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

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资助的实验性绿色数据中心Parasol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。PI将需要更新和增强Parasol当前的软件堆栈，用于监控、编程控制和远程访问，以实现新的硬件增强。具体的研究目标是绿色嵌入式系统中的资源管理，包括针对环境和负载变化的协调工作负载、冷却和能源调度，以最大限度地发挥绿色嵌入式系统的优势，并利用GPU和深度学习硬件等加速器帮助改善电网电源管理，这些加速器有望实现出色的性能/功耗比。

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 of critical concerns. NSF funded experimental green datacenter called Parasol 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 PIs will need to 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, that includes 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 leveraging accelerators such as GPUs and deep learning hardware, which promise excellent performance/watt ratios.