The Direct Energy Demand of Internet Data Flows

Coroamă, Vlad C.; Hilty, Lorenz M.; Heiri, Ernst; Horn, F. M.

doi:10.1111/jiec.12048

Cited by 62 publications

(74 citation statements)

References 21 publications

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“…Additionally, a data packet of 10 kB is sent by the user at the beginning of each video-conference to connect on IMS. For all data transmissions, the result from Coroama et al [19] has been used, which gives a consumption of 0.1993 kWh/GB.…”

Section: A) Blade Servers Usementioning

confidence: 99%

Life cycle assessment of videoconferencing with call management servers relying on virtualization

Vandromme¹,

Dandres²,

Maurice³

et al. 2014

Proceedings of the 2014 Conference ICT for Sustainability

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Abstract-Recently, data centres have been called out for their particularly high energy consumption, which already accounts for 1.5% of the total global electricity consumption and is among the world's fastest growing energy consumptions. To reduce the data centres' environmental impacts, technologies such as free cooling and sustainable power sources are used. Another newly developed strategy to improve the energy efficiency of data centres is virtualization, which makes it possible to install several operating systems, known as virtual machines (VMs), so that several tasks and users can share a single server. To evaluate the environmental advantages and burdens of this strategy, assessments tools are required. Several studies have already quantified the energetic and environmental benefits of virtualization but often only considered the use phase and CO2 improvement. This study uses life cycle assessment (LCA) to evaluate the environmental impacts of Internet use in videoconferencing (VC). Preliminary results show the advantages of virtualization in the manufacturing, use and endof-life phases. Indeed, when virtualization is implemented, one server can be allocated to several tasks. Therefore, the environmental burden of use and manufacturing will be allocated to the various tasks, decreasing the impact of each one.

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Section: A) Blade Servers Usementioning

confidence: 99%

Life cycle assessment of videoconferencing with call management servers relying on virtualization

Vandromme¹,

Dandres²,

Maurice³

et al. 2014

Proceedings of the 2014 Conference ICT for Sustainability

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“…For a system boundary that included network devices and optical fibers but no end devices, and making pessimistic assumptions in terms of energy consumption where specific data were not available, the study yielded an energy intensity of 0.2 kWh/GB for 2009. As we argued in [32], many characteristics of the study (such as an above-average number of hops) justify considering its result aboveaverage in terms of energy intensity. This implies that the case-study result, when generalized, should be considered an upper bound for the average energy intensity.…”

Section: Bottom-up Assessmentsmentioning

confidence: 99%

“…In [32], Coroamȃ and Hilty present an assessment of a 40 megabit per second (Mbps) videoconferencing transmission of the case study introduced in [33]. For a system boundary that included network devices and optical fibers but no end devices, and making pessimistic assumptions in terms of energy consumption where specific data were not available, the study yielded an energy intensity of 0.2 kWh/GB for 2009.…”

Section: Bottom-up Assessmentsmentioning

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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“…the use of the Internet (reported, e.g., in [30][31][32][33]) have emerged recently, showing the relevance of end-user devices in comparison to the entire infrastructure required in order to access Internet data. In a recent conference contribution dealing with changes of the environmental impacts from ICT over time, Lunden and Malmodin conclude that although the "impacts per connected device and data volume are lower than in the past," further decreases can be achieved only by reducing energy consumption at core sites, data centers, and in the end user devices [34].…”

Section: Lca and Ict -A Short Historical Overviewmentioning

confidence: 99%

Grey Energy and Environmental Impacts of ICT Hardware

Hischier

Coroamă

Schien

et al. 2014

Advances in Intelligent Systems and Computing

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PostprintThis is the accepted version of a chapter published in ICT Innovations for Sustainability. Citation for the original published chapter:Hischier, R., Coroama, V., Schien, D., Ahmadi Achachlouei, M. (2015) Grey Energy and Environmental Impacts of ICT Hardware. Abstract. Direct energy consumption of ICT hardware is only "half the story." In order to get the "whole story," energy consumption during the entire life cycle has to be taken into account. This chapter is a first step toward a more comprehensive picture, showing the "grey energy" (i.e., the overall energy requirements) as well as the releases (into air, water, and soil) during the entire life cycle of exemplary ICT hardware devices by applying the life cycle assessment method. The examples calculated show that a focus on direct energy consumption alone fails to take account of relevant parts of the total energy consumption of ICT hardware as well as the relevance of the production phase. As a general tendency, the production phase is more and more important the smaller (and the more energy-efficient) the devices are. When in use, a tablet computer is much more energy-efficient than a desktop computer system with its various components, so its production phase has a much greater relative importance. Accordingly, the impacts due to data transfer when using Internet services are also increasingly relevant the smaller the end-user device is, reaching up to more than 90% of the overall impact when using a tablet computer.

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The Direct Energy Demand of Internet Data Flows

Cited by 62 publications

References 21 publications

Life cycle assessment of videoconferencing with call management servers relying on virtualization

Life cycle assessment of videoconferencing with call management servers relying on virtualization

The Energy Intensity of the Internet: Home and Access Networks

Grey Energy and Environmental Impacts of ICT Hardware

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