Internet end-to-end delay prediction using support vector regression
一种基于支持向量回归的互联网端到端延迟预测算法

End-to-end packet delay of the Internet is the IP packet transmission delay along a determined path. An accurate end-to-end delay prediction is fundamental to numerous network activities, from protocol design to network monitoring, and from ensure end-to-end QoS to performance enhancement for realtime network applications. This paper presented a novel methodology for predicting end-to-end delay. The major contributions are: a) It converted the end-to-end delay prediction problem into the multivariate regression, and proposed a multivariate regression-based forecasting framework for end-to-end delay; b) It employed support vector regression (SVR) to solve the multivariate regression problem of end-to-end delay, and induced a SVR-based end-to-end delay predicting algorithm. Finally, it used the actual RTT data collected from Internet to validate the proposed algorithm. Simulation results show that the proposed algorithm has fast and accurate prediction characteristics, which is very suit for practical applications.