Network Electricity Use Associated with Wireless Personal Digital Assistants

Koomey, Jonathan; Chong, Huimin; Loh, Woonsien; Nordman, Bruce; Blazek, M.

doi:10.1061/(asce)1076-0342(2004)10:3(130)

Cited by 32 publications

(35 citation statements)

References 8 publications

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“…The two assessments mentioned above, [11] and [16], both used the same methodology: dividing an estimate of the overall US Internet energy consumption by the estimated total Internet traffic in the US. Since then, several more studies used the same approach, while other studies deployed different methodologies.…”

Section: Existing Assessments and Methods Usedmentioning

confidence: 99%

“…First attempts to understand the energy consumption of this distributed and heterogeneous power-consumption system were undertaken a decade ago, in [2003][2004]. Starting from statistical data and studies on the ICT equipment in use, both [16] and [11] estimated the yearly power consumption of the Internet in the US. Dividing this value by an estimate of the US Internet traffic for that year resulted in estimates of the energy intensity of the Internet, i.e., the energy consumed throughout the Internet per amount of data transferred.…”

Section: Definitions and System Boundariesmentioning

confidence: 99%

“…While [16] considered only networking equipment (i.e., Ethernet hubs, LAN and WAN switches in offices and buildings, together with the routers of the Internet), [11] also took into account devices in data centers such as servers or data storage (see Figure 1). As we discuss in more detail later, this definitional discrepancy across studies persists until today, and is one of the main causes for the large spread of the published results.…”

Section: Definitions and System Boundariesmentioning

confidence: 99%

“…The earliest top-down assessments were introduced above: Gupta et al published their results in 2003 [16], and Koomey et al in 2004 [11]. The two studies used…”

Section: Top-down Assessmentsmentioning

confidence: 99%

“…Existing studies of the Internet energy intensity give results ranging from 136 kWh/GB [11] down to 0.0064 kWh/GB [12], a factor of more than 20,000. Whether and to what extent it is more energy efficient to download a movie rather than buying the DVD, for example, or more sustainable to meet via videoconference instead of travelling to a face-to-face meeting are questions that cannot be satisfyingly answered with such diverging estimates of the substitute's impact.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Existing Assessments and Methods Usedmentioning

confidence: 99%

Section: Definitions and System Boundariesmentioning

confidence: 99%

Section: Definitions and System Boundariesmentioning

confidence: 99%

“…The earliest top-down assessments were introduced above: Gupta et al published their results in 2003 [16], and Koomey et al in 2004 [11]. The two studies used…”

Section: Top-down Assessmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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