用自回归高斯过程建模VBR流量

Proceedings IEEE International Conference on Networks 2000 (ICON 2000). Networking Trends and Challenges in the New Millennium Pub Date : 2000-09-05 DOI:10.1109/ICON.2000.875835

Jung-Shian Li

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引用次数: 1

摘要

以往的网络流量测量研究表明，当今的网络流量表现出远程依赖(LRD)。产生LRD流量的计算工作量与路径的长度成正比。本文提出了一种基于TES(变换-扩展-样本)和合成自回归高斯过程的轨迹生成框架。该方案既能拟合概率密度函数，又能拟合经验迹线的自相关性。此外，该方案的计算量与LRD轨迹的长度无关。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Modeling VBR traffic with autoregressive Gaussian processes

Previous studies about network traffic measurement show that today's network traffic exhibits long-range dependence (LRD). The computation effort of generating LRD traffic is directly proportional to the length of the traces. This paper presents a traces-generating framework based on TES (transform-expand-samples) and synthetic autoregressive Gaussian processes. The proposed scheme can fit both the probability density function and the autocorrelation of the empirical traces. Besides, the computation effort of this scheme is independent of the length of the LRD traces.

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Proceedings IEEE International Conference on Networks 2000 (ICON 2000). Networking Trends and Challenges in the New Millennium

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