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

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

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

Active Queue Management via Event-Driven Feedback Control

Active queue management (AQM) is investigated to avoid incipient congestion in gateways to complement congestion control run by the transport layer protocol such as the TCP. Most existing work on AQM can be categorized as (1) ad-hoc event-driven control and (2) time-driven feedback control approaches based on control theory. Ad hoc event-driven approaches for congestion control, such as RED (random early detection), lack a mathematical model. Thus, it is hard to analyze their dynamics and tune the parameters. Time-driven control theoretic approaches based on solid mathematical models have drawbacks too. As they sample the queue length and run AQM algorithm at every fixed time interval, they may not be adaptive enough to an abrupt load surge. Further, they can be executed unnecessarily often under light loads due to the time-driven nature. To seamlessly integrate the advantages of both event-driven and control-theoretic time-driven approaches, we present an event-driven feedback control approach based on formal control theory. As our approach is based on a mathematical model, its performance is more analyzable and predictable than ad hoc event-driven approaches are. Also, it is more reactive to dynamic load changes due to its event-driven nature. Our simulation results show that our event-driven controller effectively maintains the queue length around the specified set-point. It achieves shorter E2E (end-to-end) delays and smaller E2E delay fluctuations than several existing AQM approaches, which are ad hoc event-driven and based on time-driven control theory, while achieving almost the same E2E delays and E2E delay fluctuations as the two other advanced control theoretic AQM approaches. Further, our AQM algorithm is invoked much less frequently than the tested baseline

An advanced scheme for queue management inTCP/IP networks

Active Queue Management (AQM) is a key congestion control scheme that aims to find a balance between keeping high link utilization, minimizing queuing delays, and ensuring a fair share of the bandwidth between the competing flows. Traditional AQM mechanisms use only information that is present at the intermediate nodes (routers). They do not take into account the particularities of the flows composing the traffic. In this paper, we make use of a mechanism, called Explicit RTT Notification (ERN), that shares with routers information about the Round Trip Times (RTTs) of the flows. We propose a new fuzzy logic based AQM controller that relies on the RTTs of the flows to improve fairness between them. The performances of the new proposed method, FuzzyRTT, is examined and compared to existing schemes via simulation experiments

Simulation model of ACO, FLC and PID controller for TCP/AQM wireless networks by using MATLAB/Simulink

The current work aims to develop a suitable design for control systems as part of a queue management system using the transmission control protocol/and active queue management (TCP/AQM) protocol to handle the expected congestion in the network. The research also aims to make a comparison between the different control methods, including the traditional proportional integral derivative (PID) and the expert fuzzy logic control (FLC), as well as the optimal ant colony optimization (ACO) that is used according to the performance improvement criteria to reach the best values for parameters the traditional controller (kd, ki, k p), where the addition of the performance indicator time-weighted absolute error (ITAE) was adopted. The use of this method without any other optimization algorithm that can be applied to adjust the parameters of the PID to verify the possibility of improving performance and enhance that with experience and to know the level of improvement for this particular system being the subject of the study. The results showed the superiority of the optimal ACO over both the FLC expert and the conventional PID, as well as the superiority of the FLC expert over the traditional PID