Author: Olivier PHILIPPOT

Digital Sobriety Expert Books author «Green Patterns», «Green IT - Gérer la consommation d’énergie de vos systèmes informatiques», ... Speaker (VOXXED Luxembourg, EGG Berlin, ICT4S Stockholm, ...) Green Code Lab Founder, ecodesign software national association

The Impact of playing a Canal + video study

Reading Time: 16 minutes

Introduction

Logo_Canal+

This study was carried out by Greenspector, a company specializing in the impact of digital technology, and EVEA, specializing in analyzes of the environmental impact of products. This study is based on:

  • a calculation methodology initiated as part of the CONVINcE project on the energy consumption of video. A project involving several players such as Orange, Sony …
  • energy and resource consumption measurements carried out on the myCANAL application to support hypotheses
  • collection of data on the use and infrastructure of Canal +
  • a bibliographic study to identify and correlate emission factors and energy consumption

Study summary :
Methodology
Results : consumption, overall impact and projection
Areas for improvement
Conclusion – points to remember
Chief Digital Officer – Canal+ testimony

Methodology

The functional unit of this study is defined as follows: “Watch 1 hour of video, live or in a replay, on the Canal + interfaces”.

Disclaimer: The purpose of this study is to estimate, by orders of magnitude, the carbon impact of playing a video on Canal + interfaces. To date, it is based on reliable and robust sources and is intended to be representative of the reality of the Canal + infrastructure. However, this is not a complete Life Cycle Assessment (LCA). We rely on LCA methodologies but, for example, we have not performed a sensitivity analysis that would allow min-max deviations on the values. However, this study made it possible to formalize an analysis benchmark for the carbon footprint of a video service, and it could be used to monitor the evolution of the carbon footprint over time. And all this in no way detracts from the consistency of the data for its primary use.

Means of access to services

We have listed different ways to access myCANAL video services. Depending on this type of access, it is necessary to have more or less material (s). This inventory is needed to assess the impact of accessing video services. We have classified the means by category:

  • Canal + decoder
  • FAI TV Box
  • TNT Box
  • Game console
  • IP TV (Android TV, Samsung TV)
  • PC / Mac Multimedia Gateway with ISP Internet Box
  • Smartphone / Tablet with ISP Internet Box or GSM access

For Decoder, TV / TNT Box, Console, and Multimedia Gateway access, it is necessary to add a TV. We have ruled out all special cases of viewing considered as anecdotal compared to other accesses such as:

  • Display of the PC stream on a TV,
  • Multiroom viewing

Regarding the link between the terminals and the Boxes, we will consider that the Wi-Fi connection and the wired connection (Ethernet) have no impact on the consumption of the terminals (PC and TV).

Definition of visualization

Visualization quality influences end-to-end power consumption. We have taken the following 3 main definitions:

  • SD (Simple Definition)
  • HD (High Definition)
  • UHD / 4K (Ultra High Definition)

The definition depends on 3 factors:

  • compatible program availability
  • material compatibility in terms of quality
  • the quality of the connection (the quality of myCANAL videos is adapted according to the connection speed).

Greenspector’s exploratory measurements on myCanal show the impact of SD, HD, or UHD video consumption on smartphones, and then on laptops:

Impact of consuming SD, HD or UHD video on smartphones and laptops

Bibliographic sources were used for the consumption of other platforms such as Consoles (Source 1), TV (Sources 1 and 2), Set-Top-Box (Sources 1 and 2). The consumption of Canal + decoders supplied by Canal + was also used.

Depending on the different equipment configurations, we obtain energy consumption which varies from 1.3 to 108 Wh / h, i.e. a ratio of 1 to 80:

Consommations d'énergie selon les configurations d'équipements

Technologies

Several parameters were taken into account in the calculations because they influence the consumption:

  • Streaming technology: IPTV (Technology among ISPs), OTT (Over the Top), and Peer To Peer
  • Linear (Live) or Non-linear (Replay / VOD) signal
  • Broadcasting (Hertzian, Satellite, IP)

Means of connection to the network

To access the services, the infrastructure considered takes into account: the user’s equipment (Box among others), access to networks (GSM, Fiber, etc.), and the heart of the IP network.

We used calculation methods that are widespread in the scientific literature and standardized by ETSI. The principle is to take an “energy / typical use” ratio in Wh/Go. Although the network infrastructure has a fairly fixed consumption and does not depend on the use, this calculation method makes it possible to assign a global impact to a use (here one hour of video). In addition, it allows to study the improvement of networks in terms of efficiency. Beyond the search for improved energy efficiency, it also makes it possible to assess pressure on the network and to take into account the improvement in the capacity of the infrastructure.

As explained in the CONVINcE report:

“As we are looking for an order of magnitude in energy saving, we suppose that decreasing by 30% the traffic volume in the core network induces a decrease of same ratio in network dimensioning and consequently a decrease of 30% in energy consumption in the core IP network.”

We have studied the literature and listed metrics ranging from 1.3Wh / Go (figure for FTTH fiber) to 600Wh/Go. These factors fluctuate depending on the infrastructure assessment date, method (Top Down or Bottom Up) and technology. It is clear, however, that the efficiency improves over the years.

We have taken:

  • 13 Wh / Go for the core network (source CONVINcE)
  • 30 to 40 Wh / Go for the fixed network
  • 150 Wh / Go for the GSM network (source CONVINcE)

For TNT and satellite, there are few data. However, we based ourselves on a BBC study for TNT.

Content Delivery Network

Content Delivery Networks (CDNs) are servers used to limit the load on “Top of Head” servers (serving videos) and to provide a flow as close as possible to the user. Canal+ uses CDN providers on the market but also uses its own infrastructures.

The methodology for estimating the energy intensity per GB is to bring the estimated consumption of the data center down to throughput during peak periods. Canal+ indeed knows the number of physical servers, their types as well as their speeds. As the servers are hosted by a host, certain assumptions have been made to estimate the actual consumption: among other things, an assumption of PUE (Power Usage Effectiveness) of 2, consumption per server of 250W, and a server load of 50%. The consumption of routers and storage is considered negligible compared to the consumption of servers. (Sources 1, 2 , and 3)

The energy intensity obtained is 0.13 Wh / Go, with a different value between VOD and Live. Note that this value is calculated in the event of a peak (football match for example). It can be lower (use of the total capacity of the data center) or higher (during periods of low traffic).

For lack of data, the hypothesis of the same energy intensity (Wh / Go) was taken for CDNs outside Canal + (50%). For comparison and verification, the studies listed display values from 0.04 to 1 Wh / GB. These differences can be explained by various factors:

– Improving the efficiency of data centers, old studies, therefore, lead to higher figures,

– Top-Down approaches that have a higher estimate than Bottom-Up studies (like this estimate on CDNs).

AWS application servers Excluding video: using Mycanal (catalog presentation, authentication, etc.) and watching videos involve requesting services hosted on servers (rights management, etc.). The APIs used are hosted on AWS instances.

Canal+ rents AWS instances and knows the number of VMs. A part of these VMs is purely dedicated to providing ancillary services to video.

It is difficult to know the power consumption of AWS instances because no communication or information is provided by Amazon. According to an expert, we took a value of 20W (taking into account a PUE of 2) (Source: Interview of experts on virtualization from the company Easyvirt).

We have assumed a uniform distribution of consumption concerning the number of hours visualized and we obtain a value of 0.14 Wh / h.

Video encoding servers

The “Headend” servers allow you to format videos, “package” them to the user’s format … For the OTT, up to 150 packages may be available for a single video. Here is the workflow for the OTT.

The most consuming parts are video encoding and decoding. The workflow is distributed over specific servers (for encoding) and classic servers (for formatting and packaging).

We took the figures from the CONVINcE project (considering Harmony servers identical to those used by Canal +) to estimate the energy, as well as the Canal + server data:

  • For IPTV: 0.09 Wh for one hour of video
  • For the OTT: 0.30 Wh for one hour of video

This difference is explained by the fact that OTT is encoded in several formats, unlike IPTV which is encoded in a high definition format.

CO² emission factors

For energy, we used the values provided by the Open Data Networks Energies database (Work of distributors such as RTE and ADEME). We used intermittent usage which corresponds more to video usage in terms of the period (evening), ie 60g CO2eq / kWh. Part of the broadcasts for the rest of the world has been added taking into account the fact that some users are outside France.

For terminal and server manufacturing emission factors, we used factors provided by Shift Project / IEA.

These factors were brought to the time of viewing by taking into account the lifespans associated with each material.

Note: Regarding the impacts of DTT and satellite, there is a lack of data on infrastructure and emission factors. However, we have integrated this part to understand the orders of magnitude of the impacts. Certain analyzes were carried out only on the IP part in certain cases (for comparison with other studies for example).

Likewise, the impact of the data center manufacturing phase outside of the servers and the network infrastructure was not taken into account as it was considered to be shared and low given the lifespan of the buildings.

Results – global projection

The supply of renewable energy to AWS infrastructures and Canal + CDNs hosted by Interxion has not been taken into account.

In one hour, on average, here are the flows that pass through the network:

500 000 hours of video
900 TB of data
3.6 GB / hr average throughput

The number of viewing hours for Canal+ subscribers has been broken down according to the parameters listed above.

The unit data obtained previously are then projected on these uses to obtain the overall consumption. By taking these results, we can obtain the usage intensities of each part (Terminal, Network, Server) to check the consistency of the overall consumption.

Intensity analysis

The usage intensities obtained can be compared to the Shift Project and IEA studies. This intensity is calculated by taking the distribution of devices, the use of Canal +, and keeping only the IP transfer part (without Satellite or TNT).

It is clear (and shared by the IEA) that the Shift Project study overestimates grid consumption. For servers, we have a fairly low value but are quite confident considering that it is based on data from a known infrastructure (with the uncertainty from Amazon servers and third-party CDNs). The estimate of the terminal part of the Shift Project seems to us to have been underestimated (shared by IEA). It seems that the IEA also underestimated many parameters and seems not to have taken into account the influence of the definition on the consumption of the terminal, certain elements such as boxes, and the consumption of new TVs that consume more. The CONVINcE study and the Sauber/Koomey analysis also validate the consistency of our study.

Impact of playing an hour of video

Across the Canal+ fleet, the average end-to-end consumption is 214 Wh per hour of video. For comparison, the IEA study announces consumption between 120 and 240Wh per hour. The breakdown is as follows:

The impact in average CO2 equivalent by type of access is as follows (with the assumptions specified in the paragraph CO2 emission factors where the manufacture of Satellite, DTT, network and server hosting infrastructures has not been taken. into account)

By taking the type of connection:

The impact ranges from 20 to 66 g CO2 eq, it depends on several parameters:

  • the more or less energy consumption of the device (for example Smartphone vs TV),
  • embodied energy and material life,
  • the means of access to the network (for example GSM access has a greater impact than wired access),
  • the viewing quality which will influence above all the share of the network (depending on a cost per Wh / Go),
  • the number of materials to access the service.

By taking real uses (see the end of the document), the average is 28g CO2eq. If we take only the IP part, the average value is 37g CO2eq.

For comparison, the IEA study (2020) announces 8g CO2eq for France (for the use phase only). Another study for the US (2014) announces 360g CO2eq on the use phase (and 420g CO2eq with manufacturing). If we take a US energy mix, we get 202g which brings us closer to this study. The lower value can be explained by the differences in assumptions, in particular on the energy intensity of the network. For example, if we take for our study a TV with 4K reception, IEA announces 20g CO2eq while we rather estimate 14g (37g with the manufacturing phase).

If we look at the usage / manufacture ratio, in some cases the use has more impact (UHD in particular), while in others it is the manufacturing (IP TV or PC in HD for example).

In this same study, the estimate of the purchase of a DVD is 400g EqCO2, which therefore leads to the impact of watching a 2-hour video. 5 times less than that of buying a DVD in France and equivalent in the US.

Consumption and overall impact

The result of the total energy consumption of viewing Canal+ videos over IP is 900 GWh per year with a greenhouse gas impact of 159,000 tonnes CO2eq.

For comparison, annual French consumption is 473 TWh per year (Source RTE) and France’s 2017 carbon footprint (national emissions + imports) is 749 Mt CO2 eq (source: Haut Conseil pour le Climat – 2019 report). The consumption of the Canal + fleet by IP is therefore 0.18% of energy consumption and 0.016% of the French carbon footprint.

The end-to-end distribution is as follows:

Most of the consumption is on the user’s premises (terminal and part of the network access). Indeed, it is necessary to have the equipment (TV, Box, Smartphone …) which is not shared like the servers.

To validate the consistency of this projection, we took the consumption of CDNs specific to CANAL as well as to suppliers. We have a consumption of Canal + CDNs of 2.6 GWh (and estimated at 7 GWh for suppliers) excluding video head-ends which are not at Interxion. We get 12 GWh with the projection, which validates the model.

If we look at the distribution of the impact in greenhouse gases, we have the following distribution:

répartition de l’impact en gaz à effet de serre

A large part of the impact comes from the manufacture of user terminals. On the network part, the manufacture of Boxes (FAI, Satellite …) also has a significant impact.

Areas for improvement to limit the impact of services

General strategy

As we have seen in the network part, efficiency improves. The same is true for terminals. But on the other hand, 4K will become widespread, networks will continue to increase their capacity and therefore increase the overall impact of consumption. By taking one of the hypotheses of a 30% improvement in network efficiency (according to the trends set out in the studies listed in the Networks section of this report) and an increase in average throughput of 20% over 3 years as well as of 20% of the hours viewed, as well as a transfer of 75% of the hours viewed from all interfaces to that of the OTT, we estimate an increase in the energy consumed by 39% and the impact of greenhouse gases greenhouse by 23%. This simplistic projection allows us to approach a more realistic consumption in 3 years.

To offset this increase, and optimize the impact of video playback, we examined the impact of the following projects carried out by Canal+:

  1. Switching from H264 encoding to HEVC
  2. Switching to multicast for live
  3. Switch audio encoding from AAC to AC4
  4. Strengthen bitrate downsizing
  5. Improve the interface and the software layer
  6. Help the user on their digital impact

These projects are not exhaustive. Other possible areas for improvement have been identified which could be launched subsequently by Canal+.

The measurements, estimates, and models that allowed us to obtain the overall impact were used to quantify the estimated gains.

Note on optimizing video servers:

The energy consumption of video servers is very low, as is their impact, as the hardware has a high lifespan (10 years). Several optimizations are under study (video passage Just-in-Time among others). However, these optimizations provide very little end-to-end gain. They are however necessary to optimize management and reduce storage size (impact not taken into account in this study because it is low). Optimizations such as switching to HEVC have a stronger impact (this is confirmed by the CONVINcE study).

“The “Just In Time Transcoding” approach will allow to reduce the number of video representations stored in the CDN and thus its power consumption. This is an end-to-end approach to be compared to the global abovementioned one consisting of reducing the bandwidth of the network by using the most efficient encoding technology (HEVC/AVC).

On CDNs in the same way, even if the impact is low, certain actions such as increasing the rate of use of Canal + infrastructures (using a transfer of flows from supplier CDNs to own CDNs) will improve the efficiency.

Switching from H264 encoding to HEVC

The HEVC video codec is about 20% more efficient than the H264, and a large number of devices are now compatible. For a 3-year projection of consumption, we have taken an increase in the market share of HEVC compatible devices (Box, Smartphone, etc.).

For users who are on 1080p 5Mbits to date (34% of users currently) as for users restricted to 720p for technical reasons (for example a limitation of the network), a 20% drop in consumption is expected. For other 1080p compatible users, there is no decrease in consumption, but an increase in quality with superior grip.

A new even more efficient format begins to appear (AV1), saving an additional 20%. However, very rare equipment is compatible: the transition is largely premature, we would hardly gain anything to date because the fleet is almost zero.

Switching to multicast for live

Broadcasting in hybrid multicast / unicast adaptive streaming for the OTT will greatly reduce the use of bandwidth, and therefore power consumption.

Usable only for live stream, it ensures that a single stream is sent for all customers to the final delivery point. This project will be accompanied by other ancillary projects such as the switch to CMAF packaging. The CMAF audio/video packaging format allows you to have exactly the same video files for all platforms.

Switch audio encoding from AAC to AC4

Currently we mainly use AAC audio, from 96kbits to 128kbits.

The EAC3 + is widely compatible to date and improves audio quality at equivalent bitrate while allowing 5.1. Switching to EAC3+ would be at a fixed rate, without hoping to save bandwidth (while 20% more efficient than EAC3 +).

On the other hand, the new AC4 format is 50% more efficient than the EAC3 + and would allow the audio bit rate to be divided by 2. Even if the share of the video is greater, the gains at the global level are not negligible.

Strengthen bitrate downsizing

To date, except in Africa, or on a cellular network (so as not to empty the customer’s subscription), the quality of the video is adequate . On PC, quality adaptation is possible but access is not necessarily easy.

Making the possibility of reducing the size more accessible by different means (improvement of the interface, communication, etc.) would make it possible to redirect some of the users to a lower but sufficient quality.

Taking into account the increase in video quality and that of network performance, quality caping by users of Canal + services should represent in 3 years a gain of 20% in average speed compared to that generated by uncapped use.

In this axis, several elements have not been included in the gain projection but are possible to help the user in his impact. For example, the Greenspector mobile measurements show that it is better (under favorable conditions such as wifi) to use downloading rather than streaming. Indeed, here is the comparison for a 45mn video (with 5mn download):

Consommation d'énergie d'un streaming vidéo vs d'un téléchargement et lecture vidéo

This is partly explained by the fact that when viewing the downloaded video, the radio cell is not used while it is much more during streaming.

Improve the interface and the software layer

Part of the impact of viewing an hour of a video comes from the interface. Indeed, viewing the catalog, managing subscriptions (via APIs), processing playlists … are necessary functions. Greenspector measurements showed that this could represent 10% of terminal consumption:

Improving the interface on all platforms (smartphone, PC, etc.) would therefore make it possible to obtain significant gains. It would also allow, beyond reducing the impact, to limit the exclusion of certain people as well as the obsolescence of platforms.

Among the actions identified:

  • Switch to the dark mode
  • Reduce the overall impact of the software layer
  • Improve UX
  • Limit the integration of third-party libraries

Help the user on his digital impact

Much of the impact of video playback is not directly related to video playback. Canal +, through its large audience, can act by making users aware of the impact of digital technology. If some of the users take these actions into account, the impact can be reduced. Among these actions:

  • Extending the life of the user’s equipment,
  • Use Ethernet rather than Wifi, rather than 4G,
  • Extinction of equipment out of use.

Results

Here is the projection of the various energy gains:

The improvements compensate for the increase in energy consumption and even allow a gain of 14% compared to the current situation. For the greenhouse gas impact, we obtain a gain of 8%.

There are many ways to reduce the carbon impact of streaming activities for Canal + by 30%, and the actions put in place will make it possible to make a gain of 26% on the environmental footprint of services and in particular -31% for the consumption in OTT only.

Conclusion – to remember

This study has uncertainties on future use, on the emission factors of each element as well as on the potential gains. However, the objective is to challenge the choices or even to rule them out (some actions not listed in this document have, for example, already been discarded), if they had no gain. One of the first actions is to be able to measure yourself in order to improve. This is the goal of this phase. Now comes that of improvement.

The environmental interest of the sites identified by Canal+ has been confirmed. Their implementation will make it possible to offset the effects of the growth in uses, or even more, in the years to come.

The conclusions of this evaluation evolve with many technological parameters but in summary:

  • Work on video and audio compression formats while taking into account new formats optimized by manufacturers.
  • Optimize the consumption of user functions to access the content.
  • Involve/guide users in the choices according to the context of use (streaming, type of network, device screen format, etc.) and lead them to extend the life of the equipment to reduce the impact.
  • Implement hybrid multicast / unicast adaptive streaming for OTT to dramatically reduce bandwidth usage and power consumption.
  • Pursue end-to-end measures to avoid future decisions that shift the impact without reducing it

Pierre-Emmanuel Ferrand, Chief Digital Officer at CANAL+

Testimony

– How does this assessment and improvement process fit into the Group’s strategy?

CANAL+ is anchored in its time. We have decided to become more involved in a major issue of our time, dear to our subscribers and users of myCANAL: the protection of our environment. CANAL+ was very early on as a pioneer in eco-responsible approaches. first. In addition to the rental model of decoders, which is very virtuous from the point of view of the circular economy, Canal+ organized in 1988 the recovery of old decoders to ensure their return to service or their recycling. Moreover, as a publisher, We have produced eco-responsible productions including series that have been exemplary on this subject, Baron Noir, L’Effondrement, and more recently OVNI (S).

-What main lessons/contributions do we draw from this work (on the design of services)?

This work allows us first of all to identify the technical projects to prioritize to minimize the carbon footprint of myCANAL video consumption as much as possible. At the same time, the lessons guide us on the awareness messages to relay to our users, throughout our future developments. This commitment to cooperation between our technical developments and our users is the key to consumption that has less impact on the environment. 

– What role do users play?

Protecting the environment is a priority for them. Their role is central to our approach. Our challenge is to offer them a platform offering the best content and the best video quality adapted to their equipment and their reception capacities to reduce the effects on the environment. By raising their awareness and supporting them in responsible actions, they too will be able to get involved, as they do in other areas for a more eco-responsible society.

Integrate a third-party service: is it dangerous for your visitors privacy, which impact on the environment? The Youtube case.

Reading Time: 4 minutes

Third-party service integration makes it easy to quickly add functionality to a site such as a video or social network integration (see the case of Twitter integration). The providers of these tools have worked to make technology integration quick and easy. And the technique is there. But at what cost?


Energy consumption of the third-party service Youtube

We observe an increase in this type of third-party service on our measurements and abnormal overconsumption. This is the case with many sites and even government websites. 

The YouTube integration is a good case study to explain this effect. In just a few lines, it is possible to display a video on any site:

<iframe width=”560″ height=”315″ src=”https://www.youtube.com/embed/WoQHxxxxxxx-E?rel=0″ frameborder=”0″ allow=”autoplay; encrypted-media” allowfullscreen></iframe>

But what is the result in terms of user impact? Here is the result we get in terms of power consumption on a Nexus 6 smartphone:

Consommation d'énergie du services tiers Youtube

Reference: Phone discharge speed in uAh / s (OS, Browser …)
Loading: Speed of the first 20 seconds of loading
Idle Foreground: Inactive site speed in the foreground
Scroll: Speed when the user scrolls down the page
Idle Background: download speed when the browser (and therefore the site) is in the background

This is a government website. Discharge rates exceed our thresholds for many steps. For loading, the speed is more than 2 times that of reference. For the idle foreground or phase of inactivity in the foreground, the consumption should be identical to that of reference. This consumption is abnormal for a site that seems quite light.

Process CPU du services tiers Youtube

We see that Chrome’s CPU process goes up to 10% every second. This explains the overconsumption of energy. By profiling the JavaScript calls in the development tools, we observe processes from base.js which are from the YouTube framework:

Javascript framework Youtube

Note that this processing also impacts scrolling and loading. Is this an expected operation? A bug or a bad implementation? We haven’t been that far into the analysis.

When we look at the page loading, on 1.2MB, nearly 600KB is used for the YouTube plugin. 50kb of CSS and 550kb of Javascript. To the necessary processing, add the heavy CPU usage to parse and run scripts.

Significant point: No video appears on this page. The integration of the plugin is surely necessary for another page. This makes the waste even more critical, it is all the more annoying that the French tested website is public and widely used: Impots.gouv !


Best practices for integrating a video

1 – Directly embed video without third-party services

It is possible to use free solutions without plugins. Integration via HTML5 is native.

2 – Embed an image

Display an image with the same rendering as the video allows to reduce to 1 request. If the user clicks on the video, then the scripts will be loaded and the video launched ultimately lazy loading.

We also did the exercise on a page of our Greenspector website:

On one of our “Case Study” pages was embedded a YouTube video. We replaced this integration by displaying an image (opposite) representing the old integrated video. This modification allowed us to go from a Greenspector ecoscore from 59/100 to 75/100 characterized by an energy gain of -12% in the loading stage, -10% in Idle and -15% in scroll.

Page with embedded video
Page with image

3 – Integrate the plugin only on the desired page

A solution that is not ideal, but preferable to the existing one, is to only use scripts when the page requires a video.

What will it save?

First of all the performance. A large portion of processing related to site wait times is dedicated to third-party services. This is even more true for the YouTube plugin. On the audited site, the size can be reduced by 2, and the loading time reduced by at least 30%.

Power consumption will also be reduced and even more important than data size or performance. In fact, in addition to saving energy from charging, consumption in idle or inactivity phase will be reduced.

Bonus: user privacy

The other problem with this type of project is the use of tracker and user data recovery. Not integrating a third-party service resolves potential issues of data leakage and GDPR non-compliance. By the way, the YouTube plugin seems to allow version without cookies via the call to the URL: https://www.youtube-nocookie.com.

Like any third-party service, it is not that simple. Even with this no-cookie integration, user data is stored:

Données utilisateurs cookies Youtube

The audited site is therefore not GDPR compatible! To manage this, you must ask the user for consent explicitly:

Fenêtre de consentement

The solution of a hosted video or static image will also manage this.


Conclusion

If the integration of a video is necessary, think about it quietly and consider the impacts on resource consumption and GDPR. There are technical solutions more respectful of the user, they are initially perhaps a little more complex to set up, however, the solutions will naturally become simpler and more widespread.

What are the best web browsers to use in 2020?

Reading Time: 8 minutes

The 2021 edition of this ranking is available! Read the study

The web browser is a main tool on a mobile device. Not only for websites but also for new applications based on web technologies (progressive web app, games, …).

In our 30 most popular mobile apps ranking, among the mails, direct messaging, social networks, browsers categories, web browsing and social networks are on average more consuming than games or multimedia applications. The ratio would be 1 to 4 between the least and most energy consuming applications.

Decreasing the environmental impact of the digital life and increasing the autonomy of phones go in part through the choice of a good browser. Just as if you want to reduce the impact of your mode of transport, it is important to take the most efficient vehicle.

Last year we published the 2018 ranking of the least energy-consuming browsers, we made a new edition for 2020, more complete, made with our GREENSPECTOR App Mark.

Overall Ranking

The average rating is 36/100 which is pretty mediocre. It can be explained by low notes for each of the metrics.
The three least energy-consuming browsers are: Vivaldi, Firefox Preview, Duck Duck Go.

Overall energy consumption (in mAh)

The median is 47 mAh and a large part of the browsers are in this consumption level (8/18 are in the 2nd quartile).
Note that the last 3 browsers in the ranking are recognized by a consumption 75% higher than the median. Firefox, Qwant and Opera Mini are indeed very energy intensive.

Energy consumption of navigation (in mAh)

The last 3 browsers of the global ranking (Opera Mini, Firefox and Qwant) as well as Mint consume much more than the average (between 20 and 35 mAh against 16 mAh).

It is sufficient to say that for most browsers (apart from the previous exceptions), pure navigation is not going to be the reason for the difference in overall consumption. This is mainly due to the use of visualization engines. Most browsers use the Chromium engine. For Opera Mini, the specificity is that a proxy is used and can compress the size of the sites. It seems that this proxy is not good for the energy, in fact the decompression on the user’s phone consumes a lot of energy.

For the Firefox app, over-consumption of energy is a known and shared thing, this is one of the reasons why Mozilla is under development of a new browser. Internal code name is Fenix ​​and public one is Preview. The measures in this ranking are rather encouraging on consumption (in the average).
For Qwant, this is due to the use of the Firefox engine! The measurements between Qwant and Firefox are indeed very close.

Power consumption of features (in mAh)

The main feature that is browsing the web also requires other important features: new tab opening, enter an address in the taskbar … Indeed, when we open a new tab, each browser offers different features: mainly used websites, latest news, …

On pure navigation, browsers differ little, there are significant differences in energy consumption on other features with a ratio of more than 3 (between 4 mAh and 12 mAh).

Note that the first 3 (Firefox Focus, Firefox Preview and Duck Duck Go) have a simple homepage. The consumption of the browser in Idle (inactivity) is then very low. Functional sobriety pays the consequences!

When launching browsers, the energy consumptions are quite similar to each other. Note, however, that opening a tab and writing a URL are actions that are performed several times. If we take a daily projection of 30 new tabs and 10 URL entries, we can still see the difference between browsers and the large advance of Firefox Preview and Focus!

The basic features are not insignificant in the overall consumption.

Projection of autonomy (in number of hours)

If we take this energy data and project it for a navigation of several websites, we identify the maximum time that the user can navigate to the complete discharge of his battery:

Data consumption (in MB)

The difference in data consumption between browsers (8 MB difference) is explained by the pure navigation and the different features.

On the navigation, we explain this difference:

  • some applications do not manage the cache at all for reasons of data protection and confidentiality (Firefox Focus)
  • proxy usage that optimizes data (Opera Mini)
  • a difference in the implementation of cache management. It is possible that some browsers invalidate the cache and that data is loaded while they are cached.
  • additional data consumption continues in the background (idle tabs, background data not blocked …)
  • download performance differences that increase the duration of the measurement. Indeed, if a browser is powerful, the counterpart is that many more data are potentially loaded in the background.

The difference in overall consumption can also be explained by the data consumption of the basic functionalities:

Many browsers are very consuming. We note the 3 MB of Qwant that seem abnormal!
It can be considered that for browsers, this consumption should be close to 0. Indeed, the main feature of a browser is to display a website, any feature (and associated consumption) can be considered as “over-consumption”.
In this context, many browsers consume data when writing the URL. This is mainly explained by the URL proposal features. There is indeed exchange between the mobile and the servers, either directly by the browser or by the associated search engine.

For example, for the Yandex browser below, the details of data exchanges when writing a URL show more than 400 KB of data exchanged.

In contrast, below, trading for Brave is frugal with less than 2 KB.

Browser performance (in seconds)

The measures allow us to evaluate the performance of the key features:

  • Launching the browser
  • Adding a tab
  • Writing a URL
  • Removing the cache
  • Mozilla Kraken Bench

NB: This study does not evaluate the display performance of websites. However, the Mozilla Kraken benchmark allows this in part by evaluating the functionality of browsers.

Efficiency of browsers (in mAh/s)

We can evaluate the efficiency of browsers by taking the performance of the Mozilla Kraken benchmark and the associated energy. Efficiency is the energy consumption per unit of time:

Samsung, Opera Mini and Opera are the most efficient browsers. This ranking is different from that of overall energy consumption. For Samsung Internet, this first place in terms of efficiency on a Samsung hardware can be explained by the optimized link that can have the manufacturer with a pre-installed software. The Opera browser has a good positioning (2nd for overall consumption and 3rd for efficiency).

Track of improvements

It is possible to improve the consumption of navigation.

For the user :

  • Choosing an efficient browser
  • Use bookmarks or favorites to avoid going through the entry bar
  • Configure the energy saving options of browsers (mode or dark theme, data server …)

For developers of sites:

  • Eco-design their site
  • Test and measure on different browsers to identify different behaviors and take them into account

For browser editors:

  • Measure energy consumption and efficiency
  • Eco-design features
  • Reduce resource consumption of recurring features (url write, new tab …)
  • Make the homepage as simple as possible.

Measurement protocol

The measurements were carried out by the laboratory GREENSPECTOR App Mark on the basis of a protocol Standardized: Samsung S7 Smartphone, Android 8, Wi-Fi, 50% brightness. Between 4 and 8 iterations were carried out and the value used is the average of these measurements. Measurement campaigns follow a scenario that evaluates browsers in different situations.

Evaluation of features

  • Launching the browser
  • Adding a tab
  • Writing a URL in the search bar
  • Remove tabs and clean the cache

Navigation

  • Launch of 6 sites and wait for 20 seconds to be representative of a user journey

Benchmark browser

Evaluation of browser inactivity periods

  • At launch (this allows to evaluate the homepage of the browser)
  • After navigation
  • After closing the browser (to identify closing problems)

For each iteration, the following tests are performed:

  • Deleting the cache and tabs (without measurement)
  • First measure
  • Second measure to measure the behavior with cache
  • Remove cache and tabs (with measure)
  • System shutdown of the browser (and not only a closure by the user to ensure a real closing of the browser)

The average measurement therefore takes into account a navigation with and without cache.

The main metrics analyzed are: display performance, power consumption, data exchange. Other metrics such as CPU consumption, memory consumption, system data … are measured but will not be displayed in this report. Contact GREENSPECTOR for more information.

In order to improve the stability of the measurements, the protocol is completely automated. We use an abstract GREENSPECTOR test description language that allows us to fully automate this protocol. Browser configurations are the default ones. We have not changed any settings of the browser or its search engine.

Rating

A notation out of 100 makes it possible to classify the browsers between them. It is based on the notation of 3 main metrics:

MetricDefinitionUnit
PerformanceDuration required for a test stepseconds (s)
EnergyBattery discharge rate found on the device during the test step, compared to the battery discharge rate of the device before the application is launchedMeasurements in uAh / s, then classification in multiples of the reference discharge velocity
DataTotal data volume (transmitted + received) during the test stepkilo-bytes (kB)

A weighting ratio is applied to the 5 step levels (from 5 for dark green to -1 for dark red) as described in the following example table:

The score of this application is then calculated at 61/100 for the energy metric.
Once the score of each of the three metrics obtained on 100 points, the total score of the application is calculated with equal weighting of the three metrics:
Total Score = (Performance Score + Energy Score + Score Data) / 3

Browsers evaluated

Browser nameVersion
Brave1.5.2
Chrome78.0.3904.108
Duck Duck Go5.32.3
Ecosia39632
Edge42.0.4.4052
Firefox68.3.0
Firefox Focus8.0.24
Firefox Preview2.3.0
KiwiQuadea
Lilo1.0.22
Maxthon5.2.3.3241
Mint37290
Opera54.3.2672.502
Opera Mini44.1.2254.143
Qwant37714
Samsung10.1.01.3
Vivaldi2.7.1624.277
Yandex19.10.2.116

Some browsers were discarded because they did not allow the tests automation. For instance, UC Browser and Dolphin browsers could not be measured. Beyond automation, this is a symptom of a accessibility issue of the application. To improve the accessibility of applications for people with visual impairments (among others), it is necessary to set up buttons labels. The automation that we realized is based on this information. In the end, these browsers do not appear in the ranking, but we can consider that accessibility problems are in all cases a crippling problem.

Note : The 2020 ranking is hardly comparable to that of 2018. Indeed, our protocol having completely evolved, the tests are thus more advanced and automated.

Discover our lastest study: the 2019 Playstore Efficiency Report!

12 Rules to your application success

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In a previous article on this blog, we introduced you the 5 keys to success of a mobile application. We present today the 12 rules by business indicator to respect that will make the success of your application.

Inclusion

  • The application must be usable in degraded network conditions
  • The application should not require a recent OS version like Android to be used. Some users do not follow updates, either voluntarily or because of their platform that does not allow them. According to our “PlayStore Efficiency Report 2019“, only 70% of apps on the store are compatible with all versions of Android.
  • The application must comply with the accessibility rules and must not exclude users with disabilities.
  • The app should work well on older phones too only on recent and latest models. This criterion will be degraded if you do not respect that of sobriety. 1/4 of the Google PlayStore applications are 10% of the oldest mobiles. (Source: PlayStore Efficiency Report 2019)

Sobriety

  • The application must limit its energy consumption so as not to empty the user’s battery. Moreover, in case of excessive consumption, the system notifies the user of anapplication as a consumer. Some energy-intensive applications can reduce battery life to less than 3 hours. (Source: PlayStore Efficiency Report 2019)
  • The application must limit its resource consumption (number of CPUs, memory occupied, data exchanged) in order to avoid any slowness or pollution of the other applications (for instance because of the memory leak). 50% of Google PlayStore apps continue to process after the app closes. (Source: PlayStore Efficiency Report 2019)
  • The application must limit its network consumption in order to not involve any load on the data centers and thus avoid the additional costs related to the unnecessary congestion of the servers.

Performance

  • The first launch of the application must be fast: otherwise, it is possible that your users won’t go further, the inclusion criterion will not be respected either.
  • The loading times of the application must be acceptable in all network situations.

Discretion

  • The application requires few or no permission. Do you really need to consult the list of contacts of your user? It’s all the more important to optimize this since the more permissions there are, the more the application consumes resources. This will therefore negatively influence the performance criterion.
  • The application has little or no tracker. The integration of a large amount of trackers implies a greater consumption of resources but can also cause bugs. This observation is even more true that the connection is degraded. On average, adding a tracker causes an over-consumption of resources of 8.5%.(Source : PlayStore Efficiency Report 2019)

According to our “PlayStore Efficiency Report 2019“, trackers, analytics and permissions are ubiquitous (44% applications have more than 5).

Ecology

  • The application must respect the sobriety criterion, the CO2 impact linked to the use is lower as well as the pressure of the resources on the components of the equipment of the user (battery obsolescence, loss of performance). As a result, the user is less likely to renew their equipment, which reduces the risk of obsolescence of his material. Our latest study shows that mobile apps contribute at least 6% of CO2 emissions digital.

Some tracks for the improvement of its GREENSPECTOR App Mark score

Directly improve the application

Several metrics are evaluated by the GREENSPECTOR App Mark and can be directly improved.

  • Minimum SDK version: Allow Android older versions to avoid the exclusion of users using older generation platforms.
  • Number of trackers: the fewer trackers the application has, the more it will respect the user’s data as well as the protection of his privacy. In addition, trackers via processing and data exchange increase the consumption of the application.
  • APK size: the bigger the binary of the application, the more the network is solicited and the less efficient the application. In addition, a large application size will use the limited storage space of some users.
  • Loaded data: number of loaded data throughout the test run. Limiting this data will reduce the consumption of resources on both the smartphone and the network.
  • Data loaded in the background: when the application is not used, it must limit its impact and send or receive as less data as possible.

More global metrics

Some metrics are directly related to the impact of the application and its efficiency. It is possible to act on it via the previous metrics, see by other axes (functional optimization, improvement of the source code …)

  • CO2: the more the application consumes energy, the more the battery is solicited and become obsolete. This may lead to a premature renewal of the battery or even the smartphone and therefore to a higher environmental impact. Let’s not forget that most of the environmental impact of a smartphone is predominant in its manufacturing phase than in its use phase: keeping it longer reduces its overall impact.
  • Energy Overconsumption: if the application overconsumes, it increases the environmental impact but also creates discomfort for the user especially on the loss of autonomy and generates an additional stress factor.
  • Performance after the first installation: applications sometimes perform additional treatments during the first launch, so the launch time is sometimes increased. It is necessary to limit its treatments because this loss of performance can be inconvenient for the user.
  • Performance: the launch time of the application is an important data for the user. It is necessary to reduce it to the maximum while consuming the least possible resources.
  • 3G Performance: in poor network conditions, it is necessary to master the performance to maintain a good user experience. It is even possible that some users do not have access to the application in the case of degraded performance. Having a frugal service that takes into account the constraints of mobility is therefore a key to success.

What about now?

You are certainly wondering how your application is doing on these 5 indicators. Is it rather virtuous? Is there any risk? How is it ranked against its competitors? Do you have quick progress actions? If you ask us, we will tell you! Contact-us, and we will introduce you to your own inclusive, sober, fast, ecological and discreet evaluation – just like your application very soon.

Greenspector study of the energy consumption of Google Play Store mobile apps

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During the Mobile One event, GREENSPECTOR announces a survey of the major mobile consumer trends of the Google Play Store. More than 1000 applications were sifted through for Performance, Sobriety and Inclusion by the measurement tools developed by GREENSPECTOR.

Continue reading “Greenspector study of the energy consumption of Google Play Store mobile apps”

GREENSPECTOR App Scan is now available on iOS!

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This is a great first for GREENSPECTOR: one of our main solutions is now available in the Apple universe.

Long awaited by our customers, this compatibility with iOS makes it possible to complete the analyzes of type GREENSPECTOR App Scan.

The measures of performance and efficiency on the iPhone complement those on Android. Your applications and your websites benefit from a maximum coverage, representative of the uses of your users, no matter what the underlying technology.

So you can now ensure the quality of user experience across all your mobile devices, under control their performance and efficiency.
Note that because of the restrictions imposed by Apple, all the usual GREENSPECTOR metrics are not yet available. But our teams continue to work to offer you, throughout the versions, analyzes always more pointed on your applications.

The Top 10 myths of frugal ICT

Reading Time: 5 minutes

I have been working for more than 8 years in GreenIT and I have seen lately that several studies and initiatives have started. This is a very positive sign and shows that there is a real dynamic to change the impact of ICT. All actions, whether small scale, as a simple awareness, or on a larger scale such as the optimization of a website with millions of visitors, is good to take into account the climate emergency.

However it’s important to avoid any greenwashing phenomenon and to understand the impact of the good practices mentioned (are they really all green?)

Myth 1 – A powerful software is a simple software.

False

A powerful software is a software that will be displayed quickly. This gives no information on its sobriety. On the contrary, it’s possible that practices are put in place for a quick display and that they go against the sobriety. As for example put the loading of the scripts after the display of the page. The page will be displayed quickly but many processes will run in the background and will have an impact on resource consumption.

Myth 2 – Optimize the size of queries and the weight of the page, this makes the software more frugal.

True and false

True because actually fewer resources will be used on the network and servers. Which means less environmental impact. It goes in the right direction.

False because the evaluation of a simple software will not only be based on this type of technical metrics. Indeed, it is possible that certain elements have an equally important impact. A carousel on a home page could for example be quite light in terms of weight and requests (for an optimized carousel) but in any case will have a strong impact in user-side resource consumption (CPU consumption, graphics … ).

Myth 3 – Automatic control via tools allows me to be green

True and false

True because it is important to measure the elements. This will allow to know objectively where we are, and to improve.

False because the evaluation will be done on technical elements. There is a bias: we only measure what we can automate. This is the criticism that can be made for example on Lighthouse (accessible tool in Chrome) on the accessibility. We can make a totally inaccessible site by having a score of 100. This is the same criticism that we can have about the tools that are used in ecodesign. For example the website http://www.ecoindex.fr/ is an interesting tool to initiate the process, however the calculation of this tool is based on 3 technical elements: the size of the page, the number of request and the size DOM. These are important elements in the impact of the page, however several other elements can be impacting: CPU processing from script, graphic processing, more or less good solicitation of the radio cell … All elements that can create false positives.

A measurement software will be complementary 😉

Myth 4 – My software uses open-source and free code, so I’m green

False

Free software is a software in its own right. He suffers the same obesity as other software. He will therefore potentially be a consumer. On the other hand, free software has a stronger capacity to integrate good efficiency practices. Still need to implement or at least begin to evaluate the impact of its solution …

Myth 5 – The impact is more on the datacenter, on the features, on that …

True and false

Any software is different, by its architecture, its use, its implementation, its functions … no serious study can certify a generality on a domain that would have more impact than another. In some cases, the impact will be more on the datacenter (for example on calculation software) but in other cases it will be on the user side (for example mobile applications). In the same way, some software will be obese because of their multiple functionalities whereas others will be because of a bad coding or an external library too heavy.

Myth 6 – Ecodesign requires a structured and holistic approach

True and false

True because indeed it’s necessary to involve all the actors of the companies (developer but also Product Owner, Business Department) and to have a coherent strategy.

However, starting process and product improvement through unit and isolated actions is very positive. The heaviness of the software is indeed in a state where any isolated positive action is good to take.

Both approaches are complementary. Avoiding the application of certain practices while waiting for a structured approach (which can be cumbersome) would be dangerous for the optimization and competitiveness of your software.

Myth 7 – The green coding does not exist, the optimization is premature …

False

This is an argument that has existed since the dawn of time (software). Code implemented, legacy code, libraries … optimization tracks are numerous. My various audits and team accompaniments showed me that optimization is possible and the gains are significant. To believe otherwise would be a mistake. And beyond optimization, learning to code more green is a learning approach that is useful to all developers.

Myth 8 – My organization is certified green (ISO, ICT responsible, Lucie …), so my product is green.

False

All its certifications will effectively ensure that you are on the right track to produce more respectful software. Far be it from me to say that they aren’t useful. However, it must not be forgotten that these are organization-oriented certifications. In a structured industry (like agriculture, a factory …) the company’s deliverables are very aligned to the process. Certifying an AB farm will ensure that the product is AB good.

However in the mode of the software it is not so simple, the quality of the deliverables is indeed very fluctuating, even if one sets up a process of control. In addition, an organization potentially consists of a multitude of teams that are not going to have the same practices.

It’s therefore necessary to control the qualities of software products and this continuously. This is an approach that will be complementary to the certification but mandatory. Otherwise we risk discrediting the label (see going to greenwashing).

Myth 9 – Optimizing energy is useless, it’s the equivalent CO2 that is important to treat

False

The ecodesign work is mainly based on the reduction of equivalent CO2 (as well as other indicators such as eutrophication …) over the entire life cycle of the ICT service. It’s therefore important to take into account this metric. Without this, we risk missing the impacts of IT. However, on the same idea as points 5 to 7, no optimization is to be discarded. Indeed, it is necessary to understand where the impacts of the software are located. However, the integration of the energy problem in teams is urgent. Indeed, in some cases the consumption of energy in the use phase is only part of the impact (compared to gray energy for example). However in many cases, high energy consumption is a symptom of obesity. In addition, in the case of software running in mobility (mobile application, IoT) energy consumption will have a direct impact on the renewal of the devices (via the wear of the battery).

Myth 10 – I compensate so I’m green

False

It’s possible to offset its impact through different programs (financing of an alternative energy source, reforestation …). It’s a very good action. However, it is a complementary action to an ecodesign process. It is indeed important to sequence the actions: I optimize what I can and I compensate what remains.

Conclusion

The frugal ICT is simple because it’s common sense. However, given the diversity of the software world, the findings and good practices aren’t so simple. However, the good news is that, given the general cumbersome software and the delay in optimization, any action that will be taken will be positive. So don’t worry, start the process, it’s just necessary to be aware of some pitfalls. Be critical, evaluate yourself, measure your software!

Discover “Pear”, the 2.5.0 version of GREENSPECTOR!

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L’équipe GREENSPECTOR is proud to announce the release of its new release: version 2.5.0 Pear! With this new version, you can choose the type of network connectivity (Wi-Fi, 4G, 3G, 2G) when launching your tests on Power Test Bench phones. You can now verify that your application is efficient in poor network conditions.

Continue reading “Discover “Pear”, the 2.5.0 version of GREENSPECTOR!”