II-EN: Collaborative Research: Enhancing the Parasol Experimental Testbed for Sustainable Computing

II-EN：协作研究：增强可持续计算的 Parasol 实验测试台

• 批准号: 1730043
• 负责人: Thu Nguyen
• 金额: $ 69.17万
• 依托单位: Rutgers University New Brunswick
• 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: 2017-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1730043&HistoricalAwards=false
• 关键词: II; EN; Collaborative; Research; Enhancing

This project will enhance an experimental datacenter for sustainable computing. Datacenters consume vast amounts of energy, totaling about 1.8% of the US electricity usage in 2014. Thus, the energy efficiency, energy-related costs, and overall sustainability of datacenters are critical concerns. NSF funded an experimental green datacenter called Parasol, which has previously demonstrated that the combination of green design and intelligent software management systems can lead to significant reductions in energy consumption, carbon emission, and cost. The enhanced version of this project will update energy sources, network technologies and management software.Running real experiments in live conditions using Parasol led to findings that were not possible in simulation. This proposal seeks to update and enhance Parasol with current and next generation power-efficient servers, improve network connectivity and integrate software-defined networking (SDN) and Wi-Fi capabilities, increase solar energy generation capacity, add a low emission fuel cell power source, diversify energy storage, and improve the cooling system to advance green computing. The investigators will update and enhance Parasol's current software stack for monitoring, programmatic control, and remote access for the new hardware enhancements. Specific research goals are resource management in green datacenters, including coordinated workload, cooling, and energy scheduling against environmental and load variability to maximize the benefits of green datacenters and to help improve grid power management. A specific goal is to leverage accelerators such as GPUs and deep learning hardware, which promise excellent performance/watt ratios.

该项目将增强可持续计算的实验数据中心。数据中心消耗大量能源，2014年约占美国用电量的1.8%。因此，能源效率、与能源有关的成本和能源中心的总体可持续性是关键问题。NSF资助了一个名为Parasol的实验性绿色数据中心，该数据中心此前已经证明，绿色设计和智能软件管理系统的结合可以显著降低能耗、碳排放和成本。该项目的增强版将更新能源、网络技术和管理软件。使用Parasol在现场条件下运行真实的实验，得出了在模拟中无法实现的结果。该提案旨在通过当前和下一代节能服务器更新和增强Parasol，改善网络连接并集成软件定义网络（SDN）和Wi-Fi功能，增加太阳能发电容量，增加低排放燃料电池电源，使能源存储多样化，并改善冷却系统以推进绿色计算。调查人员将更新和增强Parasol目前的软件堆栈，用于监控、编程控制和远程访问新的硬件增强功能。具体的研究目标是在绿色发电厂的资源管理，包括协调工作负载，冷却，能源调度对环境和负载的变化，以最大限度地提高绿色发电厂的利益，并帮助改善电网电源管理。 一个具体的目标是利用加速器，如GPU和深度学习硬件，这些硬件保证了出色的性能/功耗比。

项目成果

Global cost/quality management across multiple applications

跨多个应用程序的全球成本/质量管理

• DOI: 10.1145/3368089.3409721
• 发表时间: 2020
• 期刊: ESEC/FSE 2020: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
• 作者: Liu, Liu;Isaacman, Sibren;Kremer, Ulrich
• 通讯作者: Kremer, Ulrich

GreenDRL: managing green datacenters using deep reinforcement learning

• DOI: 10.1145/3542929.3563501
• 发表时间: 2022-11
• 期刊: Proceedings of the 13th Symposium on Cloud Computing
• 作者: Kuo Zhang;Peijian Wang;Ning Gu;Thu D. Nguyen

An Adaptive Application Framework with Customizable Quality Metrics

具有可定制质量指标的自适应应用程序框架

• DOI: 10.1145/3477428
• 发表时间: 2022
• 期刊: ACM Transactions on Design Automation of Electronic Systems
• 影响因子: 1.4
• 作者: Liu, Liu;Isaacman, Sibren;Kremer, Ulrich
• 通讯作者: Kremer, Ulrich