GreenDIGIT, a Horizon Europe project reducing environmental footprint launches with HUN-REN SZTAKI on board

Colleagues from the Laboratory of Parallel and Distributed Systems participated in the face-to-face kick-off meeting of the new Horizon Europe project, GreenDIGIT. The 36-month-long project aspires to lower the environmental footprint of Digital Infrastructures. The project's coordinator, the University of Amsterdam welcomed the 15-member consortium in Amsterdam and launched the project with an in-person meeting in spring of 2024.

GreenDIGIT aims to keep Research Infrastructures (RIs) at the highest level of excellence while developing novel technologies and solutions to reduce their environmental footprint. The project brings together four major distributed Digital Infrastructures (EGI, SLICES, SoBigData, EBRAINS) to reduce their environmental impact and to provide universal, reusable solutions for the whole spectrum of digital services on the ESFRI (European Strategy Forum on Research Infrastructures) landscape.

The project will capture good practices and will develop new technologies and solutions for all aspects of the digital continuum: from service provisioning to monitoring, job scheduling, resource allocation, architecture, workload and Open Science practices, task execution, storage, and use of green energy. The concept is to deliver these as building blocks, with a reference architecture and guidelines for RIs to lower their environmental footprint. These new solutions will be validated through scientific use cases from diverse disciplines and promoted to providers and users via active dissemination and training programmes, to prepare the next generation of Digital RIs with a low environmental footprint. 

HUN-REN SZTAKI participates in the project as an EGI member and will lead a Work Package (WP) focusing on Technology enablers for energy efficiency in Research Infrastructures. This WP will establish and populate a database with metrics about the environmental impact of RI digital services and will improve technological solutions that use this information to lower the environmental impact of the whole chain of scientific workflows from IoT/far-edge to edge, cloud, AI and HPC.