GPU-Accelerated Support Vector Machines for Traffic Classification

doi:10.23940/ijpe.18.05.p28.10881098

Abstract

Abstract:

Machine learning model tackles traffic classification effectively. But, it consumes considerable computing resources and computing time, resulting in the difficulty to accommodate large-scale network. In the presented study, GPU-accelerated Support Vector Machines (SVM) is proposed for traffic classification. GPU is used to parallelly calculate the kernel matrix and process the grid traversal of iterative-tuning scheme, in order to accelerate the training and parameters optimization procedure of SVM. Parallel traffic classification is applied to accelerate the classification procedures through the single instruction multiple data paradigm, multithreading and the shared memory of the threads. The experimental results show that the presented method achieves the similar accuracy comparing to the existing CPU-based LibSVM. Furthermore, it ramps up the training speed to 1.53 times and the classification speed to 24 times, which is suitable for the real time classification of high speed backbone networks.

Submitted on January 26, 2018; Revised on March 2, 2018; Accepted on April 26, 2018
References: 21

Guanglu Sun, Xuhang Li, Xiangyu Hou, and Fei Lang. GPU-Accelerated Support Vector Machines for Traffic Classification [J]. Int J Performability Eng, 2018, 14(5): 1088-1098.

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