News > Thesis Defense - Guillaume Fieni
Guillaume Fieni has defended his thesis today (15 dec. 2022). Congratulation doctor! 🎉
Towards Modeling the Power Usage Efficiency of Software-Defined Computing Infrastructures
Energy is one of the biggest expenses for a data center, most of which is attributed to the cooling system, as well as the many underlying parts, such as network equipment and the large number of machines used. These infrastructures are very energy-intensive, and their number is constantly increasing around the world, especially due to the growing popularity of the Cloud Computing.
A lot of software is needed to run these infrastructures, especially for network management, data storage, task scheduling and the supervision of all hardware and software. All these software consumes a significant amount of energy, but are not taken into account in the calculation of the energy efficiency of the infrastructures. The scientific community as well as data center operators have developed many approaches to evaluate and optimize energy consumption globally, but the question of the energy cost of software infrastructures remains rarely studied.
The objective of this thesis is to propose methods to analyze the end-to-end software energy efficiency of data processing infrastructures. To do so, we propose approaches and tools to accurately estimate the energy consumption of software running on a distributed infrastructure, as well as an indicator to calculate their energy efficiency.
Firstly, we introduce SmartWatts, a software power meter to estimate the energy consumption of software containers deployed on a machine. Secondly, we propose SelfWatts, a controller to automate the configuration of software power meters to facilitate their deployment in heterogeneous infrastructures. And finally, we propose xPUE, a metric to calculate the energy efficiency of software and hardware in real-time at different levels of an infrastructure.
Through these contributions, we aim to advance the knowledge in the field of software energy consumption, and allow to accurately measure the energy consumption of software deployed at different levels of the infrastructure. This allows infrastructure operators, as well as software developers and users, to observe and analyze in detail the energy consumption and thus assist in its optimization.