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Research in Information-Centric Networking (ICN) and Named Data Networking (NDN) has produced many protocol designs and software prototypes, but they need to be validated and evaluated by real usage on the Internet, which is also critical to the realization of the ICN/NDN vision in the long run. This article presents the first Internet-scale adaptive video streaming service over NDN, which allows regular users to watch videos using NDN technology with no software installation or manual configuration. Since mid-2019, the official NDN website [1] has started using our service to deliver its videos to Internet users over the global NDN testbed, showcasing the feasibility of NDN. We conduct real-world experiments on the NDN testbed to validate the proper implementation of the client, network, and server sides of the proposed system and also evaluate the performance of our video streaming service. [ABSTRACT FROM AUTHOR]

Name lookup is an essential function but a performance bottleneck in both today’s and future network architectures. Trie is an excellent candidate data structure and has been widely used for looking up and updating names. However, the granularity of trie—at bit, byte (character), or component level—can dramatically affect the network performance in terms of memory usage and packet-processing speed, which has not yet been studied adequately. To fill this gap, we first show that the choice of trie’s granularity for name lookups and updates (i.e., insertions and removals) is not a trivial problem due to the complex performance tradeoffs involved. We also introduce a new tool, called NameGen, which uses a Markov-based name learning model and generates pseudo-real datasets with different tunable name characteristics. We compare different trie granularities based on a collection of datasets and performance metrics, highlight the strengths and weaknesses of each granularity, and draw a conclusion on the choice of granularity. Surprisingly, our experimental evaluation finds that there are only two key rules to choose the proper trie’s granularity for any kind of dataset: 1) bit-level trie is the choice when the memory requirement is a real concern and 2) character- and component-level tries are preferred for faster lookups and updates when dealing with names composed of short and long components, respectively. [ABSTRACT FROM AUTHOR]