Adapting Datacenter Capacity for Greener Datacenters and Grid

Liuzixuan Lin, A. Chien
{"title":"Adapting Datacenter Capacity for Greener Datacenters and Grid","authors":"Liuzixuan Lin, A. Chien","doi":"10.1145/3575813.3595197","DOIUrl":null,"url":null,"abstract":"Cloud providers are adapting datacenter (DC) capacity to reduce carbon emissions. With hyperscale datacenters exceeding 100 MW individually, and in some grids exceeding 15% of power load, DC adaptation is large enough to harm power grid dynamics, increasing carbon emissions, power prices, or reduce grid reliability. To avoid harm, we explore coordination of DC capacity change varying scope in space and time. In space, coordination scope spans a single datacenter, a group of datacenters, and datacenters with the grid. In time, scope ranges from online to day-ahead. We also consider what DC and grid information is used (e.g. real-time and day-ahead average carbon, power price, and compute backlog). For example, in our proposed PlanShare scheme, each datacenter uses day-ahead information to create a capacity plan and shares it, allowing global grid optimization (over all loads, over entire day). We evaluate DC carbon emissions reduction. Results show that local coordination scope fails to reduce carbon emissions significantly (3.2%–5.4% reduction). Expanding coordination scope to a set of datacenters improves slightly (4.9%–7.3%). PlanShare, with grid-wide coordination and full-day capacity planning, performs the best. PlanShare reduces DC emissions by 11.6%–12.6%, 1.56x–1.26x better than the best local, online approach’s results. PlanShare also achieves lower cost. We expect these advantages to increase as renewable generation in power grids increases. Further, a known full-day DC capacity plan provides a stable target for DC resource management.","PeriodicalId":359352,"journal":{"name":"Proceedings of the 14th ACM International Conference on Future Energy Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 14th ACM International Conference on Future Energy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3575813.3595197","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Cloud providers are adapting datacenter (DC) capacity to reduce carbon emissions. With hyperscale datacenters exceeding 100 MW individually, and in some grids exceeding 15% of power load, DC adaptation is large enough to harm power grid dynamics, increasing carbon emissions, power prices, or reduce grid reliability. To avoid harm, we explore coordination of DC capacity change varying scope in space and time. In space, coordination scope spans a single datacenter, a group of datacenters, and datacenters with the grid. In time, scope ranges from online to day-ahead. We also consider what DC and grid information is used (e.g. real-time and day-ahead average carbon, power price, and compute backlog). For example, in our proposed PlanShare scheme, each datacenter uses day-ahead information to create a capacity plan and shares it, allowing global grid optimization (over all loads, over entire day). We evaluate DC carbon emissions reduction. Results show that local coordination scope fails to reduce carbon emissions significantly (3.2%–5.4% reduction). Expanding coordination scope to a set of datacenters improves slightly (4.9%–7.3%). PlanShare, with grid-wide coordination and full-day capacity planning, performs the best. PlanShare reduces DC emissions by 11.6%–12.6%, 1.56x–1.26x better than the best local, online approach’s results. PlanShare also achieves lower cost. We expect these advantages to increase as renewable generation in power grids increases. Further, a known full-day DC capacity plan provides a stable target for DC resource management.
为更环保的数据中心和电网调整数据中心容量
云提供商正在调整数据中心(DC)容量以减少碳排放。随着超大规模数据中心单个超过100兆瓦,并且在一些电网中超过电力负荷的15%,直流适应性足以损害电网动态,增加碳排放,电价或降低电网可靠性。为了避免伤害,我们探索了直流容量在空间和时间上不同范围变化的协调性。在空间中,协调范围跨越单个数据中心、一组数据中心和网格数据中心。在时间上,范围从在线到前一天。我们还考虑了使用的直流和电网信息(例如实时和一天前的平均碳,电价和计算积压)。例如,在我们提出的PlanShare方案中,每个数据中心使用前一天的信息来创建容量计划并共享它,从而允许全局网格优化(在所有负载上,在一整天内)。我们评估直流碳减排。结果表明,地方协调范围未能显著减少碳排放(减少3.2% ~ 5.4%)。将协调范围扩展到一组数据中心略有改善(4.9%-7.3%)。具有全电网协调和全天容量规划的PlanShare表现最好。PlanShare减少了11.6%-12.6%的直流排放,比最好的本地在线方法的结果好1.56 - 1.26倍。PlanShare还实现了更低的成本。我们预计,随着电网中可再生能源发电量的增加,这些优势将会增加。此外,已知的全天数据中心容量计划为数据中心资源管理提供了一个稳定的目标。
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 求助全文
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