PFED:一种基于预测的公平的主动队列管理算法

对多个著名的主动队列管理算法进行了深入的理论分析和实验比较，对它们的优点和不足进行了总结，并在此基础上提出了一种新的主动队列管理算法PFED（prediction-based fair early drop）．PFED的主要目标是：①通过对流量较为精确的预测，结合对分组丢弃概率更为合理的计算，将队列长度的变化稳定在一个理想的水平；②对非响应流实施有效的惩罚，提高算法的公平性；③通过合理的分组丢弃将队列（分组）的到达速率控制在链路的服务速率之下．仿真实验表明，PFED很好地实现了上述3个目标．     

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.     

Fuzzy-Based Dynamic Distributed Queue Scheduling for Packet Switched Networks

Addressing the problem of queue scheduling for the packet-switched system is a vital aspect of congestion control. In this paper, the fuzzy logic based decision method is adopted for queue scheduling in order to enforce some level of control for traffic of different quality of service requirements using predetermined values. The fuzzy scheduler proposed in this paper takes into account the dynamic nature of the Internet traffic with respect to its time-varying packet arrival process that affects the network states and performance. Three queues are defined, viz low, medium and high priority queues. The choice of prioritizing packets influences how queues are served. The fuzzy scheduler not only utilizes queue priority in the queue scheduling scheme, but also considers packet drop susceptibility and queue limit. Through simulation it is shown that the fuzzy scheduler is more appropriate for the dynamic nature of Internet traffic in a packet-switched system as compared with some existing queue scheduling methods. Results show that the scheduling strategy of the proposed fuzzy scheduler reduces packet drop, provides good link utilization and minimizes queue delay as compared with the priority queuing (PQ), first-in-first-out (FIFO), and weighted fair queuing (WFQ).     

Design and analysis of a self-tuning feedback controller for the Internet

Active queue management (AQM) is an effective method used in Internet routers for congestion avoidance, and to achieve a tradeoff 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. This paper proposes a novel packet dropping scheme, called self-tuning proportional and integral RED (SPI-RED), as an extension of RED. SPI-RED is based on a self-tuning proportional and Integral feedback controller, which considers not only the average queue length at the current time point, but also the past queue lengths during a round-trip time to smooth the impact caused by short-lived traffic dynamics. Furthermore, we give theoretical analysis of the system stability and give guidelines for selection of feedback gains for the TCP/RED system to stabilize the average queue length at a desirable level. The proposed method can also be applied to the other variants of RED. Extensive simulations have been conducted with ns2. The simulation results have demonstrated that the proposed SPI-RED algorithm outperforms the existing AQM schemes in terms of drop probability and stability.     

一种改进的自适应随机早期检测算法

为了进一步减小Adaptive RED算法中队列长度的波动性,在对丢弃概率的变化率与队列长度稳定性的关系进行研究后,对Adaptive RED算法的丢弃概率计算函数进行了非线性化,使得算法能够根据平均队列长度与目标队列长度区间中值的偏离程度动态地改变丢弃概率的变化率,减小了队列长度的波动,进而提高算法的稳定性。     

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.     

TCRED:一种基于DiffServ的队列管理算法

AF PHB的实现机制是DiffServ网络的重要研究课题，目前的多数实现采用了RED或类似的队列管理技术，在分析了RED及其改进算法在公平性及分组优先级等方面存在的不足后，提出了一种采用同一组RED参数对三色分组实行队列管理的TCRED算法．TCRED对不同颜色的分组分别计算平均队列长度，并考虑了分组的到达和离去对平均队长的影响，据此计算出各色分组的丢弃几率，该算法能实现分组的相对优先级，保证用户所预定的带宽，同时能实现分组优先级的平滑升级和降级，因而很好地满足了AF PHB的要求。     

PbRED:基于优先级的RED改进算法

随机早期检测算法RED作为一种重要的主动队列管理算法,通过有效地控制队列长度,取得较好的吞吐量性能。然而,当多个业务流存在不同优先级时,不能很好地区分服务质量。提出一种新的RED改进算法—PbRED,基于业务的优先级调整丢弃概率,通过减小高优先级的丢弃概率、增大低优先级的丢弃概率,为不同优先级的业务进行区分服务。仿真实验结果表明,在获得较高吞吐量的同时,PbRED可以使不同优先级业务流的服务质量存在合理区分度,保证高优先级业务流获得更好的吞吐量性能。     

参数动态调节的随机早期检测算法

RED算法对参数的设置很敏感,不同的参数设置对RED的性能影响很大。本文在自适应RED算法的基础上,提出了一种以流量变化趋势为依据,以平均队列长度的变化幅度大小为调节参数的RED算法。本算法通过动态参数调节方式,提高了RED算法的自适应性。模拟实验结果表明,算法在丢包率、平均队列长度及链路利用率等方面都有很好的表现。     

基于云模型的被动队列管理算法

为了解决因拥塞而带来的网络性能问题,基于“弃头”方式提出了一种新的被动队列管理算法DFCM。该算法首先结合业务流队长和到达速率给出了丢包策略和丢包概率,同时利用云模型对实际队长进行求解;最后,通过利用NS2和Matlab进行仿真实验,深入分析了影响算法性能的关键因素。与“弃尾”(Drop Tail)和DFSQ算法相比,DFCM在瞬时队长方差、有效传输数据包等方面显示了较好的适应性。     

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。     

Design and Analysis of a Multiscale Active Queue Management Scheme

Since Internet is dominated by TCP-based applications, active queue management （AQM） is considered as an effective way for congestion control. However, most AQM schemes suffer obvious performance degradation with dynamic traffic. Extensive measurements found that Internet traffic is extremely bursty and possibly self-similar. We propose in this paper a new AQM scheme called multiscale controller （MSC） based on the understanding of traffic burstiness in multiple time scale. Different from most of other AQM schemes, MSC combines rate-based and queue-based control in two time scales. While the rate-based dropping on burst level （large time scales） determines the packet drop aggressiveness and is responsible for low and stable queuing delay, good robustness and responsiveness, the queue-based modulation of the packet drop probability on packet level （small time scales） will bring low loss and high throughput. Stability analysis is performed based on a fluid-flow model of the TCP/MSC congestion control system and simulation results show that MSC outperforms many of the current AQM schemes.     

一种新的被动队列管理算法

针对因网络拥塞而产生的丢包问题,基于弃头方式提出了一种新的被动队列管理算法(Drop Front n based on Wavelet and Shuffled frog leaping,DFWS)。该算法首先利用小波变换对实际流量的长相关特性进行刻画,并利用混合蛙跳方法获取网络队长,同时结合弃头方式提出丢包策略和丢包概率。最后,以实际数据进行仿真实验,深入研究了网络队长、丢包数量和服务率之间的关系。结果发现,与其他算法相比,DFWS具有较好的适应性。     

调度和拥塞控制相结合的无线网络资源分配模型

研究了基于WLAN访问Internet的网络基站处流,提出了一种基于队列长度的调度方法和基于信道容量的拥塞控制模式,以达到网络资源的公平分配,并解决由于不恰当处理基站处堆积数据包而引起的弊端。在提出的资源分配模型中,调度算法根据各条流堆积的队列长度来随机地选择将要发送的数据分组;而拥塞控制模式中,将链路使用率作为拥塞指示,通过计算,平等地反馈给每一条流的发送端。发送端根据反馈到的拥塞信息来调整发送速率,以达到资源分配的公平性。仿真的结果表明：各条流能公平地共享无线网络的带宽。此算法的最大的优点在于基站不需要按照某种特定的公平性定义来选择数据包却能达到很高的公平性。     

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．     

一种基于神经元强化学习的网络拥塞控制方法

提出了一种基于神经元强化学习(Neuron-based Reinforcement Learning,NRL)的自适应AQM算法,采用链路速率和队列长度作为拥塞指示,可根据网络环境的变化在线自动调整神经元参数,从而保持良好的队列长度稳定性和对网络负载波动的鲁棒性.该算法结构简单、易于实现,且不依赖对象的模型.仿真结果表明,该算法尤其适合于解决复杂不确定性网络的拥塞控制问题,并具有更好的队列稳定性和鲁棒性.     

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。     

Approximate analysis of a discrete-time tandem network of cut-through queues with blocking and bursty traffic

A discrete-time tandem network of cut-through queues is presented. The model allows finite capacity queues, blocking, and bursty traffic. A new bursty arrival process, IBK(k), for cut-through traffic is introduced. The tandem network is analyzed using single-node decomposition. Each queue is analyzed numerically in isolation assuming that its arrival and service processes are known. The parameters of the arrival and service processes of the queues are obtained using an iterative scheme. The results obtained are approximate and validation tests have shown that the model has good accuracy. Using this model, the packet loss, throughput, and queue length distributions were obtained for different traffic parameters and queue sizes.     

一种数据丢包情况下的交叉口排队长度均衡控制方法

针对交通数据在传输过程中随机丢包造成交通拥堵的问题,提出一种新的交叉口排队长度均衡控制方法。考虑到交叉口交通控制的重复特性和强非线性,将无模型自适应迭代学习控制方案应用于交叉口排队长度控制中,通过实时调整各交叉口的信号配时方案来调节路口车辆的排队长度,实现各交叉口排队长度的均衡。针对道路交通网络控制中排队长度差值数据在传输过程中存在的丢包现象,将数据丢失现象描述为概率已知的伯努利序列,提出数据丢失情况下的补偿算法,即利用上次迭代的输出数据、伪梯度的估计值和控制输入差值对丢失数据进行补偿,解决存在数据丢包情况下多交叉口排队长度均衡控制问题。仿真结果表明,该方法在数据丢包的情况下迭代100次左右能够收敛于期望值并达到期望控制效果,验证了补偿算法的有效性。     

基于网络概率丢弃策略的自适应控制机制

提出一种基于网络节点分组概率丢弃策略的单神经元自适应PID控制机制，并对其进行了相关性能的分析。在单节点的网络业务流模型基础上，运用OPNET仿真软件对其进行不同输入和工作条件下的仿真。结果显示，在所设计的单神经元自适应PID控制机制下，节点平均队列稳定性好且具有较好的鲁棒性。