A frequency adjustment architecture for energy efficient router

Fu, Wenliang; Song, Tian

doi:10.1145/2342356.2342383

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…The energy footprint of this global data movement is estimated at more than 100 terawatt hours per year at the current rate, costing more than 20 billion US dollars annually to the world economy in addition to the environmental side effects [27], [44], [41], [51], [22]. This fact has resulted in considerable amount of work focusing on power management and energy efficiency in hardware and software systems [12], [47], [59], [28], [16], [17], [49], [36], [30], [52], [46] as well as on power-aware networking [6], [41], [24], [31], [23], [21].…”

Section: Introductionmentioning

confidence: 99%

GreenDataFlow: Minimizing the Energy Footprint of Global Data Movement

Nine

Tacchio

Imran

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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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 applicationlayer 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.

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Section: Introductionmentioning

confidence: 99%

GreenDataFlow: Minimizing the Energy Footprint of Global Data Movement

Nine

Tacchio

Imran

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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“…The annual electricity consumed by the global data movement is estimated to be more than 100 terawatt hours at the current rate, costing more than 20 billion US dollars per year [26,29,52,55,62]. This fact has resulted in considerable amount of work focusing on power management and energy efficiency in hardware and software systems [16,19,21,30,33,39,57,58,60,63,70] as well as on power-aware networking [14,25,27,28,34,52]. On the other hand, there has been little work focusing on saving data transfer energy at the end systems (sender and receiver nodes).…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Data Transfer Algorithms for HTTP-Based Services

Kosar,

Alan

2017

Preprint

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According to recent statistics, more than 1 zettabytes of data is moved over the Internet annually, which consumes several terawatt hours of electricity, and costs billions of US dollars to the world economy. HTTP protocol is used in the majority of these data transfers, accounting for 70% of the global Internet traffic. We claim that HTTP transfers, and the services based on HTTP, can become more energy efficient without any performance degradation by application-level tuning of certain protocol parameters. In this paper, we analyze several application-level parameters that affect the throughput and energy consumption in HTTP data transfers, such as the level of parallelism, concurrency, and pipelining. We introduce SLA-based algorithms which can decide the best combination of these parameters based on user-defined energy efficiency and performance criteria. Our experimental results show that up to 80% energy savings can be achieved at the client and server hosts during HTTP data transfers and the end-to-end data throughput can be increased at the same time.

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