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• W2963078671 abstract "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 2.5x for the achieved end-to-end performance." @default.
• W2963078671 created "2019-07-30" @default.
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• W2963078671 date "2018-12-01" @default.
• W2963078671 modified "2023-09-27" @default.
• W2963078671 title "GreenDataFlow: Minimizing the Energy Footprint of Global Data Movement" @default.
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• W2963078671 doi "https://doi.org/10.1109/bigdata.2018.8622570" @default.
• W2963078671 hasPublicationYear "2018" @default.
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