CC* Networking Infrastructure: Building HPRNet (High-Performance Research Network) for Advancement of Data Intensive Research and Collaboration

由于科学仪器的改进、社交媒体和存储成本的降低等几个因素，所有科学学科的科学数据集的大小都在呈指数级增长。为了从这些地理上相距遥远的数据集中提取真正的价值，研究人员需要能够高速访问这些数据集，而这在传统的校园网络中通常是不可能的。伊利诺伊大学芝加哥分校(UIC)正在建设“HPRNet”，这是一个高性能的研究网络，为超过31个研究项目提供最后一英里的连接。HPRNet不仅提高了正在进行的研究生产率，还为未来的创新和合作奠定了基础。UIC是位于芝加哥中心地区的一所公立大学和少数族裔服务机构(MSI)，HPRNet在那里对代表性不足群体的研究培训产生了重大影响。该项目团队正在与其他NSF和机构资助的校园少数群体培训计划合作，以确保获得HPRNet资源。对于HPRNet的部署，UIC确定了13个地点，在那里建立了连接地区、国家和国际R&A；E网络的10到40千兆上行链路。HPRNet建立在科学DMZ模型的基础上，该模型与当前的校园研究网络(CRN)和称为数据传输节点(DTN)的特殊数据存储系统协同工作，为数据密集型应用提供高性能和可靠的网络路径，包括海量海量数据传输、远程实验和/或仪器控制、云计算、数据挖掘和高级可视化。

The sizes of scientific datasets are growing exponentially across all scientific disciplines due to several factors such as improved scientific instrumentation, social media and decreasing costs of storage. To extract real value from these geographically distant datasets, researchers need to have access to these datasets at high speeds which is typically not possible with traditional campus networks. The University of Illinois at Chicago (UIC) is building "HPRNet", a high performance research network providing last mile connectivity for over 31 research projects. HPRNet not only improves the ongoing research productivity, but also sets the stage for future innovations and collaborations. UIC is a public university and minority serving institution (MSI) in the heart of Chicago area where HPRNet significantly impacts the research training of underrepresented groups. The project team is working with other NSF and institutionally funded minority training programs on campus to ensure access to HPRNet resources.For HPRNet's deployment, 13 locations are identified at UIC where 10 to 40 Gigabit uplinks to regional, national and international R&E networks are established. HPRNet builds on the Science DMZ model that works in concert with the current campus research network (CRN) and a special data storage system known as Data Transfer Node (DTN) to deliver high-performance and reliable network paths for data-intensive applications, including high-volume bulk data transfer, remote experiment and/or instrumentation control, cloud computing, data-mining and advanced visualization.