拥塞控制中的随机检测算法研究

作为主动队列管理（AQM）的典型代表，RED（Random Early Detection）算法得到了较为广泛的应用。出于改进其稳定性和公平性的考虑，研究者开发出了ARED，SRED，FRED，RIO和WRED等几种算法。重点研究了RED及其多种变种方法的设计思想、算法，并比较了它们的优缺点。在此基础上，分析了IP复接器中适用的算法。     

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.     

利用可变队列长度的AQM改进与研究

AQM（主动队列管理）通过在拥塞的早期阶段丢包来告知信源。AQM改进了响应流（如TCP）和非响应流（如UDP）之间的公平性,它可以带来较高的吞吐量及有效的链路。提出了可变队列长度适应RED算法,它基于每小时的可变队列长度,并且可以更有效地应对突发流。如果将当前可变队列长度与RED及ARED相比,这种方法可以提供更为平缓的平均度列长度。试验结果表明,可变队列长度适应RED比弃尾法提高了公平性,与ARED比较性能也大为改观。这样即使有突发流存在,也可以使数据包稳定地传输。     

一种加强的主动队列管理算法--EBLUE

作为一种典型的主动队列管理算法,BLUE明显不同于其它方法,它使用丢包和连接空闲事件来控制拥塞。试验表明BLUE的丢包率明显小于RED,但是其参数设置仍然存在一些不足之处。本文在BLUE算法的基础之上,通过引进自适应的思想对其进行了改进,提出了一种加强的BLUE队列算法——EBLUE。大量的仿真实验表明本文的改进算法能够进一步提高BLUE的性能。     

自适应RED算法在OBS网络中的应用

队列管理是光突发交换(OBS)网络边缘节点的重要功能之一.对随机早期检测(RED)队列管理算法及两种自适应RED算法在OBS网络中的应用进行了研究和仿真分析.仿真结果表示:动态调整队列权重的自适应RED算法和动态调整最大丢包率的自适应RED算法均能改进RED算法的性能,但是后者的性能优于前者.     

一种基于负载的公平性主动队列管理算法

针对随机早期检测（RED,Random Early Detection）算法存在的公平性问题,提出了一种基于负载的公平性主动队列管理算法（LFED）。该算法通过引入网络负载和队列这两个概念,有效地判断当前网络拥塞程度,同时使用改进的丢包率公式,并且借鉴CHOKe算法的惩罚机制对非响应流进行有效惩罚,以保证不同数据流之间的公平。仿真实验表明,与RED和CHOKe相比,LFED可以得到稳定的瞬时队列长度和减少分组丢弃率,具有较好的公平性和稳定性。     

基于时间槽的自相似流量随机早检测算法——SFRED

分析研究了网络流量的自相似和长相关性,并基于分形布朗运动模型导出了自相似流量环境RED算法分组丢失概率的一种计算方法,从而提出了基于时间槽的自相似流量随机早检测队列管理算法SFRED.该算法在每个时间槽内计算一次分组丢失概率,大大降低系统负担.NS2仿真实验表明SFRED算法性能明显优于RED,能够很好地在自适应流背景下控制队列长度,并具有良好的吞吐量性能.     

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。     

IBLUE：一种新的主动队列管理算法

针对日益严重的网络拥塞现象,在传统的BL U E算法的基础上建立了一种新的主动队列管理方法（Im-mune clonal simulated annealing-based BLUE ,IBLUE）。该方法重新定义队列长度变化范围以及丢包概率,并且利用免疫克隆模拟退火算法来刻画队列长度变化情况。其次,以实际数据进行仿真实验,深入分析了影响该方法的关键因素,同时通过对比BLUE和BLUE＋算法性能,结果表明IBLUE具有较好的适应性。     

A comparison of active queue management algorithms using the OPNETModeler

A number of active queue management algorithms for TCP/IP networks such as random early detection (RED), stabilized RED (SRED), BLUE, and dynamic RED (DRED) have been proposed in the past few years. This article presents a comparative study of these algorithms using simulations. The evaluation is done using the OPNET Modeler, which provides a convenient and easy-to-use platform for simulating large-scale networks. The performance metrics used in the study are queue size, packet drop probability, and packet loss rate. The study shows that, among the four algorithms, SIZED and DRED are more effective at stabilizing the queue size and controlling the packet loss rate while maintaining high link utilization. The benefits of stabilized queues in a network are high resource utilization, bounded delays, more certain buffer provisioning, and,traffic-load-independent network performance in terms of traffic intensity and number of TCP connections     

主动队列管理算法性能评价：比较研究

对当前的主动队列管理算法进行了分类，然后基于NS2网络仿真器比较了它们在不同流量和网络拓扑条件下的多种性能指标。主要结论包括：ARED综合表现最好；PI和REM的瞬态性能较差，对动态流量响应较慢：REM和没有ECN支持的AVQ可以得到较小的时延，但同时牺牲了链路的带宽利用率并造成大量分组丢失；在多瓶颈网络拓扑中，大部分算法的性能有所提高。     

The BLUE active queue management algorithms

In order to stem the increasing packet loss rates caused by an exponential increase in network traffic, the IETF has been considering the deployment of active queue management techniques such as RED (random early detection) (see Floyd, S. and Jacobson, V., IEEE/ACM Trans. Networking, vol.1, p.397-413, 1993). While active queue management can potentially reduce packet loss rates in the Internet, we show that current techniques are ineffective in preventing high loss rates. The inherent problem with these algorithms is that they use queue lengths as the indicator of the severity of congestion. In light of this observation, a fundamentally different active queue management algorithm, called BLUE, is proposed, implemented and evaluated. BLUE uses packet loss and link idle events to manage congestion. Using both simulation and controlled experiments, BLUE is shown to perform significantly better than RED, both in terms of packet loss rates and buffer size requirements in the network. As an extension to BLUE, a novel technique based on Bloom filters (see Bloom, B., Commun. ACM, vol.13, no.7, p.422-6, 1970) is described for enforcing fairness among a large number of flows. In particular, we propose and evaluate stochastic fair BLUE (SFB), a queue management algorithm which can identify and rate-limit nonresponsive flows using a very small amount of state information.     

Advances in Active Queue Management (AQM) Based TCP Congestion Control

Current end-to-end Internet congestion control under tail-drop (TD) queue management experiences performance degradations such as multiple packet losses, high queueing delay and low link utilization. In this paper, we review recently proposed active queue management (AQM) algorithms for supporting end-to-end transmission control protocol (TCP) congestion control. We focus recently developed control theoretic design and analysis method for the AQM based TCP congestion control dynamics. In this context, we analyze the problems of existing AQM proposals in which congestion is detected and controlled reactively based on current and/or past congestion. Then we argue that AQM based TCP congestion control should be adaptive to the dynamically changing traffic situation in order to detect, control and avoid the current and the incipient congestion proactively. Finally, we survey two adaptive and proactive AQM algorithms, PID-controller and Pro-Active Queue Management (PAQM), designed using classical proportional-integral–derivative (PID) feedback control to overcome the reactive congestion control dynamics of existing AQM algorithms. A comparative study of these AQM algorithms with existing AQM algorithms is given. A simulation study under a wide range of realistic traffic conditions suggests that PID-controller and PAQM outperform other AQM algorithms such as random early detection (RED) [Floyd and Jacobson, 18] and proportional-integral (PI) controller [Hollot et al., 24].     

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.     

Virtual rate control algorithm for active queue management in TCPnetworks

A virtual rate control (VRC) algorithm for active queue management (AQM) to regulate the queue length with small variation and to achieve high utilisation with small packet loss is proposed. Through ns simulations, the effectiveness of the proposed VRC algorithm as compared with several well-known AQM schemes such as random early detection (RED), random exponential marking (REM), and adaptive virtual queue (AVQ) algorithms is shown     

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 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.