CC*DNI Networking Infrastructure - University of Alabama SciNet

亚拉巴马大学（UA）正在设计和部署一个科学DMZ，本文称为UA SciNet，以支持跨多个科学和工程学科的研究的快速扩展。 UA SciNet基于Internet 2和ESNet的设计模式和最佳实践。UA SciNet是一个专用的隔离网络，在校园网络边界连接到UA广域网。 它在活跃的数据密集型科学领域与高性能计算和研究存储资源之间提供40 Gbps连接。它为研究提供了初始400%的带宽增加，并为通过UA系统区域光网络和南部十字路口服务实现100 Gbps广域网连接奠定了基础。它包括一个强大的性能监控和问题解决组件使用perfSONAR。它还使用广域远程DMA（RDMA）来实现对UA大数据资源的高带宽远程访问。受该项目影响的研究包括水文气象建模、灾害事件预测和缓解、人类行为和粒子物理学，特别是利用最近在UA校园内完成的国家水中心的数据密集型操作。 该项目还与校园以及区域和州一级的其他举措协调工作，以确保其有效整合，并确保其效益得到充分利用。 此外，该项目还为本科生和研究生、研究人员以及系统和网络专业人员带来了独特的教育和培训机会，涉及先进的网络基础设施、数据密集型科学以及性能监控和诊断等领域。

The University of Alabama (UA) is designing and deploying a science DMZ herein referred to as UA SciNet to support the rapid expansion in research across multiple science and engineering disciplines. UA SciNet is based on the design patterns and best practices emerging from Internet2 and ESNet. UA SciNet is a dedicated, isolated network connecting to the UA wide-area network at the campus network border. It provides 40 Gbps connections between active data-intensive science areas and high performance computing and research storage resources. It provides an initial 400% bandwidth increase for research and lays the foundation for 100 Gbps connectivity to the wide area network via the UA System Regional Optical network and Southern Crossroads services. It includes a robust performance monitoring and problem resolution component using perfSONAR. It also uses wide-area remote DMA (RDMA) to achieve high-bandwidth remote access to UA Big Data resources.The research impacted by the project includes hydro-meteorologic modeling, disaster event prediction and mitigation, human behavior, and particle physics, specifically leveraging the data-intensive operations at the recently completed National Water Center on the UA campus. This project also works in coordination with other initiatives both on campus and at the regional and state levels to ensure its effective integration and to ensure its benefits are fully leveraged. Further, the project brings unique education and training opportunities to undergraduate and graduate students, researchers and system and network professionals to the areas of advanced network infrastructure, data-intensive science, and performance monitoring and diagnostics.