Internet Traffic Measurement Based on Non-Stationary Traffic Train Model

YANG Jia-hai; WU Jian-ping; C LIU

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Internet Traffic Measurement Based on Non-Stationary Traffic Train Model

更新时间：2025-07-16

- Internet Traffic Measurement Based on Non-Stationary Traffic Train Model
- Acta Electronica Sinica Vol. 32, Issue 5, Pages: 727-730(2004)
- 作者机构：
  
  1. 清华大学信息网络工程研究中心,北京,100084
  2. 美国贝尔实验室
  3. 清华大学信息网络工程研究中心北京,100084
  4. 美国贝尔实验室
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP393
- Published：2004
- 稿件说明：
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YANG Jia-hai, WU Jian-ping, C LIU. Internet Traffic Measurement Based on Non-Stationary Traffic Train Model[J]. Acta Electronica Sinica, 2004, 32(5): 727-730.
DOI：

YANG Jia-hai, WU Jian-ping, C LIU. Internet Traffic Measurement Based on Non-Stationary Traffic Train Model[J]. Acta Electronica Sinica, 2004, 32(5): 727-730. DOI：

摘要

针对大规模细粒度网络流量测量的现实困难

本文引入一种非平稳型流量队列模型NTT

直接对粗粒度的流量采样进行建模.NTT模型是大量活跃的流量队列的迭加

每个队列内部的流量变化过程用分形高斯噪声过程FGN描述

队列的到达过程形成非同构的泊松过程

队列的生存周期服从指数分布.对CERNET主干网的实际流量测量分析验证了NTT模型的有效性

证实了NTT模型可用于基于各种粒度的流量测量的网络行为研究.

Abstract

In order to solve the practical problem in the fine time-scale traffic measurement of large-scale networks

such as Internet

the paper introduced a non-stationary traffic train (NTT) model

which aims to directly model the coarse time-scale traffic samples.NTT model is the superposition of a large number of active traffic train segments

each of which can be described with a fractional Gaussian noise process

while train arrivals form a non-homogeneous Poisson process and train durations are exponential.The application of NTT model in the actual measurement and analysis of CERNET backbone traffic verifies the validation and effectiveness of NTT model

which confirms that NTT model can be used in the study of network behavior based on various time-scale traffic measurements.

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Related Institution

School of Computer Science and Engineering, School of Cyber Science and Engineering, Nanjing University of Science and Technology

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College of Computer Science and Technology, Zhejiang University

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