CANS 2016: 2016 Meeting of the Chinese-American Networking Symposium

秉承美国国家科学基金会推进研究和教育的使命，30多年来，美国和中国之间的研究与教育(R&A；E；)合作伙伴关系为这家高级研究/高等教育企业的全球知识交流做出了贡献。自1999年以来，华裔美国人网络研讨会(CANS)通过每隔一年在美国和中国举行的年度会议，在这些发展中发挥了不可或缺的作用。2016年的CANS研讨会在得克萨斯州休斯敦举行，两国选定的参与者将围绕R&A；E网络、它们的工程以及利用网络促进科学和教育的方式讨论共同感兴趣的话题。在过去的近二十年里，CANS 2016延续了以往研讨会的传统，促进了美国和中国之间的科学交流，并将来自多个科学学科的研究人员与网络工程师和信息技术领域的校园领导人聚集在一起，探索促进研究合作的最有效机制。该项目有可能扩大研究和教育界之间的对话，并通过更好地了解共同的科学目标，并让校园领导层参与如何通过高性能网络最好地支持这些科学目标，从而影响下一代研究人员和网络工程师。探索的技术领域包括用于推动科学应用的原生IPv6部署和端到端支持，以及基于Perfsonar主动性能测试的性能测量。

Consistent with NSF's mission to advance research and education, Research and Education (R&E) partnerships between the U.S. and China have contributed to the global exchange of knowledge in the advanced research/higher education enterprise for over 30 years. Since 1999, the Chinese American Networking Symposium (CANS) has played an integral part in these developments through annual meetings that are held in the U.S. and China in alternate years. The 2016 CANS workshop takes place in Houston, Texas and engages selected participants from both countries to discuss topics of mutual interest that revolve around R&E networks, their engineering, and ways in which network utilization can enhance science and education. In the tradition of prior workshops over the last nearly two decades, CANS 2016 fosters scientific communication between the U.S. and China, and brings together researchers from multiple scientific disciplines with network engineers and campus leaders in Information Technology to explore the most effective mechanisms to foster increased research collaborations. The project has the potential to broaden the dialogue between research and education communities as well as impact the next generation of researchers and network engineers by providing a better understanding of shared science goals, and engaging campus leadership on how best to support those science goals through high performance networking. Technical areas of exploration include native IPv6 deployment and support end-to-end for driving science applications, as well as performance measurement based on active performance testing with Perfsonar.