Figure 1: A rough estimation of the carbon FOOTPRINT of a server (top left), a laptop (top right), a monitor (bottom left) and a desktop (bottom right) for a lifetime of 4 years. Based on the Dell product carbon footprint analysis. 

Over-provisioned datacentres

Another aspect of excessive energy consumption in datacentres is related to the “user tyranny”, the fear of disruption or outage which could result in BIG FINES for cloud providers. Anne-Cécile Orgerie – a computer science researcher at the Irisa, Rennes, France – remarked in a CNRS ARTICLE in 2018: “Despite numerous studies showing that this would not affect their performance, datacentres continue to operate at 100% capacity 24 hours a day […] The reason for this is that those in charge of the equipment will not risk users having to suffer from even the slightest latency—a lag of a few seconds.” 

Datacentres can endanger the energy transition of some countries.

In Southeast Asia, some cloud providers are implementing new datacentres in countries which rely almost exclusively on fossil fuels like SINGAPORE or INDONESIA. While it is inevitable to implement new datacentres in the VALERIEPIERIS CIRCLE – a region containing more than half the world population – these implementations could put at risk the COMMITMENT of some of those countries to become carbon neutral. 
In Europe too, the datacentre industry risks jeopardising the energy transition of some countries like IRELAND and NETHERLANDS. 

The threat of a rebound effect

We noted in the last article that users could reduce their carbon-footprint by 88% by migrating their server performances to the cloud. However, not all users will effectively migrate all their server performances to the cloud and eventually close their own datacentre. Indeed, some users might prefer a hybrid approach: migrating partially to the cloud while keeping access on their own datacentre, on-premises. There are also some users who are willing to have a hybrid multi-cloud approach. There are already some solutions – such as Anthos – to monitor services spanning between clouds and/or on-premises. This poses the threat of a rebound effect.

What is the rebound effect (or Jevons paradox)?

The rebound effect is the increase of consumption of a resource, related to the reduction of the limits of this resource.

Example: high speed trains allow us to travel faster. Its rebound effect is that we spend the same amount of time using them, but we travel further.

Another example: 5G allows us to spend less energy for the same volume of communication. Its expected rebound effect is an overall increase of global communications.

Similarly, we could expect a rebound effect for the public cloud: it is less expensive, more secure and it reduces GHG emissions to move to the cloud. The rebound effect could be a larger utilization of datacentres than before or attracting new users who did not use datacentres previously.

And what if…

There are two reasons why energy consumption due to datacentres could rise exponentially in the next few decades:

• Currently, there is a compensation because on the one hand there is an increase in energy which results from users migrating to the cloud and closing their own datacentre (or considerably reducing its use) and on the other hand there is an increase in energy due to new users starting their business directly with cloud (no pre-existing datacentre) or users increasing their use of cloud computing. But at some point, when no more users will be migrating to the cloud, the energy demands for datacentres MAY RISE SIGNIFICANTLY. 
• We mentioned in the beginning of the last article that the energy efficiency of servers is in constant growth. Nevertheless, the improvement of a server’s capacity is not expected to continue indefinitely, and a LIMIT MAY BE REACHED in the next decades either due to physical efficiency limits of transistors, or theoretical limits to Koomey’s law. 

Finally, as a conclusion for this section regarding datacentres, it should be mentioned that recent studies analysing ICT or datacentres energy consumption call for more regulations from the policy makers. ARSHAD ET AL., MASSANET ET AL. or SANTARIUS ET AL. to name a few. For completeness – because not only datacentres and cloud providers should be held responsible for their success – we will also talk briefly about policy makers, companies, and data engineers.

Policy makers

We are not aiming to make a complete list of policy makers regulations in this section but only highlight a few promising ones regarding:

• The extraction of metals: since the 1ST OF JANUARY 2021, the E.U. requires European companies to ensure that their imports of minerals and metals are exclusively from responsible, conflict-free sources. Similar law exists in the U.S. since 2010 with the Dodd-Frank law. 
• Manufacturing and end-of-life: as early as 1992, the U.S. put into place the Energy Star labelling program, aiming to encourage low-energy devices. In the E.U., since the 1ST OF MARCH 2021, there is a right to repair to extend the lifecycle of electronic devices 
• GHG emissions: IN EUROPE, all datacentres should be carbon-neutral by 2030. As early as 2008, the E.U. also issued a CODE OF CONDUCT for datacentres. As part of the GREEN DEAL, E.U. has the ambition to become the first neutral carbon continent by 2050. On the other side of the world, in China – who announced to become carbon neutral by 2060, and although 73% of the electricity consumed by datacentres came from coal-fired power plants in 2018 – SOME CITIES started to ban (or will not give subsidies to) datacentres having that had too high of a PUE. More recently, under the impulse of BIDEN, the U.S. joined the Paris agreement.

We can also note that with countries passing data localization laws that require citizen’s data to be stored on servers located domestically more frequently (GDPR for EU, Personal Data Protection Law for China, Japan’s Act on Protection of Personal Information for Japan, etc.), using datacentres in colder climates, countries beyond these borders either bring an ADDITIONAL BURDEN or are simply not an option anymore. 

Companies

We do not have enough hindsight to confirm that moving to the cloud will lower the environmental footprint for companies in the long term. For sure, migrating the servers’ performances to the cloud will lower the environmental footprint as mentioned above and in the last article, but as there are additional services in the cloud, the benefits obtained by moving to the cloud could be reinvested in implementation of new products, posing the threat of a rebound effect.

What we can say however, is to not migrate to the cloud alone and unprepared. Especially for companies that have large technical debt. We are not saying that because consulting is a part of the core business of Syntio, there is a technology gap between traditional IT infrastructure and clouds that should not be neglected as they could result in a large amount of waste. Meghan Liese – Senior Director Product Marketing from HashiCorp – pointed these challenges recently in an ARTICLE in The New Stack (as much as 40% of cloud spend is sunken into over-provisioned and unused infrastructure in many organizations).

Companies concerned about their environmental impact or willing to strengthen their image can get the ISO 14001 (on the environmental management system of a company), the Eco Management and Audit Schema EMAS (tightly linked to the ISO 14001) and finally the ISO 50001 (which certifies the energy performance of a company).

In contrast to servers, where we noted earlier that the carbon footprint of the manufacturing was smaller than the one of its utilization, the CARBON FOOTPRINT OF A LAPTOP is approximately 80% due to its manufacturing and 15% its utilization, for a 4-years utilization (its transportation and end-of-life account for the remaining percentages). The same trends are observed for monitors (~65% for (a 4-years) utilization) and desktop (~50% for manufacturing / ~45% for (a 4-years) utilization). See Figure 1. Because of the large contribution in the manufacturing to the environmental footprint of the product, it is an environmental choice for companies to keep the IT material for a few more years of utilization. As noted by the SHIFT PROJECT, the average respective lifetime of the items of equipment is estimated at 3 years for a laptop computer, and 2.5 years for a smartphone. We all know that we are often changing our smartphones while they are still working perfectly. Mottainai! (What a waste! As they say in Japan). By lengthening these lifetimes to 5 and 3.5 years, respectively, this could reduce the annual related GHG emissions of 37% and 26% per employee.

The Shift Project provides three more suggestions for companies:

• Companies could also offer to employees a “bring your own device” (BYOD) option, based on the utilization of smartphones equipped with two SIM cards. This would limit the number of terminals per person, and this is expected to reduce the annual related GHG emissions of 37% per employee.
• Favouring the exchange of office documents on a shared platform to reduce the number of copies of the same document stored on the servers used by the company, and avoid documents shared by email resulting into duplicated documents is also expected to have a considerable impact on the environmental footprint of the companies.
• In the same way as financial and social criteria, indicators of the environmental impact of digital components of a product could be included during the elaboration of a project or product.

Data engineers

Data engineers are individuals before data engineers. Like everybody, they can be concerned about the digital sobriety or digital frugality or whatever you call it. Ines Leonarduzzi – author of Réparer le future (Editions de l’observatoire) – with her NGO Digital for the Planet suggests the following actions in Figure 2.

Figure 2: Actions SUGGESTED by the NGO Digital for the Planet to reduce our digital footprint.

We would like to add, especially because we did not say a lot regarding network, which accounts for nearly 40% of electricity consumption as mentioned in the previous article, and also because videos are accounting for a LARGE MAJORITY (~80%) of the network:

• using a music platform instead of a video platform to listen to music (if you are seeking for new inspiration, check out our SYNTIO PLAYLIST,
• reducing the quality of videos wherever possible (for example, you can pretty fairly watch the TOMO TALKS series with half of the best video quality without any problem enjoying it.

We will not extend the list further as many websites with good advice already exist like this. Let us instead focus on more technical and data engineering related topics. Here are a few things that data engineers can do to lower the environmental impact of their work:

• While it is more practical to start a product with a monolithic application, it becomes more relevant to break it down in favour of a distributed architecture – such as microservices or even a domain-oriented architecture like the data mesh architecture BROUGHT FORTH recently by Zhamak Dehghani from ThoughtWorks when scaling up. Indeed, microservices being independent from each other and loosely coupled (with MESSAGE BROKER for example) over time it is easier to rework a microservice while it BECOMES A BURDEN (became too big, scaling issues, applying changes become slow, the quality of the code declines, etc.) for a monolithic architecture. Our Syntio colleagues Martina Kocet and Tihana Britvic provided a list of reasons why moving to a data mesh architecture is a positive thing in a SEPARATE ARTICLE.
• Whenever possible, select availability zones in public cloud that have a low PUE and or a low carbon intensity. Some clouds have provided calculators to quantify the carbon impact of their CLOUD SERVICES or DATACENTRE LOCATIONS.
• Using a language with good energy, time, and memory efficiencies (see Table 1 From PEREIRA ET AL.. For example, if you need to use a function as a service (FaaS), as in most of the clouds you have the choice between several languages, for better energy saving, time saving, and energy efficiency. You may wish to use Go or Java instead of Python or Ruby, but of course, those languages also compare themselves differently (functional, imperative, object-oriented, scripting) and there may be architectural decisions or company choices that influence the choice of a language. ABOUT TEN YEARS AGO, a social network for professionals improved their product to be 20 times faster for some scenarios and use 3 servers instead of 30, simply by replacing their language from Ruby to Node.js.

Table 1: Benchmark of energy, time, and memory efficiencies of different languages.