Adaptive active queue management controller for TCP communication networks using PSO-RBF models
This work addresses congestion control for TCP communication networks, offering an incremental improvement over prior methods.
The paper tackled performance degradations in TCP network congestion control by proposing an adaptive active queue management controller using a PSO-optimized RBF model with an error-integral term, achieving better link utilization with small packet loss rates compared to existing controllers like ARED, REM, and PI.
Addressing performance degradations in end-to-end congestion control has been one of the most active research areas in the last decade. Active queue management (AQM) aims to improve the overall network throughput, while providing lower delay and reduce packet loss and improving network. The basic idea is to actively trigger packet dropping (or marking provided by explicit congestion notification (ECN)) before buffer overflow. Radial bias function (RBF)-based AQM controller is proposed in this paper. RBF controller is suitable as an AQM scheme to control congestion in TCP communication networks since it is nonlinear. Particle swarm optimization (PSO) algorithm is also employed to derive RBF parameters such that the integrated-absolute error (IAE) is minimized. Furthermore, in order to improve the robustness of RBF controller, an error-integral term is added to RBF equation. The results of the comparison with Drop Tail, adaptive random early detection (ARED), random exponential marking (REM), and proportional-integral (PI) controllers are presented. Integral-RBF has better performance not only in comparison with RBF but also with ARED, REM and PI controllers in the case of link utilization while packet loss rate is small.