CC-NIE Network Infrastructure: Advanced Connectivity for Texas A&M University

德州农工大学正在建立和开发先进的100 Gb/s连接，专注于为研究人员访问Internet 2的OS 3E网络。它正在推进数据密集型学科研究和网络研究，并有助于国家最佳实践和先进的连接，以支持整个LEARN社区在Texas.其主要目标是提高A M研究人员从事数据密集型科学的能力，包括校园和国家网络基础设施和大型数据集的结合使用。同样，常规使用基于VLAN的大型广域数据流将对德克萨斯A M公司的技术增长构成挑战。一个例子是将这些技术集成到LHC“数据移动”领域。第二个例子是探索联邦广域文件系统作为经典“数据移动”范例的替代方案，包括对这种方法的成功至关重要的性能和联邦身份问题。这是对实现不断发展的校园桥接愿景的贡献。为了支持Texas A M的计算科学家，该项目利用与沉浸式可视化中心的连接来实现有效的本地和远程可视化形式。最后，该项目使网络研究人员能够使用Internet 2的OS 3E的OpenFlow服务，因为它的发展。先进的连接，结合得克萨斯A M的ScienceDMZ，使新兴的数据密集型科学家在尚未成为突出的领域，摧毁物理偏远作为参与协作数据密集型科学的障碍。

Texas A&M University is establishing and exploiting advanced 100-Gb/s connectivity, focused on accessing Internet2's OS3E network for its researchers. It is furthering data-intensive discipline research and network research, and is contributing to national best practices and to advanced connectivity in support of data-intensive research throughout the LEARN community in Texas.The chief objective is to enhance the ability of A&M researchers to engage in data-intensive science, including combined use of campus and national cyberinfrastructure and large data sets. Similarly, routinely using large VLAN-based, wide-area data flows will challenge Texas A&M to grow technically. One example is the integration of these techniques into the LHC "data moving" area.A second example is exploring federated wide-area file systems as an alternative to the classic "data moving" paradigm, including performance and federated identity issues crucial to the success of this approach. This is a contribution to realizing the evolving Campus Bridging vision. In support of computational scientists at Texas A&M, the project leverages connectivity with the Immersive Visualization Center to enable effective forms of local and remote visualization. Finally, the project enables network researchers to use the OpenFlow-based services of Internet2's OS3E as it evolves.The advanced connectivity, combined with Texas A&M's ScienceDMZ, enables emerging sets of data-intensive scientists in areas that have not yet become prominent, destroying physical remoteness as a barrier to engaging in collaborative data-intensive science.