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CPS: Medium: Collaborative Research: Demand Response & Workload Management for Data Centers with Increased Renewable Penetration

CPS: Medium: Collaborative Research: Demand Response & Workload Management for Data Centers with Increased Renewable Penetration
CPS:媒介:协作研究:需求响应
批准号:
1739344
负责人:
Junshan Zhang
金额:
$50.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-01 至 2021-11-30

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中文摘要
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英文摘要
The confluence of two powerful global trends, (1) the rapid growth of cloud computing and data centers with skyrocketing energy consumption, and (2) the accelerating penetration of renewable energy sources, is creating both severe challenges and tremendous opportunities. The fast growing renewable generation puts forth great operational challenges since they will cause large, frequent, and random fluctuations in supply. Data centers, on the other hand, offer large flexible loads in the grid. Leveraging this flexibility, this project will develop fundamental theories and algorithms for sustainable data centers with a dual goal of improving data center energy efficiency and accelerating the integration of renewables in the grid via data center demand response (DR) and workload management. Specifically, the research findings will shed light on data center demand response while maintaining their performance, which will help data centers to decide how to participate in power market programs. Further, the success of data center demand response will help increase renewable energy integration and reduce the carbon footprint of data centers, contributing to global sustainability. The PIs will leverage fruitful collaboration to eventually bring the research to bear on ongoing industry standardization and development efforts. The PIs teach courses spanning networks, games, smart grid and optimization, and are strongly committed to promoting diversity by providing research opportunities to underrepresented students. Built on the PIs expertise on data centers and the smart grid, this project takes an interdisciplinary approach to develop fundamental theories and algorithms for sustainable data centers. The research tasks are organized under two well-coordinated thrusts, namely agile data center DR and adaptive workload management. The strategies and decisions of data center DR will be made based on the workload management algorithms that balance quality of service and energy efficiency and determine the supply functions. The workload management algorithms will optimize quality of service under the electric load constraints imposed by DR accordingly. This project will make three unique contributions: (1) new market programs with strategic participation of data centers in DR, instead of passive price takers, (2) fundamental understanding of the impacts of power network constraints on data center DR and new distributed algorithms for solving optimal power flow with stochastic renewable supplies, and (3) high-performance dynamic server provisioning and load balancing algorithms for large scale data centers under time-varying and stochastic electric load constraints and on-site renewable generation.
期刊论文(8)
专著(0)
科研奖励(0)
会议论文
Steady‐state analysis of load balancing with Coxian‐2 distributed service times
使用 Coxian™2 分布式服务时间进行负载平衡的稳态分析
DOI: 10.1002/nav.21986
发表时间: 2021
期刊: Naval Research Logistics (NRL
影响因子: --
作者: [Liu, Xin, Gong, Kang, Ying, Lei]
通讯作者: Ying, Lei
DOI: 10.1109/tac.2020.2996178
发表时间: 2021-04
期刊: IEEE Transactions on Automatic Control
影响因子: 6.8
作者: [Xuanyu Cao;Junshan Zhang;H. Vincent Poor]
通讯作者: Xuanyu Cao;Junshan Zhang;H. Vincent Poor
Learning Parallel Markov Chains over Unreliable Wireless Channels
通过不可靠的无线信道学习并行马尔可夫链
DOI: 10.1109/ciss48834.2020.1570614323
发表时间: 2020
期刊: CISS
影响因子: --
作者: [Wang, Weichang, Ying, Lei]
通讯作者: Ying, Lei
DOI: 10.1109/jstsp.2018.2827302
发表时间: 2018-04
期刊: IEEE Journal of Selected Topics in Signal Processing
影响因子: 7.5
作者: [Xuanyu Cao;Junshan Zhang;H. Vincent Poor]
通讯作者: Xuanyu Cao;Junshan Zhang;H. Vincent Poor
8
    CCSS: Collaborative Research: Quality-Aware Distributed Computation for Wireless Federated Learning: Channel-Aware User Selection, Mini-Batch Size Adaptation, and Scheduling
    • 批准号:
      2203238
    • 项目类别:
      Standard Grant
    • 资助金额:
      $22.0万
    • 财政年份:
      2021
    • 负责人:
      Junshan Zhang
    • 依托单位:
    Collaborative Research: MLWiNS: Distributed Learning over Multi-Access Channels: From Bandlimited Coordinate Descent to Gradient Sketching
    • 批准号:
      2203412
    • 项目类别:
      Standard Grant
    • 资助金额:
      $40.0万
    • 财政年份:
      2021
    • 负责人:
      Junshan Zhang
    • 依托单位:
    NSF-AoF: CNS Core: Small: Reinforcement Learning for Real-time Wireless Scheduling and Edge Caching: Theory and Algorithm Design
    • 批准号:
      2130125
    • 项目类别:
      Standard Grant
    • 资助金额:
      $41.5万
    • 财政年份:
      2021
    • 负责人:
      Junshan Zhang
    • 依托单位:
    CPS: Medium: Collaborative Research: Demand Response & Workload Management for Data Centers with Increased Renewable Penetration
    • 批准号:
      2202126
    • 项目类别:
      Standard Grant
    • 资助金额:
      $50.0万
    • 财政年份:
      2021
    • 负责人:
      Junshan Zhang
    • 依托单位:
    海外基金