In the framework of this project IBERGRID will contribute to deliver the EOSC Compute Platform through a federation of cloud compute and storage facilities, PaaS services and data spaces with analytics tools and federated access services. Compute and Storage facilities are provided by CESGA, LIP, CSIC and INCD.
EGI-ACE is a 30-month project coordinated by the EGI Foundation with a mission to empower researchers from all disciplines to collaborate in data- and compute-intensive research.
In the framework of this project IBERGRID will contribute to deliver the EOSC Compute Platform through a federation of cloud compute and storage facilities, PaaS services and data spaces with analytics tools and federated access services. Compute and Storage facilities are provided by CESGA, LIP, CSIC and INCD.
IBERGRID contribution to EGI-ACE include tools for federation and accessing to infrastructures such as the IM and EC3 developed by the University Polytechnique of Valencia, and Platform services oriented to serve the needs of Deep and Machine Learning applications developed by CSIC.
IBERGRID will also provide support in EGI-ACE to the deployment of a single Data Space for ecological data from GBIF.org across the Iberian Peninsula by integrating the Spanish and Portuguese databases.
All the IBERGRID services are accessible via the EOSC Portal can be found here: https://wibergrid.lip.pt/site/eosc-portal
What is the EOSC Compute Platform?
The EOSC Compute Platform builts on the EGI Federation, that is integrated by pooling the capacity of many European research centers, and thus constitutes the largest distributed computing infrastructure for research.
The platform will evolve beyond the state of the art through a data-centric approach, where data, tools and compute and storage facilities form a fully integrated environment accessible across borders thanks to virtual access.
The project foresees a number of open calls (3 per year) for additional infrastructure providers and early adopters of cloud infrastructures.
EGI-ACE receives funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101017567.
This work was produced with the support of INCD funded by FCT and FEDER under the project 01/SAICT/2016 nº 022153