Hybrid Fuzzy Congestion Controllers for Computer Networks Tuned by Modified Particle Swarm Optimization

One of the most debated issues nowadays is the quality of computer network service. The best internet service must provide a fast processing of the traffic. Each router has a queue of packets that provides a buffer space, where the packets wait for processing. Transmission Control Protocol (TCP) is a packets congestion control theory. Active Queue Management (AQM) is a mechanisms proposed to employ at gateways to improve the performance of TCP congestion control. AQM mechanisms aim to provide high link utilization with low loss rate and low queuing delay while reacting to load changes quickly. Random Early Detection (RED) is an extensively studied AQM algorithm that can detect congestion by dropping packets randomly with certain probability that serves as the function of the average queue size. In this work, hybrids Fuzzy Logic Controllers (FLC) are proposed to measure the router queue size directly by use them as a congestion controllers. A multiple hybrid fuzzy controllers are proposed, where (Proportional Integral Derivative controller (PID) -like FLC-Particle Swarm Optimization (PSO) Based, Proportional Derivative (PD)-like FLC with conventional I-PSO Based and PID tuned by Fuzzy Logic-PSO Based), which is provided to regulate the queue length, round trip time and packet loss. The Particle Swarmed Optimization (PSO) algorithm is used for tuning the gains of hybrid fuzzy logic controller which helps in reducing the error of the queue size. This is achieved through minimizing the rise time, peak time, settling time and overshoot of the AQM response. The empirical results revealed a high-performance improvement regarding the proposed method in comparison to previous works of other researchers.