Article

New perspectives on internet electricity use in 2030
Anders S.G. Andrae
Huawei Technologies Sweden AB, Kista, Sweden.; anders.andrae@huawei.com
Received: 24 April 2020; Accepted: 18 June 2020; Published: 30 June 2020.

Abstract: The main problems with several existing Information and Communication Technology (ICT) power
footprint investigations are: too limited (geographical and temporal) system boundary, overestimation of
power saving potential in the next decade, assume that historical power use can predict future global power
use in the next decade despite unprecedented data traffic growth, assume that Mooret’s law relation to digital
circuitry can continue "forever" and that no problems with extra cooling power will occur for several decades.
The highly variable outlooks for the future power consumptions depend on "starting values", disruptions,regional differences and perceptual estimations of electricity intensity reductions and data traffic increase. A
hugely optimistic scenario - which takes into account 20% annual improvement of the J/bit in data centers
and networks until 2030 is presented. However, the electric power consumption of the present ICT scope will
be significant unless great efforts are put into power saving features enabling such improvements of J/bit.
Despite evident risks, it seems though that planned power saving measures and innovation will be able tokeep the electricity consumption of ICT and the World under some kind of control. The major conclusion isbased on several simulations in the present study - that future consumer ICT infrastructure cannot slow its
overall electricity use until 2030 and it will use more than today. Data traffic may not be the best proxy metricfor estimating computing electricity. Operations and J/operation seem more promising for forecasting andscaling of bottom-up models.
Keywords: Communication, computing, data center, data traffic, devices, electricity use, electricity intensity,
5G, forecast, information, instructions, networks, operations, video streaming.

1. Introductionn recent years some controversy has emerged concerning the potential electric power use of Information
and Communication Technology (ICT) technology going forward in the present decade. The electricity
consumption is important as there are more or less sustainable ways of producing electricity. Most schools ofthought agree that with the current moderate data traffic the power consumption of ICT has - so far - been kept
more or less under control. There are conflicting messages regarding the path to a power consumption undercontrol. Depending on scope, in 2020 ICT stands for up to 7% of the total global electricity use. Researchers
have used different ways to measure, different ways to model and have also used different kind of statistics.
The rise of ICT electric power use is far from a "phantom" problem. A recent review [1] confirmed that ICT
systems - despite a large number of energy saving technologies at hand are at a critical point regarding current
and future energy consumption of telecommunication networks, data centers and user-related devices. Most
evidence speaks against flattening or reducing ICT power. For example, Weldon estimated that the electricity
use of all connected devices - including all consumer devices with network connections - would rise from
200 TWh in 2011 to 1100 TWh in 2019 and 1400 TWh in 2025 [2]. Hintemann argued credibly against too
pessimistic (e.g. expected and worst case in [3]) and optimistic scenarios for global data center power bylisting indisputable global trends such as cryptocurrency mining, relentless speed of data center construction
and cloud to hybrid cloud [4]. Moreover, for 2018 Hintemann estimated as much as 400 TWh for global data
center electricity use [4]. Then it has been argued that the efficiency gains will continue unhindered between
2022 and 2030 thanks to Artificial Intelligence (AI) [[5]. Nevertheless, on computing level Khokhriakov etal. found that multicore processor computing is not energy proportional as the optimization for performancealone results in increase in dynamic energy consumption by up to 89% and optimization for dynamic energy
alone results in performance degradation by up to 49% [6]. Actual electricity measurements from Leibniz

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Eng. Appl. Sci. Lett. 2020, 3(2), 19-31

20Supercomputing Centre in Germany showed that between 2000 and 2018 - despite higher power efficiency the increase in system density and overall performance lead to increase in electricity consumption [7]. The
electricity generated by renewable energy is increasing. In 2015 the share of hydro, wind, solar and biomass
power was 25% on average in China [8] which is of importance as the growth of ICT construction will be ofhuge significance there compared to more developed nations.
Truthfully it is challenging to make accurate predictions of global ICT electric power use as it isproblematic to account for unknown unknowns. Most researchers agree that the data traffic - no matter how it
is defined - will increase exponentially for several years as it has been doing the last decade. The disagreement
concerns how fast and how large the ICT related power use will become in around 2030. Probably there is aparallel to linear or exponential thinking of how fast some entity will increase. Further discussions concern
whether the anticipated extra electricity use by ICT really is a concern if the additional power can drive thecorresponding share of sustainable electric power in specific grids used by the ICT infrastructure. The costof electricity has to date been rather small for ICT Service providers compared to other expenditures [9], but
this could change if the electricity prices and electricity use increase. There is not much expectation that future
consumer ICT infrastructure can actually slow its overall electricity use until 2030. With the current knowledge,
there are more circumstances pointing towards rising - 1-2 PWh - power consumption of ICT than slowing or
flattening.
2030 is rather far away and unprecedented changes in economic activity is hard to predict as the firstquarters of 2020 has showed. Here it is assumed that the trend of more ICT and data will not be affected
dramatically until 2030 as a result of the slow-down Q1-2 2020. Therefore trends are more important than
"exact" use patterns and numbers, as we do not exactly know how and which devices will be used in the
future. Blockchain, artificial intelligence (AI), virtual reality (VR), and augmented reality (AR) might be
the biggest trends for ICT power use. Anyway, a proper power analysis of the ICT Sector should include
production of hardware including embedded chips, use of data centers, use of networks, and use of consumer
communication devices.
Production is today around 20% of ICTs footprint but there is room for improving the precision. The
digital revolution may possibly in itself help optimize the power use of production. However the total emissionof ICT production - and thereby the power use - may well be heavily underestimated [10]. Use stage power ofdata centers is now around 15%, but is expected to become one of the most important drivers for ICT electricity
use. Use stage power of Networks (wireless and core) is now at around 15% of ICT, but its share is expected toincrease. There is however considerable uncertainty about 5G’s power use depending on point of introduction,
learning curve and regional differences.
Use stage power of consumer devices (including Wi-Fi modems) is now at some 50% of ICT total power
use but is ideally expected to decrease thanks to advanced power saving features. Current downward trend is
expected to continue if no "dramatic processing power saving problems related to Moores law" happen around2022. The speed of electricity intensity reduction vs. the speed of data traffic increase is the determinant of ICT
power. As hypothesized in Section 5, other more fundamental determinants are possible.
1.1. Objectives
The objective of this prediction study is to estimate the global electric power use in 2030 associatedwith computing and communication - the Information and Communication Technology (ICT) infrastructure consisting of the use stage of end-user consumer devices, network infrastructure and data centers as well as
the production of hardware for all. The specific purpose is to update previous predictions [3] and understandif the power consumption is still likely to develop as previously understood.
1.2. Hypothesis
The hypothesis is that the electric power consumption of the ICT Sector will increase along something
in between the best and expected scenario as outlined by Andrae and Edler in 2015 [3] when adding new
assumptions of data traffic and electricity intensity improvements.

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Table 1. Differences between [3] expected case and the present prediction for data centers.

2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030

Global Data Center IP Traffic (ZettaBytes/year)\cite{10}Present
13
19
16
25
20
33
25
43
30
56
37
72
46
94
56
122
69
159
85
206
105
268

Electricity use (TWh)\cite10present
660
299
731
311
854
328
998
320
1166
377
1362
412
1592
471
1860
551
2173
652
2539
788
2967

2. Materials and methods
The approach follows the one outlined in [3] however with several new assumptions for parameters suchas electricity intensity improvements and data traffic growth. The expected case scenario in [3] constitute thebaseline for the present research, however, the best case scenario is also shown occasionally for entities of the
ICT Sector. The baseline year is 2020 and only one trend curve - for ICT total - will be proposed toward 2030.
All assumptions made are available in the Supplementary Information.
2.1. Alternate assumptions for data centers use stage
Compared to the expected case scenario in [3] the following assumptions have been made• The annual electricity intensity improvement taking place from - 2010 to 2022 - has been increased to 20%
instead of 10%. This implies a lower starting point in 2020 than in [3].
• A much higher amount of data will be processed in the data centers (see Table 1).
Data traffic is a crude proxy for power use but the numbers are reported frequently [10]. Operations/s [11]may be a better proxy as will be discussed in Section 5.

3. Alternate assumptions for Networks
3.1. Wireless accessCompared to expected case scenario in [3] the following assumptions have been made• The factor of historical improvement of the TWh/EB factor between 2010 and 2020 as assumed in [3] hasbeen corrected.
Andrae and Edler [3] arrived at an accumulated improvement factor of 0.083 in 2030 for 5G by assuming
22% improvement between 2010 and 2022 and 5% improvement from 2022 to 2030. However, it is wrong toassume an improvement for 5G from 2010 to 2020 as 5G did not (more or less) exist then. Due to gradually
introduced Moore’s law problems, the accumulated improvement factor is assumed to be 0.229 in 2030. On
top of this, a gradually waning Moore’s law is introduced for all mobile technology Gs from 2022 so that the
improvement factors run from 19% in 2022 to 5% in 2030, instead of 5% from 2022 to 2030. This leads to morethan 4 times more TWh from 5G in the latest understanding mentioned in [12] than in [3]. Tables 2 and 3 show
some of the new assumptions.
According to [13], in 2020 4G networks deliver 20 kbit/J while [3] predicted (better) 40 kbit/J in 2020. For
5G [13] predicted 10 Mbit/J while [3] predicted (worse) 0.8-2.8 Mbit/J for 2030. The starting point in 2020 for
5G in [3] is 0.05 Mbit/J. As shown in Table 2, the energy efficiency prediction for 5G has decreased - comparedto [3] - to 0.18-0.22 Mbit/J [12].

22Eng. Appl. Sci. Lett. 2020, 3(2), 19-31

Table 2. Differences between [3] and the present prediction for 5G mobile networks.

Best Case
Expected caseBest case
Expected case

5G Traffic EB
TWh
5G Traffic EB
TWh
5G Traffic EB
TWh
5G Traffic EB
TWh

2021
41
0
44
0
41
0
44
2

2022
164
0
189
1
164
5
189
7

2023
324
0
399
2
324
8
399
12

2024
677
1
892
4
677
15
892
23

2025
1248
1
1762
7
1248
23
1762
38

2026
2656
3
3881
15
2565
41
3881
74

2027
4316
4
6996
26
4316
62
6996
120

2028
6685
7
11609
41
6685
87
11609
181

2029
9928
9
18473
61
9928
120
18473
268

2030
14403
13
28714
91
14403
166
28714
396

Table 3. Differences between [3] expected case and the present prediction for mobile networks.

2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030

Electricity use TWh\cite{10} Present
98
98
92
94
100
92
114
95
127
102
144
116
145
142
149
181
157
237
172
320
196
446

3.2. Fixed access wired
One of the major weaknesses of the predictions done in [3] is likely the overestimation of fixed wired(core) networks. To improve this, a faster improvement of the TWh/EB is assumed between 2010 and 2022,
20% per year is used instead of 10%. However, a gradually waning Moore’s law is introduced from 2022 sothat the improvement factors run from 19% in 2022 to 5% in 2030, instead of 5% from 2022 to 2030. Overall
however, this results in a dramatically lower electricity use of these networks in 2030 compared to [3] (Table
4).
Table 4. Differences between [3] expected case and the present prediction for fixed access wired networks.

2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030

\cite{10}
439
494
588
703
843
1014
1222
1477
1789
2171
2641

Electricity use (TWh)Present best case Present expected case
134
171
129
171
126
174
125
179
126
188
129
200
138
223
152
255
169
296
192
352
224
428

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3.3. Alternate assumptions for Devices power use including Wi-Fi modems
From 2020 the improvement of kWh/unit/year for devices is assumed 3% as in [10]. The difference isthat Wi-Fi is added to the consumer devices section. Wi-Fi is overestimated in [3] as the Wi-Fi modems electric
power use is actually rather independent of handled traffic. The action taken is to increase the electricity
intensity improvement from 10% to 20% per year from 2010 to 2022 for the expected case scenario. The
resulting electricity use is shown in Table 5. As a sensitivity check, 2 billion homes globally - each with one
3 Watt Wi-Fi modem - would use on average around 52 TWh per year. This shows that the new assumption
is more reasonable than previous [3]. Table 5 shows that adding Wi-Fi (moving Wi-Fi from the Networks) toconsumer devices, both in [3] and here, suggest increasing and flattening TWh, respectively.
Table 5. Differences between [3] expected case and the present prediction for devices use stage electric power
use.

2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030

Electricity use (TWh)\cite{10}Consumer devices Present Consumer devices+ Wi-Fi modems+ Wi-Fi modems
1132
1039
1153
1051
1171
1054
1186
1049
1200
1037
1217
1017
1250
1008
1298
1008
1365
1017
1451
1038
1559
1073

Wi-Fi modems
72
75
79
84
91
99
113
133
157
190
234

This prediction is to be considered highly uncertain as the devices will of course also be affected by thepower issues related to the slow-down of Mooret’s law. This slow-down is included for Wi-Fi devices. Anyway,
the order of magnitude for the TWh is most likely correct. Still, a reduction of consumer devices power use
seems quite optimistic. It can happen though, thanks to a firm focus on power saving and updated energy
labeling requirements for end-user devices.
In the future the use stage electric power of USB dongles, smart home devices, wearables, AR & VR
devices, and Wi-Fi modems should be added systematically. Moreover, due to a strong push for longer
lifetimes for consumer devices, lifetimes may increase compared to [3].
3.4. Alternate assumptions for Production of ICT hardware
Andrae and Edler [3] overestimated the electric power used to produce ICT goods used in Networks
and Data Centers. This is improved in the present prediction by setting the so called life cycle ratio for
Networks and Data Center production to 0.02 instead of 0.15. This assumption brings down the production
TWh significantly. Assuming that 2 million base stations with ?3 MWh/unit [14] - used in wireless accessnetworks - and 60 million servers with 1 MWh/unit [15] - used in data centers - will be produced in 2030, theelectric power needed would be around 66 TWh. The present study predicts 38 TWh in 2030 - of 289 TWh - forall network and data center equipment. This suggests that a 0.02 life cycle ratio for production is reasonable
for traffic dependent calculations of data centers and networks. Table 6 shows that the production estimates
are much lower in the present study than in [3].

24Eng. Appl. Sci. Lett. 2020, 3(2), 19-31

Table 6. Differences between [3] expected case and the present prediction for production of ICT hardware.

2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030

Electricity use (TWh)\cite{10}Present
549
381
540
358
547
339
562
324
584
311
614
302
650
295
696
291
752
290
821
292
903
298

With the current twists and turns in the global economy it is almost undoable to predict parameters for
production of ICT. Still, the latest understanding [10]is that production of ICT is underestimated.

4. Results
The stability of Andrae and Edler [3] trend analysis - of how much electric power the ICT Sector mightuse in 2030 - is remarkable considering the number of changed (improved) assumptions made in the present
update and others [10–12]. In summary in 2030, all entities are predicted to use much less electricity - except
wireless access networks - than the expected scenario in [10]. The total TWhrs - for the current studied Internet
scope - are very close to best case scenario in [3].
4.1. Data centers power use
Figure 1 shows some trends for data centers 2020 to 2030.

Figure 1. Trends for data centers 2020 to 2030.

Although the electricity intensity improvements are assumed higher than in [3] the consequences causedby data traffic increase compensate, and the electricity use might still rise. 366 TWh in 2030 - for the best case are due to a very moderate data traffic growth.
4.2. Networks power use
Figures 2 and 3 show some trends for Networks 2020 to 2030.

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Figure 2. Trends for wireless access networks 2020 to 2030.

Figure 3. Trends for fixed access wired networks 2020 to 2030.

4.3. Devices power use
Figure 4 shows some trends for end-user consumer ICT goods use stage 2020 to 2030.

Figure 4. Trends for consumer ICT goods use stage 2020 to 2030.

5. Summary
Figures 5 and 6 show some trends for the synthesis per contributing category in 2020 and 2030, separately.
Andrae and Edler [3] is compared to the present update.

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Eng. Appl. Sci. Lett. 2020, 3(2), 19-31

Figure 5. Trends for ICT electric power overall 2020.

Figure 6. Trends for ICT electric power overall 2030.

Generally the values for 2020 are lower for most entities. For 2030 too except for wireless access networks
which will use more electricity. In total the electric power predictions for the ICT Sector have been reducedby 31% in 2020 and 61% 2030 in the present study compared to expected case scenario in [3]. Potential further
reductions are discussed in Section 8.1.

6. Discussion
The ideal framework for ICT electric power footprint would be based on annual shipments of each ICT
good, each lifetime and each measured annual and lifetime electric power consumption. However, it may
not be practicable to make that journey yet. Connectivity and smart metering is probably the road ahead forcollecting power data. Still, the electricity predictions need to be checked against bottom-up and national
top-down assessments too. It is crucial to find out how such national assessments are done and to which
degree ICT electric power consumption estimates are included.
The implications for researchers regarding the path to sustainable computing practices are at least four:
1. Produce research results which help reduce the electricity use and environmental impact of computing
2. Sourcing of the power
3. Power saving strategies
4. Recycling strategies for the used computers, screens etc
Knowing the high degree of variability, here follows some suggestions for future research approach of this
topic. Nissen et al. [16] suggested that process flow modelling would be the best for improving the precisionof wireless access networks energy use modeling. As for the future forecasting of ICTs electricity use, Artificial
Neural Networks seems a very useful modeling tool [17].

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7. Bottom-up considerations for research
The electricity cost of individual computing in particular might be difficult to isolate. Still, there areways with which we can implement green computing. For example, somehow mimicking the green software
coding idea "Proof of stake" - by which the cryptocurrency ethereum plan to slash its power use [18] seems
like a good idea. Nevertheless it does not seem useful for individuals to calculate their personal ICT electricity
consumption, but some measures probably can be taken. One easy measure is to turn off the video image incommunication when voice+video is possible but visual communication is not really required. Still, in Section
4.3 the overall individual and global electricity cost of video streaming is estimated.

8. Testing of the order of magnitude of worldwide ICT and data center electric power use
8.1. What if the 20% per year electricity intensity improvements continue after 2022
Figure 7 shows the summary of the present predictions. At the moment Wi-Fi based - or fixed optic fibre
broadband - computing is preferable to wireless 4G based computing from an overall electricity consumption
point of view.

Figure 7. Trends for ICT electric power use 2020 to 2030.

The "extreme positive" scenario assumes that no slowdown of electricity intensity improvements happen
after 2022 i.e. no gradually waning annual improvements from 2022 to 2030 as in the present baseline (expectedcase scenario) - and that 20% improvements still happen in Networks and Data Centers until 2030. In that case
ICT power will more or less stay flat while the total data traffic grows 14 times between 2020 and 2030. The
electricity use of networks and data centers will be 54% less in such an "extreme positive" scenario than in thepresent study.
8.2. Blockchain and cryptocurrencies
The blockchain is established on databases that are not consolidated in one server, but in a global networkof computers. The information is eternally registered, in sequential order, and in all parts of the computer
network. The computing power allocated to the specific blockchain application bitcoin is likely very high [19].
The reason is that with bitcoin every new piece of information added to the chain requires that someone
uses computer power to solve an advanced cryptographic problem via Proof of Work. The sooner thiscryptographic problem gets resolved, the greater the likelihood that the person who is in charge of the miningof bitcoin cryptocurrencies will be paid in bitcoin cryptocurrency. The demand for bitcoins - as long as it
lasts - will therefore increase the demand for electric power. Mora et al., [19] pointed out that any further
development of cryptocurrencies should critically aim to reduce electricity demand. Reducing the power useof cryptocurrencies might have a solution in the form of Proof of Stake instead of Proof of Work [18].

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Table 7. 2020 and 2030 key electricity intensity indicators relevant for video streaming

Entity usedin video
streaming
Wireless
access network
Fixed accesswired networks
Data center

2020

2030

0.18
(98 TWh/549 ExaByte)
0.07(171/2444)
0.015(299/19919)

0.0144(446/30899)
0.017(428/25901)
0.004(974/274599)

unit

Assumedshare oftotal Global
access traffic(internet traffic)
2020

Assumedshare oftotal Global
access traffic(internet traffic)
2030

kWh/GB

15%

85%

85%

15%

kWh/GB
kWh/GB

8.3. Renewable electric power and ICT
There are discussions ongoing about the possibility that ICT infrastructure can be run entirely on
renewable power. One of many challenges is that the renewable power should be located in the vicinity of
the ICT infrastructure.
Using renewable energy to power data centers and networks can reduce the environmental impacts.
However, the uneven geospatial distribution of renewable energy resources and regions with high ICT use
might create uncertainty of supply [20,21]. The relation between renewable energy resources and associatedenvironmental impacts - of data centers and networks driven by renewable energy at a global scale should be
investigated thoroughly [10].
Overall the present predictions suggest a trajectory in between the Best and Expected Case Scenarios in[3], ≈1990 TWh in 2020 and 3200 TWh in 2030 (Figure 6). The ICT Sector has and will have a considerable
share of the global electricity footprint.
8.4. Bottom-up calculation of the electricity use associated with video streaming
It is relevant to estimate how much data is generated - and associated electric power used - by normal
behavior like video streaming several hours every day. For the present estimations the following key indicators
are used (Table 7). The electricity intensities are set to decrease massively, especially for wireless accessnetworks. However, those networks are perhaps used much less extensively for video streaming in 2020 than
optic fixed access. Table 7 suggests that the electric power use of video streaming is strongly correlated to theway in which the video streaming is obtained. Streaming via a 4G router directly or with Wi-Fi is less efficientat the moment than optical broadband via a mobile phone/tablet using Wi-Fi.
Typically standard definition video use 1 GB per hour and high definition (HD) video use 3 GB per hour.
Other video formats with higher resolution (e.g. 8K 3D) might use even higher amounts. It is assumed 20 GB
per hour for the most typical video technology used in 2030.
By this information it is possible to predict the current and future data generation and electricity
consumption associated with video streaming and relate it to the total for ICT.
8.5. Data amounts and TWh from global video streaming
For 2020 it is assumed that one person watches video streaming in HD 2 hours/day in weekdays and 4
hour/day on weekends, i.e. 18 hours per week and 936 hours per year.
To provide these hours, 2808 GB per person is generated in 2020. If all entities are used in Table 7 to deliver
the stream, 285 kWh per year per person is required. Assuming that 2 billion persons have this behavior, 570
TWh is needed for 5230 ExaBytes. This suggests that video streaming is a noticeable driver for ICT electric
power use in 2020. For 2030 it is assumed that one person watches video streaming in HD 2 hours/day inweekdays and 4 hour/day on weekends, i.e. 18 hours per week and 936 hours per year.
However, due to higher GB/hour, 18720 GB per person is generated in 2030. If all entities are used inTable 7 to deliver the stream, 352 kWh per year per person is needed. Assuming that 7 billion persons will

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29

have this behavior, 2464 TWh is required for 122040 ExaBytes. These simple hypotheses shows that increasing
electricity use of the ICT Sector is unquestionably in the cards.

9. Conclusions
It is very difficult to fathom the circumstances under which the electric power use of communication andcomputing (the ICT infrastructure) cannot rise considerably until 2030. The total TWh will develop along an
average of the best and expected scenario in [3] with a strong leaning to the best case.

10. Next steps
New advances in large-scale fiber-optic communication systems [22–25] should be translated to J/bit
and used for predictions of the fixed core network. Advances in heat recovery and lowering temperatureof microchips [26] may have big implications for the global ICT power use. The reason is that the energy
consumption per transistor is strongly correlated to the temperature at which the transistor is working [11].
The overall effect of solving the Internet of Things, edge devices high computation, memory requirements andpower dilemma is not well understood [27]. Moreover, it is plausible that ICT infrastructure can help saveelectric power in society as a whole, and Ono et al. suggested 1300 TWh in 2030 [28]. These assumptions
should be further explored. Also these areas are next steps:• Find out best way to define an operation or instruction in computing• Forecast the number of different operations and instructions• Measure different J/operation or J/instruction.
Andrae [9] put forward these hypotheses for 2015: (i) the "traffic" (instructions/s) was around 1
Zettainstructions/s in total and (ii) the energy efficiency was overall around 7 Gigainstructions/J.
Falsifying in detail the above hypotheses would enable reliable forecasting of the power consumptionof computing which involve new technologies. Equation (1) may be the way forward if the data could becollected:!
( Inss ) j,i,t
ICTt = ∑ 8760 ×
(1)
( Ins
j,i
j ) j,i,t
where,
ICTt = ICT sector total global average electricity use in Wh related to processing and computing.
j =computing type; special purpose, general purpose, machine learning, dark calculations etc.
i =ICT good type.
t = year.
Ins =computing instructions.
Has data traffic reached the end of the line as proxy for ICT power forecasting? Machine learning
training done in a data center may send only a few bits of data to the data center, presumably creating a
relatively small amount of IP traffic. That is, on one hand the training process may imply many calculations
without necessarily generating a lot of IP traffic. On the other hand the training may use more energy due tothe required operations and J/operation [12]. Deep learning may use enormous amounts of electricity [29],however unclear how many Joules per instruction. Then research [30] showed that this electricity use may be
reduced 1000 times. These frameworks and speculations need more analysis and put into a global perspectiveand Equation 1. Another angle to be analyzed further is that as web page sizes increase, the metrics Page Load
Time and Page Render Time have larger impact on energy usage on the client side [31].
Conflicts of Interest: “The author declares no conflict of interest.”

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