Apollo - Connect Talent (2017-2020) -
Shadi Ibrahim (Coordinator)
PrivGen (2016-2019) - CominLabs laboratory of excellence -
Mario Sudholt (Coordinator)
The PrivGen project aims at providing new techniques for making secure and protect the privacy of shared genetic data that is processed using distributed applications, but not only. To do so, PrivGen proposes to develop:
- new means for the combination of watermarking, encryption and fragmentation techniques to ensure the security and protection of privacy of shared genetic data,
- a composition theory for security mechanisms that allows the enforcement of security and privacy properties in a constructive manner on the programming level,
- new service-based techniques for the distributed processing of shared genetic data.
Epoc - CominLabs laboratory of excellence -
Thomas Ledoux, Jean-Marc Menaud (Coordinator)
With the emergence of the Future Internet and the dawning of new IT models such as cloud computing, the usage of data centers (DC), and consequently their power consumption, increase dramatically. Besides the ecological impact, the energy consumption is a predominant criteria for DC providers since it determines the daily cost of their infrastructure. As a consequence, power management becomes one of the main challenges for DC infrastructures and more generally for large-scale distributed systems. The EPOC project focuses on optimizing the energy consumption of mono-site DCs connected to the regular electrical grid and to renewable energy sources.
SeDuCe (CPER 2015-2019) -
Jean-Marc Menaud (Coordinator)
The SeDuCe project (Sustainable Data Centers: Bring Sun, Wind and Cloud Back Together), aims to design an experimental infrastructure dedicated to the study of data centers with low energy footprint.
Hydda (PIA 2017-2020) -
Hélène Coullon, Jean-Marc Menaud.
The HYDDA project aims to develop a software solution allowing the deployment of Big Data applications (with hybrid design (HPC/CLoud)) on heterogeneous platforms (cluster, Grid, private Cloud) and the orchestration of computation tasks (like Slurm, Nova for OpenStack, or Swarm for Docker). The mains challenges addressed by the project are:
- How to propose an easy-to-use service to host application components (from deployment to supression) that are both typed Cloud and HPC?
- How to propose a service that unifies the HPCaaS (HPC as a service) and the Infrastructure as a Service (IaaS) in order to offer on-demand resources and to take into account specificities of scientific applications?
- How optimize resources usage of these platforms (CPU, RAM, Disk, Energy, etc.) in order to propose solutions at the least cost?
ANR KerStream (2017-2021) -
Shadi Ibrahim (Coordinator)
The KerStream project aims to address the limitations of Hadoop, and to go a step beyond Hadoop through the development of a new approach, called KerStream, for reliable, stream Big Data processing on clouds. KerStream keeps computation in-memory to ensure the low-latency requirements of stream data computations. Furthermore, KerStream will embrace a set of techniques that allow the running applications to automatically adapt to the performance variation and node failures/subfailures, and enable a smart choice of failure handling techniques. Moreover, KerStream will have a set of scheduling policies to allow multiple running applications to meet their QoS (low-latency for stream data processing) while achieving high resource utilization.
GRECO (ANR-16-CE25-0016 2017-2020) -
The goal of the GRECO project is to develop a reference resource manager for cloud of things. The manager should act at the IaaS, PaaS and SaaS layer of the cloud. One of the principal challenges here will consist in handling the execution context of the environment in which the cloud of things operate. Indeed, unlike classical resource managers, connected devices imply to consider new types of networks, execution supports, sensors and new constraints like human interactions. The great mobility and variability of these contexts complexify the modeling of the quality of service. To face this challenge, we intend to innovate in designing scheduling and data management systems that will use machine learning techniques to automatically adapt their behavior to the execution context. Adaptation here requires a modeling of the recurrent cloud of things usages, the modeling of the physical cloud architecture and its dynamic.
DISCOVERY (Inria Project Lab 2015-2019) -
Hélène Coullon, Shadi Ibrahim, Adrien Lebre (Coordinator), Mario Südholt
The Discovery initiative aims to overcome the main limitations of the traditional server-centric cloud solutions by revising the OpenStack software in order to make it inherently cooperative.
Bigstorage (2015-2018) -
BigStorage is an European Training Network (ETN) whose main goal is to train future data scientists in order to enable them and us to apply holistic and interdisciplinary approaches for taking advantage of a data-overwhelmed world, which requires HPC and Cloud infrastructures with a redefinition of storage architectures underpinning them – focusing on meeting highly ambitious performance and energy usage objectives.