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A Study on QoS Control in High-Speed IP Multiservice Networks

高速IP多业务网络中的QoS控制研究

基本信息

批准号：
15560328
负责人：
TAKINE Tetsuya
金额：
$ 2.37万
依托单位：
Osaka University (2004-2005)Kyoto University (2003)
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2005
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15560328/
关键词：
IP traffic time scale longrange dependence packet loss ratio closed-form solution superposition of on-off sources M G ∞ input 最関連時間スケール局所ポワソン性 IPトラヒックモデリングマルコフ型到着流周期的クラスタ到着

项目摘要

It is known that flow-aggregated IP traffic has multiple time-scale behaviors. In short time scales, it exhibits random characteristics, while it has long-range dependence in long time-scales. This fact is very important from an engineering view point Note that the packet loss probability and the tail distribution of delay in queueing models depend only on stochastic characteristics in a certain finite time-scale, and therefore under an appropriate condition, the packet loss probability in IP networks can be determined based on random characteristics of traffic in short time-scales. Compared with long-range dependent traffic, it is easy to handle traffic with random characteristics, and the analytical framework proposed by the investigator can be utilized for qualitative evaluation of QoS performance. In this study, we investigated the mechanism of appearance of local randomness in IP traffic and obtained a method to identify the range of time scales in which local randomness dominates the performance. For a part of this achievement, we received the Best Paper Award from the IEICE. We also studied analytical approaches to queueing models with multiple Markovian arrival streams. Note that the Markovian arrival stream can represent any stationary point process approximately with an arbitrary accuracy. In particular, we establish an approach to analyzing queues with time-dependent service speed. Further we derived the closed-form solution of the mean buffer contents in discrete-time queues with multiple Markovian arrival streams, and revealed how traffic parameters have an impact on the mean buffer contents. Moreover, we analyzed the geometric and subexponential asymptotics in Markov chains of M/G/1 type and proposed an algorithm to compute their transient solutions. With all those achievements, we established the foundations of quantitative evaluation of multiservice IP networks with variable-length packets.

众所周知，汇聚流的IP流量具有多个时间尺度行为。在短时间尺度上，它表现出随机性，而在长时间尺度上，它具有长期相关性。从工程角度来看，这一事实是非常重要的。请注意，排队模型中的丢包概率和延迟的尾部分布仅取决于一定有限时间尺度上的随机特性，因此在适当的条件下，可以根据短时间尺度上的业务的随机特性来确定IP网络中的丢包概率。与长距离相关业务相比，具有随机特性的业务易于处理，研究者提出的分析框架可用于定性评估服务质量性能。在这项研究中，我们研究了IP流量中局部随机性的产生机制，并得到了一种识别局部随机性主导性能的时间尺度范围的方法。作为这一成就的一部分，我们获得了IEICE颁发的最佳论文奖。我们还研究了多马氏到达流排队模型的解析方法。请注意，马尔可夫到达流可以以任意精度近似地表示任何平稳点过程。特别地，我们建立了一种分析具有时间依赖服务速度的队列的方法。在此基础上，给出了多马氏到达流离散时间排队系统平均缓冲区容量的闭合解，揭示了流量参数对平均缓冲区容量的影响。此外，我们还分析了M/G/1型马氏链的几何渐近性和次指数渐近性，并给出了计算它们的暂态解的算法。在这些研究成果的基础上，为多业务变长包IP网络的定量评估奠定了基础。