Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks

Nie, Laisen, Jiang, Dingde, Guo, Lei and Yu, Shui 2016, Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks, Journal of network and computer applications, vol. 76, pp. 16-22, doi: 10.1016/j.jnca.2016.10.006.

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Title Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks
Author(s) Nie, Laisen
Jiang, Dingde
Guo, Lei
Yu, ShuiORCID iD for Yu, Shui orcid.org/0000-0003-4485-6743
Journal name Journal of network and computer applications
Volume number 76
Start page 16
End page 22
Total pages 7
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-12
ISSN 1084-8045
1095-8592
Keyword(s) network traffic prediction
network traffic estimation
traffic matrix
deep learning
deep belief network
Summary Network traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network traffic data. Though the network traffic estimation method has been the most prevalent technique for acquiring network traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network traffic estimation problem is more deteriorated. Besides, the statistical features of network traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network traffic, and then propose a novel network traffic prediction approach based on a deep belief network. We further propose a network traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GÉANT backbone networks.
Language eng
DOI 10.1016/j.jnca.2016.10.006
Field of Research 080501 Distributed and Grid Systems
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30089899

Document type: Journal Article
Collection: School of Information Technology
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