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ISNN
2004
Springer

A Boosting-Based Framework for Self-Similar and Non-linear Internet Traffic Prediction

9 years 4 months ago
A Boosting-Based Framework for Self-Similar and Non-linear Internet Traffic Prediction
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, a boosting-based framework is proposed for self-similar and non-linear traffic prediction by considering it as a classical regression problem. The framework is based on Ada-Boost on the whole. It adopts Principle Component Analysis as an optional step to take advantage of self-similar nature of traffic while avoiding the disadvantage of self-similarity. Feed-forward neural network is used as the basic regressor to capture the non-linear relationship within the traffic. Experimental results on real network traffic validate the effectiveness of the proposed framework.
Hanghang Tong, Chongrong Li, Jingrui He
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where ISNN
Authors Hanghang Tong, Chongrong Li, Jingrui He
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