Title: A network traffic prediction method based on IFS algorithm optimised LSSVM

Authors: Zhongda Tian; Shujiang Li

Addresses: College of Information Science and Engineering, Shenyang University of Technology, Shenyang, 110870, China ' College of Information Science and Engineering, Shenyang University of Technology, Shenyang, 110870, China

Abstract: How to predict network traffic accurately is an important issue in the network congestion control and network management. A network traffic prediction method based on improved free search algorithm optimised least squares support vector machines is proposed. Firstly, the Hurst exponent calculation shows that the network traffic time series has predictability, nonlinear and long-related characteristics, so least squares support vector machines is chosen as prediction model. Then, an improved free search algorithm is introduced so that it can be applied into the parameters optimisation of prediction model based on least squares support vector machines. Finally, the actual network traffic samples data of LAN and WAN are chosen as the simulation object, the simulation results show that the improved free search algorithm has faster convergence speed and better fitness value. Compared with other prediction methods, the proposed prediction method has better predictive effect and smaller predictive error. At the same time, the complexity of computation time shows that the proposed prediction method not only improves the prediction performance, but also does not increase the complexity of the algorithm.

Keywords: network traffic; prediction; least squares support vector machines; LSSVM; improved free search; IFS; optimisation.

DOI: 10.1504/IJESMS.2017.087553

International Journal of Engineering Systems Modelling and Simulation, 2017 Vol.9 No.4, pp.200 - 213

Received: 17 Sep 2016
Accepted: 17 Apr 2017
Published online: 18 Oct 2017 *

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