数据网格中的数据复制和功耗

Susan V. Vrbsky, Ming Lei, Karl Smith, Jeff Byrd
{"title":"数据网格中的数据复制和功耗","authors":"Susan V. Vrbsky, Ming Lei, Karl Smith, Jeff Byrd","doi":"10.1109/CloudCom.2010.35","DOIUrl":null,"url":null,"abstract":"While data grids can provide the ability to solve large-scale applications which require the processing of large amounts of data, they have been recognized as extremely energy inefficient. Computing elements can be located far away from the data storage elements. A common solution to improve availability and file access time in such environments is to replicate the data, resulting in the creation of copies of data files at many different sites. The energy efficiency of the data centers storing this data is one of the biggest issues in data intensive computing. Since power is needed to transmit, store and cool the data, we propose to minimize the amount of data transmitted and stored by utilizing smart replication strategies that are data aware. In this paper we present a new data replication approach, called the sliding window replica strategy (SWIN), that is not only data aware, but is also energy efficient. We measure the performance of SWIN and existing replica strategies on our Sage green cluster to study the power consumption of the strategies. Results from this study have implications beyond our cluster to the management of data in clouds.","PeriodicalId":130987,"journal":{"name":"2010 IEEE Second International Conference on Cloud Computing Technology and Science","volume":"189 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"39","resultStr":"{\"title\":\"Data Replication and Power Consumption in Data Grids\",\"authors\":\"Susan V. Vrbsky, Ming Lei, Karl Smith, Jeff Byrd\",\"doi\":\"10.1109/CloudCom.2010.35\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"While data grids can provide the ability to solve large-scale applications which require the processing of large amounts of data, they have been recognized as extremely energy inefficient. Computing elements can be located far away from the data storage elements. A common solution to improve availability and file access time in such environments is to replicate the data, resulting in the creation of copies of data files at many different sites. The energy efficiency of the data centers storing this data is one of the biggest issues in data intensive computing. Since power is needed to transmit, store and cool the data, we propose to minimize the amount of data transmitted and stored by utilizing smart replication strategies that are data aware. In this paper we present a new data replication approach, called the sliding window replica strategy (SWIN), that is not only data aware, but is also energy efficient. We measure the performance of SWIN and existing replica strategies on our Sage green cluster to study the power consumption of the strategies. Results from this study have implications beyond our cluster to the management of data in clouds.\",\"PeriodicalId\":130987,\"journal\":{\"name\":\"2010 IEEE Second International Conference on Cloud Computing Technology and Science\",\"volume\":\"189 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"39\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE Second International Conference on Cloud Computing Technology and Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CloudCom.2010.35\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE Second International Conference on Cloud Computing Technology and Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CloudCom.2010.35","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 39

摘要

虽然数据网格可以提供解决需要处理大量数据的大规模应用程序的能力,但它们被认为是极其低效的。计算元素可以位于远离数据存储元素的地方。在这种环境中提高可用性和文件访问时间的一种常见解决方案是复制数据,从而在许多不同的站点上创建数据文件的副本。存储这些数据的数据中心的能源效率是数据密集型计算中最大的问题之一。由于传输、存储和冷却数据需要电力,我们建议通过使用数据感知的智能复制策略来最大限度地减少传输和存储的数据量。在本文中,我们提出了一种新的数据复制方法,称为滑动窗口复制策略(SWIN),它不仅具有数据感知性,而且节能。我们在Sage green集群上测量了SWIN和现有副本策略的性能,以研究这些策略的功耗。本研究的结果对云数据管理的影响超出了我们的集群。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Replication and Power Consumption in Data Grids
While data grids can provide the ability to solve large-scale applications which require the processing of large amounts of data, they have been recognized as extremely energy inefficient. Computing elements can be located far away from the data storage elements. A common solution to improve availability and file access time in such environments is to replicate the data, resulting in the creation of copies of data files at many different sites. The energy efficiency of the data centers storing this data is one of the biggest issues in data intensive computing. Since power is needed to transmit, store and cool the data, we propose to minimize the amount of data transmitted and stored by utilizing smart replication strategies that are data aware. In this paper we present a new data replication approach, called the sliding window replica strategy (SWIN), that is not only data aware, but is also energy efficient. We measure the performance of SWIN and existing replica strategies on our Sage green cluster to study the power consumption of the strategies. Results from this study have implications beyond our cluster to the management of data in clouds.
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:481959085
Book学术官方微信