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The European Commission (EC) recently highlighted, in an Expert Group’s Report on the Future of Cloud Computing, the need for coordinated open source deployments between Research and Industry. This is to promote the use of flexible commercial Cloud-based services infrastructure offerings. The RESERVOIR (Resources and Services Virtualization without Barriers) European Framework Programme (FP7) project, now in its third year, has developed open architecture and interfaces, and open source software. These are available as downloadable specifications and tools encompassed under “the RESERVOIR Framework” which presents a blueprint to help businesses build on-demand infrastructure services, at competitive costs, across disparate administrative domains, while assuring quality of service.

The RESERVOIR approach, led by a group of strong industrial and academic partners is focusing on the federation of Clouds at the infrastructure level, to enable on-demand delivery of IT services at competitive costs without requiring a large capital investment in infrastructure. With this approach, end-users can run their applications on virtual machines over the cloud, and are relieved of the need to own and manage physical resources. The software and specifications that are being released by the project offer users an opportunity to build public and private clouds by choosing specific components.

Interoperability is important to users and RESERVOIR is working closely on standard interfaces for the remote management of cloud infrastructures, working with various standards bodies including DMTF and OGF between the Virtual Infrastructure Management and the Cloud Management layers. RESERVOIR is collaborating with several related projects. One of these is OpenNebula, an open-source software application toolkit for building Cloud deployments. OpenNebula is one of the technologies being enhanced in the RESERVOIR project as a service solution for Cloud management.

More information:
http://www.reservoir-fp7.eu/

Next issue: January 2024
Special theme:
Large Language Models
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