Scientific LargeScale Infrastructure for Computing/Communication Experimental Studies

Slices-RI objectives

SLICES will make a fundamental contribution to research and innovation in Digital Sciences and Infrastructures, future Internet technologies, future smart networks and services. This encompasses the full range of network, computing, and storage functions required for “on-demand” services across many verticals, and addresses new complex research challenges, supporting disruptive science in IoT, networks and distributed systems such as, e.g., 5G & beyond, NFV, IoT, Cloud/Edge.

SLICES will provide advanced services (reinstallation of nodes, network isolation, complete description of the testbed, monitoring) to enable experiments on novel Digital Infrastructure (DI) solutions and applications in flexible environments. Moreover, workloads of HPC RIs tend to be composed of many small-size batch jobs submitted in advance. SLICES will allow flexible options, e.g., to perform large-scale experiments on the whole testbed during nights and week-ends, while giving a few resources to interactively prepare experiments during the day. Combining both kinds of usage, while maintaining comfort for both categories of users, requires to analyse usage requests, and improvements to accounting systems. None of the existing HPC RIs allows experiments to be performed with emerging Internet technologies (e.g. wireless, IoT, edge/fog/cloud, NFV). Finally, SLICES differs from data infrastructures (such as EUDAT or OpenAIRE): it adopts FAIR principles for data management, but is not focused on general data management services.


Equip researchers with a wide range of scientific and experimental resources and tools by deploying and operating a large-scale platform providing access to cutting-edge technologies in wireless networking, IoT, and Cloud;


Enable automatic infrastructure services composition and deployment; create catalogue and repository of composable services that can be easily integrated into domain specific applications;

Test & Improve

Provide advanced test tools to ensure reproducibility through an automated data repository and support an open data approach following the FAIR principles for these communities;


Build the capacity by strongly contributing to the important education effort targeting both students and engineers.


Allow the evolution of DIs following users’ needs and availability of new technologies.