Computers have become an essential part of modern life. They are used in a wide range of applications, from smartphones and laptops to data centers and supercomputers. However, the increasing usage of computers has led to a rise in energy consumption, which has significant environmental and economic consequences.

The hardware of computing systems has become more energy-efficient over the years. However, software remains a largely untapped area for energy optimization. The key to reducing energy consumption in computing systems lies in understanding the impact of software on the overall energy usage of the system. So, the need for tools and methods to measure software energy consumption is crucial. Those tools and methods should be flexible enough to be used in different computing environments.

This thesis reviews the current state-of-the-art tools for monitoring software energy consumption. After a list of their capacities, we identify the more promising tools and methods and compare their performance during experiments using different types of workloads. The thesis also presents a new tool called Geryon, which brings together the best techniques found in the tools reviewed. Geryon is a flexible and easy-to-use tool that provides multiple estimations at once. Those estimations are computed via simple power models and regression models trained on-the-fly. It also provides a way to plug in an external power model, allowing for more complex power models to be used. The tool is evaluated on the same workloads as the other tools to provide a fair comparison.