主动队列管理中的智能分组丢弃新机制

主动队列管理通过网络中间节点有控制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TCP端到端拥塞控制近来研究的一个技术热点.已有的大多数算法在判定分组丢弃时大都沿袭了RED的概率丢弃机制,具有一定计算复杂度的随机数生成过程不利于路由器性能的优化.在本文中,我们首先定义了拥塞指数这一新的测度变量来量化描述网络的拥塞状态.接着,利用模糊逻辑设计了一种新的智能分组丢弃机制,离线的合成推理使得分组丢弃的判定仅需要简单的查表操作和比较运算即可完成,为优化路由器的性能提供了便利.数字仿真的结果表明:智能分组丢弃机制的性能优于经典的RED算法,控制队列的能力强,鲁棒性好,稳定工作域大,能很好地抵抗突发性和非弹性业务的干扰,适合工作在瞬息万变的动态网络环境中.     

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 .     

一种基于模糊逻辑的主动队伍管理算法

主动队列管理（AciiveQueue Management,AQM)技术作为Internet拥塞控制的一种有效方法，对于提高Internet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伍管理算法，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。     

大时滞网络拥塞控制器的设计

几乎现有的AQM算法都忽略了大时滞对网络拥塞控制系统稳定性的负面影响,以致稳定性、响应性和鲁棒性在大时滞网络中大大降低.如PI、REM等AQM算法在大时滞环境下表现出剧烈的队列震荡和频繁的空队列情况,这些现象直接导致了链路利用率的低下和延时抖动的增大.而面向大时滞网络拥塞控制的DC-AQM算法不够恰当的参数配置使得系统输出偏离了控制的目标,出现了很高的分组丢弃概率.为解决大时滞网络拥塞控制的稳定性问题,本文基于内模控制原理,提出一种新的鲁棒AQM控制器IMC-PID来补偿网络时滞对系统稳定性的影响.仿真分析表明,随着网络时滞的增大,IMC-PID在综合性能上胜过其他算法,获得了较高的链路利用率和较低的延时抖动.     

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。     

一种顽健的自校正主动队列管理机制

现有基于控制理论的主动队列管理机制（AQM）大多数是根据简化的线性被控对象模型设计的,或者根据特定的网络条件设置算法的参数,当网络条件大范围变化时算法的性能难以保证.为了解决这些问题,提出了一种自校正的主动队列管理机制STR,通过在线估计TCP/AQM闭环系统被控对象模型的参数,并相应地调节报文丢弃概率,使路由器的缓冲区队列长度与期望值之间的方差最小.通过仿真实验验证了当网络条件大范围变化时算法的队列长度、链路利用率、报文丢弃率等性能,实验结果表明该算法具有良好的顽健性.     

基于动态输出反馈控制的主动队列管理算法

 针对网络拥塞控制的不确定时滞特性,提出了一种基于动态输出反馈控制(DOFC)的主动队列管理(AQM)算法.建立了TCP/AQM系统的时滞有界模型,给出了判定闭环AQM系统稳定的充分性条件,以及基于线性矩阵不等式的动态输出反馈控制器参数设计方法.仿真结果表明,该算法在大时滞的网络环境中,能迅速地将队列长度收敛到目标长度附近,且在特征参数变化的网络环境中具有较强的鲁棒性.     

SBlue:一种增强Blue稳定性的主动式队列管理算法

主动式队列管理(AQM)是 IETF 为解决 TCP 端到端拥塞控制机制存在的问题而提出的一种队列管理技术。Blue 是一种常用的 AQM 算法,它使用丢包事件和链路空闲事件来管理拥塞。相比较于 RED 算法,Blue 有很多优点,但由于缺乏早期拥塞检测机制,因此不能维持队列长度的稳定,特别是当 TCP 连接很多或 TCP 连接数发生突变时容易导致队列溢出或空闲。本文为此提出了一种增强 Blue 稳定性的主动式队列管理算法 SBlue。仿真实验表明,SBlue 能有效保持队列长度的稳定,大大减少队列溢出或空闲现象的发生。     

主动队列管理中的PID控制器

作为对终端系统上拥塞控制的一种补充,中间节点上的主动队列管理(AQM)策略在保证较高吞吐量的基础上有效地控制队列长度,从而实现了控制端到端的时延,保证QoS的目的。C.Hollot等人(2001)用经典控制理论中频域校正的方法设计了用于AQM的PI控制器,但参数整定上的试凑方法不免代有盲目性;算法的瞬态性能指标也不够理想。为此,该文引入了微分环节来增强系统的响应能力,同时给出了基于稳定裕度的参数整定方法,使PID控制器的稳定性有了绝对保障。仿真试验表明PID算法的调节时间远远短于PI控制器,从而为在负载瞬息万变的网络环境中实现控制分组排队等待时间的目标提供了有力的技术保障。     

一种参数自适应的主动队列管理算法-自适应BLUE

BLUE算法是一种典型的主动队列管理(Active Queue Management,AQM)算法,研究表明BLUE算法优于RED算法.BLUE算法使用丢包事件和链路空闲事件控制网络拥塞.但由于BLUE算法在参数设置方面存在不足,尤其是当TCP连接数突然剧烈变动时,容易导致队列溢出或空闲的频繁发生.该文引进参数自适应机制,提出了自适应BLUE算法,并借鉴了RED算法的早期拥塞检测机制.NS仿真实验表明该算法能有效保持队列长度的稳定,减少队列溢出或空闲现象的发生,在提高链路利用率的同时降低丢包率.     

Modeling and Analysis of Active Queue Management Schemes under Bursty Traffic

Traffic congestion arising from the shared nature of uplink channels in wireless networks can cause serious problems for the provision of QoS to various services. One approach to overcome these problems is to implement some effective congestion control mechanisms at the downlink buffer at the mobile network link layer or at gateways on the behalf of wireless network access points. Active queue management (AQM) is an effective mechanism to support end-to-end traffic congestion control in modern high-speed networks. Initially developed for Internet routers, AQM is now being also considered as an effective congestion control mechanism to enhance TCP performance over 3G links. This paper proposes an analytical performance model for AQM using various dropping functions. The selection of different dropping functions and threshold values required for this scheme plays a critical role on its effectiveness. The model uses a well-known Markov-modulated Poisson process (MMPP) to capture traffic burstiness and correlations. The validity of the model has been demonstrated through simulation experiments. Extensive analytical results have indicated that exponential dropping function is a good choice for AQM to support efficient congestion control.     

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.     

Designing a binary controller to enhance end-to-end congestion control

Active queue management (AQM) is proposed to enhance end-to-end congestion control through purposefully dropping packets in the intermediate nodes. In this letter, a novel packet dropping mechanism is developed through designing a binary controller applying the robust control theory. The new mechanism can simplify the manipulation on the AQM router so as to be helpful for implementing the high performance router. The numerical simulation results show that the binary controller can satisfy with the technical requirements for AQM     

Adaptive mean queue size and its rate of change: queue management with random dropping

The random early detection active queue management (AQM) scheme uses the average queue size to calculate the dropping probability in terms of minimum and maximum thresholds. The effect of heavy load enhances the frequency of crossing the maximum threshold value resulting in frequent dropping of the packets. An adaptive queue management with random dropping algorithm is proposed which incorporates information not just about the average queue size but also the rate of change of the same. Introducing an adaptively changing threshold level that falls in between lower and upper thresholds, our algorithm demonstrates that these additional features significantly improve the system performance in terms of throughput, average queue size, utilization and queuing delay in relation to the existing AQM algorithms.     

A stochastic approximation approach to active queue management

Recently, a dynamic adaptive queue management with random dropping (AQMRD) scheme has been developed to capture the time-dependent variation of average queue size by incorporating the rate of change of average queue size as a parameter. A major issue with AQMRD is the choice of parameters. In this paper, a novel online stochastic approximation based optimization scheme is proposed to dynamically tune the parameters of AQMRD and which is also applicable for other active queue management (AQM) algorithms. Our optimization scheme significantly improves the throughput, average queue size, and loss-rate in relation to other AQM schemes.     

A novel adaptive traffic prediction AQM algorithm

In the Internet, network congestion is becoming an intractable problem. Congestion results in longer delay, drastic jitter and excessive packet losses. As a result, quality of service (QoS) of networks deteriorates, and then the quality of experience (QoE) perceived by end users will not be satisfied. As a powerful supplement of transport layer (i.e. TCP) congestion control, active queue management (AQM) compensates the deficiency of TCP in congestion control. In this paper, a novel adaptive traffic prediction AQM (ATPAQM) algorithm is proposed. ATPAQM operates in two granularities. In coarse granularity, on one hand, it adopts an improved Kalman filtering model to predict traffic; on the other hand, it calculates average packet loss ratio (PLR) every prediction interval. In fine granularity, upon receiving a packet, it regulates packet dropping probability according to the calculated average PLR. Simulation results show that ATPAQM algorithm outperforms other algorithms in queue stability, packet loss ratio and link utilization.     

A dynamic quarantine scheme for controlling unresponsive TCP sessions

In addition to unresponsive UDP traffic, aggressive TCP flows pose a serious challenge to congestion control and stability of the future Internet. This paper considers the problem of dealing with such unresponsive TCP sessions that can be considered to collectively constitute a Denial-of-Service (DoS) attack on conforming TCP sessions. The proposed policing scheme, called HaDQ (HaTCh-based Dynamic Quarantine), is based on a recently proposed HaTCh mechanism, which accurately estimates the number of active flows without maintenance of per-flow states in a router. We augment HaTCh with a small Content Addressable Memory (CAM), called quarantine memory, to dynamically quarantine and penalize the unresponsive TCP flows. We exploit the advantage of the smaller, first-level cache of HaTCh for isolating and detecting the aggressive flows. The aggressive flows from the smaller cache are then moved to the quarantine memory and are precisely monitored for taking appropriate punitive action. While the proposed HaDQ technique is quite generic in that it can work with or without any AQM scheme, in this paper we have integrated HaDQ and an AQM scheme to compare it against some of the existing techniques. For this, we extend the HaTCh scheme to develop a complete AQM mechanism, called HRED. Simulation-based performance analysis indicates that by using a proper configuration of the monitoring period and the detection threshold, the proposed HaDQ scheme can achieve a low false drop rate (false positives) of less than 0.1%. Comparison with two AQM schemes (CHOKe and FRED), which were proposed for handling unresponsive UDP flows, shows that HaDQ is more effective in penalizing the bandwidth attackers and enforcing fairness between conforming and aggressive TCP flows.     

PAQM: an adaptive and proactive queue management for end‐to‐end TCP congestion control

Two functions, the congestion indicator (i.e. how to detect congestion) and the congestion control function (i.e. how to avoid and control congestion), are used at a router to support end‐to‐end congestion control in the Internet. Random early detection (RED) (IEEE/ACM Trans. Networking 1993; 1 (4):397–413) enhanced the two functions by introducing queue length averaging and probabilistic early packet dropping. In particular, RED uses an exponentially weighted moving average (EWMA) queue length not only to detect incipient congestion but also to smooth the bursty incoming traffic and its resulting transient congestion. Following RED, many active queue management (AQM)‐based extensions have been proposed. However, many AQM proposals have shown severe problems with detection and control of the incipient congestion adaptively to the dynamically changing network situations. In this paper, we introduce and analyse a feedback control model of TCP/AQM dynamics. Then, we propose the Pro‐active Queue Management (PAQM) mechanism, which is able to provide proactive congestion avoidance and control using an adaptive congestion indicator and a control function under a wide range of traffic environments. The PAQM stabilizes the queue length around the desired level while giving smooth and low packet loss rates and high network resource utilization. Copyright © 2004 John Wiley & Sons, Ltd.     

Rate‐based proportional–integral control scheme for active queue management

Most high‐speed links do not have adequate buffering and as a result Active Queue Management (AQM) schemes that utilize queue size information for congestion control cannot be effectively applied on these links. A high‐speed link will, typically, have small buffers in relation to the bandwidth‐delay product of the link. In this paper we argue that rate‐based AQM schemes be used for such links. The goal here is to match the aggregate rate of the active TCP connections to the available capacity while maintaining minimal queue size and high link utilization. The AQM scheme described here employs a Proportional–Integral (PI) control strategy and explicitly takes into account the time delay in the control process. Copyright © 2006 John Wiley & Sons, Ltd.