Internet traffic prediction by w-boost: classification and regression

Authors:
Hanghang Tong;Chongrong Li;Jingrui He;Yang Chen
Affiliations:
Department of Automation, Tsinghua University, Beijing, China;Network Research Center of Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

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Abstract

Internet traffic prediction plays a fundamental role in network design, management, control, and optimization. The self-similar and non-linear nature of network traffic makes highly accurate prediction difficult. In this paper, we proposed a new boosting scheme, namely W-Boost, for traffic prediction from two perspectives: classification and regression. To capture the non-linearity of the traffic while introducing low complexity into the algorithm, ‘stump' and piece-wise-constant function are adopted as weak learners for classification and regression, respectively. Furthermore, a new weight update scheme is proposed to take the advantage of the correlation information within the traffic for both models. Experimental results on real network traffic which exhibits both self-similarity and non-linearity demonstrate the effectiveness of the proposed W-Boost.