A GPU-accelerated parallel network traffic analysis system
by Jing-Jing Hu; Ru-Feng An; Lie-Huang Zhu
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 9, No. 4, 2015

Abstract: 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.

Online publication date: Sun, 03-Jan-2016

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