Adaptive Active Queue Management Research Articles

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

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.

Adaptive Hurst-Sensitive Active Queue Management.

An Active Queue Management (AQM) mechanism, recommended by the Internet Engineering Task Force (IETF), increases the efficiency of network transmission. An example of this type of algorithm can be the Random Early Detection (RED) algorithm. The behavior of the RED algorithm strictly depends on the correct selection of its parameters. This selection may be performed automatically depending on the network conditions. The mechanisms that adjust their parameters to the network conditions are called the adaptive ones. The example can be the Adaptive RED (ARED) mechanism, which adjusts its parameters taking into consideration the traffic intensity. In our paper, we propose to use an additional traffic parameter to adjust the AQM parameters—degree of self-similarity—expressed using the Hurst parameter. In our study, we propose the modifications of the well-known AQM algorithms: ARED and fractional order and the algorithms based on neural networks that are used to automatically adjust the AQM parameters using the traffic intensity and its degree of self-similarity. We use the Fluid Flow approximation and the discrete event simulation to evaluate the behavior of queues controlled by the proposed adaptive AQM mechanisms and compare the results with those obtained with their basic counterparts. In our experiments, we analyzed the average queue occupancies and packet delays in the communication node. The obtained results show that considering the degree of self-similarity of network traffic in the process of AQM parameters determination enabled us to decrease the average queue occupancy and the number of rejected packets, as well as to reduce the transmission latency.

Trade-off queuing delay and link utilization for solving bufferbloat

Abstract To solve the bufferbloat problem, active queue management (AQM) has been recommended but existing AQM algorithms suffer from poor adaptability to dynamic traffics. We develop a novel adaptive AQM algorithm named TODU for a better Trade-Off between queuing Delay and link Utilization. TODU aims to maintain a stable queue length against dynamical network traffics by adapting the drop probability to balance the expected link utilization and queuing delay. We compare TODU with benchmark algorithms in Network Simulator-2 and the results demonstrate that TODU achieves a better trade-off between low queuing latency and high link utilization in a dynamical environment.

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 integrate s the a forementioned expectations into one function and needs only one parameter change to adapt to fluctuating network condition . Hence, t his scheme provides lightweight computation and simple software and hardware implement ation . 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 .

An Adaptive Active Queue Management Based on Model Predictive Control

In recent years, active queue management (AQM) has gained more and more attention as an important part of network congestion control. Although there are many AQM algorithms, these algorithms show weaknesses to detect and control congestion due to the complexity and dynamics of the networks. Hence, this paper proposes a new AQM algorithm based on model predictive control (MPC) theory which has been widely applied in nonlinear and time-delay systems. In order to adjust the parameters of the MPC-based AQM algorithm adaptively according to network scenario variations, the adaptive mechanism is introduced into the new algorithm, named PHAQM, by using the Hebb learning rules from the neural network control theory. The simulation results show that the algorithm is effective in avoiding network congestion. Compared to the traditional AQM schemes, such as PI, REM, and GPC algorithm, the PHAQM has a faster convergence rate and smaller queue length fluctuations and outperforms especially under dynamically changing network situations.

Adaptive Chunklets and AQM for Higher-Performance Content Streaming

Commercial streaming services such as Netflix and YouTube use proprietary HTTP-based adaptive streaming (HAS) techniques to deliver content to consumers worldwide. MPEG recently developed Dynamic Adaptive Streaming over HTTP (DASH) as a unifying standard for HAS-based streaming. In DASH systems, streaming clients employ adaptive bitrate (ABR) algorithms to maximise user Quality of Experience (QoE) under variable network conditions. In a typical Internet-enabled home, video streams have to compete with diverse application flows for the last-mile Internet Service Provider (ISP) bottleneck capacity. Under such circumstances, ABR algorithms will only act upon the fraction of the network capacity that is available, leading to possible QoE degradation. We have previously explored chunklets as an approach orthogonal to ABR algorithms, which uses parallel connections for intra-video chunk retrieval. Chunklets effectively make more bandwidth available for ABR algorithms in the presence of cross-traffic, especially in environments where Active Queue Management (AQM) schemes such as Proportional Integral controller Enhanced (PIE) and FlowQueue-Controlled Delay (FQ-CoDel) are deployed. However, chunklets consume valuable server/middlebox resources which typically handle hundreds of thousands of requests/connections per second. In this article, we propose ‘adaptive chunklets’ -- a novel chunklet enhancement that dynamically tunes the number of concurrent connections. We demonstrate that the combination of adaptive chunklets and FQ-CoDel is the most effective strategy. Our experiments show that adaptive chunklets can reduce the number of connections by almost 30% and consume almost 8% less bandwidth than fixed chunklets while providing the same QoE.

An Adaptive AQM Algorithm Based on a Novel Information Compression Model

Because the bufferbloat phenomenon has significantly degraded the quality of service of interactive applications, there is pressing need for effective active queue management (AQM) strategies to be deployed at both intermediate devices and consumer edges. Existing models and AQM design methods usually lack sufficient consideration of heterogenous round-trip times, uncertain endpoint mechanisms, and time-varying network conditions. This paper proposes an innovative information compression model purely from routers’ perspective by evaluating the average effect an accepted/dropped packet has on the aggregated packet arriving rate. The proposed model is independent of round-trip time heterogeneity and specific endpoint protocols. A customized parameter identification algorithm and corresponding control strategy are developed to scale the regulating sensitivity of dropping probability adaptively. Simulation results demonstrate the performance improvements of the proposed algorithm in providing a good tradeoff between reducing the latency and maximizing the goodput, especially in large delay environments.

Optimization of Video Streaming through Bufferbloat Mitigation using Hybrid FQ-CoDel Algorithm

he major traffic in today's internet is entirely filled with video data with increased use of Video on-Demand systems. Quality Of internet video has a great impact on user engagement. Video encoding and streaming over wireless networks become a big concern. Real time video service requires low end-to-end delay and the major concern is to reduce the queuing delay which has high impact on the video quality. Bufferbloat is one of the main reasons for experiencing high queue latency at the intermediate nodes. Optimization of buffer size in the network nodes is never practiced, though several AQM were used for reducing the queue latency. In this work, Hybrid FQ-CoDel mechanism is proposed which is the combination of both CoDel and Adaptive CoDel AQM mechanism with fair queuing for the reduction of queuing delay and to optimize video transmission. Here the Adaptive CoDel is targeted for video traffic data and on the other hand the CoDel is used for other low traffic network flow. The mechanisms of network classifier and the scheduler is used to achieve fairness in the network queuing. Hybrid FQ-CoDeL is used for effective reduction of the queuing delay and to increase the network bandwidth utilization for video streams which will result in better quality of video.

Context-aware adaptive active queue management mechanism for improving video transmission over IEEE 802.11E WLAN

IEEE 802.11 wireless local area networks (WLANs) standard is now used widely in daily life. IEEE 802.11e, as a new standard of WLAN, can support different services such as audio and video. However, providing the desired quality of service (QoS) and improving the transmission efficiency for H.264 video transmission over IEEE 802.11e still present a challenge. In this paper, a new active queue management mechanism is proposed, context-aware adaptive active queue management (CAAQM), which can drop the video slices according to the video compression characteristics information and network condition. Simulation under QualNet on IEEE 802.11 network shows that CAAQM is capable of improving the quality of video transmission to enhance user experience.

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 of Active Queue Management for Robust Control on Access Router for Heterogeneous Networks

The Internet architecture is a packet switching technology that allows dynamic sharing of bandwidth among different flows with in an IP network. Packets are stored and forwarded from one node to the next until reaching their destination. Major issues in this integration are congestion control and how to meet different quality of service requirements associated with various services. In other words streaming media quality degrades with increased packet delay and jitter caused by network congestion. To mitigate the impact of network congestion, various techniques have been used to improve multimedia quality and one of those techniques is Active Queue Management (AQM). Access routers require a buffer to hold packets during times of congestion. A large buffer can absorb the bursty arrivals, and this tends to increase the link utilizations but results in higher queuing delays. Traffic burstiness has a considerable negative impact on network performance. AQM is now considered an effective congestion control mechanism for enhancing transport protocol performance over wireless links. In order to have good link utilization, it is necessary for queues to adapt to varying traffic loads. This paper considers a particular scheme which is called Adaptive AQM (AAQM) and studies its performance in the presence of feedback delays and its ability to maintain a small queue length as well as its robustness in the presence of traffic burstiness. The paper also presents a method based on the well-known Markov Modulated Poisson Process (MPP) to capture traffic burstiness and buffer occupancy. To demonstrate the generality of the presented method, an analytic model is described and verified by extensive simulations of different adaptive AQM algorithms. The analysis and simulations show that AAQM outperforms the other AQMs with respect to responsiveness and robustness.

An Adaptive Proportional Integral Active Queue Management Algorithm based on Self-Similar Traffic Rate Estimation in WSN

Wireless Sensor Network (WSN) is made up of a number of sensor nodes and base stations. Traffic flow in WSN appears self-similar due to its data delivery process, and this impacts queue length greatly and makes queuing delay worse. Active queue management can be designed to improve QoS performance for WSN. In this paper, we propose self-similar traffic rate estimating algorithm named Power-Law Moving Averaging (PLMA) to regulate packet marking probability. This algorithm improves the availability of the rate estimation algorithm under the self-similar traffic condition. Then, we propose an adaptive Proportional Integral algorithm (SSPI) based on the estimation of the Self-Similar traffic rate by PLMA. Simulation results show that SSPI can achieve lower queue length jitter and smaller setting time than PI.

An Improved Adaptive Active Queue Management Algorithm Based on Nonlinear Smoothing

According to analyzing the principle and limitations of RED and its improved algorithms, an improved adaptive RED congestion control mechanism is proposed in this paper, which imposes nonlinear smooth for packet loss rate function of RED algorithm by using the membership function of the ascend demi-cauchy of fuzzy distribution. The speed of growth of packet loss rate is relatively slow near the minimum threshold, while near the maximum threshold the speed of growth of packet loss rate is relatively faster. ThePmax will be dynamically adjusted by the length of average queue and the target for adapting the changing of network environment. NS simulation shows that this algorithm has been significantly improved for packet loss rate, throughput and other performance.

CDM-based design and performance evaluation of a robust AQM method for dynamic TCP/AQM networks

A new robust AQM strategy for dynamically varying TCP/AQM networks is proposed and its performance is investigated through computer simulations in MATLAB and ns-2 environments. The developed AQM is designed based on coefficient diagram method (CDM), which is a new indirect pole placement method that considers the speed, stability and robustness of the closed loop system in terms of time domain specifications. Simulation results indicate that the new method (CDM-AQM) performs very well for network variations both in topology and traffic. Besides, a new adaptive controller based on CDM as an AQM method is introduced. In the developed adaptive AQM (ACDM), the output feedback pole placement is implemented in an indirect model reference adaptive control (MRAC) scheme, in which the closed loop characteristic polynomial is determined by CDM and the system parameters are estimated using a modified recursive gradient method. This method preserves the existing features of CDM controller in the control of networks with unknown or time-varying parameters. Simulation results illustrate high capability of the controller in coping with the network variations.

Adaptive neural queue management for TCP networks

Active Queue Management (AQM) is a proven strategy to efficiently maintain queues and ensure high utilization of Transmission Control Protocol (TCP) network resources. The fundamental mechanism is to manage incoming packet rates at a router to prevent incipient network congestion. In this paper, we present an efficient neural network AQM system as a queue controller. The recurrent neural network has a Multi-layer Perceptron-Infinite Impulse Response (MLP-IIR) structure. Three distinct neural AQMs are trained under different network scenarios involving traffic levels. Selecting one of three neural AQMs is based on posterior probability history of traffic level. In addition, we investigate stochastic modeling of the network dynamics by a Dynamic Bayesian Network (DBN). This model allows implementation of a predictive AQM system in which queue dynamics are predicted and used for error prediction via online DBN estimation. Our AQM method is evaluated through simulation experiments both using an Ordinary Differential Equation (ODE) network model and using OPNET ©. The simulation results demonstrate that our adaptive neural AQM outperforms Random Early Detection (RED) and Proportional-Integral-Derivative (PID) based AQM.

A fuzzy logic-based AQM for real-time traffic over internet

Autonomic networking has been proposed as an approach to reduce cost and complexity of managing communication functions. An autonomic system is self-configuring, self-optimizing, self-healing and self-protecting. Such a system requires the minimum of administration, primarily involving policy-level management and AI-cognitive models. On the other hand, numerous Active Queue Management (AQM) algorithms have been proposed in the literature to address the problem of congestion in the Internet. Their performance is highly dependent on parameters' setting and tuning. Besides that, most of the AQM algorithms focus on throughput optimization and fail to provide bounded transmission delay while providing high link utilization to popular TCP-based radio/video streaming applications. Tackling the aforementioned concerns, in this paper we propose and evaluate a novel self-configuring AQM algorithm based on fuzzy logic. The proposed approach simplifies significantly the deployment and management of such complex QoS control mechanisms in the Internet providing at the same time a good tradeoff between link utilization and queuing latency. The introduced algorithm is compared with the most efficient adaptive AQM algorithms proposed to date such as ARED, REM, BLUE, PID and LRED. The performance analysis demonstrates that the proposed ''Fast and Autonomic Fuzzy Controller'' (FAFC): (1) minimizes queue fluctuation, (2) optimizes the throughput regardless of the traffic load variation and the presence of unresponsive UDP/RTP based voice and video communications, and (3) suggests the best compromise between link utilization and queuing delay.

A Load-Adaptive Active Queue Management Algorithm

Random Early Detection (RED) is the active queue management (AQM) algorithm recommended by IETF. Unfortunately, it is identified that RED is difficult to configure its parameters and the average queue length of RED is closely related to the load level. ARED (adaptive RED) is the adaptive version of RED. ARED dynamically adjust maximum packet marking probability according to the average queue length to make average queue length stable, but it still suffers from unstable instantaneous queue length and performance degradation under dynamic traffic conditions. In this paper, the cause of such problems of ARED is analyzed and a load adaptive active queue management scheme called LARED (load adaptive RED) is proposed. LARED features in adapting the load level of bottleneck link as well as quick response to queue length dynamics. Analysis and simulation results show that, compared with ARED and other AQM algorithms, LARED brings more stable queue dynamics; while keeping high link utilization and low queuing delay, it presents good responsiveness and robustness in various traffic conditions.

Adaptive AQM controllers for IP routers with a heuristic monitor on TCP flows

We propose adaptive proportional (P) and proportional-integral (PI) controllers for Active Queue Management (AQM) in the Internet. We apply the classical control theory in the controller design and choose a proper phase margin to achieve good performance of AQM. We have identified a simple heuristic parameter that can monitor the changes of network environment. Our adaptive controllers would self-tune only when the dramatic change in the network parameters drift the monitoring parameter outside its specified interval. When compared to P controller, a PI controller has the advantage of regulating the TCP source window size by adjusting the packet drop probability based on the knowledge of instantaneous queue size, thus steadying the queue size around a target buffer occupancy. We have verified our controllers by OPNET simulation, and shown that with an adaptive PI controller applied, the network is asymptotically stable with good robustness. Copyright © 2005 John Wiley & Sons, Ltd.

An adaptive AQM to provide delay guarantees for differentiated services

RIO(RED with IN and OUT) is the primary queue management mechanism proposed for assured forwarding in the DiffServ (Differentiated Service) framework. Although RIO can generally provide bandwidth guarantees, its queuing delay is sensitive to the traffic load. This paper presents a qualitative explanation for its origin. As a solution, an Adaptive RIO for Delay (ARIO-D) is proposed to provide guaranteed delay for multimedia traffic. Simulation results show that by trading loss for delay, ARIO-D can effectively improve the robustness of RIO under different and dynamic traffic, and provide stable and differentiated performance of queuing delay without any degradation in performance of throughput.