CC*DNI Campus Design: Internet2 Infrastructure to Enable Research in Big Data Science and Engineering at Tennessee State University

Tennessee State University, Nashville TN

Investigators

Abstract

This project enhances cyberinfrastructure at Tennessee State University (TSU) with high-speed access to massive and complex datasets and distributed computing infrastructure via Internet2. The two-year project accelerates outcomes of ongoing astronomy, bioinformatics and cybersecurity projects with high speed access to: instruments, such as robotic telescopes and their streaming data; high dimension datasets, such as genomic datasets; and large scale distributed computing and networking platforms. TSU is adding a dedicated network for science and engineering research traffic, upgrading the backbone network connections to the research complex housing the astronomy, bioinformatics and cybersecurity laboratories, and providing high speed access to Internet2. The enhanced infrastructure will enable TSU faculty and students to effectively engage in science and engineering research and education. The new campus network capacity enables TSU to transfer large datasets and improves quality of computational research conducted by science and engineering faculty. Specifically, researchers can connect to TSU's state-of-the art telescopes located in Southern Arizona and perform daily analysis of images of several thousand stars. Bioinformatic researchers can conduct genomic sequencing service facilities to analyze massive and complex datasets of plant tissues at Pittsburgh Supercomputing Center, Vanderbilt Technologies for Advanced Genomic, Cornell's Genomic Diversity Facility and national distributed computing platforms. Cybersecurity researchers can conduct at scale research experiments and take advantage of high speed access to computing servers and Openflow switches on the Global Environment for Network Innovations (GENI) facility. The project also advances undergraduate research, allowing students to conduct data analysis with effective use of computational resources.