Intervention de Cinchéo à “Tech4Climate?” – L’éco-conception logicielle : quelques constats et conseils pour démarrer

Le 9 juin 2022, j’étais invité à intervenir dans l’atelier “Les acteurs du numérique au service de la transition” de l’événement “Tech4Climate?” organisé par le groupe Constellation.

Pour celles et ceux qui n’ont pas eu la chance d’y assister, voici quelques photos et le résumé des messages que j’ai fait passer.

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Food for thought about Software Eco-Design

Things are not simple when it comes to Software Eco-Design.

Eco-Design is about maximizing a software Efficiency function of Usage, which can be defined as a ratio :

Efficiency(U) = Utility(U) / Resources(U) (U = Usage, a parameter)

  • Utility is related to the amount of useful work you can do in a unit of time (the word useful is important, because it depends on the context and what you need to achieve).
  • Resources, for software is the amount of computation, data, I/O you use, which in turn, corresponds to an amount of energy (electricity) and hardware resources (servers, routers, wires, satellites, laptops, smartphones, …). The more software resources your application use, the more energy you will need, and the more powerful hardware you will use.
  • Usage (U), corresponds of how your software is used. Usage is about how often your users will connect to the app, how many, from which county, etc. It is a parameter of Efficiency, Utility, and Resources because software impacts heavily depend on how the users will use it. The same software used differently may use up different amount of resources or have different utilities. Most importantly, Efficiency, Utility, and Resources might not be a linear function. Think of Amazon for instance: the utility might vary largely depending on the number of users, and the resources on where those users are located on the planet.
  • Energy and hardware resources end up to be direct impacts on the planet, contributing to climate change or environment destruction (side note: the IT sector is expected to reach 10% of GES within 5 years, that is to say the equivalent of all personal vehicles worldwide).
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MC2: a tool to remotely monitor computer resources

For more than a year now, EASYTEAM and Cinchéo have been working on a project that aims at creating new methods and tools to help IT departments to control the carbon footprint linked to digital services. It is a vast and complicated topic and a lot needs to be achieved. With MC2 (say M-C-square), we are contributing a small and modest part to the tooling ecosystem.

In this post, I will explain the principles of MC2 and how it works. The source code is fully available on Github:

NOTE: MC2 it is still work in progress and is a small part of a wider R&D project involving EASYTEAM (Constellation group) and INRIA (Spirals team).

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Back to the Future: let’s stop buying new Laptops and Smartphones ;)

When I was a teenager, I used to go to the center of Paris to buy specific parts to build my PC myself: sound cards, video cards, power blocks, hard drive, CPU, fans, memory, you name it. Not only it was fun and instructive, but it was also good for the planet.

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AI vs. Maths

Quite recently, I had the chance to discuss with Philippe de Reffye, a French researcher who spent his life studying how plants grow. He built a generic mathematical model that can simulate the growth of any plant, assuming we can mesure some environmental parameters to calibrate the model. He wrote a very complete book about it (in French).

The book of Philippe de Reffye

The interesting point from my perspective is that his work managed to lower the computational complexity of simulating plant growth from an exponential to a linear function. What would take days to calculate can now be done in seconds, and it grows linear w.r.t. the plant complexity.

Philippe and I talked about AI. His opinion is that AI can help calibrating the models. However, without the maths, you cannot really understands what happens when the plants grow and you have no chance finding an optimization of the kind he found.

Philippe warned me about the danger of replacing maths with ML when we don’t have a model. Indeed, it is a potential workaround, but it is expensive in computation time and it blurs our understanding of the world.