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Bloom-filter-based request node collaboration caching for named data networking

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Abstract

To promote data caching efficiency, caching space utilization, and data content searching speed of the content store (CS) unit in named data networking (NDN) routers, a sum-up Bloom-filter-based request node collaboration caching (BRCC) approach is proposed in this paper. BRCC realizes different forms of caching for different types of data content. It sets the data content life time in accordance with its request frequency. It thereby promotes caching efficiency by caching high-frequent requested data content around the request node. In addition, it enhances the data content matching rate and decreases the searching time by using the sum-up Bloom filter. Simulation results showed that BRCC can efficiently utilize the CS caching space of NDN routers, reduce duplicate data caching, promote the cache hit rate, and increase the data content searching speed.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 60841001; the Natural Science Foundation of State Ethnic Affairs Commission of People’s Republic of China under Grant 12ZNZ010; the Scientific and Technological Projects of Wuhan, China, under Grant 2015010101010008; the Foundation of China Scholarship Council; and the Special Fund for Basic Scientific Research of Central Colleges, South-Central University for Nationalities, under Grant No. CZP17042. The authors thank all the reviewers for their useful comments.

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Authors and Affiliations

  1. College of Computer Science, South-Central University for Nationalities, Wuhan, 430074, Hubei, China

    Rui Hou, Lang Zhang, Tingting Wu & Tengyue Mao

  2. Electronic Information and Networking Research Institute, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Jiangtao Luo

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  1. Rui Hou
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  2. Lang Zhang
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Correspondence to Rui Hou.

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Hou, R., Zhang, L., Wu, T. et al. Bloom-filter-based request node collaboration caching for named data networking. Cluster Comput 22 (Suppl 3), 6681–6692 (2019). https://doi.org/10.1007/s10586-018-2403-9

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  • DOI: https://doi.org/10.1007/s10586-018-2403-9

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