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.     

A novel numerical algorithm based on self-tuning controller to support TCP flows

Active queue management (AQM) is an effective method used in Internet routers for congestion control, and to achieve a trade off between link 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.     

On the use of a full information feedback to stabilize RED

RED and most of its family algorithms use only the average queue length as a congestion meter. Since the average queue length considers only long-term behavior of the queue, these algorithms fail to see instantaneous changes of the queue length and hence their reaction to the congestion is not fast enough. In other words the feedback generated by using only the average queue length does not reflect the network congestion precisely and hence leads to a poor performance and stability. This paper solves this problem by designing a RED-based active queue management (AQM) algorithm, called FUF-RED that provides a Full Information Feedback. This algorithm not only considers the average queue length but also it takes into account growth rate of the instantaneous queue length to calculate its congestion feedback. The proposed algorithm is supported by a theoretical stability analysis which gives those feedback gains that guarantees the network stability. Extensive packet level simulations, done by using ns-2 simulator, show that the proposed algorithm outperforms existing AQM algorithms in terms of stability, average queue length, number of dropped packets and bottleneck utilization.     

LRED: A Robust and Responsive AQM Algorithm Using Packet Loss Ratio Measurement

Active queue management (AQM) is an effective means to enhance congestion control, and to achieve trade-off between link utilization and delay. The de facto standard, random early detection (RED), and many of its variants employ queue length as a congestion indicator to trigger packet dropping. Despite their simplicity, these approaches often suffer from unstable behaviors in a dynamic network. Adaptive parameter settings, though might solve the problem, remain difficult in such a complex system. Recent proposals based on analytical TCP control and AQM models suggest the use of both queue length and traffic input rate as congestion indicators, which effectively enhances stability. Their response time generally increases however, leading to frequent buffer overflow and emptiness. In this paper, we propose a novel AQM algorithm that achieves fast response time and yet good robustness. The algorithm, called Loss Ratio-based RED (LRED), measures the latest packet loss ratio, and uses it as a complement to queue length for adaptively adjusting the packet drop probability. We develop an analytical model for LRED, which demonstrates that LRED is responsive even if the number of TCP flows and their persisting times vary significantly. It also provides a general guideline for the parameter settings in LRED. The performance of LRED is further examined under various simulated network environments, and compared to existing AQM algorithms. Our simulation results show that, with comparable complexities, LRED achieves shorter response time and higher robustness. More importantly, it trades off the goodput with queue length better than existing algorithms, enabling flexible system configurations     

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.     

一种基于负载和队列的模糊主动队列管理算法

针对目前已有的模糊主动队列管理算法(AQM)大多只考虑队列长度及其变化率作为模糊输入,很少同时考虑包到达速率的影响,结合队列长度和包到达速率,提出一种更为有效的模糊主动队列管理算法(FQL-AQM)。FQL-AQM以瞬时队列长度和网络平均负载因子作为模糊输入来调整包丢弃概率,并采用参数自校正技术,将队列长度维持在期望的队列水平上、包到达速率维持在队列服务速率附近,使算法对网络状态的变化具有很好的适应能力,从而提高网络的鲁棒性。仿真结果表明,FQL-AQM算法具有比FQ-AQM算法更快的响应速度、更高的链路利用率和更好的队列稳定性,从而减少了分组延时抖动和分组丢弃率。     

Nonlinear RED: A simple yet efficient active queue management scheme

Among various active queue management schemes (AQM), random early detection (RED) is probably the most extensively studied. Unlike the existing RED enhancement schemes, we replace the linear packet dropping function in RED by a judicially designed nonlinear quadratic function. The rest of the original RED remains unchanged. We call this new scheme Nonlinear RED, or NLRED. The underlying idea is that, with the proposed nonlinear packet dropping function, packet dropping becomes gentler than RED at light traffic load but more aggressive at heavy load. As a result, at light traffic load, NLRED encourages the router to operate in a range of average queue sizes rather than a fixed one. When the load is heavy and the average queue size approaches the pre-determined maximum threshold (i.e. the queue size may soon get out of control), NLRED allows more aggressive packet dropping to back off from it. Simulations demonstrate that NLRED achieves a higher and more stable throughput than RED and REM, another efficient variant of RED. Since NLRED is fully compatible with RED, we can easily upgrade/replace the existing RED implementations by NLRED.     

一种提高稳定性和公平性的主动队列管理机制

结合平均队列和负载衡量拥塞,实现选择性丢包,提出早期选择性丢包算法(ESD)．ESD采用指数函数计算丢包概率,使丢包概率随拥塞程度增大而指数递增;引入记录活跃连接状态信息的虚队列,并在虚队列上假轮转,以检测发送速率大的连接;区分非响应性连接和响应性连接,挑选丢包的候选连接,拥塞时优先丢弃候选连接在队列头部的数据包．实验结果表明,ESD可提高队列稳定性,降低Web流和RTT较大连接的丢包率,提高连接占用带宽的公平性,缩短应用响应时间．     

一种链路负载自适应的主动队列管理算法

随机早检测(random early detection,简称RED)是IETF推荐部署的主动队列管理(active queue management,简称AQM)算法.RED存在参数难以配置、无法得到与流量无关的平均队长等问题.ARED(adaptive RED)是RED的自适应版本,它根据平均队长动态调节最大标记概率参数,从而得到稳定的平均队长.但ARED没有克服瞬时队列长度振荡问题,且在动态流量环境下性能明显降低.分析了ARED性能问题的原因,并提出了一种链路负载自适应的主动队列管理算法LARED(load adaptiveRED).LARED具有两个特点:自适应链路负载、快速响应队长变化.分析和仿真实验表明,与ARED等其他AQM算法相比,LARED在保持高链路利用率和低时延的同时可以得到稳定的瞬时队长,并且具有良好的响应性和鲁棒性.     

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.     

Effective RED: An algorithm to improve RED's performance by reducing packet loss rate

Random early detection (RED) is an effective congestion control mechanism acting on the intermediate gateways. We describe a new active queue management scheme, Effective RED (ERED) that aims to reduce packet loss rates in a simple and scalable manner. We made a few change to packet drop function of existing RED scheme. The rest of the original RED remains unchanged. We achieve this by making several refinements and by controlling packet dropping function both with average queue size and instantaneous queue size. Simulations demonstrate that ERED achieves a highest throughput and lowest packet drops than RED, Blue, REM, FRED, LDC and SRED. Since ERED is fully compatible with RED, we can easily upgrade/replace the existing RED implementations by ERED.     

Adaptive Head-to-Tail: Active Queue Management based on implicit congestion signals

Active Queue Management is a convenient way to administer the network load without increasing the complexity of end-user protocols. Current AQM techniques work in two ways; the router either drops some of its packets with a given probability or creates different queues with corresponding priorities. Head-to-Tail introduces a novel AQM approach: the packet rearrange scheme. Instead of dropping, HtT rearranges packets, moving them from the head of the queue to its tail. The additional queuing delay triggers a sending rate decrease and congestion events can be avoided. The HtT scheme avoids explicit packet drops and extensive retransmission delays. In this work, we detail the HtT algorithm and demonstrate when and how it outperforms current AQM implementations. We also approach analytically its impact on packet delay and conduct extensive simulations. Our experiments show that HtT achieves better results than Droptail and RED methods in terms of retransmitted packets and Goodput.     

RED队列稳态误差分析

主动队列管理在保证较高吞吐量的同时，通过在交换节点上主动丢弃数据包来控制队列长度，从而实现对端到端的延时和抖动的控制．RED算法是目前应用最为广泛的主动队列管理(AQM)算法．RED算法以平均队列长度作为衡量网络拥塞的指标，其参数设置对算法性能有较大影响．利用现代控制工程理论，将RED算法看做一种单位反馈控制系统，并将期望队列长度作为系统输入，将瞬时队列长度作为输出，对该系统的稳态误差进行了分析．实验结果表明在稳定状态下，RED队列的波动受分组丢弃概率函数的斜率影响．在稳定条件边界附近，系统的稳态误差急剧增加．     

基于ARED的主动队列管理改进算法

随机早期检测（Random Early Detection,RED）是IETF推荐部署的主动队列管理（Active Queue Management,AQM）算法。 RED存在参数难以配置、无法适应动态网络环境的缺点。 ARED（ Adaptive RED）是RED的自适应版本,通过平均队列长度来动态调整最大丢弃概率,从而达到稳定平均队列长度的目的,但是存在瞬时队列长度振荡的问题。文中研究了拥塞控制中的主动队列管理,对ARED算法进行了改进,优化丢弃概率计算函数,提出TTS-ARED算法,实现在动态网络环境下队列长度的稳定以及丢包率降低。 NS2的仿真结果表明,TTS-ARED算法显著地降低了丢包率,队列长度稳定性比ARED算法更优越。     

一种适用于无线通信网络的随机提前检测算法*

在无线通信网络环境下,提出了一种改进的基于平均队列长度和等待时间的随机提前检测算法.这种算法根据平均队列长度和等待时间计算数据包的丢弃概率.仿真结果表明,与单纯基于平均队列长度的RED算法相比较,在大的数据业务负荷条件下可以获得相对更大的吞吐量、更低的丢包率以及较低的时延抖动,从而能更有效地实现无线网络中的拥塞控制.     

基于RED算法的非线性拥塞控制

由于RED算法是采用丢包率随平均队列长度线性变化的方法,因此导致网络在拥塞并不严重的时候丢包率较大,在拥塞比较严重的时候丢包率较小,拥塞控制能力较低。该文提出非线性平滑算法通过对RED算法的丢包率函数进行非线性平滑,在最小阈值时丢包率增长速度比较小,在最大阈值时丢包率增长速度比较大,有效地控制了平均队列长度,具有较好的拥塞控制能力。NS2仿真结果表明该算法对丢包率、端到端时延、吞吐量以及时延抖动等性能均有较明显的提高。     

Rate-based versus queue-based models of congestion control

Mathematical models of congestion control capture the congestion indication mechanism at the router in two different ways: Rate-based models, where the queue-length at the router does not explicitly appear in the model, and queue-based models, where the queue length at the router is explicitly a part of the model. Even though most congestion indication mechanisms use the queue length to compute the packet marking or dropping probability to indicate congestion, we argue that, depending upon the choice of the parameters of the active queue management (AQM) scheme, one would obtain a rate-based model or a rate-and-queue-based model as the deterministic limit of a stochastic system with a large number of users.     

NS2中新协议的实现

深入分析了网络仿真器NS2的组成和结构,以及NS2中队列管理算法的实现机制,在此基础上,通过一个新的主动队列管理算法的设计与实现阐述了在NS2中实现新协议、新算法的方法。新算法主要通过更为准确的拥塞检测和更为合理的分组丢弃概率计算来提高主动队列管理算法的稳定性,最后通过仿真实验对新算法的性能进行了测试,实验结果表明新算法的性能要由于RED算法。     

Stochastic controller as an active queue management based on B-spline kernel observer via particle swarm optimization

Given the fact that the current Internet is getting more difficult in handling the traffic congestion control, the proposed method is compatible with the stochastic nature of network dynamics. Most conventional active queue management is based on the first stochastic moment. In stochastic theory, the first moment is not efficient for non-Gaussian systems that are the same as the network queue size. We propose a new stochastic active queue management technique, based on stochastic control and B-spline window observer, called intelligent probability density function AQM (IPDF-AQM). The IPDF-AQM is based on a PDF control and particle swarm optimization, which not only considers the average queue length at the current time slot, but also takes into consideration the PDF of queue lengths within a round-trip time. We provide a guideline for the selection of the probability of dropping as control input for TCP/AQM system to make the PDF of queue length converge at a certain PDF target based on B-spline approximation and improve the network performance. Simulation results show that the proposed stochastic AQM scheme does improve the end-to-end performance.     

基于反馈的区分服务网络拥塞管理方案研究

为了有效解决RED算法拥塞响应滞后以及在聚集类之间公平性不足的问题,提出建立一种基于早期拥塞指示反馈的区分服务网络模型,并在该模型上采用优先级早期随机检测算法。在网络入口节点对分组进行分类、聚集与监测,各队列按优先级设置不同队列长度阈值,当平均队列长度或各队列长度达到相应阈值范围时,立即触发不同程度拥塞指示包向源端反馈。使用改进的NS进行了仿真实验,实验结果表明该算法使得拥塞指示能尽可能快地到达TCP源,有效地降低路由器中的丢包率。提高网络利用率,并且为不同的聚集类提供不同优先级服务,保障了聚集类的公平性。