可持续微云数据中心的弹性用电

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE IEEE Transactions on Sustainable Computing Pub Date : 2023-01-13 DOI:10.1109/TSUSC.2023.3236598
Tuhin Chakraborty;Adel N. Toosi;Carlo Kopp
{"title":"可持续微云数据中心的弹性用电","authors":"Tuhin Chakraborty;Adel N. Toosi;Carlo Kopp","doi":"10.1109/TSUSC.2023.3236598","DOIUrl":null,"url":null,"abstract":"Efficient utilization of renewable energy when powering Cloud Data Centers is a challenging problem due to the variable and intermittent nature of both workload demand and renewable energy supply. This work aims to develop an innovative dynamic resource management algorithm to provide energy flexibility to data center operators for shaping their energy demand to match renewable energy supply. We present a novel framework, called \n<italic>Elastic Power Utilization</i>\n (\n<italic>EPU</i>\n), to serve this purpose. \n<italic>EPU</i>\n utilizes energy source information to dynamically manage data center resources for matching the renewable energy supply with the energy demand to serve the workload. We propose a resource management algorithm that exploits overbooking, consolidation and migration of virtual machines (VMs) to implement the power elasticity required by the \n<italic>EPU</i>\n framework. We compare our approach to a state-of-the-art algorithm and baseline approaches with three different workloads. The results from extensive simulations show that our proposed algorithm outperforms the state-of-the-art approach in saving brown energy by 23.1%, 21.3%, and 27.0% for \n<italic>Google</i>\n, \n<italic>Wikipedia</i>\n, and \n<italic>Nectar</i>\n workloads, respectively.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 3","pages":"465-478"},"PeriodicalIF":3.0000,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Elastic Power Utilization in Sustainable Micro Cloud Data Centers\",\"authors\":\"Tuhin Chakraborty;Adel N. Toosi;Carlo Kopp\",\"doi\":\"10.1109/TSUSC.2023.3236598\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Efficient utilization of renewable energy when powering Cloud Data Centers is a challenging problem due to the variable and intermittent nature of both workload demand and renewable energy supply. This work aims to develop an innovative dynamic resource management algorithm to provide energy flexibility to data center operators for shaping their energy demand to match renewable energy supply. We present a novel framework, called \\n<italic>Elastic Power Utilization</i>\\n (\\n<italic>EPU</i>\\n), to serve this purpose. \\n<italic>EPU</i>\\n utilizes energy source information to dynamically manage data center resources for matching the renewable energy supply with the energy demand to serve the workload. We propose a resource management algorithm that exploits overbooking, consolidation and migration of virtual machines (VMs) to implement the power elasticity required by the \\n<italic>EPU</i>\\n framework. We compare our approach to a state-of-the-art algorithm and baseline approaches with three different workloads. The results from extensive simulations show that our proposed algorithm outperforms the state-of-the-art approach in saving brown energy by 23.1%, 21.3%, and 27.0% for \\n<italic>Google</i>\\n, \\n<italic>Wikipedia</i>\\n, and \\n<italic>Nectar</i>\\n workloads, respectively.\",\"PeriodicalId\":13268,\"journal\":{\"name\":\"IEEE Transactions on Sustainable Computing\",\"volume\":\"8 3\",\"pages\":\"465-478\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2023-01-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Sustainable Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10016768/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Sustainable Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10016768/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 1

摘要

在为云数据中心供电时,可再生能源的高效利用是一个具有挑战性的问题,因为工作负载需求和可再生能源供应都是可变的和间歇性的。这项工作旨在开发一种创新的动态资源管理算法,为数据中心运营商提供能源灵活性,使其能源需求与可再生能源供应相匹配。我们提出了一个新的框架,称为弹性功率利用(EPU),以达到这一目的。EPU利用能源信息来动态管理数据中心资源,以使可再生能源供应与服务于工作负载的能源需求相匹配。我们提出了一种资源管理算法,该算法利用虚拟机(VM)的超预订、整合和迁移来实现EPU框架所需的功率弹性。我们将我们的方法与最先进的算法和三种不同工作负载的基线方法进行了比较。广泛模拟的结果表明,对于谷歌、维基百科和Nectar的工作负载,我们提出的算法在节省棕色能源方面分别优于最先进的方法23.1%、21.3%和27.0%。
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
Elastic Power Utilization in Sustainable Micro Cloud Data Centers
Efficient utilization of renewable energy when powering Cloud Data Centers is a challenging problem due to the variable and intermittent nature of both workload demand and renewable energy supply. This work aims to develop an innovative dynamic resource management algorithm to provide energy flexibility to data center operators for shaping their energy demand to match renewable energy supply. We present a novel framework, called Elastic Power Utilization ( EPU ), to serve this purpose. EPU utilizes energy source information to dynamically manage data center resources for matching the renewable energy supply with the energy demand to serve the workload. We propose a resource management algorithm that exploits overbooking, consolidation and migration of virtual machines (VMs) to implement the power elasticity required by the EPU framework. We compare our approach to a state-of-the-art algorithm and baseline approaches with three different workloads. The results from extensive simulations show that our proposed algorithm outperforms the state-of-the-art approach in saving brown energy by 23.1%, 21.3%, and 27.0% for Google , Wikipedia , and Nectar workloads, respectively.
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
IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing Mathematics-Control and Optimization
CiteScore
7.70
自引率
2.60%
发文量
54
期刊最新文献
Editorial Dynamic Event-Triggered State Estimation for Power Harmonics With Quantization Effects: A Zonotopic Set-Membership Approach 2024 Reviewers List Deadline-Aware Cost and Energy Efficient Offloading in Mobile Edge Computing Impacts of Increasing Temperature and Relative Humidity in Air-Cooled Tropical Data Centers
×
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