CC-NIE Integration: Improved Infrastructure for Data Movement and Monitoring

该项目开发了一个基础设施，以支持管理大数据的传输和分析，以满足加州大学戴维斯分校的几个项目的要求。例如，大型天文观测望远镜(LSST)项目将很快产生每晚30太字节的数据。加州大学戴维斯分校的科学家们检查数据，测试新的搜索和分析算法，以发现新的或意想不到的现象。这一合作项目需要与参与该项目的其他22个机构进行高速连接。同样，Genome项目和其他项目也将受益于或将很快受益于对内部网络基础设施进行的更改。该项目还为研究网络的校园研究小组提供了数据。例如，强大而无处不在的网络实验室和计算机安全实验室在他们的研究中使用网络流量和性能测量，网络的增强使他们能够以不违反大学隐私法规的方式收集这些数据。此外，网络架构使管理人员能够部署增强的监控功能，以检测网络流量问题。包括本科生在内的支持人员充当了使用这种增强型网络的研究项目和网络管理人员之间的桥梁，因此研究人员可以获得他们需要的最佳网络性能，并且网络本身可以进行适当的调整，以最大限度地减少开销。

This project develops an infrastructure to support managing the transfer and analysis of big data as required by several projects at the University of California at Davis. For example, the Large Synoptic Survey Telescope (LSST) Project will soon produce 30 terabytes of data each night. Scientists at UC Davis examine the data, testing new search and analysis algorithms to find new or unexpected phenomena. This collaborative project requires high-speed connections to the other 22 institutions involved in the project. Similarly, the Genome project and other projects benefit, or will shortly benefit, from the changes being made to the internal networking infrastructure. The project also provides data for campus research groups that study networks. For example, the Robust and Ubiquitous Networking Laboratory and the Computer Security Laboratory use network traffic and performance measurements in their research, and the enhancements to the network enable them to gather this data in ways that do not violate University privacy regulations. Further, the network architecture enables administrative staff to deploy enhanced monitoring capabilities to detect network traffic problems. Support personnel including undergraduates act as a bridge between the research projects using this enhanced network and the network managers, so researchers can obtain the optimum network performance that they need, and the network itself can be tuned as appropriate to minimize overhead.