GreenDataFlow: Minimizing the Energy Footprint of Global Data Movement
GreenDataFlow: Minimizing the Energy Footprint of Global Data Movement
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DOI：
10.1109/bigdata.2018.8622570
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
M. S. Q. Z. Nine;Luigi Di Tacchio;A. Imran;T. Kosar;Muhammed Fatih Bulut;Jinho Hwang
中科院分区：
文献类型：
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作者：
M. S. Q. Z. Nine;Luigi Di Tacchio;A. Imran;T. Kosar;Muhammed Fatih Bulut;Jinho Hwang
互联网上的全球数据运动每年的能源足迹估计为100个小时，使世界经济数十亿美元以及涉及数据传输贡献的源头和目的地节点的数十亿美元。在本文中，最大程度地减少了末端系统的功能，我们介绍了一种新颖的应用程序，基于历史分析和实时调整，旨在实现高数据传输吞吐量，同时保持最小的级别的级别的服务水平（SLAS）结果表明，GreendataFlow的表现优于该领域最接近的最新解决方案，可节省50％，而实现的端到端性能为2.5倍。
The global data movement over Internet has an estimated energy footprint of 100 terawatt hours per year, costing the world economy billions of dollars. The networking infrastructure together with source and destination nodes involved in the data transfer contribute to overall energy consumption. Although considerable amount of research has rendered power management techniques for the networking infrastructure, there has not been much prior work focusing on energy-aware data transfer solutions for minimizing the power consumed at the end-systems. In this paper, we introduce a novel application-layer solution based on historical analysis and real-time tuning called GreenDataFlow, which aims to achieve high data transfer throughput while keeping the energy consumption at the minimal levels. GreenDataFlow supports service level agreements (SLAs) which give the service providers and the consumers the ability to fine tune their goals and priorities in this optimization process. Our experimental results show that GreenDataFlow outperforms the closest competing state-of-the art solution in this area 50% for energy saving and 2.5× for the achieved end-to-end performance.