Descripción del proyecto
Creación de un servicio de nube híbrida para los investigadores europeos
En los últimos años, la Unión Europea ha reconocido la importancia de apoyar a los investigadores proporcionándoles los datos y recursos necesarios para desempeñar su labor. Sin embargo, debido al gran número de investigadores existente, puede resultarles difícil acceder a equipos informáticos con la potencia suficiente para aplicar técnicas y procesos de cálculo intensivo. El objetivo del proyecto DEEP-HybridDataCloud, financiado con fondos europeos, es crear un servicio de nube híbrida para los investigadores europeos. Este servicio les proporcionará acceso a recursos informáticos a través de la nube, lo que les permitirá llevar a cabo su trabajo de forma más eficiente. Además, en el proyecto se aplicará un método de operaciones de desarrollo (DevOps, por sus siglas en inglés) que permitirá mejorar la eficiencia y el mantenimiento.
Objetivo
The key concept proposed in the DEEP Hybrid DataCloud project is the need to support intensive computing techniques that require specialized HPC hardware, like GPUs or low latency interconnects, to explore very large datasets. A Hybrid Cloud approach enables the access to such resources that are not easily reachable by the researchers at the scale needed in the current EU e-infrastructure.
We also propose to deploy under the common label of “DEEP as a Service” a set of building blocks that enable the easy development of applications requiring these techniques: deep learning using neural networks, parallel post-processing of very large data, and analysis of massive online data streams.
Three pilot applications exploiting very large datasets in Biology, Physics and Network Security are proposed, and further pilots for dissemination into other areas like Medicine, Earth Observation, Astrophysics, and Citizen Science will be supported in a testbed with significant HPC resources, including latest generation GPUs, to evaluate the performance and scalability of the solutions.
A DevOps approach will be implemented to provide the chain to ensure the quality of the software and services released, that will also be offered to the developers of research applications.
The project will evolve to TRL8 existing services and technologies at TRL6+, including relevant contributions to the EOSC by the INDIGO-DataCloud H2020 project, that the project will enrich with new functionalities already available as prototypes, notably the support for GPUs and low latency interconnects. These services will be deployed in the project testbed, offered to the research communities linked to the project through pilot applications, and integrated under the EOSC framework, where they can be further scaled up in the future.
Ámbito científico
- natural sciencescomputer and information sciencessoftwaresoftware development
- natural sciencescomputer and information sciencescomputer securitynetwork security
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- social sciencespolitical sciencespolitical policiescivil society
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-EINFRA-2017
Régimen de financiación
RIA - Research and Innovation actionCoordinador
28006 Madrid
España