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CAREER: Coordinated Power Management in Colocation Data Centers

职业：托管数据中心的协调电源管理

基本信息

批准号：
1551661
负责人：
Shaolei Ren
金额：
$ 40.4万
依托单位：
University of California-Riverside
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2015
资助国家：
美国
起止时间：
2015-07-10 至 2022-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1551661&HistoricalAwards=false
关键词：
CAREER Coordinated Power Management Colocation

项目摘要

CAREER: Coordinated Power Management in Colocation Data CentersThis project focuses on optimizing power management in colocation data centers, which provide facility support to multiple tenants that manage their own servers in shared space. As an integral segment of data center industry supporting the exploding information technology demand, multi-tenant colocation data centers have been massively expanding in both number and scale. The significant concerns are both growing operational costs and environmental issues. Nonetheless, the existing energy-efficiency efforts have been dominantly focused on owner-operated data centers, like Google, whose operators have full and coordinated control over both computing resources and facilities, and hence they are not applicable for colocation data centers in which individual tenants manage their own servers and power consumption in a non-coordinated manner. Consequently, colocation data centers are far behind state-of-the-art owner-operated data centers in energy efficiency and sustainability, which, if left unaddressed, is a major hurdle for sustainable growth in digital economy.To achieve energy sustainability in colocation data centers, this project proposes novel market-based approaches to coordinate individual tenants' power management, in concert with optimizing colocation operator's own server management and non-IT control (e.g., cooling system). This project investigates three complementary research thrusts: (i) Holistically optimizing colocation operator's facility management and economic incentives offered to tenants for energy saving, to limit the usage of carbon-intensive electricity; (ii) Enabling colocation data center demand response to minimize colocation operator's operating cost, increase the adoption of renewable energy, and enhance the power grid reliability; and (iii) Experimentation and validation based on combined software simulation, system prototyping, and real-world implementation.As part of the collaborative efforts from both industry and academia to enable sustainable computing, this project uniquely extends the exploration of power management from owner-operated data centers to multi-tenant colocation data centers. This project can catalyze a shift in future data center management and transform the way that the digital economy evolves, bearing great economic, environmental and societal impacts. This project also contains a significant educational component and provides broad opportunities to attract a diverse population of students to engage in computer science.

职业：主机托管数据中心的协调电源管理该项目专注于优化主机托管数据中心的电源管理，这些数据中心为在共享空间中管理自己的服务器的多个租户提供设施支持。作为支撑爆炸性信息技术需求的数据中心行业的一个组成部分，多租户托管数据中心在数量和规模上都在大规模扩张。日益增长的运营成本和环境问题都是令人担忧的问题。尽管如此，现有的能效努力主要集中在所有者运营的数据中心，如谷歌，其运营商对计算资源和设施都有完全和协调的控制，因此不适用于托管数据中心，在这种数据中心中，单个租户以非协调的方式管理自己的服务器和功耗。因此，托管数据中心在能效和可持续性方面远远落后于最先进的所有者运营的数据中心，如果不加以解决，这是数字经济可持续增长的主要障碍。为了实现托管数据中心的能源可持续性，该项目提出了基于市场的新方法来协调单个租户的电源管理，并优化托管运营商自己的服务器管理和非IT控制(例如冷却系统)。该项目研究了三个互补的研究方向：(I)全面优化代管运营商的设施管理和为租户提供的节能经济激励，以限制碳密集型电力的使用；(Ii)支持代管数据中心需求响应，以最小化代管运营商的运营成本，增加对可再生能源的采用，并提高电网可靠性；以及(Iii)基于软件模拟、系统原型和真实世界实施相结合的实验和验证。作为产业界和学术界合作实现可持续计算的一部分，该项目独特地将电源管理的探索从所有者运营的数据中心扩展到多租户托管数据中心。该项目可以催化未来数据中心管理的转变，并改变数字经济的发展方式，带来巨大的经济、环境和社会影响。该项目还包含一个重要的教育组成部分，并提供了广泛的机会，以吸引不同群体的学生从事计算机科学。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Increasing the Trustworthiness of Deep Neural Networks via Accuracy Monitoring

DOI：
发表时间：
2020-07
期刊：
ArXiv
影响因子：
0
作者：
Zhihui Shao;Jianyi Yang;Shaolei Ren
通讯作者：
Zhihui Shao;Jianyi Yang;Shaolei Ren

On the Latency Variability of Deep Neural Networks for Mobile Inference

关于移动推理深度神经网络的延迟可变性

DOI：
发表时间：
2020
期刊：
Workshop on Hot Topics in Edge Computing (1-page Poster
影响因子：
0
作者：
Yang, Luting;Lu, Bingqian;Ren, Shaolei
通讯作者：
Ren, Shaolei

Multi-Feedback Bandit Learning with Probabilistic Contexts

DOI：
10.24963/ijcai.2020/427
发表时间：
2020-07
期刊：
影响因子：
0
作者：
Da Yu;Huishuai Zhang;Wei Chen;Tie-Yan Liu;Jian Yin
通讯作者：
Da Yu;Huishuai Zhang;Wei Chen;Tie-Yan Liu;Jian Yin

Your Noise, My Signal: Exploiting Switching Noise for Stealthy Data Exfiltration from Desktop Computers?

你的噪音，我的信号：利用开关噪音从台式计算机中秘密窃取数据？

DOI：
10.1145/3410048.3410094
发表时间：
2020
期刊：
ACM SIGMETRICS Performance Evaluation Review
影响因子：
0
作者：
Shao, Zhihui;Islam, Mohammad A.;Ren, Shaolei
通讯作者：
Ren, Shaolei

Your Noise, My Signal: Exploiting Switching Noise for Stealthy Data Exfiltration from Desktop Computers

你的噪音，我的信号：利用开关噪音从台式计算机中秘密窃取数据

DOI：
10.1145/3393691.3394178
发表时间：
2020
期刊：
SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems
影响因子：
0
作者：
Shao, Zhihui;Islam, Mohammad A.;Ren, Shaolei
通讯作者：
Ren, Shaolei