Section: Partnerships and Cooperations
Inria International Labs
UNIFY: An associated team involved in the JLESC international lab
- Inria International Lab:
- International Partner:
Argonne National Laboratory (USA) — Department of Mathematics, Symbolic Computation Group — Tom Peterka
- Start year:
- See also:
The landscape of scientific computing is being radically reshaped by the explosive growth in the number and power of digital data generators, ranging from major scientific instruments to the Internet of Things (IoT) and the unprecedented volume and diversity of the data they generate. This requires a rich, extended ecosystem including simulation, data analytics, and learning applications, each with distinct data management and analysis needs.
Science activities are beginning to combine these techniques in new, large-scale workflows, in which scientific data is produced, consumed, and analyzed across multiple distinct steps that span computing resources, software frameworks, and time. This paradigm introduces new data-related challenges at several levels.
The UNIFY Associate Team aims to address three such challenges. First, to allow scientists to obtain fast, real-time insight from complex workflows combining extreme-scale computations with data analytics, we will explore how recently emerged Big Data processing techniques (e.g., based on stream processing) can be leveraged with modern in situ/in transit processing approaches used in HPC environments.
Second, we will investigate how to use transient storage systems to enable efficient, dynamic data management for hybrid workflows combining simulations and analytics.
Finally, the explosion of learning and AI provides new tools that can enable much more adaptable resource management and data services than available today, which can further optimize such data processing workflows.
Inria Associate Teams Not Involved in an Inria International Labs
- International Partner:
Instituto Politécnico Nacional (Mexico) — Centro de Investigación en Computación — Rolando Menchaca-Mendez
See also: https://team.inria.fr/smartfastdata/
The proliferation of small sensors and devices that are capable of generating valuable information in the context of the Internet of Things (IoT) has exacerbated the amount of data flowing from all connected objects to private and public cloud infrastructures. In particular, this is true for Smart City applications, which cover a large spectrum of needs in public safety, water and energy management. Unfortunately, the lack of a scalable data management subsystem is becoming an important bottleneck for such applications, as it increases the gap between their I/O requirements and the storage performance.
The vision underlying the SmartFastData associated team is that, by smartly and efficiently combining the data-driven analytics at the edge and in the cloud, it becomes possible to make a substantial step beyond state-of-the-art prescriptive analytics through a new, high-potential, faster approach to react to the sensed data.
The goal is to build a data management platform that will enable comprehensive joint analytics of past (historical) and present (real-time) data, in the cloud and at the edge, respectively, allowing to quickly detect and react to special conditions and to predict how the targeted system would behave in critical situations.
In 2019, the first objective of the associated team (i.e., exploring analytical models for performance evaluation of stream storage and ingestion systems) was achieved by means of the two internships of José Canepa and Edgar Romo (described in the New Results section) as well as the visit of Mario Rivero as an Invited Professor, who set up the main research agenda for those internships.