Diffluent Internet Traffic and Characteristics Computation Based on Hadoop

Yong Liu; Jiangtao Luo; Shengxiong Deng; Xiaoping Wang

doi:10.3969/j.issn.1000-0801.2014.12.011

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Diffluent Internet Traffic and Characteristics Computation Based on Hadoop

research and development | 更新时间：2024-06-05

- Diffluent Internet Traffic and Characteristics Computation Based on Hadoop
- Telecommunications Science Vol. 30, Issue 12, Pages: 76-81(2014)
- 作者机构：
  
  1. 重庆市高校通信网测试技术工程研究中心重庆400065
  2. 重庆邮电大学电子信息与网络工程研究院重庆400065
  3. 重庆邮电大学通信与信息工程学院重庆400065
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1000-0801.2014.12.011
  CLC：
- Published Online：2014-12，
  
  Published：15 December 2014
- 稿件说明：
移动端阅览
Yong Liu, Jiangtao Luo, Shengxiong Deng, et al. Diffluent Internet Traffic and Characteristics Computation Based on Hadoop[J]. Telecommunications science, 2014, 30(12): 76-81.
DOI：

Yong Liu, Jiangtao Luo, Shengxiong Deng, et al. Diffluent Internet Traffic and Characteristics Computation Based on Hadoop[J]. Telecommunications science, 2014, 30(12): 76-81. DOI： 10.3969/j.issn.1000-0801.2014.12.011.

摘要

网络流量特征计算是网络流量分析的一个重要步骤，对于海量网络流量数据，并行化计算网络流量特征是高效网络流量分析的重要方法。针对传统单机处理成本高、可扩展性差的问题，提出一种基于MapReduce编程模型的网络流量分析方法，并行实现网络分流和流量特征计算。通过使用Hadoop平台对实际数据进行分析，统计常用网络流量属性特征，实验表明，该方法分析网络流量特征的结果准确可信，且适合分析大流量数据。

Abstract

Internet traffic characteristics computation is an important step of internet traffic analysis

in the face of massive internet traffic data

parallel computing internet traffic characteristics is base to prompt the performance of internet traffic analysis. In order to solve the problems of poor expansibility and high cost that caused by the traditional stand-alone

an internet traffic characteristics analysis method based on MapReduce

which parallel processes diffluent internet traffic and characteristics computing

was proposed. By using the Hadoop platform

the actual data was analyzed and the common internet traffic characteristics were computed. Experiments show that the method is reliable to analyze the characteristic of the internet traffic and it is suitable for analyzing large traffic data.

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references

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