TY - GEN
T1 - MapReduce enabling content analysis architecture for information-centric networks using CNN
AU - Zhao, Chengcheng
AU - Dong, Mianxiong
AU - Ota, Kaoru
AU - Wu, Jun
AU - Li, Jianhua
AU - Li, Gaolei
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61431008, 61571300, the National Key Research and Development Program of China 2016QY01W0104 and partially supported by the JSPS KAKENHI Grant Number JP16K00117, JP15K15976, KDDI Foundation.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/27
Y1 - 2018/7/27
N2 - 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.
AB - 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.
KW - Big data
KW - Content analysis
KW - ICN
KW - MapReduce
UR - http://www.scopus.com/inward/record.url?scp=85051416817&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051416817&partnerID=8YFLogxK
U2 - 10.1109/ICC.2018.8422234
DO - 10.1109/ICC.2018.8422234
M3 - Conference contribution
AN - SCOPUS:85051416817
SN - 9781538631805
T3 - IEEE International Conference on Communications
BT - 2018 IEEE International Conference on Communications, ICC 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Communications, ICC 2018
Y2 - 20 May 2018 through 24 May 2018
ER -