{"title":"Managing Server Clusters on Renewable Energy Mix","authors":"Chao Li, Rui Wang, D. Qian, Tao Li","doi":"10.1145/2845085","DOIUrl":null,"url":null,"abstract":"As climate change has become a global concern and server energy demand continues to soar, many IT companies have started to explore server clusters running on various renewable energy sources. Existing green data center designs often yield suboptimal performance as they only look at a certain specific type of energy source. This article explores data centers powered by hybrid renewable energy systems. We propose GreenWorks, a framework for HPC data centers running on a renewable energy mix. Specifically, GreenWorks features a cross-layer power management scheme tailored to the timing behaviors and capacity constraints of different energy sources. Using realistic workload traces and renewable energy data, we show that GreenWorks could provide a near-optimal workload performance (within 3% difference) on average. It can also reduce the worst-case performance degradation by 43% compared to the state-of-the-art design. Moreover, the performance improvements are based on carbon-neutral operations and are not at the cost of significant efficiency degradation and reduced battery lifecycle. Our technique becomes more efficient when servers become more energy proportional and can effectively handle the ever-increasing depth of renewable power penetration in green data centers.","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":"129 1","pages":"1:1-1:24"},"PeriodicalIF":2.2000,"publicationDate":"2016-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Autonomous and Adaptive Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/2845085","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 30
Abstract
As climate change has become a global concern and server energy demand continues to soar, many IT companies have started to explore server clusters running on various renewable energy sources. Existing green data center designs often yield suboptimal performance as they only look at a certain specific type of energy source. This article explores data centers powered by hybrid renewable energy systems. We propose GreenWorks, a framework for HPC data centers running on a renewable energy mix. Specifically, GreenWorks features a cross-layer power management scheme tailored to the timing behaviors and capacity constraints of different energy sources. Using realistic workload traces and renewable energy data, we show that GreenWorks could provide a near-optimal workload performance (within 3% difference) on average. It can also reduce the worst-case performance degradation by 43% compared to the state-of-the-art design. Moreover, the performance improvements are based on carbon-neutral operations and are not at the cost of significant efficiency degradation and reduced battery lifecycle. Our technique becomes more efficient when servers become more energy proportional and can effectively handle the ever-increasing depth of renewable power penetration in green data centers.
期刊介绍:
TAAS addresses research on autonomous and adaptive systems being undertaken by an increasingly interdisciplinary research community -- and provides a common platform under which this work can be published and disseminated. TAAS encourages contributions aimed at supporting the understanding, development, and control of such systems and of their behaviors.
TAAS addresses research on autonomous and adaptive systems being undertaken by an increasingly interdisciplinary research community - and provides a common platform under which this work can be published and disseminated. TAAS encourages contributions aimed at supporting the understanding, development, and control of such systems and of their behaviors. Contributions are expected to be based on sound and innovative theoretical models, algorithms, engineering and programming techniques, infrastructures and systems, or technological and application experiences.