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.

• Type: Conference
• Year: 2004
• Where: ISNN
• Authors: Hanghang Tong, Chongrong Li, Jingrui He
• Keywords: ISNN 2004, Network Traffic, Self-similar, Traffic Prediction