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FABRIC is Adaptive Programmable Research Infrastructure for Computer Science and Science Applications

FABRIC (FABRIC is Adaptive ProgrammaBle Research Infrastructure for Computer Science and Science Applications) is an International infrastructure that enables cutting-edge experimentation and research at-scale in the areas of networking, cybersecurity, distributed computing, storage, virtual reality, 5G, machine learning, and science applications.

The FABRIC infrastructure is a distributed set of equipment at commercial collocation spaces, national labs and campuses. Each of the 29 FABRIC sites has large amounts of compute and storage, interconnected by high speed, dedicated optical links. It also connects to specialized testbeds (5G/IoT PAWR, NSF Clouds), the Internet and high-performance computing facilities to create a rich environment for a wide variety of experimental activities.

FABRIC Across Borders (FAB) extends the network to 4 additional nodes in Asia and Europe.

Advances Cybersecurity

Enables New Internet and Science Applications

Integrates HPC, Wireless, and IoT

Integrates Machine Learning & Artificial Intelligence

Promotes Education

More about FABRICMore about FAB

Recent News

NSF FABRIC project announces groundbreaking high-speed network infrastructure expansion

The NSF-funded FABRIC project has completed installation of a unique network infrastructure connection, called the…   Read More >

NGI Enrichers program seeks host organizations for funded researchers

Next Generation Internet (NGI) Enrichers is an initiative that supports transatlantic research cooperation in areas…   Read More >

Anticipated FABRIC Topology


Development Timeline

10/01/2019 - 09/30/2020

Finalize node and topology designs and deployment plans, collect community feedback. Design and begin implementation of control and measurement software. Prototype and test different FABRIC nodes.

FABRIC Contributors

Facility Partners


FABRIC is made possible by collaborations with the following organizations.

FABRIC is supported in part by a Mid-Scale RI-1 NSF award under Grant No. 1935966.

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