CC*IIE Engineer: A Software-Defined Campus Network for Big-Data Sciences

Princeton University, Princeton NJ

Investigators

Abstract

Scientific researchers on university campuses create, analyze, visualize, and share large and diverse datasets from experimental devices like brain scanners, particle colliders, and genome sequencers. However, these "big data" applications place strain on traditional campus networks, due to rapidly increasing volumes of data, the need for either predictably low latency (to adapt experiments in real time) or high throughput (to transfer large data sets between locations), and sophisticated access-control policies (to protect the privacy of human subjects). To enable the next wave of scientific advances, university campuses must find effective ways to meet these challenging demands, at reasonable cost. The emerging technology of Software-Defined Networking (SDN) lowers the barrier to innovation in network management, and can substantially reduce cost through (i) inexpensive commodity network switches, (ii) greater automation of network configuration, and (iii) novel network-management applications that optimize bandwidth usage. Yet, existing innovation in SDN focuses primarily on the needs of commercial cloud providers, rather than the unique requirements of university campuses and scientific researchers. Princeton University is creating a software-defined campus network that can enable the next generation of data-driven scientific research. The initiative brings together big-data science researchers, computer scientists who are experts in SDN, and the campus Office of Information Technology. Princeton is deploying an open-source SDN platform for monitoring and configuring the network, conducting trials of new ways to support big-data applications, and bridging with the larger community, on and off campus, to support the sharing of scientific data, SDN software, and operational experiences.