Award Number1246396
Funding AgencyNational Science Foundation
Effective Date2013-01-01
Expiration Date2014-12-31
Funding Amount$499,995

Abstract

Prism@UCSD creates a campus-wide "Big Data freeway" composed of high-bandwidth end-to-end optical connections routed by a next-generation Arista switch. This creates an optical fabric capable of more than 10 Terabit/s of aggregate bandwidth, has full bisection similar to in-machine room clusters but is deployed at a campus scale. This researcher-defined network unites users of in-lab scientific instruments such as genome sequencers and microscopes with remote compute, visualization, data-storage and analysis systems. Prism bridges to, augments, and protects the existing campus production network by providing a complementary, specialized, cost-effective, massive-capacity network to a targeted group of data-intensive labs.

Prism builds upon and upgrades the Quartzite "campus-scale network laboratory" NSF MRI (awarded 2006) that was motivated by applications with extreme-scale bandwidth requirements. Compared to Quartzite, Prism not only adds IPv6 capability and support for software defined networks via OpenFlow, but also increases port capacity by 4x, lowers power consumption by 3x, and removes all card-to-switch-backplane over-subscription at the core switch. In addition, the existing optical fiber connection to the San Diego Supercomputer Center is being expanded to 120Gbps as a high-bandwidth bridge to cloud/parallel storage and NSF XSEDE resources. This fundamentally enables research in multiple disciplines, including physics, chemistry, biology, climate change, oceanography, and computer science to address big-data challenges.

Workshops will be held with regional optical networks and EDUCAUSE to disseminate experience to research campuses and fully describe this cost-effective, expandable and replicable infrastructure. In addition, a summer workshop aimed at minority serving institutions will build on Calit2 / SDSC's tradition of diversity outreach. Prism will form a model for others to follow in building their own big-data transportation systems.