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CC*IIE Networking Infrastructure: Enabling Scientific Discovery through a UW-DMZ

CC*IIE Networking Infrastructure: Enabling Scientific Discovery through a UW-DMZ
CC*IIE 网络基础设施:通过 UW-DMZ 实现科学发现
批准号:
1440610
负责人:
Craig Douglas
金额:
$50.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-09-01 至 2018-08-31

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英文摘要
The University of Wyoming is developing an environment providing researchers and students with access to local, regional, national, and international computational, networking, and data storage resources needed to transform scientific discovery and education. The university is developing a perimeter network named UW-DMZ (University of Wyoming DeMilitarized Zone) that contains and exposes the university's external facing services to the Internet. UW-DMZ enhances Big Computing and Big Data based research by providing infrastructure needed to leverage high performance, dynamic network services. Research is informed by a wide variety of data collected from field work and sensor networks, mined from databases, observed by lab experiments, or generated by simulations. The movement of data from their source to computational and visualization resources and then to the scientific world is critical. The university has good connectivity from the main computer center to the outside world, but currently researchers transfer data through a general purpose campus network. UW-DMZ creates a research optimized, devoted high speed network that is explicitly designed for Big Data applications and provides researchers access to existing wide area science services on campus.Class projects in upper division and graduate level networking classes help monitor, evaluate, and optimize UW-DMZ for different users and applications, thus providing students with deeper, enriching experiences. Through the university's significant research collaborations with Jackson State University on environmental hydrology, their students and faculty are trained in accessing and using computational and visualization resources and gain firsthand experience in how to facilitate scientific discovery through management and integration of Big Computing and Big Data.
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