北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2015, Vol. 38 ›› Issue (5): 54-57.doi: 10.13190/j.jbupt.2015.05.009

• 论文 • 上一篇    下一篇

网络冗余流量的柯西-拉普拉斯多分形小波模型

邢玲1,2, 马强1,2, 徐蕾3, 姜春晓3   

  1. 1. 西南科技大学 信息工程学院, 四川 绵阳 621010;
    2. 特殊环境机器人技术四川省重点实验室, 四川 绵阳 621010;
    3. 清华大学 电子工程系, 北京 100084
  • 收稿日期:2015-01-06 出版日期:2015-10-28 发布日期:2015-10-28
  • 作者简介:邢玲(1978—),女,博士,E-mail:xingling_my@163.com.
  • 基金资助:

    国家自然科学基金项目(61171109);四川省科技厅应用基础项目(2014JY0215);四川省教育厅重点项目(13ZA0161);绵阳市科技计划项目(13zd3107);西南科技大学项目(13zx9101,13zxtk07,12zxwk01)

A Cauchy-Laplace Multifractal Wavelet Model for Network Redundant Traffic

XING Ling1,2, MA Qiang1,2, XU Lei3, JIANG Chun-xiao3   

  1. 1. School of Information Engineering, Southwest University of Science and Technology, Sichuan Mianyang 621010, China;
    2. Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Sichuan Mianyang 621010, China;
    3. Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
  • Received:2015-01-06 Online:2015-10-28 Published:2015-10-28

摘要:

为了刻画网络冗余流量在小时间尺度下的局部特性,提出了基于柯西-拉普拉斯多分形小波模型及其参数估计算法. 采用联合分布函数来描述冗余流量的局部表征,即分别用柯西分布和拉普拉斯分布计算冗余流量的重尾和尖峰参数乘法因子;通过概率比较方法获取小波系数和尺度系数的比例参数阈值,以界定两种不同分布的参数范围. 实验结果表明,提出的模型能准确且有效地描述网络冗余流量在小时间尺度下的多分形特性.

关键词: 网络冗余流量, 尖峰特性, 重尾特性, 柯西-拉普拉斯多分形小波模型

Abstract:

In order to characterize local features of network redundant traffic on small-time scale more accurately, a new Cauchy-Laplace multifractal wavelet model was proposed. An algorithm for estimating parameters of wavelets was also put forward. A joint distribution function was adopted to describe local features, i.e., Cauchy and Laplace distributions were used to obtain the parameter multiply factors for heavy-tailed and spike features, respectively. A threshold for ratio of wavelets to scaling parameters, which decides how these two distributions affected redundant traffic modeling, was achieved by probability comparison. Experiments show that the proposed model can well characterize small-time scale multifractal features of network redundant traffic.

Key words: redundant traffic, spike feature, heavy-tailed feature, Cauchy-Laplace multifractal wavelet model

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