Abstract
Named data network (NDN) replaces the original IP address with the content name, which solves the security, mobility and other problems exposed by the current TCP/IP network, and becomes a typical representative of the future network. All routers in NDN support the caching function, which can cache the passing content and provide content services for users. Users can obtain the content from the router nearby without going to the remote content server. The current default caching strategy of NDN is Leave Copy Everywhere (LCE) strategy. This strategy does not distinguish the popularity of contents and the importance of nodes, and caches all contents on all nodes without difference, resulting in a high content redundant data that wastes cache resources and causes the cache hit rate to be unsatisfactory. In response to above problems, this paper proposes an improved cache strategy, which fully considers the popularity of the content, divides the content into different popularity levels, and divides the router nodes according to the distance from the user, and proposes a cache matching algorithm, which matches the two levels and combines the degree of the node to determine the cache nodes to place the content on. A large number of simulation experiments show that compared with LCE strategy, the strategy improves the content hit rate, reduces the average hit hop, cache replacement frequency and throughput.
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Acknowledgment
This work is supported by the Inner Mongolia Natural Science Foundation of China under Grant No. 2018MS06024, the Research Project of Higher Education School of Inner Mongolia Autonomous Region under Grant NJZY18010, the National Natural Science Foundation of China under Grant No. 61862046 and the CERNET Innovation Project under Grant No. NGII20180626.
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Feng, M., Li, R., Hu, Y., Yu, M. (2021). A Caching Strategy Based on Content Popularity Level for NDN. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1424. Springer, Cham. https://doi.org/10.1007/978-3-030-78621-2_61
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DOI: https://doi.org/10.1007/978-3-030-78621-2_61
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