NDN is one instance of ICN, which is a cleanslate approach that promises to reduce inefficiencies in the current Internet. NDN provides intelligent data retrieval using the principles of name-based symmetrical forwarding of Interest/ Data packets and in-network caching. The continually increasing demand for the rapid dissemination of large-scale scientific data is driving the use of NDN in big science experiments. In this article, we establish the first intercontinental NDN testbed to offer complete insight into NDN construction for big science. In the testbed, an NDN-based application that targets climate science as an example big-science application is designed and implemented with differentiated features compared to previous works on NDNbased application design for big science. We first attempt to systematically address detailed analysis of why or how NDN benefits fit in big science and issues that must be resolved to improve each advantage, mostly based on lessons learned from establishing the NDN testbed for climate science. We extensively justify the needs of using NDN for large-scale scientific data in the intercontinental network, through experimental performance comparisons between classical deliveries and NDNbased climate data delivery, and detailed analysis of why or how NDN benefits fit in big science.

中文翻译：

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大型科学的NDN构建：从建立测试平台中学到的经验教训

NDN是ICN的一个实例，它是一种彻底的方法，有望减少当前Internet的效率低下。NDN使用基于名称的兴趣/数据包对称转发和网络内缓存的原理提供智能数据检索。快速传播大规模科学数据的需求不断增长，这驱使NDN在大型科学实验中的使用。在本文中，我们建立了第一个洲际NDN测试平台，以全面了解大型科学的NDN构建。在测试平台中，与以前关于大科学的基于NDN的应用程序设计的工作相比，设计和实现了以气候科学为例的针对NDN的应用程序，该应用程序以大科学为例。我们首先尝试系统地解决NDN优势为何或如何适用于大型科学的详细分析，以及必须解决的问题以提高每种优势，主要是基于建立气候科学NDN测试平台的经验教训。通过经典交付与基于NDN的气候数据交付之间的实验性能比较，以及对NDN为何或如何适合大型科学的详细分析，我们广泛证明了使用NDN进行洲际网络中大规模科学数据的需求。