A model for green design of online news media services

Schien, Daniel; Shabajee, Paul; Wood, Steve; Preist, Chris

doi:10.1145/2488388.2488485

Cited by 15 publications

(15 citation statements)

References 24 publications

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“…In our future work, we will be using analysis of home network data alongside associated carbon foot-printing of Internet based services (impacts of geographical location on the energy footprint of digital media [22]) to explore to what extent we can begin associating indirect carbon contributions with particular devices and practices, further filling in the holistic view of media and IT impacts.…”

Section: Resultsmentioning

confidence: 99%

“…It is possible to roughly estimate the indirect energy impacts of practices involving Internet-based services by drawing on impact estimates from current scholarly articles, such as those derived by Schien et al [22]. For example, Ellie's streaming of two hours of TV per day might have added 50% to her typical daily direct energy impact; while Darren's frequent social networking might have incurred an additional 10% to his direct energy impact.…”

Section: Frequency Of Access and Total Data Amountmentioning

confidence: 99%

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Towards an holistic view of the energy and environmental impacts of domestic media and IT

Bates

Hazas

Friday

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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To date, research in sustainable HCI has dealt with ecofeedback, usage and recycling of appliances within the home, and longevity of portable electronics such as mobile phones. However, there seems to be less awareness of the energy and greenhouse emissions impacts of domestic consumer electronics and information technology. Such awareness is needed to inform HCI sustainability researchers on how best to prioritise efforts around digital media and IT. Grounded in inventories, interview and plug energy data from 33 undergraduate student participants, our findings provide the context for assessing approaches to reducing the energy and carbon emissions of media and IT in the home. In the paper, we use the findings to discuss and inform more fruitful directions that sustainable HCI research might take, and we quantify how various strategies might have modified the energy and emissions impacts for our participants.

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Section: Resultsmentioning

confidence: 99%

Section: Frequency Of Access and Total Data Amountmentioning

confidence: 99%

Towards an holistic view of the energy and environmental impacts of domestic media and IT

Bates

Hazas

Friday

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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show abstract

“…This energy has to be somehow distributed among the services provided during a certain period [37]. We choose to distribute the idle energy consumption among the services provided over a given period of time proportionally to the time those services are active.…”

Section: The Energy Intensity Of Cpe and Access Networkmentioning

confidence: 99%

“…For the power of the customer premises equipment (P CP E ), a few older studies consider only modems [14], while the more recent studies consider both modems and WiFi routers [24,31,37]. We follow [24] who assumes that only a few users use a modem without a WiFi router and that their number is comparable to those with multiple WiFi routers or WiFi repeaters.…”

Section: The Energy Intensity Of Cpe and Access Networkmentioning

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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“…This allows comparison to take place with alternatives, such as electronic vs paper-based news delivery [2], CDs vs music streaming [3] and postal vs online software delivery [4]. It also allows alternative IT architectures for delivery to be compared, and so allowing 'design for environment' of digital services [5].…”

Section: Introductionmentioning

confidence: 99%

A Review of Top-Down Models of Internet Network Energy Intensity

Schien¹,

Preist²

2014

Advances in Computer Science Research

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Abstract-Assessments of digital services wishing to take into account an energy footprint of the Internet typically require models of the energy intensity of the Internet. A common approach to the modeling of the energy intensity is to combine estimates of market surveys of installed network devices on a national or global scale and their related power consumption with the total data volume transported at the same scale. These are commonly referred to as top-down models. In order to be applicable, the system boundaries applied in the modeling of energy intensity need to match the system that delivers the particular digital service. The system boundaries applied in past studies vary considerably, resulting in increased uncertainty in the assessment results. In this text, we firstly reconsider the components of the network relevant for the delivery of general public digital services that should be included in an energy footprint. We then reinterpret the existing models in the light of those altered system boundaries. Secondly, we review existing assumptions about annual reduction of energy intensity and consider the effect of uncertainty in these on the overall estimates of energy intensity. We find that the energy intensity of the general Internet network in the year 2014 varies between 0.05 and 0.6 kWh/GB when top-down modeling is applied which is considerably lower than previous estimates had indicated.

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A model for green design of online news media services

Cited by 15 publications

References 24 publications

Towards an holistic view of the energy and environmental impacts of domestic media and IT

Towards an holistic view of the energy and environmental impacts of domestic media and IT

The Energy Intensity of the Internet: Home and Access Networks

A Review of Top-Down Models of Internet Network Energy Intensity

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