CC*DNI Networking Infrastructure - University of Alabama SciNet

阿拉巴马大学（UA）正在设计和部署一个科学非军事区（此处称为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Internet2和ESNet的设计模式和最佳实践。UA SciNet是一个专用的、隔离的网络，连接到校园网边界的UA广域网。它在活跃的数据密集型科学领域和高性能计算和研究存储资源之间提供40gbps的连接。它为研究提供了400%的初始带宽增长，并通过UA系统区域光网络和南方十字路口服务为100gbps的广域网连接奠定了基础。它包括使用perfSONAR的强大性能监控和问题解决组件。同时采用广域RDMA （wide-area remote DMA）技术，实现UA大数据资源的高带宽远程接入。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，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、地区和州一级的其他倡议协调工作，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提供了独特的教育和培训机会，使他们能够进入先进的网络基础设施、数据密集型科学以及性能监测和诊断领域。

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.