MapReduce enabling content analysis architecture for information-centric networks using CNN

Chengcheng Zhao, Mianxiong Dong, Kaoru Ota, Jun Wu, Jianhua Li, Gaolei Li

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

Information Centric Network (ICN) is one of the promising architectures in the next generation networks. The content-based routing in ICN can satisfy the content distribution of large-scale data. For prompt content obtainment, it is important to realize the content analysis before the content reaches application layer. The novel characteristics of data naming in ICN make it possible to search and analyse content during the transmission of content, which can directly get the critical content without the process of the application layer. In this paper, we propose a MapReduce enabling content analysis architecture for ICN. MapReduce framework can realize the parallelization of content collection and analysis during the routing process. For more efficient content collection, we put forward an optimal selection for mapper nodes. Moreover, Convolutional Neural Network (CNN) is deployed in the MapReduce architecture providing further analysis for ICN content. The simulation result shows the advantages of the proposed architecture.

本文言語English
ホスト出版物のタイトル2018 IEEE International Conference on Communications, ICC 2018 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(印刷版)9781538631805
DOI
出版ステータスPublished - 2018 7月 27
外部発表はい
イベント2018 IEEE International Conference on Communications, ICC 2018 - Kansas City, United States
継続期間: 2018 5月 202018 5月 24

出版物シリーズ

名前IEEE International Conference on Communications
2018-May
ISSN(印刷版)1550-3607

Other

Other2018 IEEE International Conference on Communications, ICC 2018
国/地域United States
CityKansas City
Period18/5/2018/5/24

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 電子工学および電気工学

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