EAGER: GreenDataFlow: Minimizing the Energy Footprint of Global Data Movement

该项目填补了对数据传输能效理解的重要空白。 作为该项目的一部分开发的模型，算法和工具将有助于提高性能并降低端到端数据传输期间的功耗，这将节省大量资源（估计仅在美国经济中就可节省千兆瓦时的能源和数百万美元）。 这些新技术的适用性和效率将在实际应用中进行评估，在与IBM的合作伙伴关系。该项目探讨了最大限度地减少全球数据移动的能源使用足迹的选项。这项工作的重点是在数据传输期间节省终端系统（发送器和接收器节点）的能量。 它探索了一种通过应用层能量感知吞吐量优化实现低能耗端到端数据传输的新方法。研究团队调查并分析了影响端到端数据传输性能和能耗的因素，例如CPU频率缩放，多核调度，I/O块大小，TCP缓冲区大小，并行度，并发性和流水线水平，沿着网络路由器，交换机和集线器的数据传输速率。 评估这些参数如何在不牺牲传输性能的情况下降低终端系统和网络基础设施的能耗。 该项目将创建新的应用层模型、算法和工具，用于：预测端系统和协议参数的最佳组合，以实现具有能效约束的最佳数据传输吞吐量;准确预测由于活动链路上的数据传输速率增加而导致的网络设备功耗，并动态调整传输速率以平衡能效比;以及-向服务提供商提供基于服务等级协议（SLA）的能量高效传输算法。作为该项目的一部分开发的模型、算法和工具将有助于提高端到端数据传输过程中的性能并降低功耗，从而节省大量资源。 由于这些工具专注于应用层，因此它们不需要改变现有的基础设施，也不需要改变低级别的网络堆栈，并且开发的系统的广泛部署应该是容易实现的。这个奖项反映了NSF的法定使命，并且通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project fills an important gap in the understanding of data transfer energy efficiency. The models, algorithms and tools developed as part of this project will help increase performance and decrease power consumption during end-to-end data transfers, which should save significant quantities of resources (estimated to be gigawatt-hours of energy and millions of dollars in the US economy alone). The applicability and efficiency of these novel techniques will be evaluated in actual applications, in a collaborative partnership with IBM.The project explores options for minimizing the energy-use footprint of global data movement. The effort is focused on saving energy at the end systems (sender and receiver nodes) during data transfer. It explores a novel approach to achieving low-energy end-to-end data transfers, through application-layer energy-aware throughput optimization. The research team investigates and analyzes the factors that affect performance and energy consumption in end-to-end data transfers, such as CPU frequency scaling, multi-core scheduling, I/O block size, TCP buffer size, and the level of parallelism, concurrency, and pipelining, along with the data transfer rates at the network routers, switches, and hubs. How these parameters decrease energy consumption in the end systems and networking infrastructure, without sacrificing transfer performance, are assessed. The project will create novel application-layer models, algorithms, and tools for: - predicting the best combination of end-system and protocol parameters for optimal data transfer throughput with energy-efficiency constraints; - accurately predicting the network device power consumption due to increased data transfer rate on the active links, and dynamic readjustment of the transfer rate to balance the energy performance ratio; and - providing service level agreement (SLA) based energy-efficient transfer algorithms to service providers. The models, algorithms and tools developed as part of this project will help increase performance and decrease power consumption during end-to-end data transfers, saving significant quantities of resources. Since the tools focus on the application layer, they will not require changes to the existing infrastructure, nor to the low-level networking stack, and wide deployment of the developed system should be readily attainable.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.