一种基于速率的主动队列管理机制

提出了一种基于速率的主动队列管理机制。主要思想是根据每流过去的输入速率和输出速率预测下一个采样间隔内的输入速率和输出速率,用上一个采样间隔末的每流队列长度、预计输入、预计输出和期望的队长来决定数据包的丢弃概率,达到稳定队列长度和实现公平性的目的：仿真结果证明在多种网络条件下,算法能够实现稳定队列长度和每流公平性两个目的。     

Active Queue Management技术的研究与发展

ActiveQueueManagement(AQM)技术通过有效控制输出队列的丢包时间和丢包方式，对拥塞进行早期通告，这在TCP拥塞控制的实现中至关重要。目前对AQM进行较全面介绍和总结的文献尚不多见，以RandomEarlyDetection(RED)为重点介绍了这种第一代AQM技术的设计思想、优缺点以及为此出现的多种RED变种方法，另外还简单介绍了其它几种与RED设计思路不同的AQM方法，以期对AQM技术的研究和发展进行较全面的总结，并促进国内学者以及设备制造商对这一技术的关注。     

基于主动队列管理的集群计算负载平衡系统

在分析了分组到达率和服务率的关系及主动队列管理AQM（Active Queue Management）的Balanced RED算法的基础上,提出了一个计算连接权重的动态分配算法“基于AQM加权时序动态法”（Weighted Opportunity Dynamic Algorithm based on AQM）,它具有对终端用户透明、能自动找出最佳服务器来处理请求的特点,从而实现集群计算服务器的负载平衡。另外据此算法设计并实现了一个支持服务器集群的动态负载平衡原型系统,实验结果表明该算法在负载平衡的效能上有明显的提高。     

基于内模控制的自适应主动队列管理算法

实际网络具有大时滞性和动态特性。针对大时滞特性,根据内模控制(IMC)和改进的TCP/AQM控制理论模型设计了一种适合于大延时网络环境的主动队列管理算法。而对于网络的动态特性,分析了网络参数的变化给算法带来的影响,并以此对算法参数进行在线修正,得到符合大时滞网络的自适应AQM算法。最后通过NS2仿真实验验证了设计的AQM算法的可靠性。     

控制时延的主动队列管理算法

针对现有的主动队列管理(AQM)算法造成的队列时延无法满足VoIP、音视频等流媒体传输需求的问题,提出一种直接控制队列时延的主动队列管理算法--DCQA。该算法使用PID控制器计算路由器缓存的数据包丢弃概率,用其对即将进入缓存排队的数据包做丢包判断并采取相应动作,以控制队列时延在期望值以下。实验仿真了3种网络环境下DCQA的性能,链路利用率分别是99.93%、99.88%和99.95%。并且,队列时延分别有50.45%、51.59%、52.4%被控制在期望值以下,比CoDel算法分别提高了3.6%、40.53%、50.69%。实验结果表明,DCQA在不同的网络环境中都可以获得较高的链路利用率,而且控制队列时延的能力优于CoDel算法,适用于流媒体的传输。     

一种新的主动队列管理算法

提出一种新的主动队列管理（AQM）算法。本算法以V. Misra的TCP流量随机微分方程模型为基础,在计入窗口限制的约束条件下,由局部线性化方法导出本文主动队列管理模型的传递函数,并以其作为受控对象,利用比例积分微分（PID）调节器控制该传递函数输出与理想队列长度之差。仿真分析表明,本算法性能优于RED和ARED算法。     

改进的CHOKe公平性主动队列管理算法

针对主动队列管理算法中的CHOKe算法对非响应流的惩罚力度不够、精确度不高的问题,提出一种新的惩罚非响应流算法——LRU-CHOKe。该算法以最近最少使用(LRU)击中取代CHOKe击中,提高CHOKe击中的有效性;利用路由器队列击中,自适应丢包,并结合新的丢包策略来加强对非响应流的惩罚,从而提高网络带宽公平性。仿真结果表明,LRU-CHOKe相比CHOKe和HCHOKe算法能更加有效地惩罚非响应流,实现更为公平的带宽分配。     

一种基于内模PID控制的主动队列管理算法

针对传统主动队列管理中PID控制存在的参数不易整定等缺点,通过引入内模控制思想,提出了一种基于内模控制的PID控制器（IMC－PID）,其突出特点是控制器仅有一个参数需要整定。将IMC－PID应用于网络拥塞控制中,得到了一种新的主动队列管理（AQM）算法——IMC－PID算法。仿真实验表明,IMC－PID算法有较强的鲁棒适应性及较快的队长调节速率。     

基于神经元自适应变结构控制的主动队列管理算法

针对网络TCP模型的非线性以及回路延时和负载波动等不确定性因素,提出一种基于神经元自适应变结构控制(VSC)的主动队列管理(AQM)算法。通过非线性变结构控制以保证路由器队列响应的快速性和鲁棒性;同时考虑到滑模控制中存在的抖振会引起队列波动和控制精度降低等问题,引入神经元在线调整控制器参数以减弱抖振,从而减小队列延时和模型不确定性的影响,提高AQM系统的鲁棒性和性能。最后通过NS-2仿真实验验证了算法的有效性。     

基于微分先行PI的主动队列管理算法

主动队列管理(AQM)作为一种重要的IP层拥塞控制策略,对于提高Internet的服务质量起到了关键性的作用。简要介绍了AQM算法的研究现状和AQM中的PI算法,依据TCP拥塞控制策略基于数据包丢弃的窗口变化机制,设计了一种基于微分先行PI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采用一定的微分校正原则,实时调整进入该路由器数据包的丢弃概率,使路由器中的队列长度能够稳定在参考值附近。仿真结果表明,该算法与PI算法相比具有更小的超调量,可以明显的加快收敛的速度,从而使Internet的服务质量有更大程度的提高。     

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.     

Congestion control of TCP flows in Internet routers by means of index policy

In this paper we address the problem of fast and fair transmission of flows in a router, which is a fundamental issue in networks like the Internet. We model the interaction between a source using the Transmission Control Protocol (TCP) and a bottleneck router with the objective of designing optimal packet admission controls in the router queue. We focus on the relaxed version of the problem obtained by relaxing the fixed buffer capacity constraint that must be satisfied at all time epoch. The relaxation allows us to reduce the multi-flow problem into a family of single-flow problems, for which we can analyze both theoretically and numerically the existence of optimal control policies of special structure. In particular, we show that for a variety of parameters, TCP flows can be optimally controlled in routers by so-called index policies, but not always by threshold policies. We have also implemented the index policy in Network Simulator-3 and tested in a simple topology their applicability in real networks. The simulation results show that the index policy achieves a wide range of desirable properties with respect to fairness between different TCP versions, across users with different round-trip-time and minimum buffer required to achieve full utility of the queue.     

基于粒子群优化的主动队列管理方法

针对网络拥塞现象,基于粒子群优化(PSO)提出了一种新的主动队列管理算法RQQM。该算法首先通过粒子群优化和变异算子来计算当前队列长度,并且基于到达速率和当前队列长度给出了丢包策略和丢包概率。最后,以实际数据将RQQM算法与基于速率的早期检测公平队列管理(RFED)算法和自适应主动队列管理(ABLUE)算法进行仿真实验,
发现丢包率受利用率和缓冲区影响较大;同时实验结果表明RQQM算法的公平性远远优于其他两种算法,其平均丢包率降低至12.21%。     

基于队列敏感性的无线接入网络拥塞控制算法

由于无线接入网络存在强非线性、大时延以及随机链路丢包等因素,导致经典主动队列管理(AQM)算法在实际控制时存在队列收敛速度慢、响应时间长等问题。通过分析随机指数标记(REM)算法在无线接入网中的特点,在原先REM价格模型的基础上对其进行了改进,以队列误差的平方项来克服价格对队列变化不敏感的缺陷,从而提出了一种基于队列敏感性的无线接入网络拥塞控制算法,并利用单神经网络对其参数进行了优化。最后,通过NS2仿真平台对所提算法与REM、PI算法进行对比,实验表明所提算法拥有队列收敛快、鲁棒性强的优点。     

显示拥塞指示标记的主动队列管理研究

主动队列管理对于解决网路拥塞具有重要意义。针对PID主动队列管理算法在调节队列长度时有较大的丢包率这一缺点,提出一种显示拥塞指示标记即ECN标记的PID主动队列管理算法。该算法用显示拥塞指示标记取代丢包机制,用于通知源端网络即将发生拥塞,采用PID控制器实现反馈控制,保证系统的稳定性。仿真结果表明,显示拥塞指示标记的主动队列管理算法适用于多变的网络环境,比PID算法具有低丢包率、低延时和高吞吐量的特点。     

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。     

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.     

A policy approximation method for the UMTS connection admission control problem modelled as an MDP

This article presents a connection admission control (CAC) algorithm for UMTS networks based on the Markov decision process (MDP) approach. To deal with the non-stationary environment due to the time-varying statistical characteristics of the offered traffic, the admission policy has to be computed periodically based on on-line measurements, and the optimal policy computation is excessively time-consuming to be performed on-line. Thus, this article proposes a reduction of the policy space coupled with an aggregation of the state space for the fast computation of a sub-optimal admission policy. Theoretical results and numerical simulations show the effectiveness of the proposed approach.     

A framework for systems analysis for decision support systems

Comprehensive and elaborate systems analysis techniques have been developed in the past of routine and operational information systems. Developing support systems for organizational decision-making requires new tools and methodologies. We present a new framework for data collection and decision analysis which is useful for developing decision support systems. This task analysis methodology encompasses (1) event analysis, (2) participant analysis, and (3) decision content analysis. With a proper coding manual, it provides a framework for collecting relevant and detailed information required for decision support design and implementation. Further research is suggested for application and evaluation of the methodology in real-life DSS environments.     

A new LP formulation of the admission control problem modelled as an MDP under average reward criterion

The admission control problem can be modelled as a Markov decision process (MDP) under the average cost criterion and formulated as a linear programming (LP) problem. The LP formulation is attractive in the present and future communication networks, which support an increasing number of classes of service, since it can be used to explicitly control class-level requirements, such as class blocking probabilities. On the other hand, the LP formulation suffers from scalability problems as the number C of classes increases. This article proposes a new LP formulation, which, even if it does not introduce any approximation, is much more scalable: the problem size reduction with respect to the standard LP formulation is O((C?+?1)²/2 ^C ). Theoretical and numerical simulation results prove the effectiveness of the proposed approach.