Active queue management algorithm based on particle swarm optimization

Abstract

In order to mitigate the network congestion,a new active queue management algorithm named RQQM(Rate and Queue-based Queue Management) was proposed by Particle Swarm Optimization(PSO).In this algorithm,the actual queue length was deducted with PSO and variation factor,and the dropping strategy and dropping rate were given based on arrival rate and actual queue length.Then,a simulation with actual data was conducted to compare the algorithm performance between RQQM algorithm and RFED(Rate-based Fair Early Detection) algorithm,as well as ABLUE(Adaptive BLUE) algorithm.The results show that the dropping rate is greatly influenced by the utilization rate and buffer size,and the fairness of RQQM is much better than that of the other two algorithms,its average packet loss rate is decreased to 12.21%.