Authors: Jing-Jing Hu; Ru-Feng An; Lie-Huang Zhu
Addresses: School of Software, Beijing Institute of Technology, Beijing, China ' School of Software, Beijing Institute of Technology, Beijing, China ' School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
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.
Keywords: flow analysis; parallel computing; MapReduce; GPU; graphics processing unit; network traffic analysis.
International Journal of Wireless and Mobile Computing, 2015 Vol.9 No.4, pp.343 - 348
Received: 26 Jun 2015
Accepted: 19 Jul 2015
Published online: 03 Jan 2016 *