基于模糊神经网络的拥塞控制算法研究

本文介绍一种基于模糊神经网络的主动队列管理(AQM)算法,实现网络拥塞控制。利用神经网络来实现模糊推理,可自适应修正隶属函数的参数和加权系数,优化模糊逻辑控制器,从而达到某种性能指标的最优化。仿真结果表明,采用模糊神经网络进行流量速率预测的拥塞控制策略能够使缓冲器队列长度快速收敛到目标值,并且维持小的队列震荡。结果也表明该方法与传统的PD控制器相比具有更好的性能和鲁棒性。     

一种简化PID模糊控制器的研究与设计

在介绍模糊控制基本原理及模糊控制器设计与分类的基础上,推导出一种简化PID型模糊控制器。为了验证简化PID型模糊控制器的性能,将其与PD及PI型模糊控制器进行比较。其仿真结果最后表明,在控制器参数选取相同的情况下,简化PID型模糊控制器的性能要优于PD型和PI型模糊控制器。     

基于FREM的自适应AFREM算法

设计了一种基于模糊控制技术的自适应FREM（AFREM）算法。该方案用平均队列长度和丢包比例的变化速率作为拥塞指示,对FREM算法输出的丢弃概率进行动态调整。仿真分析结果表明在动态环境下AFREM算法也能有效地将队列长度钳制到固定TQL值附近,改善了FREM算法的控制性能。     

RRM交换机中MCI算法的性能分析

在ABR相对速率标记算法交换机中,用CI和NI来描述网络的拥塞状态。由于CI,NI只有一个比特,只有表示网络状态拥塞或不拥塞的能力,不足以全面反映网络的各种拥塞程度。论文指出的多比特拥塞指示算法用多个比特将网络的拥塞程度进行刻画,比用单个比特刻画的算法精确。仿真结果表明多比特拥塞指标算法性能更优。     

基于控制论的ATM拥塞控制算法

ATM网络的多业务复用特性及其对QoS的承诺,使得其流量和拥塞控制变得非常复杂。控制论是分析和设计复杂系统的强有力工具,文章运用控制理论闭环反馈理论,设计了PI调节器,实现了基于PI调节器的ATM拥塞控制算法,实验及理论分析结果表明,该算法具有较强的稳定性与鲁棒性。     

TCP-H：基于三重判决的TCP拥塞控制

在异构无线网络中存在高误码、切换、信号衰落等链路特性,使传统的TCP拥塞控制机制受到了挑战。在不增加开销的情况下,基于丢包检测、RTT时间和ACK返回速率三重判决,提出了一种新的TCP拥塞控制机制（TCP—H）。仿真结果表明,TCP—H增强了对拥塞和随机差错的区分能力,满足公平性要求,改进的最小RTT计算方法解决了在低延迟向高延迟网络切换的时Vegas,Westwood等算法存在的最小RTT更新问题,有效提高了在异构无鲅网络环埔下TCP的性能．     

无线传感器网络中基于拥塞程度分级的速率调节机制

文中提出一种基于拥塞程度分级的速率调节算法.首先,对缓冲区进行多尺度排队分析,计算出缓冲区的溢出概率.其次,根据溢出概率的值,把节点拥塞程度分成三级.最后,针对每一级拥塞采取相应的速率调节方案来缓解拥塞.实验结果表明,该算法可以有效缓解拥塞,提高无线传感器网络的数据包投递率.     

面向SDN的拥塞控制算法研究

随着SDN技术飞速发展,SDN拥塞控制技术成为网络研究的一个热点。文章围绕基于SDN的数据中心网络拥塞问题,首先归纳总结产生拥塞的原因;其次,设计一种新的拥塞控制算法,即当网络中有节点处于拥塞状态,控制器选择一条或者多条合适的数据流在拥塞节点进行重路由传输并降低发送速率;仿真结果表明,所提出的算法降低了丢包率,提高了带宽利用率。     

主动队列管理机制在无线系统中的优化

主动队列管理(AQM)是对抗拥塞的重要手段,其经典算法之一是随机早期丢弃,简称RED。为使其能适应无线传输高突发误帧的特点,本文提出了一种基于平均队列长度和平均包到达速率的改进RED算法,分析了其相对于传统RED算法在预防和处理拥塞时的优势。同时针对无线信道的时变特性,提出自适应MIR速率调整,以提高频谱利用率和进一步降低拥塞概率,并讨论了其对于系统性能的改善。     

基于FPGA的模糊PID控制器设计

针对实现传统模糊PID控制器时,需要建立比例、积分和微分三个模糊控制器,存在模糊规则较繁杂、运算量大、速度慢等问题,提出了以PD模糊控制器代替PI模糊控制器,采用两个PD模糊控制器,并引入FPGA技术,实现模糊PID控制器.通过Quartus Ⅱ和Matlab联合仿真,比较了基于FBC和SBC实现的模糊PID控制器的控制效果,验证了设计方案的正确性和可行性.     

A fuzzy-logic control algorithm for active Queue Management in IP networks

Active Queue Management （AQM） is an active research area in the Internet community. Random Early Detection （RED） is a typical AQM algorithm, but it is known that it is difficult to configure its parameters and its average queue length is closely related to the load level. This paper proposes an effective fuzzy congestion control algorithm based on fuzzy logic which uses the predominance of fuzzy logic to deal with uncertain events. The main advantage of this new congestion control algorithm is that it discards the packet dropping mechanism of RED, and calculates packet loss according to a preconfigured fuzzy logic by using the queue length and the buffer usage ratio. Theoretical analysis and Network Simulator （NS） simulation results show that the proposed algorithm achieves more throughput and more stable queue length than traditional schemes. It really improves a router＇s ability in network congestion control in IP network.     

传感器网络拥塞避免与控制的模糊AQM算法

传感器网络节点通信能力有限,当数据到达速率持续超过节点转发能力时网络会发生拥塞;传感器网络是任务型网络,对不同优先级的信息具有不同的服务质量要求.针对传感器网络信息传输的上述特性,提出了一种新的拥塞避免与控制算法FAQM（Fuzzy Active Queue Management）.该算法在综合考虑数据包的随机指数标记概率和优先级权值的基础上,建立了模糊逻辑推理系统,并以数据包丢弃因子作为参量来实现数据流的智能调控.NS2仿真实验结果表明：FAQM算法能减少高优先级数据包的丢弃率和节点间链路的时延,稳定节点队列长度,在有效避免与控制拥塞网络的同时提升网络整体QoS（Quality of Service）性能.     

一种基于模糊逻辑的主动队列管理算法

主动队列管理 (ActiveQueueManagement,AQM)技术作为Internet拥塞控制的一种有效方法 ,对于提高In ternet的服务质量具有十分重要的作用 .本文根据TCP拥塞控制算法基于数据包丢失的窗口变化机制 ,设计了一种基于模糊逻辑的主动队列管理算法 .该算法依据路由器中队列长度的变化情况 ,根据一定的模糊自校正原则来调整数据包的丢弃概率 ,从而使路由器中的队列长度稳定在参考值附近 .仿真结果表明该算法不但十分有效 ,而且对不同的网络状况具有很好的适应能力 .     

Effective control of traffic flow in ATM networks using fuzzyexplicit rate marking (FERM)

We describe the fuzzy explicit fate marking (FERM) traffic flow control algorithm for a class of best effort service, known as available bit rate (ABR), proposed by the ATM Forum. FERM is an explicit rate marking scheme in which an explicit rate is calculated at the asynchronous transfer mode (ATM) switch and sent back to the ABR traffic sources encapsulated within resource management (RM) cells. The flow rate is calculated by the fuzzy congestion control (FCC) module by monitoring the average ABR queue length and its rate of change, then by using a set of linguistic rules. We use simulation to compare the steady-state and transient performance of FERM with EPRCA (a current favourite by the ATM Forum) in the presence of high priority variable bit rate (VBR) video and constant bit rate (CBR) in both a local-area network (LAN) and a wide-area network (WAN) environment. Our experiments show that FERM exhibits a robust behavior, even under extreme network loading conditions, and ensures fair share of the bandwidth for all virtual channels (VCs) regardless of the number of hops they traverse. Additionally, FERM controls congestion substantially better than EPRCA, offers faster transient response, leads to lower end-to-end delay and better network utilization     

基于T-S模糊神经网络的ATM网络拥塞控制

本文充分考虑了模糊神经网络的学习功能，提出了利用T－S模糊神经网络算法对ATM网络进行拥塞控制的方案。仿真结果表明，该方法改善了网络对拥塞的实时处理能力，又增加了网络资源的利用率。     

一种基于QoS的空间延迟/中断容忍网络拥塞控制方法

为缓解网络拥塞对空间延迟/中断容忍网络产生的影响,该文提出一种基于QoS的网络拥塞控制算法。该算法包括接触拥塞判断和基于QoS的数据转发两种机制,分别从接触剩余可用容量和节点剩余存储空间两方面对每一段接触的拥塞程度进行预测,将接触划分为不同的拥塞等级。在计算路由时,以整段路径中所包含接触的最高拥塞等级为该路径的拥塞等级,并根据该拥塞等级发送不同优先级的数据。实验表明,基于QoS的拥塞控制算法可以提高低优先级数据的传递率并在节点存储空间不足时降低最高优先级数据的传递时延。     

Adaptive Congestion Prediction in Vehicular Ad-hoc Networks (VANET) Using Type-2 Fuzzy Model to Establish Reliable Routes

Urban areas are more prone to accidents and traffic congestions due to ever-increasing vehicles and poor traffic management. The increase in the emission of harmful gases is another important issue associated with vehicular traffic. Attaining a level of QOS is often challenging as it has to meet the eco-friendly factors along with reliable and safe transportation. Smart and accurate congestion management systems in VANET can significantly reduce the risk of accidents and health issues. To fulfil the requirements of QOS the congestion control methods should consider the properties such as fairness, decentralization, network characteristics, and application demands in VANET. We proposed an Adaptive Congestion Aware Routing Protocol (ACARP) for VANET using the dynamic artificial intelligence (AI) technique. The ACARP presents the adaptive congestion detection algorithm using the type-2 fuzzy logic AI technique. The fuzzy model detects the congestion around each vehicle using three fuzzy inputs viz. bandwidth occupancy, link quality, and moving speed. This is followed by inference model to estimate congestion probability for each vehicle. Finally, defuzzification determines status of congestion detection using the pre-defined threshold value for each vehicle. The status of congestion and its probability values were utilized to establish safe and reliable routes for data transmission. It also saves significant communication overhead and hence congestions in the network. The simulation results provide the evidence that the proposed protocol improves the QOS and assist in reduction of traffic congestions significantly.

     

Design of a fuzzy traffic controller for ATM networks

This paper presents the design of a fuzzy traffic controller that simultaneously manages congestion control and call admission control for asynchronous transfer mode (ATM) networks. The fuzzy traffic controller is a fuzzy implementation of the two-threshold congestion control method and the equivalent capacity admission control method extensively studied in the literature. It is an improved, intelligent implementation that not only utilizes the mathematical formulation of classical control but also mimics the expert knowledge of traffic control. We appropriately choose input linguistic variables of the fuzzy traffic controller so that the controller is a closed-loop system with stable and robust operation. We extract knowledge of conventional control methods from numerous analytical data using a clustering technique and then use this knowledge to set parameters of the membership functions and fuzzy control rules via fuzzy set manipulation (linguistically stated but mathematically treated) with the aid of an optimization technique named genetic algorithm (GA). Simulation results show that the proposed fuzzy admission control improves system utilization by a significant 11%, while maintaining the quality of service (QoS) contract comparable with that of the conventional equivalent capacity method. The performance of the proposed fuzzy congestion control method is also 4% better than that of the conventional two-threshold congestion control method     

Convergence of REM flow control at a single link

Various TCP congestion control schemes can be interpreted as approximately carrying out a certain basic algorithm to maximize aggregate source utility, different schemes corresponding to different choices of utility functions. The basic algorithm consists of a link algorithm that updates a congestion measure based on its traffic load, and a source algorithm that adapts the source rate to congestion in its path. Though convergent, this algorithm can lead to large equilibrium backlogs. This problem can be eliminated by modifying the basic algorithm to include backlog in the update of the congestion measure. This article proves that the modified algorithm converges when the network can be modeled as a single bottleneck link. Moreover, in equilibrium, the source rates are matched to the link capacity while the buffer is cleared