A Comparative Study among Four Controllers Intended for Congestion Control in Computer Networks

Maryam Abd Al-majeed, Laith Saud


Computer networks efficiency is an vital part of today’s information services technology, with this comes multiple issues, among them is the congestion problem. This paper will discuss the designing and evaluating of four controllers to deal with this issue. The design starts with modeling the Transmission Control Protocol /Active Queue Management (TCP/AQM) which is intended for dynamics modeling of the average TCP window size and the queue size in the bottleneck router. Apart from modeling, the work comprises of two parts. In the first, three controllers Random Early Detection, Proportional-Integral and Proportional-Integral-Derivative  (RED, PI, and PID) are designed, tested, evaluated, and compared among each other, with the use of the TCP/AQM model developed. The second part considers designing a fuzzy logic based online tuned PID controller and comparing its performance with a PID controller tuned offline with three tuning methods, Ziegler Nichols (Z-N), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). The Integral Square Error (ISE) is used as the objective function for optimization. The controllers’ performance is evaluated using the following parameters for system’s response, rise time, settling time, and maximum peak overshoot. The performance of the controllers is also examined by applying a disturbance as an exceptional condition. To test and evaluate the controllers, the system as all is implemented using MatLab (Version 2014).  The results obtained indicated that the PID gave a better performance, compared to the RED and the PI, in following changes in the desired queue level, and in reducing the loss of packets. The PID gave a settling time 20% lesser than that of the PI and 60% lesser than that of the RED. Regarding the tuning methods, and under the settings considered for each in this work, the ACO-PID gave the least overshoot (1.545%) compared to the others methods [ZN-PID (40%), PSO-PID (13.85%), Fuzzy-PID (5%)].

The PSO and ACO managed to cause great reduction in settling time () and rise time (). The ratios of and  of PSO-PID to PID before tuning are (16.5%), (23.43%) and the ratio of  and  of ACO-PID to PID before tuning are (11.5%), (44.56%). The intelligent tuning methods [PSO & ACO] gave better  and  compared to Fuzzy or Ziegler–Nichols. Despite the indicated relative performance of the Fuzzy PID controller, it has some important privileges. Firstly, it is an online tuning method, as it continuously adapts the PID controllers’ parameters as long as the system is running. Secondly, its performance can still be improved by optimizing the fuzzy part. Thirdly, it represents a nonlinear controller (as its parameters are changing), and so it can even suit the nonlinear model.


Random Early Detection (RED); Proportional-Integral (PI); Proportional-Integral-Derivative (PID); Fuzzy Logic (FL); Ziegler Nichols Method (ZN); Particle Swarm Optimization (PSO); and Ant Colony Optimization (ACO).

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