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首页> 外文学位 >Fractional stable noise processes and their application to traffic modeling and fast simulation of broadband telecommunications networks.
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Fractional stable noise processes and their application to traffic modeling and fast simulation of broadband telecommunications networks.

机译：分数稳定噪声过程及其在宽带电信网络流量建模和快速仿真中的应用。

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摘要

A great amount of research has been focused during the last few years on obtaining realistic models for the traffic generated by the users of telecommunications networks. Self-similarity and long-range dependence have been proved to be important features of aggregated traffic, and several models of this type have been proposed. These models, however, are applicable only to relatively slowly-varying traffic, since they are unable to capture the high level of burstiness of some types of information flows.;The main purpose of this research is to prove that aggregate traffic traces that present very high burstiness can also be accurately modeled with long-range dependent processes by using infinite-variance distributions for the marginal probability density function. Namely, we propose the use of alpha-stable distributions.;In order to prove the appropriateness of the model that we are proposing, different types of self-similar traffic traces (LAN/WAN, WWW, VBR video) are analyzed by estimating their self-similarity coefficient H, as well as the parameters of their marginal distributions. When comparing the real traces with our artificial traces, the agreement is evaluated both qualitatively (visually) and quantitatively (by means of the sample PDF to compare their marginal distribution, and the periodogram to compare their dynamic behavior). By analyzing different types of traffic traces, the model is shown to be flexible enough to be applied to simulate a variety of communications scenarios. An analytical proof, from basic principles, of the appropriateness of the proposed model is also given, as well as the conditions under which the Gaussian assumption is applicable.;As additional tools for the application of this novel model to the solution of a variety of telecommunications problems, algorithms for prediction of traffic behavior, fast generation of artificial traces, and fast simulation of systems involving traffic compatible with our model are given. In order to find an efficient algorithm for the generation of artificial traces of FSN processes, we approximate our random process by an auto regressive (AR) model based on the minimum dispersion (MD) principle. In order to derive this algorithm, we describe the dependence structure and define a prediction algorithm for both Linear- and Log-Fractional Stable Noise processes. The way this algorithm works is by estimating the next sample using values from the past, and by adding a random (alpha-stable) increment to this estimated value. To compare the artificial traces generated by the fast and the direct algorithms, we use again their sample PDF and their periodogram.;The prediction algorithm used in the fast traffic-generation method is generalized to estimate now the value of the random process at an arbitrary distance into the future. Prediction can be applied to the solution of a variety of problems, such as dynamic bandwidth allocation, buffer and bandwidth optimization inside a switch, etc.;Finally, a fast simulation algorithm is proposed based on the Importance Sampling technique. Previous theoretical results do not allow for a direct application of this technique to alpha-stable stochastic processes; therefore, mathematical manipulation and approximations are needed in order to achieve our goal. The appropriateness of our proposed method is evaluated by comparing results obtained via our algorithm and those acquired using direct (long) simulations.
机译：在过去的几年中，大量的研究集中在获得电信网络用户产生的业务量的现实模型上。事实证明，自相似性和远程依赖性是聚合流量的重要特征，并且已经提出了几种此类模型。但是，这些模型仅适用于相对缓慢变化的流量，因为它们无法捕获某些类型的信息流的高突发性。这项研究的主要目的是证明存在大量流量的总流量跟踪通过将无限方差分布用于边际概率密度函数，还可以使用长期依赖的过程对高突发性进行精确建模。即，我们建议使用alpha稳定分布。为了证明我们提出的模型的适当性，通过估计不同类型的自相似流量跟踪（LAN / WAN，WWW，VBR视频）来分析它们的分布。自相似系数H以及其边际分布参数。在将真实痕迹与人工痕迹进行比较时，将对定性（视觉）和定量进行评估（通过样本PDF来比较其边际分布，以及使用周期图来比较其动态行为）。通过分析不同类型的流量跟踪，该模型显示出足够的灵活性，可以应用于模拟各种通信场景。还给出了从基本原理上对所提出的模型的适用性进行分析的证明，以及适用高斯假设的条件。作为将这种新颖模型应用于各种形式的解的附加工具给出了电信问题，交通行为预测算法，人工痕迹的快速生成以及涉及与我们模型兼容的交通的系统的快速仿真。为了找到一种有效的算法来生成FSN流程的人工痕迹，我们通过基于最小分散（MD）原理的自回归（AR）模型来近似随机过程。为了导出该算法，我们描述了相关性结构并定义了线性和对数分数稳定噪声过程的预测算法。该算法的工作方式是使用过去的值估计下一个样本，并向此估计值添加随机（alpha稳定）增量。为了比较快速算法和直接算法生成的人工痕迹，我们再次使用它们的样本PDF和它们的周期图。；快速流量生成方法中使用的预测算法被广义化为现在估计任意值下随机过程的值。距离未来。可以将预测应用于各种问题的解决，例如动态带宽分配，交换机内部的缓冲区和带宽优化等。最后，提出一种基于重要性采样技术的快速仿真算法。先前的理论结果不允许将该技术直接应用于α稳定的随机过程。因此，为了实现我们的目标，需要进行数学上的操纵和近似。通过比较通过我们的算法获得的结果和使用直接（长时间）仿真获得的结果，评估了我们提出的方法的适当性。

著录项

  • 作者

    Gallardo, Jose Rosario.;

  • 作者单位

    The George Washington University.;

  • 授予单位 The George Washington University.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 D.Sc.
  • 年度 2000
  • 页码 111 p.
  • 总页数 111
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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