A GPU-accelerated parallel network traffic analysis system

被引：0

作者：

Hu, Jing-Jing ^{[1
]}

An, Ru-Feng ^{[1
]}

Zhu, Lie-Huang ^{[2
]}

机构：

[1] School of Software, Beijing Institute of Technology, Beijing

[2] School of Computer Science and Technology, Beijing Institute of Technology, Beijing

来源：

International Journal of Wireless and Mobile Computing | 2015年 / 9卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Flow analysis; GPU; Map/reduce; Parallel computing;

D O I：

10.1504/IJWMC.2015.074037

中图分类号：

学科分类号：

摘要：

With the rapid increasing of network scale, the size of traffic data also expands a lot. In traditional traffic data analysis, there are some problems, such as high computation complexity, low analysis efficiency, long learning period, and difficulty of development. To address these problems, we design and implement a GPU-accelerated parallel analysis scheme for network traffic - EasyAnalyze. In EasyAnalyze, we introduce GPU parallel computing, Map/ Reduce architecture into network traffic analysis, which greatly improves the efficiency but does not increase the difficulty in programming. In the experiments, EasyAnalyze shows very promising results: (1) the speed is 6-17 times faster than conventional serial analysis in network traffic data analysis; and (2) the size of code is only 2% of the mainstream GPU Map/Reduce. © Copyright 2015 Inderscience Enterprises Ltd.

引用

页码：343 / 348

页数：5

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