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
基本信息
- 批准号:2202126
- 负责人:
- 金额:$ 50万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-10-01 至 2022-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
两个强大的全球趋势的汇合,(1)云计算和数据中心的快速增长,能源消耗飙升,以及(2)可再生能源的加速渗透,正在创造严峻的挑战和巨大的机遇。快速增长的可再生能源发电提出了巨大的运营挑战,因为它们将导致供应的大,频繁和随机波动。另一方面,数据中心在网格中提供了大量灵活的负载。利用这种灵活性,该项目将开发可持续数据中心的基础理论和算法,其双重目标是提高数据中心能源效率,并通过数据中心需求响应(DR)和工作负载管理加速可再生能源在电网中的整合。 具体而言,研究结果将揭示数据中心的需求响应,同时保持其性能,这将有助于数据中心决定如何参与电力市场计划。此外,数据中心需求响应的成功将有助于增加可再生能源整合,减少数据中心的碳足迹,为全球可持续发展做出贡献。PI将利用富有成效的合作,最终将研究成果用于正在进行的行业标准化和开发工作。PI教授的课程涵盖网络,游戏,智能电网和优化,并通过为代表性不足的学生提供研究机会来促进多样性。 该项目以PI在数据中心和智能电网方面的专业知识为基础,采用跨学科的方法为可持续数据中心开发基础理论和算法。研究任务是在两个协调良好的推动下组织的,即敏捷数据中心DR和自适应工作负载管理。数据中心灾难恢复的策略和决策将基于平衡服务质量和能源效率并确定供应功能的工作负载管理算法。负载管理算法将在DR施加的电力负载约束下相应地优化服务质量。该项目将作出三个独特的贡献:(1)具有数据中心在DR中的战略参与的新市场计划,而不是被动的价格接受者,(2)对电力网络约束对数据中心DR的影响的基本理解以及用于解决具有随机可再生供应的最优电力流的新分布式算法,以及(3)在时变和随机电力负载约束以及现场可再生发电下的用于大规模数据中心的高性能动态服务器供应和负载平衡算法。
项目成果
期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Stochastic Modeling and Analysis of Public Electric Vehicle Fleet Charging Station Operations
- DOI:10.1109/tits.2021.3099825
- 发表时间:2022-07
- 期刊:
- 影响因子:8.5
- 作者:Tianyang Zhang;Xi Chen;Bin Wu;M. Dedeoglu;Junshan Zhang;L. Trajković
- 通讯作者:Tianyang Zhang;Xi Chen;Bin Wu;M. Dedeoglu;Junshan Zhang;L. Trajković
Multimicrogrid Load Balancing Through EV Charging Networks
- DOI:10.1109/jiot.2021.3108698
- 发表时间:2021-08
- 期刊:
- 影响因子:10.6
- 作者:Xi Chen;Haihui Wang;Fan Wu;Yujie Wu;Marta C. González;Junshan Zhang
- 通讯作者:Xi Chen;Haihui Wang;Fan Wu;Yujie Wu;Marta C. González;Junshan Zhang
Impact of Social Learning on Privacy-Preserving Data Collection
- DOI:10.1109/jsait.2021.3053545
- 发表时间:2019-03
- 期刊:
- 影响因子:0
- 作者:Abdullah Basar Akbay;Weina Wang;Junshan Zhang
- 通讯作者:Abdullah Basar Akbay;Weina Wang;Junshan Zhang
Differentially Private ADMM for Regularized Consensus Optimization
用于正则化共识优化的差分私有 ADMM
- DOI:10.1109/tac.2020.3022856
- 发表时间:2020
- 期刊:
- 影响因子:6.8
- 作者:Cao, Xuanyu;Zhang, Junshan;Poor, H. Vincent;Tian, Zhi
- 通讯作者:Tian, Zhi
Distributed Q-Learning with State Tracking for Multi-agent Networked Control
- DOI:10.5555/3463952.3464203
- 发表时间:2020-12
- 期刊:
- 影响因子:0
- 作者:Hang Wang;Sen Lin;H. Jafarkhani;Junshan Zhang
- 通讯作者:Hang Wang;Sen Lin;H. Jafarkhani;Junshan Zhang
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Junshan Zhang其他文献
Privacy-aware Data Trading(中国计算机学会认定的网络与信息安全领域最高级别的三大A类国际期刊之一,中科院一区TOP,影响因子:7.178)
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:6.8
- 作者:
Shengling Wang;Lina Shi;Junshan Zhang;Xiuzhen Cheng;Jiguo Yu - 通讯作者:
Jiguo Yu
Networked Information Gathering in Stochastic Sensor Networks: Compressive Sensing, Adaptive Network Coding and Robustness
- DOI:
10.21236/ada590144 - 发表时间:
2013-09 - 期刊:
- 影响因子:0
- 作者:
Junshan Zhang - 通讯作者:
Junshan Zhang
CL-LSG: Continual Learning via Learnable Sparse Growth
CL-LSG:通过可学习的稀疏增长持续学习
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Li Yang;Sen Lin;Junshan Zhang;Deliang Fan - 通讯作者:
Deliang Fan
A two-phase utility maximization framework for wireless medium access control
无线媒体访问控制的两阶段效用最大化框架
- DOI:
10.1109/twc.2007.05159 - 发表时间:
2007 - 期刊:
- 影响因子:10.4
- 作者:
D. Zheng;Junshan Zhang - 通讯作者:
Junshan Zhang
Critical behavior of blind spots in sensor networks.
传感器网络盲点的关键行为。
- DOI:
10.1063/1.2745232 - 发表时间:
2007 - 期刊:
- 影响因子:2.9
- 作者:
Liang Huang;Y. Lai;Kwangho Park;Junshan Zhang;Zhifeng Hu - 通讯作者:
Zhifeng Hu
Junshan Zhang的其他文献
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{{ truncateString('Junshan Zhang', 18)}}的其他基金
CCSS: Collaborative Research: Quality-Aware Distributed Computation for Wireless Federated Learning: Channel-Aware User Selection, Mini-Batch Size Adaptation, and Scheduling
CCSS:协作研究:无线联邦学习的质量感知分布式计算:通道感知用户选择、小批量大小自适应和调度
- 批准号:
2203238 - 财政年份:2021
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
Collaborative Research: MLWiNS: Distributed Learning over Multi-Access Channels: From Bandlimited Coordinate Descent to Gradient Sketching
协作研究:MLWiNS:多访问通道上的分布式学习:从带限坐标下降到梯度草图
- 批准号:
2203412 - 财政年份:2021
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
NSF-AoF: CNS Core: Small: Reinforcement Learning for Real-time Wireless Scheduling and Edge Caching: Theory and Algorithm Design
NSF-AoF:CNS 核心:小型:实时无线调度和边缘缓存的强化学习:理论和算法设计
- 批准号:
2130125 - 财政年份:2021
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
NSF-AoF: CNS Core: Small: Reinforcement Learning for Real-time Wireless Scheduling and Edge Caching: Theory and Algorithm Design
NSF-AoF:CNS 核心:小型:实时无线调度和边缘缓存的强化学习:理论和算法设计
- 批准号:
2203239 - 财政年份:2021
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
CCSS: Collaborative Research: Quality-Aware Distributed Computation for Wireless Federated Learning: Channel-Aware User Selection, Mini-Batch Size Adaptation, and Scheduling
CCSS:协作研究:无线联邦学习的质量感知分布式计算:通道感知用户选择、小批量大小自适应和调度
- 批准号:
2121222 - 财政年份:2021
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
Collaborative Research: MLWiNS: Distributed Learning over Multi-Access Channels: From Bandlimited Coordinate Descent to Gradient Sketching
协作研究:MLWiNS:多访问通道上的分布式学习:从带限坐标下降到梯度草图
- 批准号:
2003081 - 财政年份:2020
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
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 - 财政年份:2017
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
TWC SBE: Small: Towards an Economic Foundation of Privacy-Preserving Data Analytics: Incentive Mechanisms and Fundamental Limits
TWC SBE:小型:迈向隐私保护数据分析的经济基础:激励机制和基本限制
- 批准号:
1618768 - 财政年份:2016
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
EARS: Joint Optimization of RF Design and Smartphone Sensing: From Adaptive Sniffing to WAZE-Inspired Spectrum Sharing
EARS:射频设计和智能手机传感的联合优化:从自适应嗅探到受 WAZE 启发的频谱共享
- 批准号:
1547294 - 财政年份:2015
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
An Exchange Market Approach for Mobile Crowdsensing
移动群智感知的交易市场方法
- 批准号:
1408409 - 财政年份:2014
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
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