Project description
Creating a hybrid cloud service for EU researchers
In recent years, the European Union has recognised the importance of supporting researchers by providing them with the necessary data and resources they need to carry out their work. However, due to the large number of researchers, it can be challenging for them to access computer equipment with sufficient strength for intensive computing techniques and processes. The EU-funded DEEP-HybridDataCloud project aims to develop a hybrid cloud service for EU researchers. This service will provide them with access to computing resources through the cloud, enabling them to perform their work more efficiently. Additionally, the project will implement a DevOps approach that would allow for improved efficiency and maintenance.
Objective
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.
Fields of science
- 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
Programme(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
28006 Madrid
Spain