Queue Management Algorithm for Satellite Networks Based on Traffic Prediction

Abstract

In connexion with the effect of the self-similar characteristic of satellite network service traffic on queueing performance, a prediction model with optimised triple exponential smoothing is first established in this paper. This model performs network traffic prediction based on the dynamic triple exponential smoothing model and optimises the smoothing coefficient of the model through the differential evolution algorithm; a cubic function based on traffic prediction is further proposed to improve the adaptive random early detection (ARED) queue management algorithm. Based on the network traffic prediction results and the ARED, this algorithm uses the cubic function to perform nonlinear processing on the packet drop probability function. The simulation results show that the prediction model with optimised triple exponential smoothing has a high prediction accuracy, and the improved ARED algorithm based on the cubic function of traffic prediction in the presence of data bursts in self-similar traffic. It can effectively reduce the packet loss rate and improve the throughput, so as to better control the network congestion caused by self-similar traffic in satellite network.

Keywords

• Dynamic triple exponential smoothing model
• differential evolution algorithm
• cubic function
• adaptive random early detection