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

Version 2 2024-06-05, 05:26
Version 1 2016-12-01, 17:00
journal contribution
posted on 2024-06-05, 05:26 authored by L Nie, D Jiang, L Guo, S Yu
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.

History

Journal

Journal of network and computer applications

Volume

76

Pagination

16-22

Location

Amsterdam, The Netherlands

ISSN

1084-8045

eISSN

1095-8592

Language

eng

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2016, Elsevier

Publisher

Elsevier