Description
This track will focus on the development of cloud infrastructures and on the use of cloud computing and virtualization technologies in large-scale (distributed) computing environments in science and technology. We solicit papers describing underlying virtualization and "cloud" technology including integration of accelerators and support for specific needs of AI/ML and DNN, scientific applications and case studies related to using such technology in large scale infrastructure as well as solutions overcoming challenges and leveraging opportunities in this setting. Of particular interest are results exploring the usability of virtualization and infrastructure clouds from the perspective of machine learning and other scientific applications, the performance, reliability and fault-tolerance of solutions used, and data management issues. Papers dealing with the cost, price, and cloud markets, with security and privacy, as well as portability and standards, are also most welcome.
As part of the strategic refactoring and modernization of the INFN Cloud orchestration system, the Federation Manager has been developed to enhance the flexibility, scalability, and interoperability of the distributed DataCloud infrastructure. This initiative represents a key step in the long-term evolution of INFN Cloud toward a more modular, service-oriented architecture capable of...
The integration of artificial intelligence (AI) into biomedical research is transforming the analysis of complex datasets such as high-resolution images of tumor tissues. As part of a collaboration between the Italian EOSC and BBMRI-ERIC nodes, INFN and BBMRI-ERIC have launched a joint initiative to define and deploy a secure and scalable infrastructure capable of supporting AI-driven...
An astronomical observatory requires not only state-of-the-art telescopes but also robust computing infrastructure to archive and analyze the vast amounts of astronomical observation data. Consequently, optimizing the operation of these computing systems is a crucial issue. Adopting public cloud services is expected to reduce the Total Cost of Ownership (TCO) and allow the use of cutting-edge...
The FAIR principles provide a foundational framework for ensuring that scientific data is accessible and reusable, and their implementation is a central objective of the European Open Science Cloud (EOSC). However, enabling access to sensitive or confidential data while simultaneously preserving privacy, confidentiality, and usability for researchers remains an open challenge. Existing...
