Schien, D., Shabajee, P. J. S., & Preist, C. W. (2022). Rethinking
Allocation in High-Baseload Systems: A Demand-Proportional
Network Electricity Intensity Metric. Paper presented at IETF Internet
Architecture Board workshop on Environmental Impact of Internet
Applications and Systems.

Peer reviewed version

Link to publication record on the Bristol Research Portal
PDF-document

University of Bristol – Bristol Research Portal
General rights
This document is made available in accordance with publisher policies. Please cite only the
published version using the reference above. Full terms of use are available:
http://www.bristol.ac.uk/red/research-policy/pure/user-guides/brp-terms/

Rethinking Allocation in High-Baseload Systems: A
Demand-Proportional Network Electricity Intensity Metric
DANIEL SCHIEN, University of Bristol, Great Britain
PAUL SHABAJEE, University of Bristol, Great Britain
CHRIS PREIST, University of Bristol, Great Britain
Additional Key Words and Phrases: Environmental sustainability, environmental assessment, Internet energy
consumption

1

INTRODUCTION

As companies and and entire economic sectors begin to respond to the urgent need to reducecarbon emissions and support global decarbonisation, robust assessments of environmental impact
are essential to guide their actions. Network operators are increasingly aware and take action
to decarbonise their infrastructure, for example by procuring renewable electric. However, theorganisations providing the application services that run over the networks, and for which the
networks are built and operated, do not currently know how they can support the reduction of theenvironmental impact. This lack of alignment in decision making is inefficient and undermines
the sector’s decarbonisation goals. For high-throughput digital services, such as downloading
video games or streaming videos, the current electricity intensity metrics for assessing the energy
consumption from use of internet networks allocate a share of energy consumption per averagedata volume over a duration of typically one year. These metrics thus make no distinction when aservice is using the network. However, absolute energy consumption of network devices is strongly
dependent on their maximum bandwidth. Increases of peak-time throughput thus drive future
expansion of the network capacity, electricity consumption and hardware replacements. By ignoring
the variability of demand, electricity intensity metrics disregard the influence that peak-time use of
the network has on future environmental impact and overestimate the environmental effects ofoff-peak use. In this text we investigate the design of an alternative energy intensity metric that
redistributes burden of baseline power consumption proportional to data throughput. Such metrics
can incentivise demand-shifting of data traffic and thus reduce the pressure on network expansion,
which can contribute to a reduction of carbon emissions long-term. We illustrate this approachwith an example and consider how it can be combined with carbon intensity metrics for carbon
footprinting.
2
2.1

BACKGROUND
Carbon Footprinting Approaches

A set of standardised methods exist to quantitatively estimate environmental impacts due to thelife-cycle of a physical good or service.. The standard methodology for analysis of the environmental
impacts of a product or service over their lifetime is Life Cycle Assessments (LCA). Besides LCA some
informal practices that are loosely associated to streamlined LCA [Gradin and Björklund 2021] canbe found in the body of related carbon footprinting studies. While full LCA often consider a range
of environmental impact categories besides global warming potential, such as eutrophication orozone depletion, carbon footprints are exclusively focused on estimating greenhouse gas emissions.
Environmental assessments serve a variety of goals, including “informational” (e.g. marketingclaims and consumer information) as well as “change-oriented” uses such as product and policy
development, design choices, hot spot elimination [Weidema 1998]. The use of carbon footprints
to support decarbonisation initiatives would fall into this change-oriented category. In order to1

IAB ’22, Dec 05–07, 2022, Online

Schien et al.

enable this goal, carbon footprint methods must provide relevant information that stakeholders
require to take action towards reducing carbon emissions.
For the assessment of impact during the use phase carbon footprints of energy-using products(i.e. electricity), or services built using them, usually develop an electricity footprint of the total
electricity consumption to which then carbon intensity factors for electrical energy [DEFRA et al.
2014] are applied.
Carbon footprints can be carried out for an organisation or a system in total, such as the entire
networking infrastructure devices operated by an ISP, or for individual services using the infrastructure, such as the download of a large file; so-called product carbon footprints. In this text we areparticularly interested in product carbon footprints of application services and the data transfer that
their constituent connections involve. Examples of product carbon footprints for digital services
include [Schien et al. 2021; Williams and Tang 2013].
Networked software services, such as the World Wide Web, video streaming or social media, depend
on a product system in which user devices are connected to an electronic supply chain of network
devices and servers that consume electricity and are run by separate organisations. Our mainconcern in this text is to enable pro-environmental decision making by providers of software
services and consumers through greater transparency of the relationship between infrastructure
use and environment impact. In this analysis of electricity intensity metrics we focus on networking
devices in shared networked (ISP networks). However, the principles apply to other types of inelastic
energy-using infrastructure - such as servers in datacentres.
2.2

Electricity Intensity Coefficients

An electricity intensity is a coefficient that can be multiplied with a measure of system-use to
calculate output electricity consumption. For core networks, mainly data volume has been usedas the intensity metric. With this metric the electric energy can be calculated as 𝐸 = 𝑣 · 𝐼 𝑣 with 𝐼 𝑣
being the energy intensity coefficient per unit of data volume, and 𝑣 the data volume of the service
for which the carbon footprint is being calculated. Note, for access networks, time has been used tomodel electric energy, as in 𝐸 = 𝑡 · 𝐼𝑡 [Schien et al. 2013]. Then 𝐼𝑡 is equivalent to allocating a shareof the power consumption of the infrastructure during the duration 𝑡. This could be the time forwatching a movie.
The estimation of these coefficient has as long a history as their use in estimating energy footprints
of services, with [Koomey and Berkeley 2009] being among the earliest. These intensities have been
derived via top down or bottom-up approaches [Schien and Preist 2014a,b]. Roughly, a bottom-up
model sums the energy intensity of network components along an average route through a network,
e.g. [Baliga et al. 2009]. While top-down approaches estimates the energy consumption of an entire
system over a time frame (normally a year) and divide this by the total data volume transported
during this time. Recently [Aslan et al. 2018] provide a review of intensity values. Their electricity
intensity value is also recommended by the GHG Protocol ICT sector guidance [Carbon Trust 2012]in assessments of services.
Common to all electricity intensity coefficients is that they are scalar values. This approach has
been widely criticised [Carbon Trust 2021; Koomey and Masanet 2021; Malmodin 2020] for ignoring
the low energy proportionality of networks, a concept usually associated with [Barroso and Hölzle
2007]. While the energy intensity coefficient in their current popular form suggests that the total
power consumption 𝑃𝑇 of a system is a linear function of the data volume that it transports, inpractice power draw of network devices is largely independent from throughput (hence of low
energy proportionality). Instead, the power consumption is better understood as the sum of a static
baseline power consumption 𝑃𝑏 that is independent from use, and a dynamic portion that scales2Rethinking Allocation in High-Baseload Systems

IAB ’22, Dec 05–07, 2022, Online

with utilisation 𝑃𝑢 , such that 𝑃𝑇 = 𝑃𝑏 + 𝑢 · 𝑃𝑢 . For network devices, baseline power is close to 80%
of total power consumption [Chan et al. 2016].

3

COUPLING ELECTRICITY INTENSITY WITH CHANGE OF BASELOAD POWER

This baseload electricity use needs to be addressed as part of the decarbonisation of the ICT sector
for at least two reasons:(1) It contributes directly to carbon emissions through electricity grid emissions(2) It uses electricity that could be used elsewhere, thus indirectly increasing the overall carbon
intensity of generated electricity

Reducing baseload power consumption is a challenge for engineers building networking devices
and network architects. However, the network co-evolves with the digital software services thatuse it [Preist et al. 2016]. The designers and software engineers building the software services needto also be empowered to make decisions that help reduce baseload power consumption. Electricity
intensity coefficients in their current form are not providing this change-oriented decision support
that is required of carbon footprints.
The current intensity coefficients suggest a direct proportionality between electricity consumption
and throughput that does not correspond to the actual behaviour of network device electricity
consumption. Any projection of electricity footprints that extrapolate this proportionality bychanging data volumes are thus inappropriate. For example [Obringer et al. 2021] apply suchreasoning and estimate that network throughput reductions from turning off video in video calls,
would reduce monthly carbon emissions from 9.4 kgCO2e to 377 gCO2e for a typical individual.
However, due to the lack of energy proportionality, changes of data throughput do not result in
substantial changes in electricity consumption in the near term. This can be illustrated by theabsence of increases of electricity consumption during Covid - despite significant increases in data
throughput [GSMA 2020].
In response to this, [Malmodin 2020] propose a model that separates a constant, per-subscriber
portion of inelastic baseline power draw 𝑃𝑏 that is combined with service use time, and calculate
an intensity metric for only the dynamic power consumption 𝐼 𝑣𝑑 that scales with data volume
in: 𝐸 = 𝑃𝑏 · 𝑡 + 𝑣 · 𝐼 𝑣𝑑 . This approach offers a better representation of the (small) instantaneouschange in energy consumption from a change in transported data volume. However, that model
does not improve on the more substantial problem of baseload power consumption. By making
baseload independent from data volumes, the model in fact decouples the use of the system fromits electricity consumption. This is clearly not representing the the effect that demand for network
bandwidth exerts on the evolution of networks. 1 illustrates how demand drives capacity and in turnpower consumption. As the throughput from services increases, additional bandwidth (capacity) is
provided by replacing network nodes. After upgrades, the baseline power consumption steps up.
The dynamic power consumption contributes to a relatively much smaller degree to overall power
consumption.

3

IAB ’22, Dec 05–07, 2022, Online

Schien et al.

Capacity

Baseline
UpgradesPower

Dynamic

Service C

Service B

DemandService A

Time
Fig. 1. Scale-free schematic of capacity (blue), power consumption (red) and demand (black) over time. As thethroughput from services increases, additional bandwidth (capacity) is provided by replacing network nodes.
After upgrades, the baseline power consumption steps up. The dynamic power consumption contributes to a
relatively much smaller degree to overall power consumption.

To meet the need for carbon footprints to provide change-oriented decision support we need to
identify the key casual factors that determine the change in the baseload power draw and explore
how those factors can be fed back into the impact metric - in this case the energy and carbon
footprints. We argue that networks could have a lower power consumption at off-peak periods(i.e. baseload), if the peak demand was lower. Electric energy consumption of network devices
is defined by their peak throughput. Notwithstanding background efficiency improvements, andassuming similar functionality, a network devices with lower peak capacity will tend to have alower power consumption. It is thus the maximum capacity that determines the device class andit’s base and dynamic energy consumption. And the choice of network device thus depends on thepeak capacity the network is designed to provide.
In order to address the lack of causal connection between use of service at peak and baseline power
consumption, we present a transformation of the electricity intensity metric. This updated intensity
metric has the potential to incentivise alternative system design and behaviour change, leading
towards a lower growth in peak and baseline power consumption.
4

A TRANSFORM FUNCTION FOR A CHANGE-ORIENTED INTENSITY METRIC

The transformation burdens data traffic at peak time with proportionally higher share of thebaseline power consumption than traffic at other times. For this, we consider patterns of variabilityof demand as our starting point. For this current investigation we use a representative shape of
diurnal demand.
We model the electricity consumption of the network as the sum of the baseload (𝐸𝑏 ; straightforward
to measure during idle periods) and the dynamic remainder due to utilisation (𝐸𝑢 ). This information
is available to the network operator. Total energy consumption is then given by 𝐸𝑇 = 𝐸𝑏 + 𝐸𝑢 andfor each 30-minute interval 𝑖 of metered electricity consumption (𝑖) this is: 𝐸𝑇𝑖 = 𝐸𝑏𝑖 + 𝐸𝑢𝑖
Given a pattern of demand with peak throughput 𝑉𝑃 , we then reallocate baseload energy consumption. For this we define a transform function 𝐶𝑖 that 1) scales the data volume in each 30-minutetime window inverse proportionally to peak traffic and then re-normalises, so that the overall
volume remains constant.
4

Rethinking Allocation in High-Baseload Systems

IAB ’22, Dec 05–07, 2022, Online( 𝑉𝑉𝑖 ) · 𝑉𝑖
𝐶𝑖 = Í48 𝑃 𝑉𝑖𝑖=1 [( 𝑉𝑃 ) · 𝑉𝑖 ]

1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5

DemandRelative Energy Intensity

This transform function is then applied to scale the baseline energy 𝐸𝑏𝑖 with 𝐶𝑖 in each 30-min′
interval as 𝐸𝑏𝑖 = 𝐸𝑏𝑖 · 𝐶𝑖 . We then add the adjusted baseline power to the dynamic power as′′
𝐸𝑇𝑖 = 𝐸𝑏𝑖 + 𝐸𝑢𝑖 . Finally, we divide the adjusted total energy consumption by the data volume per′′′
half-hourly interval to calculate the new intensity metric 𝑉𝑖 as 𝐼𝑖 = 𝐸𝑇𝑖 /𝑉𝑖
In Figure 2 we show the demand-proportional intensity metric relative to the constant electricity
intensity metric.

0

10

20

30

Basic Mean
Transformed
Demand40

Fig. 2. Network Electricity Intensity Metric over 48 half-hourly intervals. Basic Mean (current practice),
Transformed (reallocated baseline energy). Demand - Typical diurnal traffic shape for core networks. From
London Internet Exchange (LINX) https://datasciencecampus.ons.gov.uk/projects/what-can-internet-use-tellus-about-our-society-and-the-economy/. Note, this is only for illustration, core traffic will be likely to follow
slightly different patterns than domestic environments, in particular during working hours.

The result of this simple transform is that each unit of activity in any given period is weighed
such that it is largest at periods of peak demand and lowest at the lowest period. Other kinds oftransform might shift the intensity differently, e.g. a steeper function would weight peak activty
even more highly.
4.1

Demand Proportional Carbon Emissions

It should be noted that here we are simply looking at energy use, in order to extend the analysis
on GHG emissions we could either use the results of the energy transform function and calculate
the emissions intensity in each 30-minute interval from the re-allocated energy intensity values ineach period. Alternatively we could calculate the total emissions in each 30 minute interval prior
to the transform (mean kgCO2e/kWh intensity varies over the day) and use the transform function
to re-allocate the GHG emissions as we have the electrical energy.
Taking this yet one step further, we could use dynamic carbon intensity, i.e. mean hourly carbon
intensity (gCO2e/kWh by hour of the day). Such data is now readily available for many grids,
e.g. the UK National Grid. In Figure 3 we compare the resulting carbon intensity metrics for datawhen combined with a dynamic carbon intensity. In this case the carbon intensity is the 30-minute5

IAB ’22, Dec 05–07, 2022, Online

Schien et al.

200

Carbon Intensity [gCO2e/kWh]

Relative Carbon Intensity of Data

average over the year 2021. The carbon intensity of electricity varies significantly, thus amplifying
the effects of the transform function.

150
100

0

10

Basic Mean
50
Transformed
Electricity Carbon Intensity
0
30
40

20

Fig. 3. Comparison of Data Demand Carbon Intensity Metrics. Carbon intensity for electricity as the 30minute average of the UK National Grid in 2021. This electricity carbon intensity is combined with basic
mean electricity intensity of data and the transformed electricity intensity, respectively.

5

DISCUSSION

A near ubiquitous feature of electrical energy using components in digital service system is thatthey have a baseload of energy demand. These include individual, low level, components such as
internet routers, servers and end user devices and also large scale aggregate components including
data-centres which are composed of servers, networking equipment, data storage devices, building
infrastructures and services, etc. By baseload we mean that there is a minimum of energy use
regardless of demand or utilisation of a component. Within carbon accounting, such baseline power
consumption forms an overhead that needs to be allocated to services in the product system. Note embodied GHG emissions (from raw material extraction, manufacturing and transport) as well as
disposal form similar overheads.
As we have illustrated, the existing electricity intensity metrics used for carbon footprints of digital
services do not support the stakeholders in taking causally informed action during design and use ofdigital services that can support reducing peak capacity growth and thus support decarbonisation.
Our very simplistic example illustrates how such signals and metrics could be developed in GHG
impact allocation. We present an approach, rather than a finalised method. More work is required
to understand the causal mechanisms that drive network capacity growth.
Creating metrics that are more causally related to infrastructure overheads is not at all new. Suchapproaches are widely used, for example, so called ’use-of-system’ charges used in electricity
grids to incentivise behaviours that reduce use of electricity at times of peak demand with theexplicit goal of constraining growth in the daily and annual peak demand and so reducing the needfor unnecessary infrastructure development and reducing costs (and also carbon emissions). For
example, in the UK grid transmission system, the TNUoS charge system consider the three top peak
30-minute intervals in a given year and distributes high charges to consumers during these periods.
Such an approach has various side-effects. Comparing this to our proposed transform function, theequivalent for data would be that baseline power is allocated exclusively to the services that use atpeak.
6Rethinking Allocation in High-Baseload Systems

IAB ’22, Dec 05–07, 2022, Online

The electricity intensity metric we illustrate incentivises demand-shifting. This is a well-establishedactivity that supports the decarbonisation of the electricity sector; for example with variable timepricing.
Our illustrative metric can be combined with a time-of-use metric as presented by [Malmodin 2020]
by applying the illustrated transform to the constant base power consumption and adding a term
to more accurately model dynamic energy use:′

𝐸𝑖 = 𝐼𝑏𝑖 · 𝑣𝑖 + 𝑣𝑖 · 𝐼 𝑣𝑑
The choice of time-window that is considered during the analysis affects the design of the metric.
We chosen to consider diurnal variability. However, those diurnal peaks vary steadily increase frombackground increases in consumption. Over the course of a year there are also seasonal patterns
that have their own peaks. And thirdly, peak demand is increasingly observed during live sport
events and game releases; that exacerbate high demand from other more consistent demand suchas that of video streaming.
The foundation for the practical application of such metrics would be a regular publication ofa transformed electricity (or carbon) intensity metric by ISPs, which would then be applied totime-of-use data on service consumption. This would enable service providers (e.g. media companies) to integrate the carbon effects from varying time-of-use into their decisions. To consumers
understanding of variable carbon intensities could increase acceptance for design interventions.
5.1

Conclusions

In this text we investigate the design of an electricity intensity metric with the goal to include theeffect of peak-time data demand on baseline power consumption. We apply a transform functionthat re-allocates baseline energy towards peak periods and illustrate it’s effect on carbon intensity.
We suggest that the principle might be further explored to help the ICT sector more effectively
reason about what drives its carbon emissions and how to reduce them.
REFERENCES
Joshua Aslan, Kieren Mayers, Jonathan G. Koomey, and Chris France. 2018. Electricity intensity of internet data transmission
untangling the estimates. Journal of Industrial Ecology 22, 4 (2018), 785–798. https://doi.org/10.1111/jiec.12630
J. Baliga, R. Ayre, K. Hinton, W.V. Sorin, and R.S. Tucker. 2009. Energy Consumption in Optical IP Networks. Journal ofLightwave Technology 27, 13 (7 2009), 2391–2403. https://doi.org/10.1109/JLT.2008.2010142
Luiz André Barroso and Urs Hölzle. 2007. The Case for Energy-Proportional Computing. IEEE Computer 40, 12 (12 2007),
33–37. https://doi.org/10.1109/MC.2007.443
Carbon Trust. 2012. GHG Protocol Product Life Cycle Accounting and Reporting Standard ICT Sector Guidance. https://ghgprotocol.org/sites/default/files/GHGP-ICTSG-ALLChapters.pdf
Carbon Trust. 2021. Carbon impact of video streaming. Technical Report. https://prod-drupal-files.storage.googleapis.com/
documents/resource/public/Carbon-impact-of-video-streaming.pdf
Chien Aun Chan, André F. Gygax, Christopher Leckie, Elaine Wong, Ampalavanapillai Nirmalathas, and Kerry Hinton. 2016.
Telecommunications energy and greenhouse gas emissions management for future network growth. Applied Energy 166(2016), 174–185. https://doi.org/10.1016/j.apenergy.2016.01.007
DEFRA, RICARDO-AEA, and carbon smart. 2014. Greenhouse Gas Conversion Factor Repository.
http://www.
ukconversionfactorscarbonsmart.co.uk/
Katja Tasala Gradin and Anna Björklund. 2021. The common understanding of simplification approaches in published LCA
studies—a review and mapping. International Journal of Life Cycle Assessment 26, 1 (2021), 50–63. https://doi.org/10.
1007/s11367-020-01843-4
GSMA. 2020.
COVID-19 Network Traffic Surge Isn’t Impacting Environment Confirm Telecom Operators. https://www.gsma.com/gsmaeurope/latest-news-2/covid-19-network-traffic-surge-isnt-impacting-environmentconfirm-telecom-operators/
Jonathan Koomey and Eric Masanet. 2021. Does not compute : Avoiding pitfalls assessing the Internet’s energy and carbon
impacts. Joule (2021), 1–4. https://doi.org/10.1016/j.joule.2021.05.007
7

IAB ’22, Dec 05–07, 2022, Online

Schien et al.

Jonathan G. Koomey and Lawrence Berkeley. 2009. Whole system redesign for electricity efficiency: Some lessons from theworld of cloud computing. (2009).
Jens Malmodin. 2020. The power consumption of mobile and fixed network data services - The case of streaming video anddownloading large files. Proceedings of the Electronics Goes Green 2020+ Conference, Berlin, September 1 (2020), 87–96.
Renee Obringer, Benjamin Rachunok, Debora Maia-Silva, Maryam Arbabzadeh, Roshanak Nateghi, and Kaveh Madani. 2021.
The overlooked environmental footprint of increasing Internet use. Resources, Conservation and Recycling 167, January(2021), 105389. https://doi.org/10.1016/j.resconrec.2020.105389
Chris Preist, Daniel Schien, and Eli Blevis. 2016. Understanding and Mitigating the Effects of Device and Cloud Service
Design Decisions on the Environmental Footprint of Digital Infrastructure. In CHI’16 Proceedings of the SIGCHI Conferenceon Human Factors in Computing Systems. ACM, San Jose, 1324–1337. https://doi.org/10.1145/2858036.2858378
Daniel Schien and Chris Preist. 2014a. A Review of Top-Down Models of Internet Network Energy Intensity. In ICT for
Sustainability (ICT4S). Stockholm, 1–8.
Dan Schien and Chris Preist. 2014b. Approaches to energy intensity of the internet. IEEE Communications Magazine 52, 11(11 2014), 130–137. https://doi.org/10.1109/MCOM.2014.6957153
Daniel Schien, Paul Shabajee, Jigna Chandaria, Dan Williams, and Chris Preist. 2021. Using behavioural data to assess theenvironmental impact of electricity consumption of alternate television service distribution platforms. Environmental
Impact Assessment Review 91, September (2021), 106661. https://doi.org/10.1016/j.eiar.2021.106661
Daniel Schien, Paul Shabajee, Mike Yearworth, and Chris Preist. 2013. Modeling and Assessing Variability in Energy
Consumption During the Use Stage of Online Multimedia Services. Journal of Industrial Ecology 17, 6 (12 2013), 800–813.
https://doi.org/10.1111/jiec.12065
Bo Weidema. 1998. Application typologies for life cycle assessment. International Journal of Life Cycle Assessment 3, 4
(1998), 237–240. https://doi.org/10.1007/BF02977574
DR Williams and Yinshan Tang. 2013. The Impact of Office Productivity Cloud Computing on Greenhouse Gas Emissions.
Environmental Science & Technology 47, 9 (2013), 4333 – 4340. http://henleymba.com.hk/web/FILES/tsbe/Williams_
TSBE_Conference_Paper_2012.pdf

8