基于灰色预测可变裕度PID网络自适应算法

为了综合控制拥塞链路的队列长度,提高AQM系统对动态网络环境的自适应能力,提出了一种基于灰色预测和考虑可变裕度PID控制的自适应TCP网络主动队列管理。首先,建立相角和幅值裕度与网络参数相关的PID自适应主动队列(TCP/AQM)控制论模型,该模型可以根据网络参数的变化而动态改变控制参数,以提高AQM网络动态自适应能力,及系统的鲁棒性;其次,将灰色预测引入该模型,实现路由器队列长度的超前预测,补偿带有PID反馈模块的AQM算法给队列造成的时滞影响。与其他算法的仿真结果相比较,该设计算法能够使信息流在较短的时间内稳定在期望队列长度阈值附近。     

大时滞网络自适应主动队列管理新算法

针对PID控制器无法严格处理主动队列管理（AQM）中的大时滞情况,且不能随着变化的网络环境在线调节参数,提出了一种基于增益自适应Smith预估控制和模糊控制的大时滞网络的自适应PID主动队列管理（GAS-FPID）算法。引入增益自适应Smith预估控制器实现滞后补偿,模糊控制器来实现PID参数动态网络环境的在线调整;NS2仿真表明,所提出算法能克服滞后的影响,能快速的适应动态网络环境,具有很好的稳定性和鲁棒性。     

基于延时补偿结构的大时滞网络拥塞控制

研究和分析了几种典型的主动队列管理AQM(Active Queue Management)算法在大时滞网络下的稳定性、性能及响应速度,发现如RED、PI等AQM算法在大时滞网络环境下队列出现剧烈的振荡和空队列增多,这些现象直接导致链路利用率降低和丢包率增大.针对上述的大时滞网络,将具有正反馈的延时补偿结构用于TCP/AQM拥塞窗口动态模型,实现对延时的补偿,仿真表明采用该控制结构提高了网络的性能.     

具有最小时间加权平方误差积分的主动队列管理算法ISTE-PI

针对现有基于控制论的主动队列管理(AQM)算法的不足,在不忽略TCP/AQM传递函数模型中时滞环节的条件下,将TCP短流和UDP流作为干扰信号引入AQM控制器的设计过程,并借助最小"时间加权平方误差积分(ISTE)"控制方法,设计了一个基于PI控制器的AQM算法ISTE-PI.与现有算法相比,ISTE-PI算法同时具备响应速度快、抗干扰能力强、能用于大时滞网络环境且鲁棒性好等特点.鉴于Internet网络流量由TCP长流、TCP短流和UDP流构成,且是大时滞网络,因此ISTE-PI算法能更好地适应Internet网络环境下的主动队列管理.     

大时滞网络中的拥塞控制算法

主动队列管理(AQM)通过网络中间节点有目的的分组丢弃实现了较低的排队延时和较高的有效吞吐量,是近年来TCP端到端拥塞控制的一个研究热点.已有的大多数AQM算法在设计过程中都没有充分考虑到大时滞对算法性能的影响.首先通过仿真试验证实了已有的几种典型算法控制的队列在大时滞网络中无一例外地出现了剧烈的振荡,导致瓶颈链路利用率下降和延时抖动加剧.为此,在进行了适当模型拟合处理的基础上,应用控制理论中的内模补偿原理设计了鲁棒的延时补偿主动队列管理(delay compensation-active queue management,简称DC-AQM)算法,克服了大时滞给队列稳定性造成的不利影响.仿真实验结果表明,新算法在大时滞小期望队列长度的网络配置中表现出的综合性能明显优于已有的算法,链路利用率是其他算法的3～4倍.     

基于模型有效性评价的主动队列管理算法

TCP/AQM系统是一多时滞回路耦合时变系统,源端传输控制协议(TCP)的拥塞控制机制也各不相同,因此其机理模型复杂.设计控制器所采用动态近似模型的参数变化范围很大,这对辨识算法提出了很高要求.本文先从模型误差分配方式这一新的角度对经典辨识算法进行了分析.然后运用新分析框架,结合TCP/AQM系统特征和控制要求,设计了一种基于模型有效性评价机制的辨识算法.该辨识算法能够对控制系数进行自适应调节,满足了主动队列管理(AQM)算法的设计需求.仿真结果表明,基于模型有效性评价机制的AQM算法,能有效应对时滞和不确定性带来的负面影响.与潜在标准化AQM算法相比,新算法在保持低排队时延和高效链路利用率方面取得了更好的平衡.     

基于预测的时滞系统拥塞控制算法

为了克服TCP/AQM控制系统中时滞环节带来的不利影响,将Smith预估器与Dahlin算法相结合,提出了一种新的AQM机制——PPI算法,该算法能消除时滞环节对系统性能的影响,并减少整定参数的数量,通过仿真结果显示出PPI控制器能有效避免网络拥塞,提高系统的稳定性,并在系统变化时具有较好的鲁棒性。     

TCP/AQM系统大时间尺度模型

网络传输连接的往返时延(round-trip times,RTT)大小各不相同,因此TCP/AQM系统本质为一多时滞回路耦合系统.由于RTT分布范围远大于控制量调节周期,这给准确评估控制效果带来很大困难.已有基于控制理论的主动队列管理(active queue management,AQM)算法多以流体流模型为基础进行设计,没有充分考虑RTT和采样周期对系统性能的影响.对于TCP/AQM系统,合理的评价方法是对调节过程进行评价,而非仅评价单个采样周期内的控制量是否合适.本文结合数据驱动控制思想和系统自身特征,统一从路由视角对TCP与AQM之间的交互进行抽象,通过时间扩展从更大的时间尺度去评价控制量调节过程,然后基于此模型设计自适应AQM算法–—大时间尺度AQM算法(large time scale AQM,LTSAQM).仿真结果表明,该算法收敛速度快,排队时延抖动小,特别是在长时滞网络环境下,性能明显改善.     

基于单神经元预测PID的主动队列管理算法

针对网络中普遍存在的大时滞现象以及网络参数实时变化问题,将单神经元PID预测控制器用于主动队列管理算法(AQM).利用单神经元的自学习能力解决网络实时变化时算法参数的在线整定问题,并利用Levinson预测器有效地解决了大时滞对网络性能的影响,最后通过仿真进一步验证了提出算法的有效性.     

基于速率和队长的大时滞网络AQM算法

针对网络拥塞控制系统在大时滞网络中产生的不利影响,提出一种基于速率和队长的大时滞网络AQM算法。该算法采用缓冲区队列长度和包到达速率作为网络拥塞的判别依据,在结合Smith预估的模糊PID控制方法中加入速率控制项。仿真表明该算法在大时滞和网络动态变化的环境中拥塞响应较快、收敛时间短,并能较好地将队列长度稳定到期望值附近,提高缓冲区的利用率。     

基于PID神经元网络和内模控制的拥塞控制算法*

针对网络系统的大时滞和非线性特性,设计了一种新的拥塞控制算法,将PID神经元网络与内模控制相结合应用于主动队列管理中,并使用Lyapunov理论证明了此算法的稳定性。NS仿真结果表明,这种算法的稳态和瞬态性能都优于PID算法,并且在参数变化和负载扰动时具有很强的鲁棒性。     

Congestion control algorithm in large-delay uncertain networks

Based on Smith-fuzzy controller, a new active queue management （AQM） algorithm adaptable to the large-delay uncertain networks is presented. It can compensate the negative impact on the queue stability caused by the large delay, and it also maintains strong robustness under the condition of dynamic network fluid. Its stability is proven through Lyapunov method. Simulation results demonstrated that this method enables the queue length to converge at a preset value quickly and keeps the queue oscillation small, the simulation results also show that the scheme is very robust to disturbance under various network conditions and large delay and, in particular, the algorithm proposed outperforms the conventional PI control and fuzzy control when the network parameters and network delay change.     

Congestion control algorithm in large-delay uncertain networks

Based on Smith-fuzzy controller, a new active queue management (AQM) algorithm adaptable to the large-delay uncertain networks is presented. It can compensate the negative impact on the queue stability caused by the large delay, and it also maintains strong robustness under the condition of dynamic network fluid. Its stability is proven through Lyapunov method. Simulation results demonstrated that this method enables the queue length to converge at a preset value quickly and keeps the queue oscillation small, the simulation results also show that the scheme is very robust to disturbance under various network conditions and large delay and, in particular, the algorithm proposed outperforms the conventional PI control and fuzzy control when the network parameters and network delay change.     

基于Smith预估的模糊PID主动队列管理算法研究

为解决网络拥塞控制系统中由于网络大时滞对主动队列管理算法产生不利影响的问题,提出了一种基于Smith预估的模糊PID主动队列管理算法.该算法将Smith预估控制与模糊控制相结合,利用Smith预估器补偿网络时滞,同时运用模糊控制在一定程度上克服了传统Smith预估器对模型结构与参数的精确性过于敏感、鲁棒性差的缺点,使主动队列管理算法控制性能有明显提高.仿真实验结果表明,该算法在大时滞的网络环境下能很好地将路由器队列长度收敛于期望值,并能适应突发流和非弹性业务流的干扰,适用于动态变化的网络环境.     

大时滞网络中的自适应拥塞控制算法

根据内模控制器(IMC)的设计方法,设计出适合于大时滞网络,具有时滞补偿能力的主动队列管理(AQM)控制器。为了使控制器适合网络的动态变化,再使其系数根据网络的变化自适应地做出相应的修正。最后通过NS-2仿真,验证了该算法的可靠性。     

A novel self-tuning feedback controller for active queue management supporting TCP flows

Wireless access points act as bridges between wireless and wired networks. Since the actually available bandwidth in wireless networks is much smaller than that in wired networks, there is a bandwidth disparity in channel capacity which makes the access point a significant network congestion point. The recently proposed active queue management (AQM) is an effective method used in wired network and wired-wireless network routers for congestion control, and to achieve a tradeoff between channel utilization and delay. The de facto standard, the random early detection (RED) AQM scheme, and most of its variants use average queue length as a congestion indicator to trigger packet dropping. In this paper, we propose a Novel autonomous Proportional and Differential RED algorithm, called NPD-RED, as an extension of RED. NPD-RED is based on a self-tuning feedback proportional and differential controller, which not only considers the instantaneous queue length at the current time point, but also takes into consideration the ratio of the current differential error signal to the buffer size. Furthermore, we give theoretical analysis of the system stability and give guidelines for the selection of feedback gains for the TCP/RED system to stabilize the instantaneous queue length at a desirable level. Extensive simulations have been conducted with ns2. The simulation results have demonstrated that the proposed NPD-RED algorithm outperforms the existing AQM schemes in terms of average queue length, average throughput, and stability.     

Adaptive generalized minimum variance congestion controller for dynamic TCP/AQM networks

Generic generalized minimum variance-based (GMV) controllers have been adopted as efficient control mechanisms especially in presence of measurement noise. However, such controllers exhibit degraded performance with change in process dynamics. To overcome this problem, a novel congestion controller based on active queue management (AQM) strategy for dynamically varying TCP/AQM networks known as adaptive generalized minimum variance (AGMV) is proposed. AGMV is the combination of the real-time parameter estimation and GMV. The performance of the proposed scheme is evaluated and compared with its adaptive minimum variance (AMV) counterpart under two distinct scenarios: TCP network with unknown parameters and TCP network with time varying parameters. Simulation results indicate that, in either case, AGMV is able to keep the queue length around the desired point. In addition, the superior performance of the proposed controller has been shown with regard to the PI controller, which is well-known in the AQM domain.