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CSR: Small: Green Farms: Towards a Stable Energy Optimization Architecture for Data Centers

CSR: Small: Green Farms: Towards a Stable Energy Optimization Architecture for Data Centers
CSR:小型:绿色农场:迈向数据中心稳定的能源优化架构
批准号:
0916028
负责人:
Tarek Abdelzaher
金额:
$42.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-09-01 至 2013-08-31

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中文摘要
翻译
该奖项是根据2009年美国复苏和再投资法案(公法111-5)资助的。该项目开发技术,以提高现代数据中心的能源效率与性能限制。计算和冷却能量都被考虑。根据美国环境保护署的数据,美国的数据中心每年的能源成本约为45亿美元,相当于580万美国家庭的平均总消费量。在没有干预的情况下,数据中心消费预计将在五年内翻一番。高达80%的预计能源支出是可以避免的,这意味着全国二氧化碳排放量每年可减少高达4700万公吨(MMT);这相当于一个小国所有燃料燃烧的年度碳排放足迹。本项目中开发的能源效率解决方案的观察动机是,服务器安装中单个节能机制的扩散可能导致整体能源管理越来越次优。问题在于性能的可组合性或缺乏可组合性;这是一个挑战,因为单独的优化在组合时通常不能很好地组合,从而导致改进的机会。一个理论的发展,以定义可组合性的先决条件和一个整体的分布式方法进行了研究,以协调跨多个组件,跨越计算和冷却子系统的节能决策。关于网络物理系统的研究生课程旨在涵盖所处理的挑战和解决方案。鼓励妇女和少数民族学生利用这些机会。
英文摘要
This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This project develops techniques for increasing energy-efficiency of modern data centers with performance constraints. Both computing and cooling energy are considered. According to the U.S. Environmental Protection Agency, data centers in the United States incur an annual energy cost of approximately $4.5 billion, which is comparable to the total consumption of 5.8 million average US households. In the absence of intervention, data center consumption is expected to double in five years. Up to 80% of this projected energy expenditure is avoidable, which constitutes a prospective reduction in nationwide carbon dioxide emissions by up to 47 million metric tons (MMT) per year; an amount comparable to the annual carbon emissions footprint of all fuel combustion in a small nation. The observation motivating the energy-efficiency solutions developed in this project is that the proliferation of individual energy-saving mechanisms in server installations can lead to increasingly suboptimal overall energy management. The problem lies in performance composability or lack thereof; a challenge that arises because individual optimizations generally do not compose well when combined, leading to opportunities for improvement. A theory is developed to define preconditions of composability and a holistic distributed approach is investigated for coordinating energy-saving decisions across multiple components that span both the computing and cooling subsystems. A graduate course on cyber-physical systems is designed to cover the addressed challenges and solutions. Women and minority students are encouraged to benefit from these opportunities.
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Collaborative Research: CPS: Medium: Real-time Criticality-Aware Neural Networks for Mission-critical Cyber-Physical Systems
CSR: Small: Data Services for Reliable Crowdsensing in Urban Spaces
Need-Based Sponsorship of Student Travel to IEEE MASS 2015; October 19-22, 2015; Dallas, TX
FIA-NP: Collaborative Research: Named Data Networking Next Phase (NDN-NP)
国内基金
海外基金
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