计算机应用 ›› 2014, Vol. 34 ›› Issue (5): 1251-1254.DOI: 10.11772/j.issn.1001-9081.2014.05.1251

• 网络与通信 • 上一篇    下一篇

基于高斯过程回归的网络流量预测模型

李振刚   

  1. 天津城建大学 信息中心，天津 300384
  • 收稿日期:2013-12-06 修回日期:2014-02-16 发布日期:2014-05-30 出版日期:2014-05-01
  • 通讯作者: 李振刚
  • 作者简介:李振刚(1974-)，男(回族)，河北沧州人，讲师，硕士，主要研究方向：网络管理、入侵检测。
  • 基金资助:

    天津市高等学校科技发展基金计划项目

Network traffic forecasting model based on Gaussian process regression

LI Zhengang   

  1. Information Center, Tianjin Chengjian University, Tianjin 300384, China
  • Received:2013-12-06 Revised:2014-02-16 Online:2014-05-30 Published:2014-05-01
  • Contact: LI Zhengang

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摘要：

针对传统网络流量预测精度低难题，为了获得理想的网络流量预测结果，提出一种基于高斯过程回归(GPR)的网络流量预测模型。该模型首先计算延迟时间和嵌入维数，构建高斯过程回归的学习样本；然后采用高斯过程回归对网络流训练集进行学习，并采用入侵杂草优化对高斯过程回归的参数进行优化；最后采用经典的网络流量测试集对该模型性能进行实验测试。实验结果表明，高斯过程回归模型提高了网络流量的预测精度。

Abstract:

To solve the defect of traditional network traffic prediction forecasting, and obtain good forecasting results of network traffic, a network traffic forecasting model based on Gaussian Process Regression (GPR) was proposed. Firstly, the time delay and embedding dimension of network traffic were calculated to construct the learning samples of GPR, and then training samples were input to Gaussian process to learn in which Invasive Weed Optimization (IWO) algorithm was used to optimize the parameters of Gaussian process, and finally, the forecasting model of network traffic was established based on the optimal parameters, and the performance was tested by network traffic data. The results show that the proposed model can improve the forecasting precision of network traffic and it has great practical application value.

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