Multi-indicator Active Queue Management Method

A considerable number of applications are running over IP networks. This increased the contention on the network resource, which ultimately results in congestion. Active queue management (AQM) aims to reduce the serious consequences of network congestion in the router buffer and its negative effects on network performance. AQM methods implement different techniques in accordance with congestion indicators, such as queue length and average queue length. The performance of the network is evaluated using delay, loss, and throughput. The gap between congestion indicators and network performance measurements leads to the decline in network performance. In this study, delay and loss predictions are used as congestion indicators in a novel stochastic approach for AQM. The proposed method estimates the congestion in the router buffer and then uses the indicators to calculate the dropping probability, which is responsible for managing the router buffer. The experimental results, based on two sets of experiments, have shown that the proposed method outperformed the existing benchmark algorithms including RED, ERED and BLUE algorithms. For instance, in the first experiment, the proposed method resides in the third-place in terms of delay when compared to the benchmark algorithms. In addition, the proposed method outperformed the benchmark algorithms in terms of packet loss, packet dropping, and packet retransmission. Overall, the proposed method outperformed the benchmark algorithms because it preserves packet loss while maintaining reasonable queuing delay.     

基于传输控制协议的无线自组网主动队列管理建模

主动队列管理（AQM）通常研究队列控制器的设计．作为被控对象,传输控制协议（TCP）往往利用网络仿真器（NS）的仿真实现,因此有必要研究无线自组网的TCP及AQM特性．基于TCP窗口加性增一乘性减算法及排队原理,推导了TCP窗口及队列的微分方程,再基于比例积分AQM控制,推导了拥塞丢弃概率的微分方程,通过建立联立微分方程组,提出了AdHoc网络TCP／AQM微分模型．对比仿真显示,新模型能较好地估计无线白组网的性能．模型研究也表明,网络跳数,无线丢失和过小的队列成为AQM性能瓶颈,队列信息则有助于TCP区分无线自组网的拥塞丢弃与无线丢失．     

Multi-step Predictive Control of a PDF-shaping Problem

A predictive control strategy is proposed for the shaping of the output probability density function (PDF) of linear stochastic systems. The B-spline neural network is used to set up the output PDF model and therefore converts the PDF-shaping into the control of B-spline weights vector. The Diophantine equation is then introduced to formulate the predictive PDF model, based on which a moving-horizon control algorithm is developed so as to realize the predictive PDF tracking performance.     

输出概率密度函数形状的多步预测控制

A predictive control strategy is proposed for the shaping of the output probability density function (PDF) of linear stochastic systems. The B-spline neural network is used to set up the output PDF model and therefore converts the PDF-shaping into the control of B-spline weights vector. The Diophantine equation is then introduced to formulate the predictive PDF model, based on which a moving-horizon control algorithm is developed so as to realize the predictive PDF tracking performance.     

基于区分服务的主动队列管理算法研究

分析了几种主动队列管理算法。RIO算法是用于支持区分服务确保转发逐跳行为的主动队列管理算法,它是对RED算法的简单扩充,但是该算法的性能对配置参数敏感。PI算法是基于控制论的主动队列管理算法,具有队列长度抖动小的特点。PIP算法是PI算法的改进,比PI算法具有更快的收敛速度。为了更好地满足AF PHB的要求,基于PIP算法,结合三色标记器的功能,提出一个新的主动队列管理算法PIPGYR（PIP with Green ＆ Yellow ＆ Red）。通过仿真验证,该算法队列长度抖动小,同时能够保护高优先级分组。     

Performance evaluation for DRED discrete-time queueing network analytical model

Due to the rapid development in computer networks, congestion becomes a critical issue. Congestion usually occurs when the connection demands on network resources, i.e. buffer spaces, exceed the available ones. We propose in this paper a new discrete-time queueing network analytical model based on dynamic random early drop (DRED) algorithm to control the congestion in early stages. We apply our analytical model on two-queue nodes queueing network. Furthermore, we compare between the proposed analytical model and three known active queue management (AQM) algorithms, including DRED, random early detection (RED) and adaptive RED, in order to figure out which of them offers better quality of service (QoS). We also experimentally compare the queue nodes of the proposed analytical model and the three AQM methods in terms of different performance measures, including, average queue length, average queueing delay, throughput, packet loss probability, etc., aiming to determine the queue node that offers better performance.     

Reducing Queue Oscillation at a Congested Link

Queue length oscillation at a congested link causes many undesirable properties such as large delay jitter, underutilization of the link and packet drops in burst. The main reason of this oscillation is that most queue management schemes determine the drop probability based on the current traffic without consideration on the impact of that drop probability on the future traffic. In this paper, we propose a new active queue (AQM) scheme to reduce queue oscillation and realize stable queue length. The proposed scheme measures the current arrival and drop rates, and uses them to estimate the next arrival rate. Based on this estimation, the scheme calculates the drop probability which is expected to realize stable queue length. We present extensive simulation with various topologies and offered traffic to evaluate performance of the proposed scheme. The results show that the proposed scheme remarkably reduces queue length oscillation compared to other well-known AQMs. It is also shown that the proposed scheme improves fairness among TCP flows due to the stable drop probability, and maintains high utilization with small queue length.     

Ad-hoc网络PSD拥塞控制算法

神经元PID算法能较好地控制队列长度,但其神经元增益对被控对象的状态较为敏感,基于试凑和经验的设定往往使控制效果难以保证。基于TCP拥塞窗口加法增大、乘法减小原则和排队机制,推导出拥塞窗口与丢弃概率、队列长度的微分方程,再对方程进行线性化,获得Ad-hoc网络TCP/AQM控制系统模型。基于该模型,将递推计算修正功能引入神经元PID,设计了一种神经元自适应PSD的AQM。该算法可以在线调整神经元增益。NS仿真表明,在无线分组丢失、突发流及链路容量变化的Ad-hoc网络中,PSD队列管理性能优于神经元PID。     

几种主动队列管理算法的仿真及性能评估

采用仿真分析的方法,系统地研究了当前提出的用于Internet路由器缓冲管理的一系列主动队列管理(AQM)算法的性能。根据AQM的设计原理,将当前的AQM算法划分为3类：基于队列长度,基于网络负载和同时基于队列长度和网络负载的AQM算法。仿真研究和分析表明,现有的AQM算法不能适应网络流量的动态变化。     

一种基于双模控制的主动队列管理新算法

随机指数标记算法(REM)是一种有效的主动队列管理算法,但由于TCP/IP网络流量模型呈现非线性特性,故而其控制效果不佳,存在队列稳定性差,对动态流量响应慢等问题.为了解决上述问题,提出了基于双模控制的主动队列管理算法(Fuzzy-REM).算法采用分段控制策略,在瞬时队列偏差大于阈值时,采用模糊控制,反之采用REM控制,从而将模糊控制的快速响应和REM稳态性能好的优点结合起来.NS2中的仿真实验表明,相对于REM算法,Fuzzy-REM提高了队列稳定性,加快了收敛速度,增强了算法对网络环境变化的适应性.     

基于智能预测控制的网络拥塞主动队列管理算法研究

路由队列管理是保证网络性能、避免网络拥塞的重要手段,目前采用的主要队列管理方法为被动式队列管理,同时主动式队列管理已经成为近来的主要研究热点. 随机早侦测(RED)作为最早提出的主动队列管理方法,更获得了普遍的关注. 使用严格的数学模型来描述由端系统和网关组成的系统,并进行队列管理性能分析. 提出一种采用快速广义预测控制的RED控制器( FGPC2RED控制器) ,进行网络拥塞控制的研究. 介绍了系统的结构及系统的辨识, 并通过仿真证明了FGPC算法在路由队列管理中应用的可行性,可以有效控制队列长度,避免路由拥塞及减小往返延迟.     

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.     

PIPIO：一个新的面向区分服务确保转发的主动队列管理算法

RIO是用于支持区分服务确保转发逐跳行为的主动队列管理算法，该算法是对RED算法的简单扩充。由于RED算法的性能对配置参数敏感，因此基于RED算法的RIO算法必然具有配置参数敏感的特点。PI算法是基于控制论的主动队列管理算法，具有队列长度抖动小的特点。PIP算法是PI算法的改进，比PI具有更快的收敛速度。本文基于PIP算法设计了一个新的主动队列管理算法PIPIO。该算法队列长度抖动小，同时能保护高优先级报文。     

A robust fractional-order PID controller design based on active queue management for TCP network

In this paper, a robust fractional-order controller is designed to control the congestion in transmission control protocol (TCP) networks with time-varying parameters. Fractional controllers can increase the stability and robustness. Regardless of advantages of fractional controllers, they are still not common in congestion control in TCP networks. The network parameters are time-varying, so the robust stability is important in congestion controller design. Therefore, we focused on the robust controller design. The fractional PID controller is developed based on active queue management (AQM). D-partition technique is used. The most important property of designed controller is the robustness to the time-varying parameters of the TCP network. The vertex quasi-polynomials of the closed-loop characteristic equation are obtained, and the stability boundaries are calculated for each vertex quasi-polynomial. The intersection of all stability regions is insensitive to network parameter variations, and results in robust stability of TCP/AQM system. NS-2 simulations show that the proposed algorithm provides a stable queue length. Moreover, simulations show smaller oscillations of the queue length and less packet drop probability for FPID compared to PI and PID controllers. We can conclude from NS-2 simulations that the average packet loss probability variations are negligible when the network parameters change.     

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.     

基于模糊推理的鲁棒主动队列管理算法

基于常规控制理论的主动队列管理(AQM)算法在复杂动态网络环境下对参数变化比较敏感,难以保证队列稳定性且缺乏鲁棒性。针对上述问题提出基于队列长度和链路速率相对变化率的模糊AQM算法,以队列长度与期望队列长度以及链路速率与链路容量的相对误差量作为网络拥塞指示,采用模糊推理得出中间节点的丢包概率。仿真实验表明,该算法具有良好的队列稳定性和较小的队列延时,对网络的非线性和负载波动等不确定因素具有鲁棒性。     

基于模型算法控制的主动队列管理算法

通过深入分析TCP/AQM系统的动态特性,提出一个新的预测模型。基于该模型,结合模型算法控制(MAC)提出一种新的主动队列管理算法(MACAQM),并给出了MACAQM的详细设计过程和参数选取的原则。大量不同网络环境的仿真实验表明了MACAQM算法的有效性。与PI, RaQ和REM等算法相比较, MACAQM具有收敛速度快、队列抖动小的优点。同时, MACAQM的采样间隔相对较大,算法实现简单,所以计算量较小,占用的路由器资源也较少。     

Design of robust active queue management controllers for a class of TCP communication networks

This paper describes the design of active queue management (AQM) controllers for a class of TCP communication networks. In TCP/IP networks, the packet-dropping probability function is considered as a control input. Therefore, a TCP AQM controller was modeled as a time-delayed system with a saturated input. The objective of the work described here was to design robust controllers capable of achieving the desired queue size and guaranteeing asymptotic stability of the operating point. To achieve this aim, we have proposed two control strategies, namely a static state feedback controller and an observer-based controller. By applying the Lyapunov-Krasovskii functional approach and the linear matrix inequality technique, control laws and delay-independent stability criteria for the AQM controllers were derived. The performance of the two control schemes was evaluated in various network scenarios via a series of numerical simulations. The simulation results confirm that the proposed schemes outperform other AQM schemes.     

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。