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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：
1739189
负责人：
Rayadurgam Srikant
金额：
$ 25万
依托单位：
University of Illinois at Urbana-Champaign
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
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1739189&HistoricalAwards=false
关键词：
CPS Medium Collaborative Research Demand

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.

云计算和数据中心的快速增长与能源消耗的急剧上升，以及可再生能源的加速渗透，这两大全球趋势的融合既带来了严峻的挑战，也带来了巨大的机遇。快速增长的可再生能源发电带来了巨大的运营挑战，因为它们将导致供应的大规模、频繁和随机波动。另一方面，数据中心在网格中提供大量灵活的负载。利用这种灵活性，该项目将开发可持续数据中心的基本理论和算法，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，研究结果将阐明数据中心在保持其性能的同时需求响应，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的整合，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，并致力于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PIs在数据中心和智能电网方面的专业知识为基础，采用跨学科方法开发可持续数据中心的基本理论和算法。研究任务组织在两个协调良好的重点下，即敏捷数据中心容灾和自适应工作负载管理。数据中心容灾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。负载管理算法将在容灾带来的电力负荷约束下优化服务质量。这个项目将做出三个独特的贡献：(1)数据中心战略性参与容灾的新市场方案，而不是被动的价格接受者；(2)对电网约束对数据中心容灾影响的基本认识，以及解决随机可再生能源最优潮流的新分布式算法；(3)针对时变随机电力负荷约束和现场可再生发电的大型数据中心高性能动态服务器配置和负载均衡算法。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Delay Asymptotics and Bounds for Multi-Task Parallel Jobs

多任务并行作业的延迟渐近和界限

DOI：
10.1145/3308897.3308901
发表时间：
2019
期刊：
ACM SIGMETRICS Performance Evaluation Review
影响因子：
0
作者：
Wang, Weina;Harchol-Balter, Mor;Jiang, Haotian;Scheller-Wolf, Alan;Srikant, R.
通讯作者：
Srikant, R.

Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning

DOI：
发表时间：
2019-07
期刊：
ArXiv
影响因子：
0
作者：
Harsh Gupta;R. Srikant;Lei Ying
通讯作者：
Harsh Gupta;R. Srikant;Lei Ying

Mean-Field Analysis of Coding Versus Replication in Large Data Storage Systems

大数据存储系统中编码与复制的平均场分析

DOI：
10.1145/3159172
发表时间：
2018
期刊：
ACM Transactions on Modeling and Performance Evaluation of Computing Systems
影响因子：
0.6
作者：
Li, Bin;Ramamoorthy, Aditya;Srikant, R.
通讯作者：
Srikant, R.

Optimal Load Balancing with Locality Constraints

具有局部性约束的最佳负载平衡