Queue Management Algorithm for Network Based on Traffic Self-similarity
The self-similarity of network traffic result in a continuous burst state of data in the network.In this context,conventional queue management algorithms cannot predict the network traffic burst state in advance,thus resulting in end-to-end delays,high packet loss rates,and deteriorated network throughputs.To solve these problems,an Active Queue Management(AQM)algorithm based on prediction of the network traffic,P-ARED,is proposed.Based on the mean value and variance of network traffic,the concept of network traffic level is proposed.The relationship between the probability of network traffic level transition and Hurst parameters is discussed,and a network traffic level prediction method based on Bayesian estimation is proposed.On this basis,based on the optimization of parameters such as the average queue length and the minimum threshold for cache queue length in self-similar network environments,and based on the Hurst parameters and self-similar traffic level prediction results,the calculation method for group dropout probability in the ARED algorithm is redesigned to improve the stability of cache queue length.Simulation results show that,compared with the AQM algorithms in the existing literatures,the P-ARED algorithm reduces the end-to-end delay and packet loss rate,improves the end-to-end throughput performance,and increases the average throughput by 7.63%at the maximum.Additionally,it reduces the average packet loss rate by 17.52%at the maximum.
network trafficself-similarityActive Queue Management(AQM)Random Early Detection(RED)traffic level