Stochastic hyperbolic random early detection controller based on probability density function estimator and particle swarm optimisation algorithm

Abstract

Most conventional active queue management (AQM) methods are established on the first stochastic moment (mean value) of queue length variation. Yet, these conventional methods have been inefficient so far. Regarding the stochastic attributes of communication networks, AQM methods based on stochastic theory perform better in comparison with conventional AQM methods. A new stochastic AQM advances is advised for the control of queue length probability density function (PDF) for transmission control protocol (TCP). The Parzen window estimate of PDFs exploitation the kernel function is used to constitute the queue PDFs of the TCP/AQM routers. This study proposes a novel stochastic AQM algorithm called hyperbolic random early detection (PDF-HRED) as an appendage of random early detection (RED). The PDF-HRED is based on a stochastic PDF estimation, an HRED controller and particle swarm optimisation (PSO). The numerical simulation results by NS 2 demonstrate that the PDF-HRED controller outperforms the existing AQM schemes with respect to performance characteristics.