Methodologies for Assessing the Use-Phase Power Consumption and Greenhouse Gas Emissions of Telecommunications Network Services

Chan, Chien Aun; Gygax, André F.; Wong, Elaine; Nirmalathas, Ampalavanapillai; Kilper, Daniel C.

doi:10.1021/es303384y

Cited by 41 publications

(23 citation statements)

References 28 publications

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“…Digital Object Identifier 10.1109/JLT.2015.2469145 undisclosed network configurations operated by a number of carriers and service providers [1]- [3]. Two representative methods of estimating network energy consumption have been studied.…”

Section: Introductionmentioning

confidence: 99%

Unifying Top-Down and Bottom-Up Approaches to Evaluate Network Energy Consumption

Ishii

Kurumida

Sato

et al. 2015

J. Lightwave Technol.

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A novel method for estimating energy consumption of networks at the national scale is presented. Accurate estimations of network energy consumption are increasingly important because of continuously increasing network traffic and the need to assess emerging energy technologies to achieve overall energy efficiency. However, direct estimations are not practical because of the complex, widely diverse, and undisclosed configurations of actual networks. The impact of each network's energy overhead, which is the energy consumed above that required to accommodate network traffic, on the energy consumption associated with these complex configurations must be inferred. In the proposed method, the energy overhead is quantitatively evaluated and introduced as overhead factors by comparing the energy consumption estimated from network configuration models (bottom-up methods) with reports of actual energy consumption (top-down methods). This proposed "unified" method is capable of long-term predictions of future technology trends, including network architecture changes. In this paper, we consider different overhead factors that serve as fitting parameters for different network areas, and demonstrate the procedure for determining each of these parameters through an estimation of the fixed broadband Internet in Japan. This unified method consistently estimates the long-term evolution of energy consumption from 2000 to 2030.Index Terms-Communication networks, Internet, network energy consumption, optical fiber communication.

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Section: Introductionmentioning

confidence: 99%

Unifying Top-Down and Bottom-Up Approaches to Evaluate Network Energy Consumption

Ishii

Kurumida

Sato

et al. 2015

J. Lightwave Technol.

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“…According to [19], top-down analyses are based on two estimates: 1) the overall energy demand of either the entire Internet or a part of it (e.g., a country or a continent), and 2) the total Internet traffic of that region.…”

Section: Existing Assessments and Methods Usedmentioning

confidence: 99%

The Energy Intensity of the Internet: Home and Access Networks

Coroamă

Schien

Preist

et al. 2014

Advances in Intelligent Systems and Computing

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Estimates of the energy intensity of the Internet diverge by several orders of magnitude. We present existing assessments and identify diverging definitions of the system boundary as the main reason for this large spread. The decision of whether or not to include end devices influences the result by 1-2 orders of magnitude. If end devices are excluded, customer premises equipment (CPE) and access networks have a dominant influence. Of less influence is the consideration of cooling equipment and other overhead, redundancy equipment, and the amplifiers in the optical fibers. We argue against the inclusion of end devices when assessing the energy intensity of the Internet, but in favor of including CPE, access networks, redundancy equipment, cooling and other overhead as well as optical fibers. We further show that the intensities of the metro and core network are best modeled as energy per data, while the intensity of CPE and access networks are best modeled as energy per time (i.e., power), making overall assessments challenging. The chapter concludes with a formula for the energy intensity of CPE and access networks. The formula is presented both in generic form as well as with concrete estimates of the average case to be used in quick assessments by practitioners. The following chapter develops a similar formula for the core and edge networks. Taken together, the two chapters provide an assessment method of the Internet's energy intensity that takes into account dierent modeling paradigms for dierent parts of the network. Abstract. Estimates of the energy intensity of the Internet diverge by several orders of magnitude. We present existing assessments and identify diverging definitions of the system boundary as the main reason for this large spread. The decision of whether or not to include end devices influences the result by 1-2 orders of magnitude. If end devices are excluded, customer premises equipment (CPE) and access networks have a dominant influence. Of less influence is the consideration of cooling equipment and other overhead, redundancy equipment, and the amplifiers in the optical fibers. We argue against the inclusion of end devices when assessing the energy intensity of the Internet, but in favor of including CPE, access networks, redundancy equipment, cooling and other overhead as well as optical fibers. We further show that the intensities of the metro and core network are best modeled as energy per data, while the intensity of CPE and access networks are best modeled as energy per time (i.e., power), making overall assessments challenging. The chapter concludes with a formula for the energy intensity of CPE and access networks. The formula is presented both in generic form as well as with concrete estimates of the average case to be used in quick assessments by practitioners. The following chapter develops a similar formula for the core and edge networks. Taken together, the two chapters provide an assessment method of the Internet's energy intensity that takes into account different ...

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“…ICT contributes with 2% of global greenhouse gas (GHG) emissions annually [2]- [4], which is a value that exceeds the GHG emission of the aviation sector [7]. Telecoms infrastructure and devices, in particular, contribute alone to at least one third of this value.…”

Section: Introductionmentioning

confidence: 99%

Measurement-based energy consumption profiling of mobile radio networks

Capone

D’Elia

Filippini

et al. 2015

2015 IEEE 1st International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow (RTSI)

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Abstract-The exponential growth of mobile traffic is forcing operators to increase quickly the capacity of their network and extend it with new technologies and improved topologies, such as heterogeneous layouts with small cells. However, since revenues cannot grow at the same rate of traffic, the main challenge is to manage capacity expansion with reduced costs. In addition to fixed costs for the new network infrastructures, operational costs are becoming critical, mainly for energy bill component. Moreover, the carbon footprint of mobile access networks is considered one of the largest of the whole ICT (Information and Communications Technology) sector and its reduction is fundamental for the environmental sustainability of the Internet economy. Due to these reasons, improving the energy efficiency of the access network is crucial for mobile operators. In order to do that, monitoring the energy consumption of the network components and defining models of energy profile are valuable approaches for estimating energy costs and identifying the most efficient configurations.In this paper, we present an energy consumption monitoring system that has been designed and implemented in three different countries using separate sensors for the radio and base-band components of second, third and fourth generation systems. We also propose an energy profiling approach that simplifies the characterization of the different components and allows the estimation of the energy efficiency based on traffic statistics.

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Methodologies for Assessing the Use-Phase Power Consumption and Greenhouse Gas Emissions of Telecommunications Network Services

Cited by 41 publications

References 28 publications

Unifying Top-Down and Bottom-Up Approaches to Evaluate Network Energy Consumption

Unifying Top-Down and Bottom-Up Approaches to Evaluate Network Energy Consumption

The Energy Intensity of the Internet: Home and Access Networks

Measurement-based energy consumption profiling of mobile radio networks

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