Adaptive Active Queue Management based on Queue Ratio of Set-point Weighting

Presently, active queue management (AQM) is one of the important considerations in communication networks. The challenge is to make it simple and robust in bursty traffic and uncertain network conditions. This paper proposes a new AQM scheme, an adaptive ratio proportional integral (ARPI), for adaptively controlling network congestion in dynamic network traffic conditions. First, AQM was designed by adding a set-point weighting structure to a proportional integral (PI) controller to reduce the burstiness of network traffic. Second, an adaptive set-point weighting based on the ratio of instantaneous queue length to the set-point queue and the buffer size was proposed to improve the robustness of a non-linear network. The proposed design integrates the aforementioned expectations into one function and needs only one parameter change to adapt to fluctuating network condition. Hence, this scheme provides lightweight computation and simple software and hardware implementation. This approach was analyzed and compared with the PI AQM scheme. Evaluation results demonstrated that our proposed AQM can regulate queue length with a fast response, good stability under any traffic conditions, and small queuing delay

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

PSO algorithm-based robust design of PID controller for variable time-delay systems: AQM application

This paper formulates a robust control for variable time-delay system models. An automatic tuning method for PID-type controller is proposed. The adopted method integrates robust control design using Quantitative Feedback Theory (QFT) with Particle Swan Optimization heuristic algorithms (PSO) to systematize the loop-shaping stage. The objective of the design method is to reach a good compromise among robust stability, robust tracking and disturbance rejection with minimal control effort. The resulting algorithm has attractive features, such as easy implementation, stable convergence characteristic and good computational efficiency. In particular, the results of the control design for active queue management (AQM) systems are presented. Simulations show improved congestion control and quality of service in TCP communication networks.Facultad de Informátic

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

Particle Swarm Optimization (PSO) Based Robust Active Queue Management Design for Congestion Control in TCP Network

Active queue management (AQM) is an effective solution for the congestion control problem. It can achieve high quality of service (QoS) by reducing the packet dropping probability and network utilization. Robust particle swarm optimization (PSO) algorithm is proposed in this paper in order to design a robust AQM schemes. Robust PSO controllers can achieve desirable time-response specifications with a simple design procedure and low-order controller in comparison to the conventional H∞ controller. Ranges of system parameters change and iterations are used to show the robustness of the designed controllers. The ability of the designed controllers to meet the specified performance is demonstrated in this paper by simulations using MATLAB, (R2016a). Finally, it was shown that the proposed robust PSO can achieve desirable performance. Keywords: Active queue Management (AQM), Quality of Service (QoS) Particle Swarm Optimization(PSO), Transmission control protocol (TCP), MATLAB

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

Enhancing AQM to combat wireless losses

In order to maintain a small, stable backlog at the router buffer, active queue management (AQM) algorithms drop packets probabilistically at the onset of congestion, leading to backoffs by Transmission Control Protocol (TCP) flows. However, wireless losses may be misinterpreted as congestive losses and induce spurious backoffs. In this paper, we raise the basic question: Can AQM maintain a stable, small backlog under wireless losses? We find that the representative AQM, random early detection (RED), fails to maintain a stable backlog under time-varying wireless losses. We find that the key to resolving the problem is to robustly track the backlog to a preset reference level, and apply the control-theoretic vehicle, internal model principle, to realize such tracking. We further devise the integral controller (IC) as an embodiment of the principle. Our simulation results show that IC is robust against time-varying wireless losses under various network scenarios. © 2012 IEEE.published_or_final_versio