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

这个项目将加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，数据中心的能源效率、能源相关成本和整体可持续性是关键问题。由美国国家科学基金会资助的名为Parasol的实验性绿色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减少能源消耗、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在实际条件下进行的真实实验得出了在模拟中不可能得到的结果。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，增加太阳能发电能力，增加低排放燃料电池电源，多样化能源存储，并改进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，用于监视、编程控制和远程访问新的硬件增强。具体的研究目标是绿色数据中心的资源管理，包括协调工作负载、冷却和针对环境和负载可变性的能源调度，以最大限度地提高绿色数据中心的效益，并帮助利用gpu和深度学习硬件等加速器改善电网电源管理，这些加速器承诺了出色的性能/瓦特比。

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.