Predictive congestion control of ATM networks: multiple sources/single buffer scenario

This paper proposes a neural network (NN)-based adaptive control methodology to prevent congestion in high-speed asynchronous transfer mode (ATM) networks. The buffer dynamics at the switch is modeled as a nonlinear discrete-time system and a NN-based predictive controller is designed to predict the explicit values of the transmission rates of the sources so as to prevent congestion. Tuning methods are provided for the NN weights to estimate the unpredictable and statistically fluctuating network traffic. Mathematical analysis is given to demonstrate the stability of the closed-loop system so that a desired quality of service (QoS) can be guaranteed. The QoS is defined in terms of cell loss ratio (CLR) and latency.We derive design rules mathematically for selecting the NN tuning algorithm such that the desired performance is guaranteed during congestion and potential tradeoffs are shown. Simulation results are provided to justify the theoretical conclusions for single source/single switch scenario using ON/OFF data. Finally, comparison studies are also included to show the effectiveness of the proposed method over conventional rate-based and thresholding techniques during simulated congestion.     

A neural network controller for congestion control in ATM multiplexers

This paper presents and adaptive approach to the problem of congestion control arising at the User-to Network Interface (UNI) of an ATM multiplexer. We view the ATM multiplexer as a non-linear stochastic system whose dynamics are ill-defined. Real-time measurements of the arrival rate process and the queueing process, are used to identify, and minimize congestion episodes. The performance of the system is evaluated using a performance-index function which is a quantative measure of “how well” the system is performing. A three-layers backpropagation neural network controller generates a signal that attempts to minimize congestion without degrading the quality of the traffic. During periods of buffer over-load the control signal, adaptively, modulates the arrival process such that its peak-rate is throttled-down. As soon as congestion is terminated, the control signal is adjusted such that the coding rates are restored back to their original values. Adaptability is achieved by continuously adjusting the weights of the neural network controller such that the performance of the system, measured by its performance index function, is maximized over a certain optimization period. The performance index function is defined in terms of two main objectives: (1) to minimize the cell loss rate (CLR), i.e., minimize congestion episodes, and (2) to maintain the quality of the video/audio traffic by maintaining its original source coding rate. The neural network learning process can be viewed as a specialized form of reinforcement learning in the sense that the control signal is reinforced if it tends to maximize the performance index function. Performance evaluation results prove that this approach is effective in controlling congestion while maintaining the quality of the traffic.     

一种基于FNN的高速网络拥塞控制策略

以ATM（asynchronous transfer mode)为研究对旬,同种基于模糊神经网络（fuzzy neural network,简称FNN）的流量预测和拥塞控制策略,拥塞控制是高速网络（如ATM）研究中的关键问题之一,传统的基于BP神经网络的流量预测方法因其收敛速度较慢且具有较大的误差,影响了拥塞控制效果,而模糊神经网络由于具有处理不确定性问题和很强的学习能力,很好地解决这一问题,最后通过仿真,比较和分析了基于BP神经网络和基于FNN方法和性能,证明此方法是有效的。     

Fuzzy neural network based traffic prediction and congestion control in high-speed networks

Congestion control is one of the key problems in high-speed networks,such as ATM.In this paper,a kind of traffic prediction and preventive congestion control scheme is proposed using neural network approach.Traditional predictor using BP neural network has suffered from long convergence time and dissatisfying error.Fuzzy neural network developed in this paper can solve these problems satisfactorily.Simulations show the comparison among no-feedback control scheme,reactive control scheme and neural network based control scheme.     

A neuro-adaptive congestion control scheme for round trip regulation

A novel neuro-adaptive congestion controller is presented, capable of regulating the per packet round trip time (RTT) around a piecewise constant desired RTT, thus achieving almost piecewise constant delay. The controller is implemented at the source and is proven robust against modeling imperfections, exogenous disturbances (UDP traffic) and delays (propagation, queueing). The notion of communication channels is introduced for throughput improvement. The analysis is nonlinear and the tools used are approximation-based control and linear-in-the-weights neural networks. The proposed controller is guaranteed to be saturated. Moreover, modifications are also provided to achieve rate reduction whenever congestion is detected. Simulation studies illustrate the performance of the proposed control scheme and compare it with other well-established congestion control mechanisms.     

ABR流量控制中的变结构控制器

自适应比特(available bit rate,简称ABR)业务的流量控制是ATM网络中一种有效的拥塞控制机制和流量管理手段.在高速的ATM网络中,算法的简洁性在很大程度上决定着交换机的性能.尽管二进制ABR流量控制的简洁性具有相当大的吸引力,但标准的EFCI算法控制的队列长度和允许信元速率(allowed cell rate,简称ACR)却容易出现大幅振荡的现象,这势必会降低链路的利用率,严重影响交换机的性能.进而又有了相对复杂却有效的显式速率反馈机制.在此研究中,以已有的ABR流量控制模型为基础,应用概率拥塞判定机制,并借助鲁棒控制理论中滑模变结构控制器的设计方法,为ABR流量控制设计了一种新的二进制算法,避免了标准EFCI算法中非线性环节诱发的自激振荡,这对于充分发挥二进制流控算法的简洁性以及优化交换机的性能是极为有利的.仿真实验表明:二进制流量控制中的滑模变结构算法大幅度地抑制了ACR和队列的振荡,平滑了由此而引入的时延抖动,为实现ATM网络中的服务质量提供了可靠的实现机制.     

Connection admission control of ATM network using integrated MLP and fuzzy controllers

This paper presents a new approach to the problem of call admission control (CAC) of variable bit rate (VBR) traffic in an asynchronous transfer mode (ATM) network. Our approach employs an integrated neural network and fuzzy controller to implement the CAC controller. This scheme capitalizes on the learning ability of a neural network and the robustness of a fuzzy controller. Experiments show that this scheme is able to achieve high throughput and low cell loss while achieving fairness among different classes of VBR traffic. For comparison, we have also implemented four other CAC schemes: (1) peak bandwidth method, (2) equivalent bandwidth method, (3) average bandwidth method and (4) neural network quality of service (QoS) predictor. Results of these experiments are presented in this paper.     

Performance of ATM available bit rate for bursty TCP sources and interfering traffic

The Available Bit Rate (ABR) service has been shown to enable persistent, greedy data sources to efficiently utilize ATM network resources. However, the throughput for bursty TCP over ABR data traffic in the presence of interfering traffic may not be as good as for persistent ABR sources without interfering traffic. This paper shows that in a comparison of rate based ABR schemes (EPRCA and ERICA) and a credit based ABR scheme (QFC), QFC performance is significantly better than rate based ABR schemes for bursty data traffic with bursty interfering traffic.     

Performance bounds for rate envelope multiplexing

In QoS guaranteed communication networks, such as ATM, several classes of traffic streams with widely varying characteristics share common transmission resources. To achieve high utilization of these networks, while providing appropriate grade of service for all connections, the development of powerful traffic management algorithms is a central issue. Due to scalability reasons traffic control functions like flow, congestion and admission control often need simple and efficient characterization of traffic using mainly aggregate characteristics instead of using information about all the individual flows. In this paper, the saturation probability as a possible performance measure of aggregate traffic on a communication link is discussed. This performance metric, also referred to as the tail distribution of aggregate traffic, is essential in traffic control and management algorithms of high-speed networks including also the prospective QoS Internet. In this paper, using the Chernoff bounding method, efficient closed-form bounds have been derived for the saturation probability for the case when little information is available on the aggregate traffic. The performance of these estimates is also shown by means of numerical examples.     

Congestion control based dynamic routing in ATM networks

In this paper we describe briefly a dynamic multi-path algorithm that has been considered for connection oriented asynchronous transfer mode (ATM) networks. Our scheme takes advantage of a cell multiplexing capability that has particular advantage in networks supporting variable bit rate (VBR) traffic. The fundamental objective of the scheme is to propose a congestion control based scheme that bridges the gap between routing and congestion control as the network becomes congested. The proposed routing scheme works as a shortest path first algorithm under light traffic conditions. However, as the shortest path becomes congested under unbalanced heavy traffic, the source uses multiple paths when and if available to distribute the calls and reduce cell loss. This mechanism will provide good Quality of Service for clients within the given constraints. We compare the performance of the proposed scheme with other competitive schemes. The throughput and cell loss performance are compared via simulations. These have been carried out concentrating on a five node network, each with varying traffic patterns, with the intention of gaining insight into the strengths and weaknesses of the various schemes.     

Stable Queue Management in communication networks

Since Active Queue Management (AQM) was recommended by the Internet Engineering Task Force (IETF) as an efficient way to overcome performance limitations of Transmission Control Protocol (TCP), several studies have proven control theory to be a promising field for the design and analysis of congestion control in homogenous communication networks. AQM is gaining increased importance due to reports of buffer-induced latencies throughout the Internet. The increasing volume and diversity of traffic types (i.e., data, voice, and video) suggests that traffic management mechanisms, in general, and AQM schemes, specifically, must not only focus on the critical issue of congestion control but must also consider the QoS demands of heterogeneous traffic. However, to combine quality-of-service provisioning with congestion control, AQM design needs to be reconsidered. In this paper, we propose a state feedback controller design scheme for heterogeneous networks preserving the closed-loop system stability. Delay dependant stability conditions of the closed loop system are derived based on the Lyapunov-Krasovskii method. The proposed approach offers flexible choice of control parameters allowing the network administrator to control fairness and response time for each individual source node in a network of multiple links with different delay properties. The performance and robustness of the proposed controller were illustrated and analyzed using event-based computer simulations.     

基于Additive2multipl icative 模糊 神经网的ATM 网络拥塞控制

考虑了模糊神经网络的学习功能，提出利用Additive-multiplicative模糊神经网络(AMFNN)对ATM网络进行拥塞控制的方案．在拥塞控制过程中，利用AMFNN模糊神经网络预测下一个将要到达流的特征，结合当前缓冲区的队列信息预测网络是否发生拥塞．一旦预测出将有拥塞发生，控制器则向源端反馈拥塞控制信息，信源根据拥塞信息适当降低传输速率，从而避免了拥塞的发生．仿真结果表明，该方法可改善网络对拥塞的实时处理能力，提高网络资源的利用率．     

一种新的基于BP神经网络的拥塞控制算法

针对计算机高速互联网中发送端速率调节的问题，在一般网络模型基础上，将BP（Back Propagation神经网络运用到计算机网络的拥塞控制中，提出了一种基于BP神经网络的动态资源管理机制以解决网络的拥塞问题，对所提出的拥塞控制方案，进行了仿真分析，仿真结果显示，控制方案有较好的可扩展性，有效性，并使网络性能表现良好。     

Nonlinear magnetic storage channel equalization using minimalresource allocation network (MRAN)

This letter presents the application of the recently developed minimal radial basis function neural network called minimal resource allocation network (MRAN) for equalization in highly nonlinear magnetic data storage channels. Using a realistic magnetic channel model, MRAN equalizer's performance is compared with the nonlinear neural equalizer of Nair and Moon (1997), referred to as maximum signal-to-distortion ratio (MSDR) equalizer. MSDR equalizer uses a specially designed neural architecture where all the parameters are determined theoretically. Simulation results indicate that MRAN equalizer has better performance than that of MSDR equalizer in terms of higher signal-to-distortion ratios.     

Dynamic buffer allocation in an ATM switch

Efficient and fair use of buffer space in an Asynchronous Transfer Mode (ATM) switch is essential to gain high throughput and low cell loss performance from the network. In this paper a shared buffer architecture associated with threshold-based virtual partition among output ports is proposed. Thresholds are updated based on traffic characteristics on each outgoing link, so as to adapt to traffic loads. The system behavior under varying traffic patterns is investigated via simulation; cell loss rate is the quality of service (QoS) measure used in this study. Our study shows that the threshold based dynamic buffer allocation scheme ensures a fair share of the buffer space even under bursty loading conditions.     

ATM网络ABR业务拥塞控制算法的PI调节器实现

基于速率反馈的流量控制是ATM网络ABR业务标准的流量控制方案。在目前绝大多数拥塞控制方案不能用数学工具进行分析的情况下，本文根据经典控制理论的有关原理，使用一个PI控制器实现了拥塞控制算法。我们对系统进行了分析，证明了此时系统带宽和输入数据速率之间将是无差的；最后通过仿真说明了控制器参数的变化对系统的影响。     

一种基于ANFIS的ABR拥塞控制

由于ATM网络环境的复杂性、多变性,用常规的数学模型对网络模型、可用带宽的获取以及控制器设计的描述具有很大的局限性,因此论文提出了一种基于自适应模糊推理系统(AdaptiveNeuralFuzzyInferenceSystems,ANFIS)的ABR业务拥塞控制方法,该方法结合模糊推理系统的规则结构化及神经网络强泛化能力的优点,克服了模糊推理模型的偶然性和神经网络收敛速度慢、训练时间过程长等缺点。仿真结果表明使用ANFIS进行拥塞控制的可行性,增加了系统稳定性并减小了信元丢失率。     

ATM communications network control by neural networks

A learning method that uses neural networks for service quality control in the asynchronous transfer mode (ATM) communications network is described. Because the precise characteristics of the source traffic are not known and the service quality requirements change over time, building an efficient network controller which can control the network traffic is a difficult task. The proposed ATM network controller uses backpropagation neural networks for learning the relations between the offered traffic and service quality. The neural network is adaptive and easy to implement. A training data selection method called the leaky pattern table method is proposed to learn precise relations. The performance of the proposed controller is evaluated by simulation of basic call admission models.     

ATM网络拥塞控制中PID控制器的设计

自适应比特（ABR）业务的流量控制是ATM网络中一种有效的拥塞控制机制和流量管理手段。在大规模的高速网络中，算法的简洁性对优化交换机的性能是至关重要的。尽管二进制ABR流量控制的简洁性具有相当的吸引力，但显式前向拥塞标识（Explicit Forward Congestion Indication,EFCI)算法控制的队列长度和允许信元速率（Allowed Cell Rate,ACR)大幅振荡，降低了链路利用率，严重的影响了交换机的性能，为此有了相对复杂却有效的显式速率反馈机制，在该文中，引入了拥塞的概率判定机制，并运用经典控制理论为拥塞判定概率的实量更新设计了线性的PID控制器，避免了非线性的控制规律可能诱发的系统自激振荡，在PID控制器的参数整定上，因为使用常用处受到限制，进而给出了一种基于确定稳定裕度的参数整定方法，仿真试验表明：二进制流量控制中的PID算法在保持了算法简洁性的前提下，大幅度地抑制了ACR和队列长度的振荡，提高了链路利用率，减小了队列系统引入的时延抖动，为保证ATM网络中的服务质量（Quality of Service,Qos)提供了必要的技术支持。     

A neural-fuzzy system for congestion control in ATM networks

We propose the use of a neural-fuzzy scheme for rate-based feedback congestion control in asynchronous transfer mode (ATM) networks. Available bit rate (ABR) traffic is not guaranteed quality of service (QoS) in the setup connection, and it can dynamically share the available bandwidth. Therefore, congestion can be controlled by regulating the source rate, to a certain degree, according to the current traffic flow. Traditional methods perform congestion control by monitoring the queue length. The source rate is decreased by a fixed rate when the queue length is greater than a prespecified threshold. However, it is difficult to get a suitable rate according to the degree of traffic congestion. We employ a neural-fuzzy mechanism to control the source rate. Through learning, membership values can be generated and cell loss can be predicted from the status of the queue length. Then, an explicit rate is calculated and the source rate is controlled appropriately. Simulation results have shown that our method is effective compared with traditional methods.