Active Queue Management Controller Research Articles

Adaptive neuro-fuzzy controller trained by genetic-particle swarm for active queue management in internet congestion

Routers are vital during network congestion. All routers have input and output packet buffers. V<span lang="EN-US">Various congestion control strategies have been suggested. Some controller-based proportional-integral derivative (PIDs) have recently been offered as active queue management (AQM) solutions to alleviate the deterioration of transmission control protocol (TCP) congestion management system performance. However, the time delay is large, the data retention decreases, and oscillation occurs, suggesting that the present PID-controller is unable to fulfill quality of service (QoS) criteria. Some research is developed on new control technologies such as neural networks and fuzzy logic. This paper proposes the adaptive neuro-fuzzy inference system (ANFIS) like PID controller for AQM. This model employs genetic algorithms (GAs) and particle swarm optimization (PSO) to learn and optimize all variables for ANFIS like PID controller. Simulations were used to investigate the effects of using fuzzy like PID based on single sign-on (SSO), and (ANFIS like PI, ANFIS like PID with GA-PSO) controllers on the length of the queue for an AQM router, respectively. Then we compared the findings to see which approach should be utilized to manage the queue length for AQM routers. In simulations, ANFIS like PID has superior stability, convergence, resilience, loss ratio, goodput, lowest rising time, overshoot, and settling time.</span>

Quality of service aware adaptive target queue length generation for active queue management

Active queue management techniques have been proposed to mitigate serious congestion issues caused by transmission control protocol flows. Active queue management controllers based on control theory are superior in terms of control performance. Such a controller utilizes a target queue length as a control input, and the congestion control system attempts to maintain the actual queue length close to the target value. However, the specific setting of the target queue length has not been discussed sufficiently. For example, raising or lowering the target queue length may increase goodput or reduce queueing delay, respectively. Therefore, the target queue length should be appropriately generated according to the required quality of services and buffer status. This paper proposes a quality of services–aware adaptive target queue length generation algorithm for an active queue management controller based on control theory. The proposed algorithm comprises two modes (loss-aware and delay-aware modes) and attempts to raise or lower the target queue length by considering the buffer status to increase the goodput or reduce the queueing delay. Simulation results revealed that the proposed algorithm could increase goodput or reduce queueing delay on each mode by adapting to the buffer status, which is in contrast to conventional methods.

Fuzzy Like Pid Controller Based on Sso Design for Congestion Avoidance in Internet Router

today The topic of contention is the quality of computer network service. Efficient data handling is important. Each router offers a buffer space where packets can be held before being sent until they are processed. The theory of packet congestion control is TCP (Transmission Control Protocol) .(AQM) is a mechanism proposed for gateways to improve congestion management. AQM (active queue management) is a crucial to minimize packet loss in TCP/IP networks and improving network efficiency. This paper offers a hybrid intelligent (PID) with type1 fuzzy logic controller which is designed to decrease network congestion. As a router (AQM), social spider optimization (SSO) is used to tune the control parameters used to reduce queue size error. The designed controller based on SSO algorithm provides a good tracking performance for AQM desired value.The observational findings indicated a high-performance increase in the proposed process. In MATLAB(R2020a), a simulation of a linearized TCP/AQM model is presented. NS2 is used to perform verification for the fuzzy logic controllers for the nonlinear TCP/AQM model under a more practical simulation and to analyze and examine network behavior in different scenarios. Index Terms—AQM controller, congestion control, fuzzy logic control, PID Controller; SSO.

Improved Variable Structure Proportional–Integral Controller for TCP/AQM Network Systems

This study proposes the design of a new variable structure proportional-integral (VSPI) controller, which combines the advantages of a proportional-integral (PI) controller with the more flexible variable structure (VS) controller for transmission control protocol (TCP) router-based active queue management (AQM) network systems. The objective of the proposed controller is to improve the performance of routers by reducing packet loss and queuing delay and increasing throughput. The performance of the VSPI controller is verified and compared with the PI controller using Network Simulator 2 (NS–2). The results demonstrate that the proposed controller is stable and robust under various scenarios and is a suitable design approach for TCP/AQM network systems. Moreover, the VSPI controller is flexible and easy to use owing to the trade-off between stability and performance of the AQM control system.

Comparative analysis of the performance of various active queue management techniques to varying wireless network conditions

This paper demonstrates the robustness of active queue management techniques to varying load, link capacity and propagation delay in a wireless environment. The performances of four standard controllers used in Transmission Control Protocol/Active Queue Management (TCP/AQM) systems were compared. The active queue management controllers were the Fixed-Parameter Proportional Integral (PI), Random Early Detection (RED), Self-Tuning Regulator (STR) and the Model Predictive Control (MPC). The robustness of the congestion control algorithm of each technique was documented by simulating the varying conditions using MATLAB® and Simulink® software. From the results obtained, the MPC controller gives the best result in terms of response time and controllability in a wireless network with varying link capacity and propagation delay. Thus, the MPC controller is the best bet when adaptive algorithms are to be employed in a wireless network environment. The MPC controller can also be recommended for heterogeneous networks where the network load cannot be estimated.

Design and analysis of a stable AQM controller for network congestion control

In this work, a stable active queue management (AQM) controller named SAQM is proposed to control the congestion in the network and improve the stability of the queue length at internet router. It is an integrated rate and queue-based AQM technique which is stable and robust under dynamic environment where a number of TCP connections, bottleneck bandwidth, round trip time (RTT), and target queue length keeps changing. The proposed AQM controller is implemented in NS2 network simulator. In this paper, the control theory is used for stability analysis of TCP/SAQM system and finally validated through MATLAB Simulink model. The simulation results show that the SAQM outperforms other existing AQM controllers in terms of achieving queue stability, less oscillatory with faster response and at the same time it responds quickly to traffic change.

Design and analysis of a stable AQM controller for network congestion control

In this work, a stable active queue management (AQM) controller named SAQM is proposed to control the congestion in the network and improve the stability of the queue length at internet router. It is an integrated rate and queue-based AQM technique which is stable and robust under dynamic environment where a number of TCP connections, bottleneck bandwidth, round trip time (RTT), and target queue length keeps changing. The proposed AQM controller is implemented in NS2 network simulator. In this paper, the control theory is used for stability analysis of TCP/SAQM system and finally validated through MATLAB Simulink model. The simulation results show that the SAQM outperforms other existing AQM controllers in terms of achieving queue stability, less oscillatory with faster response and at the same time it responds quickly to traffic change.

Robust Stability and Design of State Feedback Controller for Straightforward Active Queue Management

The straightforward active queue management ($AQM$), which is based on the prediction of arrival rate is investigated by means of state-space approach. We formulate the feedback control design problem for linearized system of additive increase multiplicative decrease ($AIMD$) dynamic models as state-space model. Then the Lyapunov-Krasovskii method is provided to achieve the robust stability and sufficient stabilization condition and afterwards the term of linear inequality matrix ($LMI$) is used to show the results. We present the simulation results and show the superiority of our proposed method to other control mechanisms.

An AQM Controller Based on Feed-Forward Neural Networks for Stable Internet

As the nature of Internet becomes nonlinear and complex, designing an intelligent controller at the router which can learn the traffic pattern of the network and predict the correct value and stabilize the system is a difficult task. In this, a new active queue management (AQM) controller is proposed based on feed-forward neural network called (FFNN-AQM) to control the network congestion efficiently by stabilizing the queue length. It learns the traffic pattern of the nonlinear and dynamic network and predicts the future value of current queue length. The parameters of neurons adjusted depending on the time-varying environment to stabilize the queue length. The NS2 network simulator is used to analyze the performance of FFNN-AQM along with existing techniques. The simulation experiment results demonstrate that FFNN-AQM is stable and achieve faster convergence with small settling time in varying network conditions. The proposed controller outperforms existing AQM proportional integral (PI), intelligent adaptive PI and neural network PI techniques.

Robust Stability and Stabilization Of TCP‐Networked Control Systems with Multiple Delay System Modeling

AbstractThis paper presents a new model for networked control systems (NCSs) under transmission control protocol (TCP) as a multiple‐delay system by considering both sensor to controller and controller to actuator delays. An analytical TCP model has been considered for the network part, and an active queue management (AQM) controller is designed to regulate the desired queue length, which ensures holding the network induced delay and its variation within their lower bounds. The model is assumed to possess structured uncertainties due to the stochastic nature of the network. Robust stability and stabilization conditions are derived in terms of linear matrix inequalities (LMIs) by applying the Lyapunov‐Krasovskii stability criterion. Illustrative examples are presented and it has been shown that the proposed method will obtain less conservative results compared to the existing approaches in the literature.

Research for AQM based on MiniMax method

This paper proposes an active queue management (AQM) controller for a class of linearized congestion router network systems in the presence of unknown time-varying link number and disturbances. Bas...

A simple active queue management based on the prediction of the packet arrival rate

In this paper, we predict the change in the packet arrival rate at the link through the analysis of the network congestion control mechanism. An appropriate expression for dropping probability is derived based on this prediction to stabilize the queue length to the desired value. A new Active Queue Management (AQM) algorithm named as Straightforward AQM (SFAQM) is proposed with simple implementation. Its analysis of the stability is also carried out, and the necessary and sufficient condition for the system to be stable is presented. The simulation results using NS2 demonstrate that SFAQM is effective in different network environments. Compared to the conventional AQM controllers, such as PI, RaQ and CARED, SFAQM has smaller queue length jitter and faster convergence speed.

Fighting the bufferbloat: On the coexistence of AQM and low priority congestion control

Nowadays, due to excessive queuing, delays on the Internet can grow longer than the round trip time between the Moon and the Earth – for which the “bufferbloat” term was recently coined. Some point to active queue management (AQM) as the solution. Others propose end-to-end low-priority congestion control techniques (LPCC). Under both approaches, promising advances have been made in recent times: notable examples are CoDel for AQM, and LEDBAT for LPCC.In this paper, we warn of a potentially fateful interaction when AQM and LPCC techniques are combined: namely, AQM resets the relative level of priority between best-effort and low-priority congestion control protocols. We validate the generality of our findings by an extended set of experiments with packet-level ns2 simulation, considering 5 AQM techniques and 3 LPCC protocols, and carry on a thorough sensitivity analysis varying several parameters of the networking scenario. We complete the simulation via an experimental campaign conducted on both controlled testbeds and on the Internet, confirming the reprioritization issue to hold in the real world at least under all combination of AQM policies and LPCC protocols available in the Linux kernel. To promote cross-comparison, we make our scripts and dataset available to the research community.

Active Queue Management of TCP Flows with Self-scheduled Linear Parameter Varying Controllers

Control-theoretic approaches to Active Queue Management (AQM) are typically based on linearizations of fluid flow models around design conditions. These conditions depend on the Round Trip Time (RTT), and the AQM performance is known to degrade if RTT values during actual operation depart substantially from design values. To overcome this difficulty a self-scheduled LPV controller for AQM is considered in this paper, where the controller is modified in real-time based on RTT. Simulations show that the self-scheduled LPV controller has good performance for both constant and time-varying RTTs, and outperforms two other common control-theoretic approaches to AQM.

AQM controller design for TCP networks based on a new control strategy

When the network suffers from congestion, the core or edge routers signal the incidence of congestion through the active queue management (AQM) to the sources. The time-varying nature of the network dynamics and the complex process of retuning the current AQM algorithms for different operating points necessitate the development of a new AQM algorithm. Since the non-minimum phase characteristics of the network dynamics restrict direct application of the proportional-integral-derivative (PID) controller, we propose a compensated PID controller based on a new control strategy addressing the phase-lag and restrictions caused by the delay. Based on the unstable internal dynamics caused by the non-minimum phase characteristics, a dynamic compensator is designed and a PID controller is then allowed to meet the desired performance objectives by specifying appropriate dynamics for the tracking error. Since the controller gains are obtained directly from the dynamic model, the designed controller does not require to be tuned over the system operating envelop. Moreover, simulation results using ns2 show improvements over previous works especially when the range of variation of delay and model parameters are drastic. Simplicity, low computational cost, self-tuning structure and yet considerable improvement in performance are exclusive features of the proposed AQM for the edge or core routers.

A compensated PID active queue management controller using an improved queue dynamic model

SUMMARYBeside the major objective of providing congestion control, achieving predictable queuing delay, maximizing link utilization, and robustness are the main objectives of an active queue management (AQM) controller. This paper proposes an improved queue dynamic model while incorporating the packet drop probability as well. By applying the improved model, a new compensated PID AQM controller is developed for Transmission Control Protocol/Internet Protocol (TCP/IP) networks. The non‐minimum phase characteristic caused by Padé approximation of the network delay restricts the direct application of control methods because of the unstable internal dynamics. In this paper, a parameter‐varying dynamic compensator, which operates on tracking error and internal dynamics, is proposed to not only capture the unstable internal dynamics but also reduce the effect of uncertainties by unresponsive flows. The proposed dynamic compensator is then used to design a PID AQM controller whose gains are obtained directly from the state‐space representation of the system with no further gain tuning requirements. The packet‐level simulations using network simulator (ns2) show the outperformance of the developed controller for both queuing delay stability and resource utilization. The improved underlying model leads also to the faster response of the controller. Copyright © 2013 John Wiley & Sons, Ltd.

Comparison of PI controllers designed for the delay model of TCP/AQM networks

One of the major problems of communication networks is congestion. In order to address this problem in TCP/IP networks, Active Queue Management (AQM) scheme is recommended. AQM aims to minimize the congestion by regulating the average queue size at the routers. To improve upon AQM, recently, several feedback control approaches were proposed. Among these approaches, PI controllers are gaining attention because of their simplicity and ease of implementation. In this paper, by utilizing the fluid-flow model of TCP networks, we study the PI controllers designed for TCP/AQM. We compare these controllers by first analyzing their robustness and fragility. Then, we implement these controllers in ns-2 platform and conduct simulation experiments to compare their performances in terms of queue length. Taken together, our results provide a guideline for choosing a PI controller for AQM given specific performance requirements.

Adaptive mechanism-based congestion control for networked systems

In order to assure the communication quality in network systems with heavy traffic and limited bandwidth, a new ATRED (adaptive thresholds random early detection) congestion control algorithm is proposed for the congestion avoidance and resource management of network systems. Different to the traditional AQM (active queue management) algorithms, the control parameters of ATRED are not configured statically, but dynamically adjusted by the adaptive mechanism. By integrating with the adaptive strategy, ATRED alleviates the tuning difficulty of RED (random early detection) and shows a better control on the queue management, and achieve a more robust performance than RED under varying network conditions. Furthermore, a dynamic transmission control protocol–AQM control system using ATRED controller is introduced for the systematic analysis. It is proved that the stability of the network system can be guaranteed when the adaptive mechanism is finely designed. Simulation studies show the proposed ATRED algorithm achieves a good performance in varying network environments, which is superior to the RED and Gentle-RED algorithm, and providing more reliable service under varying network conditions.

Adaptive active queue management controller for TCP communication networks using PSO-RBF models

Addressing performance degradations in end-to-end congestion control has been one of the most active research areas in the last decade. Active queue management (AQM) aims to improve the overall network throughput, while providing lower delay and reduce packet loss and improving network. The basic idea is to actively trigger packet dropping (or marking provided by explicit congestion notification (ECN)) before buffer overflow. Radial bias function (RBF)-based AQM controller is proposed in this paper. RBF controller is suitable as an AQM scheme to control congestion in TCP communication networks since it is nonlinear. Particle swarm optimization (PSO) algorithm is also employed to derive RBF parameters such that the integrated-absolute error (IAE) is minimized. Furthermore, in order to improve the robustness of RBF controller, an error-integral term is added to RBF equation. The results of the comparison with Drop Tail, adaptive random early detection (ARED), random exponential marking (REM), and proportional-integral (PI) controllers are presented. Integral-RBF has better performance not only in comparison with RBF but also with ARED, REM and PI controllers in the case of link utilization while packet loss rate is small.

Design and analysis of a model predictive controller for active queue management

Model predictive (MP) control as a novel active queue management (AQM) algorithm in dynamic computer networks is proposed. According to the predicted future queue length in the data buffer, early packets at the router are dropped reasonably by the MPAQM controller so that the queue length reaches the desired value with minimal tracking error. The drop probability is obtained by optimizing the network performance. Further, randomized algorithms are applied to analyze the robustness of MPAQM successfully, and also to provide the stability domain of systems with uncertain network parameters. The performances of MPAQM are evaluated through a series of simulations in NS2. The simulation results show that the MPAQM algorithm outperforms RED, PI, and REM algorithms in terms of stability, disturbance rejection, and robustness.