A genetic algorithm for the design of a fuzzy controller for active queue management

Active queue management (AQM) policies are those policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the hosts on the network borders, and the adoption of a suitable control policy. This paper proposes the adoption of a fuzzy proportional integral (FPI) controller as an active queue manager for Internet routers. The analytical design of the proposed FPI controller is carried out in analogy with a proportional integral (PI) controller, which recently has been proposed for AQM. A genetic algorithm is proposed for tuning of the FPI controller parameters with respect to optimal disturbance rejection. In the paper the FPI controller design metodology is described and the results of the comparison with random early detection (RED), tail drop, and PI controller are presented

Simulation Model of Enhancing Performance of TCP/AQM Networks by Using Matlab

Internet networks are becoming more crowded every day due to the rapid development of modern life, which causes an increase in the demand for data circulating on the Internet. This creates several problems, such as buffer overflow of intermediate routers, and packet loss and time delay in packet delivery. The solution to these problems is to use a TCP/AQM system. The simulation results showed that there were differences in performance between the different controllers used. The proposed methods were simulated along with the required conditions in nonlinear systems to determine the best performance. It was found that the use of optimization Department of Electro-mechanical Engineering, University of Technology - Iraq tools (GA, FL) with a controller could achieve the best performance. The simulation results demonstrated the ability of the proposed methods to control the behavior of the system. The controller systems were simulated using Matlab/Simulink. The simulation results showed that the performance was better with the use of GA-PIDC compared to both FL-PIDC and PIDC in terms of stability time, height, and overrun ratio for a network with a variable queue that was targeted for comparison. The results were: the bypass ratio was 0, 3.3 and 21.8 the settling time was 0.002, 0.055, and 0.135; and the rise time was 0.001, 0.004 and 0.008 for GA-PIDC, FL-PIDC and PIDC, respectively. These results made it possible to compare the three control techniques

Simulation Model of Enhancing Performance of TCP/AQM Networks by Using Matlab

Internet networks are becoming more crowded every day due to the rapid development of modern life, which causes an increase in the demand for data circulating on the Internet. This creates several problems, such as buffer overflow of intermediate routers, and packet loss and time delay in packet delivery. The solution to these problems is to use a TCP/AQM system. The simulation results showed that there were differences in performance between the different controllers used. The proposed methods were simulated along with the required conditions in nonlinear systems to determine the best performance. It was found that the use of optimization Department of Electro-mechanical Engineering, University of Technology - Iraq tools (GA, FL) with a controller could achieve the best performance. The simulation results demonstrated the ability of the proposed methods to control the behavior of the system. The controller systems were simulated using Matlab/Simulink. The simulation results showed that the performance was better with the use of GA-PIDC compared to both FL-PIDC and PIDC in terms of stability time, height, and overrun ratio for a network with a variable queue that was targeted for comparison. The results were: the bypass ratio was 0, 3.3 and 21.8 the settling time was 0.002, 0.055, and 0.135; and the rise time was 0.001, 0.004 and 0.008 for GA-PIDC, FL-PIDC and PIDC, respectively. These results made it possible to compare the three control techniques

GA-PSO-Optimized Neural-Based Control Scheme for Adaptive Congestion Control to Improve Performance in Multimedia Applications

Active queue control aims to improve the overall communication network throughput while providing lower delay and small packet loss rate. The basic idea is to actively trigger packet dropping (or marking provided by explicit congestion notification (ECN)) before buffer overflow. In this paper, two artificial neural networks (ANN)-based control schemes are proposed for adaptive queue control in TCP communication networks. The structure of these controllers is optimized using genetic algorithm (GA) and the output weights of ANNs are optimized using particle swarm optimization (PSO) algorithm. The controllers are radial bias function (RBF)-based, but to improve the robustness of RBF controller, an error-integral term is added to RBF equation in the second scheme. Experimental results show that GA- PSO-optimized improved RBF (I-RBF) model controls network congestion effectively in terms of link utilization with a low packet loss rate and outperform Drop Tail, proportional-integral (PI), random exponential marking (REM), and adaptive random early detection (ARED) controllers.Comment: arXiv admin note: text overlap with arXiv:1711.0635

Proportional-integral genetic algorithm controller for stability of TCP network

The life development and increase the number of internet users imposed an increase in data circulating on the internet network and then make the network more congestion. As a result of all this, some problems arose such as time delay in packets delivery, loss of packets, and exceed the buffer capacity for the middle routers. To overcome those problems, transmission control protocol and active queue management (TCP/AQM) have been used. AQM is the main approach used to control congestion and overcome those problems to improve network performance. This work proposes to use the proportional-integral (PI) controller with a genetic algorithm (GA) as an active queue manager for routers of the Internet. The simulation results show a good performance for managing the congestion with using proportional-integral genetic algorithm (GA-PI) controller better than the PI controller

Flower Pollination Algorithm to Tune PID Controller of TCP/AQM Wireless Networks

The current study aims to conduct a simulation that is useful in developing an appropriate design that addresses the problem of congestion in the Internet network through controlling the queue of the router. The simulation is conducted through the proposed model for simulation with different control systems that help in raising the quality of performance such as traditional Proportional Integral Derivative (PID) and advanced optimal by Flower Pollination Algorithm  (FPA). It depends for Transmission Control Protocol/ Active Queue Management( TCP/AQM )simulation model for a linear system and another non-linear system. To adjust the network work and raise the level of performance, different control systems were chosen, taking into account all the things that appear through conducting experiments and for different purposes. One of the most important things that must be taken into consideration is the system disturbances as a result of the volume and values of the data, causing congestion . It was shown through the results of the experiments that were conducted considering the cases of the linear and nonlinear system to pass data traffic in the network and by adopting the different techniques of the control units, the preference of optimizasion systems over the traditional ones, as well as the preference of the traditional over  without control in close loop, is the improvement of the performance of linear systems compared to the open and closed system without control. The simulation results showed that very clear the superiority of the optimization by FPA-PID controller over the conventional system (PID)  , as well as very clear the superiority of  the traditional system (PID)over closed system without control and open loop system

Fuzzy Fractional-Order PID Congestion Control Based on African Buffalo Optimization in Computer Networks

Congestion is the primary factor that slows down data transfer in communication networks. Transmission Control Protocol and Active Queue Management (TCP/AQM) collaborated to resolve this issue. The fuzzy-Fractional-Order-PID (FFOPID) controller is developed in this paper to control the linearized TCP/AQM model. The strategy is founded on a combination of fractional-order PID and fuzzy logic controllers. The primary objective of the proposed controller is to maintain the queue length of the router within the appropriate queue threshold for a congestion model. The control parameters are tuned using African Buffalo optimisation (ABO). The suggested controller is compared to other controllers (PID, Fuzzy-PID, and Fractional-order PID) to demonstrate the controller's efficiency, and all of these controllers are optimised using African Buffalo Optimisation (ABO). In MATLAB (R2017b), the simulation of the linearized model is introduced. Comparing the results of the Fuzzy-Fractional-Order-PID controller with those of other controllers in the same network scenarios reveals that the Fuzzy-FOPID is robust for a wide variety of TCP flows