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

EAGER：GreenDataFlow：最大限度地减少全球数据移动的能源足迹

• 批准号: 1842054
• 负责人: Tevfik Kosar
• 金额: $ 29.36万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1842054&HistoricalAwards=false
• 关键词: EAGER; GreenDataFlow; Minimizing; Energy; Footprint

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.

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端到端数据传输过程中的性能并降低功耗，这将节省大量资源（估计仅在美国经济中就节省了千兆瓦时的能源和数百万美元）。在与IBM的合作伙伴关系中，将在实际应用中评估这些新技术的适用性和效率。该项目探索最小化全球数据移动的能源使用足迹的选择。这项工作的重点是在数据传输过程中节省终端系统（发送方和接收方节点）的能源。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中性能和能耗的因素，例如CPU频率缩放、多核调度、I/O块大小、TCP缓冲区大小、并行性、并发性和流水线级别，以及网络路由器、交换机和集线器的数据传输速率。评估了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。该项目将创建新颖的应用层模型、算法和工具，用于：预测终端系统和协议参数的最佳组合，以实现能效约束下的最佳数据传输吞吐量；-准确预测由于活动链路上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量性能比；向服务提供商提供基于服务水平协议（SLA）的节能传输算法。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。由于这些工具关注于应用程序层，因此它们不需要更改现有的基础设施，也不需要更改低级网络堆栈，并且开发的系统的广泛部署应该很容易实现。该奖项反映了美国国家科学基金会的法定使命，并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。

项目成果

Cross-Layer Optimization of Big Data Transfer Throughput and Energy Consumption

• DOI: 10.1109/cloud.2019.00017
• 发表时间: 2019-07
• 期刊: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD)
• 作者: Luigi Di Tacchio;M. S. Q. Z. Nine;T. Kosar;Muhammed Fatih Bulut;Jinho Hwang

GreenDataFlow: Minimizing the Energy Footprint of Global Data Movement

• DOI: 10.1109/bigdata.2018.8622570
• 发表时间: 2018-10
• 期刊: 2018 IEEE International Conference on Big Data (Big Data)
• 作者: M. S. Q. Z. Nine;Luigi Di Tacchio;A. Imran;T. Kosar;Muhammed Fatih Bulut;Jinho Hwang

Energy-Efficient Data Transfer Optimization via Decision-Tree Based Uncertainty Reduction

通过基于决策树的不确定性降低实现节能数据传输优化

• DOI: 10.1109/icccn54977.2022.9868866
• 发表时间: 2022
• 期刊: 2022 International Conference on Computer Communications and Networks (ICCCN
• 作者: Jamil, Hasibul;Rodolph, Lavone;Goldverg, Jacob;Kosar, Tevfik
• 通讯作者: Kosar, Tevfik

Energy-saving Cross-layer Optimization of Big Data Transfer Based on Historical Log Analysis

基于历史日志分析的大数据传输节能跨层优化

• DOI: 10.1109/icc42927.2021.9500693
• 发表时间: 2021
• 期刊: ICC 2021 - IEEE International Conference on Communications
• 作者: Rodolph, Lavone;Zulkar Nine, MD S;Di Tacchio, Luigi;Kosar, Tevfik
• 通讯作者: Kosar, Tevfik

FastHLA: Energy-Efficient Mobile Data Transfer Optimization Based on Historical Log Analysis

FastHLA：基于历史日志分析的节能移动数据传输优化

• DOI: 10.1145/3265863.3265871
• 发表时间: 2018
• 期刊: MobiWac'18 Proceedings of the 16th ACM International Symposium on Mobility Management and Wireless Access
• 作者: Guner, Kemal;Nine, MD S;Bulut, M. Fatih;Kosar, Tevfik
• 通讯作者: Kosar, Tevfik