Energy Consumption

Vendredi 27 février 2026, Jérôme Maquoi était en direct chez TechnoMinds sur Boukè pour parler de sa recherche en green coding.

Ces derniers temps, on parle beaucoup d’intelligence artificielle, mais on oublie souvent qu’elle n’est que la partie visible d’un système bien plus vaste. Entraîner un modèle revient à produire un fichier gigantesque à partir d’une masse de données, mobilisant des serveurs, du stockage et des processeurs qui tournent longtemps. Une fois entraîné, même son utilisation quotidienne n’est pas neutre : plus un modèle est volumineux, plus il consomme d’énergie pour répondre.

Green software engineering is emerging as a crucial response to the rising energy impact of digital technologies, which may soon rival aviation and shipping combined. While several tools aim to help developers track energy consumption and detect regressions, they all have their own limitations. This motivated the development of EnergyTrackr, a fully modular and automated tool designed to detect statistically significant energy changes.

Energy efficiency in computing is an important subject that is increasingly being addressed by researchers and developers. Nowadays, the majority of websites are built using the Wordpress CMS, while other developers prefer to use more secure and energy-efficient site generators. This study focuses on the server-side energy consumption of these two methods of creating websites. A detailed analysis of the results will make it possible to identify borderline cases and suggest recommendations on the best technology to use, depending on the type of project.

In recent years, sustainability concerns have emerged in the software engineering community. However, little research focuses on the link between the evolution of a source code and energy consumption. Consequently, most developers don’t know the energy consumption of their software during its development. To address this problem, we want to build a theory that binds the evolution of a source code to its energy consumption profile. This relies on the measurement of the energy consumption evolution of multiple projects using repository mining approaches, the analysis of the reasons for this evolution, the design of proper data visualization to make this information available for the developers, and the evaluation of these objectives through the development of a framework for developers. This theory and the associated framework may help developers have better knowledge of the energy consumption evolution of their software and tools to reduce its consumption.