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GOALI: Models, Metrics and Control Strategies for Energy Efficient Data Centers

GOALI: Models, Metrics and Control Strategies for Energy Efficient Data Centers
目标:节能数据中心的模型、指标和控制策略
批准号:
0925964
负责人:
Bruno Sinopoli
金额:
$49.0万
依托单位:
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:
2009-09-15 至 2014-08-31

项目摘要

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中文摘要
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英文摘要
Project Title: GOALI: Models, Metrics, and Control Strategies for Energy Efficient Data CentersPIs: Bruno Sinopoli and Bruce H. KroghObjective: This project aims to improve the energy efficiency of data centers significantly by introducing coordinated control of the cooling systems and the information technology. This requires new integrated, control-oriented models of data center dynamics representing thermal interactions between the computational system and the cooling system as a function of the control inputs to each system. New metrics will also be developed to capture effectively the impact of control decisions on computational performance in the context of service level agreements (SLAs) and other objectives and constraints that arise in practice. Multi-level, distributed control strategies will be developed to deal with the wide range of time-scales and spatial distributions that characterize data center dynamics.Intellectual Merit: This transformative research will lead to the first data center control strategies that explicitly coordinate cooling system actions with workload and power state allocations to achieve effective tradeoffs between computational performance and energy consumption. These strategies will also leverage new degrees of freedom by relaxing performance requirements as allowed by SLAs. The control hierarchy is based on abstractions that reflect the range of spatial and temporal scales common in many large-scale cyber-physical systems. Broader Impact: The academic-industry collaboration in this GOALI project assures the results will be implementable in future data centers. This will lead to significant reductions in the energy required to meet the demands for computation and storage in the U.S. for years to come. Through courses and internships, engineering students will be introduced emerging concepts in cyber-physical systems research.
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AI Institute: Planning: TRustworthy Autonomous Systems Engineering (TRASE)
  • 批准号:
    2020289
  • 项目类别:
    Standard Grant
  • 资助金额:
    $50.0万
  • 财政年份:
    2020
  • 负责人:
    Bruno Sinopoli
  • 依托单位:
CPS: Medium: Collaborative Research: Mitigation strategies for enhancing performance while maintaining viability in cyber-physical systems
  • 批准号:
    1932530
  • 项目类别:
    Standard Grant
  • 资助金额:
    $80.0万
  • 财政年份:
    2019
  • 负责人:
    Bruno Sinopoli
  • 依托单位:
CPS: Synergy: Information Flow Analysis for Cyber-Physical System Security
  • 批准号:
    2002495
  • 项目类别:
    Standard Grant
  • 资助金额:
    $17.36万
  • 财政年份:
    2019
  • 负责人:
    Bruno Sinopoli
  • 依托单位:
CPS: Synergy: Information Flow Analysis for Cyber-Physical System Security
  • 批准号:
    1646526
  • 项目类别:
    Standard Grant
  • 资助金额:
    $80.0万
  • 财政年份:
    2016
  • 负责人:
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国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
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