Context Data centers are computing infrastructures that host most of the services available on the internet. As data centers may include thousands of servers, both their energy consumption and their carbon footprint are significant. This has led many research projects to focus on optimizing these two objectives. This PhD takes place in the context of centralized (Cloud) and decentralized (Edge, Fog) infrastructures.
Various ideas emerge from research / R&D to reduce the impact of computing infrastructures, and these ideas must be evaluated.
Context High Performance Computing usage is growing from climate science studies to chemical research. The increased impact of these computation opens the field of research on how to manage and reduce their energy consumption. In the NumPEx project we aim at developing state-of-the-art skills and infrastructures in the field of exascale computing. One of the pillars of NumPEx focuses on making exascale computing sustainable.
To make informed cluster-level scheduling decisions and to provide feedback to users, information on the whole infrastructure is needed.
Context There is an increasing interest in a new distributed ML paradigm called Federated Learning (FL)[La17], in which nodes compute their local gradients and communicate them to a central server. This centralized server then orchestrates rounds of training over large data volumes created and stored locally at a large number of nodes. This training procedure repeats until some criterion are met. This enables the participating nodes (e.g., IoT devices, mobile phones, etc) to protect their data and solve the data security and privacy issues imposed by law.
