CC*IIE Integration: Dynamically Optimizing Research Data Workflow with a Software Defined Science Network

Yale University, New Haven CT

Investigators

Abstract

Intelligent management of campus research networks has become a major challenge for many institutions, as their networks grow rapidly in size and complexity in order to meet the demands of on-campus scientists who are conducting research, collaborating with peers, and fulfilling their mission of scientific education. Traditional, static network management approaches are no longer adequate, since they often result in low efficiency, poor usability, and unpredictable network application performance. The goal of this project is to design and deploy a novel intelligent network cyberinfrastructure that greatly expands the ability of scientists to rapidly and efficiently move the large quantities of data required for computation- and data-intensive scientific workflows. To ensure a broad impact, the project includes specific focus on a range of science drivers in diverse fields such as astronomy, climatology, and genomics. The project achieves its goal by leveraging and validating several prior networking research and development efforts. These include Maple, a novel Software Defined Networking (SDN) programming framework developed at Yale, and an Application Layer Traffic Optimization (ALTO) protocol and framework pioneered at Yale and now incorporated in a proposed standard for the Internet by the Internet Engineering Task Force. Maple simplifies network programming for end-to-end, complex, dynamically constructed network services, while ALTO enables network applications to adapt dynamically, according to network states, to deliver network efficiency and application quality of service. In addition, the project builds on prior Yale and NSF investments in high-speed physical network cyberinfrastructure, the widely-adopted InCommon authentication framework, and IPv6 technology.