You can find here an overview of my research, the list of my PhD students, and a list and description of the projects I am/was involved in Overview An overview of my research work is available in my Habilitation (document in french) or in the associated slides (slides in french), defended on 12th Nov 2015 My focus is on Energy Efficiency in ICT and with ICT. I mainly focuses on HPC and large scale infrastructures (clusters, grids, clouds).

Funded PhD position: Energy and performance monitoring and models towards sustainable Exascale computing

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.

Funded PhD position: Exploring the tradeoffs between energy and performance of federated learning algorithms

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.

Game Theory for Green Datacenters

In order to operate a datacenter only with renewable energies, a negotiation has to be undertaken between the sources providing and storing the energy (solar panels, wind turbine, batteries, hydrogen tanks) and the consumers of the energy (basically the IT infrastructure). In the context of the ANR DATAZERO2 project (, a negotiation module has to be improved, starting from a existing proof of concept already published. The improvement will be included in a dedicated module, interoperable with a functioning middleware developed in the project.

Federation of clouds: Multi-Clouds overflow

To cover data analytics needs, Cloud providers need to adapt their IaaS services to resources consumption fluctuations and demands. This requests geographical distribution of tasks excutions and flexible services. Having a federation of cloud providers allows to provide such services to users. In this project, users submit their applications on a cloud broker. The aim is to find resources in one or many clouds to be able to answer the request.

DVFS-aware performance and energy model of HPC applications

Power consumption of computers is becoming an major concern. To optimise their power consumption it is necessary to have precise information on the behavior of applications. With this information, it is possible to choose the right frequency of a processor. The speed of some applications is not really impacted by changes of this frequency, while for some application it has an important effect. The goal of this internship is to model the fine grained behavior of applications and to link this behavior with the impact (on performance and energy) of frequency changes.

Fast scheduling under energy and QoS constraints in a Fog computing environment

Location: LAAS-CNRS - Team SARA or IRIT - Team SEPIA Supervisors: Da Costa Georges ( / Guérout Tom ( Duration: 6 months, possibility of thesis afterwards. Context The explosion of the volume of data exchanged within today’s IT systems, due to an increasingly wide use and by an increasingly wide audience (large organizations, companies, general public etc.), has led for several years to question the architectures used until now. Indeed, for the past few years, Fog computing [1], which extends the Cloud computing paradigm to the edge of the network, has been developing steadily, offering more and more possibilities and thus extending the field of Internet of Things applications.

Impact of processor temperature on HPC application performance and energy consumption

Large scale datacenters manage applications as black boxes. Most of the time, they assume that application behavior is not linked to the state of the underlying hardware. When an applications runs on a hot processors, it can be slowed down arbitrary by the processor as it tries to protect itself. The goal of this internship is to evaluate the impact of temperature on the speed of the code, the impact of the execution of the code on temperature, and the possibility to reduce the frequency of the processor to cool down the processor at key points to cool down the processor (and thus speed up the application)

Performance and energy models of colocated applications

Large scale datacenters manage applications as black boxes. Most of the time, they assume that applications have no cross impact. When multiple applications are using the memory, their speed is reduces because of the bottleneck of the memory bus. In the other direction, two applications on the same core might not go at half the speed each: if one uses only floting point operations, while the other only memory access for example.

Sufficient cloud: off-grid scheduling for environmentally responsible users

Topic Avoiding the ecological catastrophe will require an joined effort from every actor in the society – the ICT industry included. We postulate that some environmental-aware individuals are willing to reflect upon and reduce the footprint associated to their usage of new technologies. Similarly to the Low-tech Magazine[1], a solar-powered and very lightweight website, this internship will study an off-grid “sufficient”[2] data center in which a part of the users accepts to delay, degrade or even cancel the execution of their tasks to reduce the overall footprint of the infrastructure