Acceleration of decision tree searching for IP traffic classification

Abstract

Traffic classification remains a hot research problem, especially when facing new traffic trends and new hardware architectures. We propose a classification tree search method called explicit range search, motivated by the characteristics of machine learning based classification approaches. Our method differs from previously known algorithms such as HiCut and HyperCut in how to cut the ranges within a dimension and how to search within the ranges. By storing explicit marks and performing hardware supported parallel comparison, the explicit range search can reduce the worst-case number of memory accesses from 26 to 5 on a number of realistic rule sets generated from a well-known machine learning algorithm (C4.5). We also describe in this paper the proposed design based on FPGA devices.