Download The Effect of Ad Blockers on the Energy Consumption of Mobile Web

The Effect of Ad Blockers on the Energy Consumption
of Mobile Web Browsing
Arthur Visser
University of Twente
P.O. Box 217. 7500 AE Enschede
The Netherlands
[email protected]
ABSTRACT
Online advertising forms the economic backbone of the internet.
Content and services the user should normally pay for can now
be accessed for free. However, advertisements are not always
appreciated by the user. Due to the disturbances caused by
advertisements ad blockers are becoming popular.
Downloading and rendering advertisements costs energy. An ad
blocker prevents the browser from downloading advertisements.
This might lead to less energy consumed while browsing
websites that serve ads.
This paper researches the effect of ad blockers on the energy
consumption of mobile web browsing. An energy model is
developed to estimate the energy consumption of advertisements.
This model includes the network infrastructure energy usage and
energy consumed in the data centers. The client side energy
usage and data transmission is measured during the experiments.
With these values and the determined model, the energy
reduction by using ad blockers is estimated.
The results indicate that on average, the energy saved by using
an ad blocker is 28J for Wi-Fi and 324J for 4G/LTE. From the
results it becomes clear that advertisements are responsible for
15 ~ 53 percent of the total energy consumed when browsing a
website with advertisements on a smartphone. The average
energy consumed by advertisements is 27% of the total energy
consumed by smartphones.
Therefore, using an ad blocker largely brings down the energy
consumption of mobile web browsing.
Keywords
Internet, ad blockers, online advertisements, energy consumption
model, smartphone, mobile, web browsing, experiment, data
transmission, data centers
1. INTRODUCTION
Almost every user of the internet has experienced them, online
advertisements. Major technology companies rely heavily on
income from online advertising. 89% of Google’s Q1 2016
revenue came from online advertising [1]. Facebook’s revenue
came for 96% out of online ads in Q4 2015 [2]. Internet
advertising revenues in the United States totaled $27.5 billion for
the first six months of 2015. This is a 19% increase over the first
six months of 2014 [3]. It’s safe to say that online advertising is
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25thTwente Student Conference on IT, July 1st, 2016, Enschede, The
Netherlands.
Copyright 2016, University of Twente, Faculty of Electrical Engineering,
Mathematics and Computer Science.
the economic backbone for a very large portion of the companies
active on the internet.
However, online advertisements are not always appreciated by
the users and are considered disturbing. Most advertisements
take up screen real estate, are heavy in terms of bandwidth, CPU
and energy usage, take a long time to load, are distracting and are
often served by third-party ad providers. This last fact influences
the privacy of internet users, because most ad providers place
tracking cookies (trackers): web scripts for building user profiles,
serving targeted advertisements, tracing browsing behaviors and
website statistics [4]. Also, ads threat the user’s security caused
by cybercriminals who serve malicious content as ads.
The above disadvantages of advertisements have led to the rise
of ad blockers. Ad blockers are pieces of software that remove
advertisings of websites by amongst other things stopping the
network requests for the advertisements. Examples of ad
blockers are: Adblock Plus [5], Ghostery [6] and uBlock [7].
Adblock plus has reported in May 2016 that they have 100
million active users (active device installations) [8]. A 2015
report by PageFair and Adobe states that the number of people
using ad blockers globally grew by 41% year over year. In the
Netherlands there were 2.2 million monthly active users of ad
blockers [9].
The Reuters Institute for the Study of Journalism at the
University of Oxford talks about the following key finding in
their Digital News Report 2015:
“Finally we find significant consumer dissatisfaction with online
advertising, expressed through the rapid take up of ad blockers.”
This research shows that consumer annoyance with advertising
has led to large numbers of ad blockers installations on their PC,
mobile or tablet. 39% of the UK respondents (n = 2149) and 47%
of the US respondents (n=2295) have installed ad blockers on
their PC, mobile or tablet. The figures are even higher for the
people between 18 and 24 (56% and 55% respectively) [10].
There is also a downside from blocking advertisements on the
internet. Ad blocking results in decreased revenue to apps and
websites that are dependent on advertisements for their finance.
The report by PageFair and Adobe states that the estimated loss
of global revenue due to blocked advertising during 2015 was
21.8 billion dollars. The global cost of ad blocking is expected to
be 41.4 billion dollars by 2016 [9].
1.1 The problem
Although there are positive and negative aspects about online
advertisings and ad blockers, there is another aspect that is often
ignored: the impact of online advertisements on the energy
consumption and the environmental footprint of the internet.
Figure 1: Sequence diagram for displaying ads when downloading a website
Advertisements are often animated and contain much graphical
elements to attract and seduce the website visitor. This leads to
advertisements that consume much client resources such as CPU,
battery and bandwidth.
1.2 Related work
Online advertisements require energy for:
Microsoft Research in association with UC Berkeley, looked at
the energy consumption of popular apps on Android and
Windows phones. The researchers found that a typical mobile
app refreshes its ads every 12-120 seconds, which forces the
radio network to be constantly re-awakened, after which the app
will keep its 3G radio connection open for another 25 seconds.
This period, known as ‘tail time’, results in the high-energy
overhead incurred by ads. On average, ads consume 23% of the
total energy, or 65% of the communication energy, of an app
[15]. This is not about advertisements during mobile web
browsing, but show results that advertisements do actually
impact the energy consumption.
-
the client for downloading, displaying and processing ads
(CPU, memory, battery, networking)
-
the infrastructure that is required for the data transmission
between the client and the data centers of the ad providers
-
the data centers that serve the online advertisements
Advertisements also lead to unnecessary extra connections from
the client to the ad providers. This is shown in the sequence
diagram of figure 1.
With this increase in energy consumption, the impact on the
environment increases as well. Energy generation from coal, gas
and nuclear power remains the status quo to power the internet.
The International Energy Agency states that in 2013 80% of the
worlds power consumption comes from non-renewable energy
[11, 12]. In 2007, Gartner estimated that the pollution caused by
the world’s internet usage was as high as the pollution of the
entire airline industry [13].
Most surveyed studies focus solely on the client side energy
consumption. Advertisements also require energy for the data
transmission and the data centers. This research does include
those factors and estimates the energy consumption of the whole
system. Another focus point of most surveyed studies is the
energy usage of ads on PCs and in-app advertisements on
smartphones, but not the energy consumption of advertisement
during mobile web browsing [14, 15]. This can be seen in the
next section related work. This research focusses on the energy
consumption of advertisements during web browsing on the
smartphone.
This research focusses on smartphones when speaking about
client side devices. This is the fastest growing platform when it
comes to displaying online advertisements [3].
Research done at the University of Twente approximates that the
power required to download and display web advertisements
increase the total energy consumption of PCs by 3.4% [14].
Dean Murphy, the developer of one of the first ad blocker for iOS
9 (Crystal), revealed benchmarks from a small experiment
showing that it makes webpages load 3.9 times faster on average.
The time to load is reduced by 74% and the bandwidth is reduced
by 53%. By loading ten pages with ad blocker instead of without,
70 seconds and 35MB was saved [16]. Also network activity
stops after the page is loaded with an ad blocker. This should
reduce the impact on the battery of the device [17].
The New York times did a test with the iOS ad blockers Purify,
Crystal and 1Blocker about their results on the 50 most popular
news sites. They found out that more than half of all data came
from ads and other content filtered by ad blockers. The battery
life of the used iPhone increased by 21% when ad blockers were
used, this is in the case the user only uses his iPhone to browse
the web. They did not take into account the extra battery used by
ads after the initial page has loaded [18].
Researchers of the Simon Fraser university showed that there is
a 25% to 40% reduction in bytes downloaded when using ad
blockers in a university network [19]. They used PCs in the setup,
but the number might count for mobile as well.
1.3 Research questions
In this paper we try to answer the following research questions:
1.
What are the factors that play a role in the energy
consumption of online advertisements?
2.
How can a model be developed to determine the energy
usage of online advertisements?
3.
Looking at the variables of the model developed in question
2, what is the effect of an ad blocker on the determined
variables?
4.
What is the influence of using an ad blocker on the energy
consumption of the internet?
1.4 Approach and structure
It is difficult to directly measure the exact energy necessary for
the processing on the client, the data transmission and the servers
that serve the ads. Instead we determined the variables that
influence the energy consumption of ads. This leads to a link
between the determined variables and energy usage. Then we
conduct an experiment to measure the effect of ad blockers on
the determined variables. With these results we can determine the
effect of ad blockers on the energy consumption of internet
usage.
The rest of this paper is organized as follows. Chapter 2 explains
the setup of the experiment to measure the effect of ad blockers.
In chapter 3 we investigate the energy usage of online
advertisements in depth and declare an energy consumption
model for online advertisements. The results of this experiment
are shown in chapter 4 and linked with the model from chapter
3. In chapter 5 the results are discussed. Chapter 6 contains the
conclusion and answers to our research question.
2. TESTING METHODOLOGY
The challenge is to compare the total energy consumption of web
browsing with and without an ad blocker enabled. To measure
this, we need to separate the regular website content from the
advertisements. By this we can measure the client side energy
usage with and without advertisements and also the bandwidth
costs in both scenarios. By measuring these values and
comparing our developed energy model we can determine the
energy consumption with and without an ad blocker.
2.1 Ad blocking
There are two main techniques used for ad blocking:
-
URL blocking – With this method, every URL the browser
requests is checked against a black list with known ad
providers. If the URL is on the list, the request is blocked
and the ad cannot be loaded.
-
Content blocking – The website content is filtered by
HTML and CSS rules. When an advertisement is found, the
HTML will be removed and thus the browser won’t show
the ad.
With content blocking the advertisement is downloaded but not
rendered. When using URL blocking, the advertisement
providers never receives a request and thus the advertisements is
not even downloaded. URL blocking is more common [3] and is
more energy efficient because it reduces the unnecessary load on
the network infrastructure.
There are multiple ways to block web advertisements by URL
blocking on a smartphone. Browser plugins like Purify [20] and
Crystal [21] are popular options. However, we do not use these
options in this research for the following reason: the plugins
change the way the mobile browser works and thus our
measurements can become inaccurate. It requires extra activities
and computation power on the client for these plugins to work.
Instead of handling the URL blocking on the client we will use a
separate device for the ad blocking process. An option is to use a
HTTP proxy server to filter content. The proxy can block
advertisements because all traffic flows through the proxy server.
Disadvantages are that SSL traffic and JavaScript generated
content are harder to block.
Instead of a proxy we use a Raspberry Pi 2 model B [22] with Pihole [23] installed. The Raspberry Pi functions as our own
external Domain Name System (DNS) server. Users identify and
remember websites by their URL (e.g. http://google.com).
Servers in data centers are accessible by an IP address (e.g.
192.168.0.1). The DNS system is a mapping between the URL
and the corresponding IP address. Before the browser can fetch
the content of a website, it has to know the IP address that
corresponds to the given URL. Therefore, the browser has to first
do a DNS query to lookup the IP address. This process is shown
in step 1 and 4 of the sequence diagram in figure 1. After the
requested website is loaded (step 1-3), the JavaScript code that
came with the requested website instructs the browser to load the
advertisements from the advertisement providers. Again a URL
(that leads to the ad provider) is given and the corresponding IP
address is requested at the DNS server, our Raspberry Pi (step 4).
The Pi checks every DNS request with the blacklist that contains
all known advertisement providers. If the requested domain name
is on the blacklist, it returns a domain name not found error. This
behavior prevents that the advertisement is even requested,
because the necessary IP address is not known.
We modified our rooted Android smartphone to use the
Raspberry Pi as DNS server.
2.2 Client side energy consumption
We measure the energy consumption with and without
advertisements with the mobile application PowerTutor [24].
The application can be used to rapidly, accurately, and
conveniently determine the power consumption of an app. It is
developed by researchers of the University of Michigan and
Google for research purposes [25]. The app is optimized for the
HTC G1, HTC G2 and Nexus one, the power measurements on
other phone types can be less accurate because they have not
been tested in the research. We conducted the tests with a rooted
Samsung Galaxy S5 [26]. Because all tests are conducted on the
same phone, the accuracy will not be an issue.
2.3 Bandwidth measurements
The data transmitted is measured by using Wireshark [27]
installed on a MacBook pro 2012 retina 15 inch [28]. Wireshark
runs in monitor mode which allows to capture all the packets
exchanged between the smartphone browser and the webservers.
By running Wireshark in monitor mode, all packet sniffing is
passive and thus does not require any activities on the
smartphone.
2.4 Web browsing
The webpages to visit should be realistic. This means that the
websites should be common and well known and the amount of
advertisements should be realistic. We visit 10 common websites
in this research (see results). The websites are selected from
Alexa [29], a ranking for popular websites. We chose the most
accessed Dutch news and entertainment websites.
The list of websites visited is due to resources limited.
Automated testing was not possible due to the EU Cookie Law
which requires every website to ask their visitors whether they
accept cookies. The automated script we built was not able to
skip these pop-ups because they are not standardized.
Every website is visited on the Android Smartphone with the
Chrome browser over Wi-Fi and 4G/LTE. The web browsing
process should be like an average person’s browsing habits. The
websites are opened for 30 seconds and are scrolled from top to
bottom. The cache is cleared before each visit so that the entire
website is fetched and rendered every time. Every website is
visited 5 times to filter out inconsistencies. The average is used
as final result for each website.
3. ENERGY CONSUMPTION MODEL
To determine the factors that play a role in the energy
consumption of advertisements on the internet we have to look
at the energy consumption of the internet in general. We can
define three major components in the internet system: energy
consumption of end user devices when displaying
advertisements, energy consumption of the network
infrastructure to connect the client to the advertisements
providers and the energy incurred in data centers [30–34]. The
next sections explain for each component what variables
influence the energy consumption of online advertisements.
Precisely estimating the power consumption of the entire internet
is extremely hard because every data center is different, the route
of each bit can differ over the network infrastructure and client
side consumption differs per device. The model defined below
tries to estimate the energy consumption as accurate as possible.
The internet itself has a certain idle energy consumption. The
core and metro/edge network require power; no matter what data
is transferred. We estimate the extra energy per bit in the model
below, not the total energy per bit.
3.1 Client side energy consumption
The process for displaying an ad is described in the sequence
diagram in figure 1. The extra steps necessary specifically for the
ad are shown in the bottom part of the figure. To display an
advertisement on a smartphone, it needs to be fetched via the
network interface (Wi-Fi or 4G), rendered by the CPU and
displayed with the CPU and GPU [35–37]. To display ads there
are extra network requests necessary because ads are not served
by the content provider itself, but by third party advertisements
networks. Examples are Google display ads [38] and AdRoll
[39]. The display and network interface are the most energy
consuming components of a mobile device [40, 41]. These
components are used to display and fetch advertisements.
Where 𝑃 max is the maximum power consumption, 𝑃 idle is the
power consumption in idle state and 𝐶 max is the mean network
element maximum capacity.
Because datacenters should be capable to handle a surge in
activity that could slow or crash their operations, datacenters are
often over provisioned [44, 46, 47]. This means there is a
utilization threshold of servers for adding new servers (𝜌). 20%
is a value often used for 𝜌 [35]. For the idle power necessary for
the overprovisioning to handle a surge in activity, the extra
energy per bit is given by:
.
/idle
0max
When we add these two formulas to estimate the mean
incremental energy per bit (𝐸𝑏) for both the extra energy
necessary for the active server handling the request and the
overprovisioning, we get:
𝐸𝑏 =
45
.
/idle6/max
0max
[35]
Given the estimated data above, we state that the extra energy per
bit is 1.2 µJ/bit. This number can be used to estimate the extra
power necessary for processing an advertisement request on the
data center side.
3.3 Data transmission
When a user visits a website, the browser first downloads the
content from the company’s webservers. This content contains
the location of the advertisements. So after the actual webpage is
loaded, the browser sends a request to the content servers of the
ad providers.
When we look at the energy consumption of the network
infrastructure to connect the client to the advertisements
providers, there are 3 major components: the access network, the
metro/edge network and the core network [48].
We estimate the energy usage on the client in the context of ads
and without ads.
3.2 Data centers
The impact of data centers will be a fast growing part of the
global IT sector energy footprint; their energy demand will
increase 81% by 2020. Estimations are that the aggregated
electricity demand of the cloud (data centers + networks, not
devices) was 685 billion kWh in 2011 [42].
The power consumption and capacity of a general content server
are gathered from [30]. The maximum power consumption is
225W and the maximum capacity 800Mbps. These numbers are
based on typical content servers for advertisement servers [30,
43]. The baseline power usage with these servers is more than
80% of the overall power usage at peak load [30, 31, 44]. Thus
we state the idle power usage to be 180W for a conservative
estimation.
The power consumption of network devices increases almost
linearly when the load increases [30, 31, 45]. Thus when looking
at the active server that handles the request, the extra energy per
bit is given by:
𝑃 max −𝑃idle
𝐶max
Figure 2: Internet network infrastructure (we include
backbone in the core network). From [49]
There is a large amount of literature available about the energy
consumption of the internet network infrastructure. To determine
the energy consumption necessary for transmitting the
advertisements, we will look at the results from these studies.
This gives a more accurate estimation of the power consumption
of the network infrastructure when keeping resources for this
paper in mind.
The access network equipment connects homes and business to
the local internet exchanges. Three technologies dominate the
access network: fiber, copper and wireless [48].
The metro/edge network concentrates a large amount of access
network connections. The local connections are routed around
through the local network, the rest is routed into the core network
[48].
The core network is used for intercity and international
communications. This internet traffic is exchanged between the
core routers before being routed back to the metro/edge network
[48].
A smartphone accesses the internet either via Wi-Fi or 2G, 3G or
4G/LTE. We will look at the energy consumption of
downloading ads via Wi-Fi and 4G/LTE. For the metro/edge and
core network, there is no difference in energy usage when
sending data over 4G/LTE or Wi-Fi. Both traffic uses the same
equipment. The energy usage of the access network equipment
does differ when comparing Wi-Fi to 4G/LTE.
The EARTH (Energy Aware Radio and network technologies)
project estimates in 2010 the energy consumption of a 4G/LTE
connection under typical circumstances to be around 328 µJ/bit
and 615 µJ/bit [50]. This difference is caused by a difference in
traffic demand. The higher the traffic demand, the more efficient
4. RESULTS
The most important results from the experiment are shown in
table 2. The values are given by measurements without an ad
blocker minus the values with an ad blocker. This gives the extra
energy consumption and data traffic that is a consequence of
downloading and rendering advertisements.
The total energy in joules is the sum of the client side energy
consumption plus the energy consumed in the network
infrastructure and data center. The energy consumption of the
network infrastructure and data center is estimated with the
variables estimated in chapter 3 (see table 1) and the extra
bandwidth used because of advertisements. The ad overhead is
the percentage of energy used for downloading and rendering
advertisements.
On average, the energy saved by using an ad blocker was 28J for
Wi-Fi and 324J for 4G/LTE. From the results it becomes clear
that advertisements are responsible for 15 ~ 53 percent of the
total energy consumed when browsing a website with
advertisements on a smartphone. The average energy consumed
by advertisements is 27% of the total energy consumption.
Table 2: Extra energy consumption and data traffic due to
advertisements
Website
nu.nl
the cell towers function. The safer estimation of 328 µJ/bit is
used in this research (20% heavy users).
tweakers.net
When looking at energy efficiency improvements per year of
wireless systems, we use the value of 26% [51]. When
calculating the improved energy efficiency in 2016 with the 2010
value, we get the estimation for energy usage of 54 µJ/bit.
parool.nl
For Wi-Fi, we assume that a commercial Wi-Fi system (hotspot
in airport for example) is used. From [52] we find that the energy
per bit of a commercial Wi-Fi system (802.11.n using 2x2
MIMO, 300 Mb/s capacity at 30% network load) is 0.4 µJ/bit.
The Ethernet switch adds around 0.007 µJ/bit. Thus, the energy
usage of transmitting an ad via Wi-Fi is estimated at 0.4 µJ/bit.
Based on [30, 34] by using power consumption figures for
representative commercial equipment in the edge and core
network, the estimated energy required for transmitting
advertisements over the metro/edge and core network is equal to
2.7 µJ/bit. This includes the Ethernet aggregation switch that
forms the entry point to the metro/edge network and the Ethernet
switches in the LAN inside the data center. Redundancy for all
network elements is taken into account.
Table 1: energy consumption model summarized
Type
µJ/bit
Datacenter
1.2
Metro/edge+corenetwork
2.7
Wi-Fiaccessnetwork
0.4
4G/LTEaccessnetwork
54
rtlnieuws.nl
nrc.nl
ad.nl
telegraaf.nl
vkmag.com
voetbalzone.nl
volkskrant.nl
Connection
Wi-Fi
4G/LTE
Client
side
energy
usage (J)
6
10
Wi-Fi
2
4G/LTE
4
Wi-Fi
4G/LTE
Wi-Fi
4G/LTE
Wi-Fi
4G/LTE
7
10
4
12
6
13
Wi-Fi
3
4G/LTE
6
Wi-Fi
4G/LTE
5
11
Wi-Fi
6
4G/LTE
7
Wi-Fi
3
4G/LTE
9
Wi-Fi
5
4G/LTE
6
Bandwidth
(MB)
1,2
0,5
0,6
0,7
0,7
0,5
0,7
0,9
0,6
0,4
Total
energy
(J)
Ad
overhead
47
30%
566
33%
19
41%
236
53%
28
26%
288
29%
28
18%
336
20%
30
16%
337
16%
20
33%
238
43%
29
21%
335
22%
37
16%
424
15%
24
30%
287
38%
19
24%
191
26%
5. DISCUSSION
The results show that there is a remarkable energy impact caused
by displaying advertisements on a website. The estimations for
the energy consumption model from chapter 3 are based on
conservative assumptions. An assessment [53] of multiple
internet energy consumption researches shows that the there is a
substantial difference between estimations for the internet energy
usage. Partly because the included components of the internet
differ per research. In this paper all components are included and
the estimations are on the lower side of the spectrum.
Ad blocking itself costs energy too. This research used an
external ad blocking mechanism which is not very common.
Most ad blockers run in the browser and thus require energy from
the client. We estimate that this energy consumption is relatively
low compared to the entire energy usage of advertisements, but
for a complete overview it should be researched further.
is saved on average solely by the Dutch daily active internet
population.
What has not been discussed is that by improving battery life by
using an ad blocker, there is a positive effect on the battery
replacement cycle which might eventually lead to even less
energy consumed when users use an ad blocker [54]. This is not
in the scope of this research however.
6.1 Further work
Advertisement is one of the most popular business models on the
internet, but not all websites have ads. This research only looked
at websites that server advertisements. Regular internet users also
visit websites without advertisements.
6. CONCLUSION
The factors that play a role in the energy consumption of online
advertisements are:
-
the client side energy consumption for downloading,
displaying and processing advertisements
-
the core, metro/edge and access network that is required for
the data transmission between the client and the data centers
of the ad providers
-
the processing and overprovisioning in the data centers that
serve the online advertisements
In this paper a model is developed to estimate the energy
consumption of advertisements when transported over the
network infrastructure and the power consumption of the data
centers that serve the advertisements. Most works on the topic of
energy consumption of advertisements have ignored the energy
consumption of advertisements in the access network,
edge/metro network and core network. Also data center energy
usage is not always included. This paper includes these
components as well as the client side energy usage.
The estimated energy saved by using ad blockers is 28J when the
user browses the web over Wi-Fi and 324J when using 4G/LTE.
The average energy consumed by advertisements is 27% of the
total energy consumption. This number is an estimation but
indicates that ad blockers do have a significant positive impact
on reducing the energy consumption of mobile web browsing.
Most energy is saved because of the lower amounts of data
traffic. The most energy is consumed in the network
infrastructure. When data traffic is less, this is reduced. Also 4G
is an energy hog compared to Wi-Fi. With 54 µJ/bit, every
advertisement that is not downloaded leads to a large energy
consumption saving.
The results in this paper show that an ad blocker should be used
when users want to lower their energy consumption while web
browsing and eventually want to lower their impact on the
environment. Reducing the size of advertisements would also
help to reduce the energy impact of advertisements.
The implications of this paper become clear after reflecting the
results on a real world example. Based on data [55] of the
Statistics Netherlands we state that 94% of the Dutch population
has access to the internet. 81% of these internet users use it
(almost) every day. When we estimate that the average user visits
5 websites with advertisements a day, we find that 12.943.800
Dutch daily active internet users visit roughly 24.000.000.000
websites with ads a year. We assume 1/3 of the websites is
accessed over 4G and 2/3 is accessed over Wi-Fi. When all Dutch
internet users use ad blockers, 3.036.000.000.000 Joules a year
The average energy consumption of a Dutch household was 3050
kWh in 2014 [56]. 3.036.000.000.000 Joules a year is equal to
843333 kWh. This is the yearly energy consumption of roughly
275 Dutch households.
Due to practical limitations of the EU cookie law automated
testing was not possible, thus the amount of websites researches
is limited. For future work it will be interesting to conduct the
same experiment with more websites to visit.
Also it would be interesting to look at the actual energy cost of
ad blocking when executed on the client. Although most energy
is saved in the network infrastructure, ad blocking will costs
some energy.
When conducting the experiment, the number of requests made
and time to load were also logged. From the results becomes clear
that with an ad blocker enabled, the number of request was
between 36% and 56% lower than without an ad blocker. Not
only the amount of bits transported but also the number of
requests made might influence the energy consumption. Further
research should be done to conclude this.
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