基于自适应参数设置的AIMD算法

为提高TCP（Transfer Control Protocol）流的平均发送数率,减少发送数率的波动,提高网络性能,提出了一种基于自适应参数设置的AIMD（Additive Increase Multiplicative Decrease）算法（A-AIMD算法）。仿真结果表明,在网络处于稳态运行时,使用A-AIMD算法能提高TCP流的平均发送速率,减小发送速率的波动性;在网络中有可利用的链路资源时,使用A-AIMD算法能快速地对可用资源进行最大占用,同时与Reno算法保持一定的TCP友好性。     

基于学习的扩展AIMD拥塞控制机制

在Internet中,主要传输控制协议采用了和式增加积式减少(AIMD)的拥塞控制机制,直接应用该机制不能适应多媒体通信的要求,为了解决好Internet中的多媒体通信以及满足媒体流服务质量的要求,该文针对实时视频传输的实际应用,在点对点的单播速率控制方面和扩展AIMD速率控制基础上,提出一种以丢包为参数具有一定学习能力的速率控制机制即基于学习的扩展AIMD拥塞控制机制,并研究其实现原理和方法。应用表明,该机制具有平滑数据发送速率、降低丢包数等特点。     

Internet拥塞控制系统在不同源控制算法作用下的资源竞争分析

在未来的Internet拥塞控制协议中,不同的用户群根据不同的QoS需求,可以实现不同的控制算法.系统地研究了拥塞控制系统在AIMD和MIMD两类源算法共同作用下的稳态和动态特性,这些特性揭示了配置不同源控制算法的用户群对网络资源的竞争.内容包括AIMD,MIMD算法共同作用下的系统模型、系统稳态分析、系统稳定性分析等.基于NS2系统的仿真结果,证实了给出的分析方法的有效性,并揭示了不同源算法对网络资源的竞争情况.     

一种改进的TCP拥塞控制算法

TCP Reno拥塞控制机制是目前互联网中采用的主流拥塞控制算法.根据TCP Reno实现拥塞避免与拥塞控制的AIMD算法中加性因子与减性因子过于武断,对可用带宽的探测缺乏细分,造成信道利用率未达合理水平等缺陷,为此,提出一种根据拥塞窗口的增长情况进行更为平滑的信道容量探测算法,采用基于对数的增长算法和下降算法,通过N...     

一种改进的TCP拥塞控制算法

传统的TCP拥塞控制算法在遇到丢包时发送速率会有剧烈抖动,不适合传输实时多媒体数据流。本文在TCP拥塞控制算法加增乘减(AIMD)基础上提出一种改进算法慢增慢减(SISD)算法。仿真结果表明:该算法同传统TCP拥塞控制算法相比,有较好的发送速率平滑性,减小了数据延时抖动,加入了多媒体流的拥塞调整机制。本算法可作为实时多媒体数据传输研究的基础,同时也改善了TCP流的传输质量。     

Allocating Network Resources by Weight Between TCP Traffics

Under the current TCP/IP architecture,all TCP traffics compete for network resources completely fairly,which makes it difficult to satisfy applications‘‘‘‘‘‘‘‘ versatile communication requirements.This paper presents an improved TCP congestion control mechanism where the congestion window becomes ω(1-b)W rather than(1-b)W for every window W containing a packet loss.Theoretical analysis and simulation results show that it cann be easily implemented with less additional overhead and can easily perform network resource allocation by weighted parameter ω for traffics under the similar communication environments,whic can efficiently lead to guaranteed relative quqlity of scrviccs and improve network performances.     

SWAN模型速率控制模块性能分析及改进

SWAN是Ad Hoe网络中的一种无状态网络协议,利用分布式控制算法来传递分类服务。这种QoS模型把数据业务分为两类进行不同处理,即对尽力而为的UDP和TCP业务采用速率控制的策略而对实时的UDP业务采用基于源节点的接纳控制策略。本文修改了SWAN模型的速率控制模块,引入队列调度机制,对从上层来的尽力而为数据包进行排队、调度,使其尽可能公平地占用新到资源。在分析中具体给出了修改后速率控制模块的实现过程。通过仿真分析了AIMD算法的参数对修改后速率控制模块性能的影响。     

Adaptive AIMD Congestion Control

The main objectives of a congestion control algorithm are high bandwidth utilization, fairness and responsiveness in a changing environment. However, these objectives are contradicting in particular situations since the algorithm constantly has to probe available bandwidth, which may affect its stability. This paper proposes a novel congestion control algorithm that achieves high bandwidth utilization providing fairness among competing connections and, on the other hand, is sufficiently responsive to changes of available bandwidth. The main idea of the algorithm is to use adaptive setting for the additive increase/multiplicative decrease (AIMD) congestion control scheme, where parameters may change dynamically, with respect to the current network conditions.     

PMIMD拥塞控制算法

分析了目前在Internet上占主导地位的AIMD算法，指出了其在保持数据流稳定性上的不足，这一缺点不能适应当前的媒流体文件传输；提出了一种新的算法--PMIMD（比例乘性增加乘性减少）。仿真结果表明，本算法能减少控制的超调量，从而降低传输率的波动。     

τ‐AIMD for asynchronous receiver feedback

In this work we first describe an asynchronous‐feedback model which corresponds to the diverse roundtrip times (RTTs) of competing flows within the same communication channel. We show that even when the feedback of the receivers/network is asynchronous, the duration of a congestion epoch represents ‘common knowledge’ for the competing flows. Based on this property, we propose a diverse linear increase scheme in proportion to the RTT of each flow and we adjust periodically the windows of the competing flows accordingly, in order to enhance fairness of asynchronous‐feedback environments. We name this scheme τ‐AIMD and show that fairness of flows in asynchronous systems is improved since the increase‐rate diversity of long and short RTT flows is largely canceled. We use the max–min notion of fairness to show that τ‐AIMD can constitute the foundation for other window‐based, end‐to‐end algorithms that target a good balance between efficiency and fairness. Copyright © 2004 John Wiley & Sons, Ltd.