the strong heterogeneity of jobs. The distribution of job execution duration shows a high disparity, as well as the job waiting time before being scheduled. The resource requests in terms of CPU and memory are also analyzed. The knowledge of all these features is needed to design models of jobs, machines and resource requests that are representative of a real data center.","PeriodicalId":321027,"journal":{"name":"2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)","volume":"14 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCGRID.2018.00049","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

Data collected from an operational Google data center during 29 days represent a very rich and very useful source of information for understanding the main features of a data center. In this paper, we highlight the strong heterogeneity of jobs. The distribution of job execution duration shows a high disparity, as well as the job waiting time before being scheduled. The resource requests in terms of CPU and memory are also analyzed. The knowledge of all these features is needed to design models of jobs, machines and resource requests that are representative of a real data center.
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
谷歌数据中心数据分析
从运行中的谷歌数据中心在29天内收集的数据为了解数据中心的主要特性提供了非常丰富和非常有用的信息源。在本文中,我们强调了工作的强烈异质性。作业执行时间分布差异较大,作业被调度前的等待时间分布差异较大。从CPU和内存两个方面分析了资源请求。要设计代表真实数据中心的作业、机器和资源请求模型,就需要了解所有这些特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Extreme-Scale Realistic Stencil Computations on Sunway TaihuLight with Ten Million Cores RideMatcher: Peer-to-Peer Matching of Passengers for Efficient Ridesharing Nitro: Network-Aware Virtual Machine Image Management in Geo-Distributed Clouds Improving Energy Efficiency of Database Clusters Through Prefetching and Caching Main-Memory Requirements of Big Data Applications on Commodity Server Platform
×
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