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Accelerating Decision Tree Based Traffic Classification on FPGA and Multicore Platforms

机译：在FPGA和多核平台上加速基于决策树的流量分类

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摘要

Machine learning (ML) algorithms have been shown to be effective in classifying a broad range of applications in the Internet traffic. In this paper, we propose algorithms and architectures to realize online traffic classification using flow level features. First, we develop a traffic classifier based on C4.5 decision tree algorithm and Entropy-MDL (Minimum Description Length) discretization algorithm. It achieves an overall accuracy of 97.92 percent for classifying eight major applications. Next we propose approaches to accelerate the classifier on FPGA (Field Programmable Gate Array) and multicore platforms. We optimize the original classifier by merging it with discretization. Our implementation of this optimized decision tree achieves 7500+ Million Classifications Per Second (MCPS) on a state-of-the-art FPGA platform and 75-150 MCPS on two state-of-the-art multicore platforms. We also propose a divide and conquer approach to handle imbalanced decision trees. Our implementation of the divide-and-conquer approach achieves 10,000+ MCPS on a state-of-the-art FPGA platform and 130-340 MCPS on two state-of-the-art multicore platforms. We conduct extensive experiments on both platforms for various application scenarios to compare the two approaches.
机译：机器学习（ML）算法已被证明可以有效地对Internet流量中的各种应用进行分类。在本文中，我们提出了使用流量级别特征来实现在线流量分类的算法和体系结构。首先，我们基于C4.5决策树算法和Entropy-MDL（最小描述长度）离散化算法开发流量分类器。对于八个主要应用程序进行分类，它的整体准确性达到97.92％。接下来，我们提出在FPGA（现场可编程门阵列）和多核平台上加速分类器的方法。我们通过将原始分类器与离散化合并来优化原始分类器。我们在最先进的FPGA平台上实现的优化决策树实现了每秒7500+百万个分类（MCPS），在两个最新的多核平台上达到了75-150 MCPS。我们还提出了一种分而治之的方法来处理不平衡的决策树。我们实施的分而治之方法在最先进的FPGA平台上实现10,000+ MCPS，在两个最新的多核平台上达到130-340 MCPS。我们在两种平台上针对各种应用场景进行了广泛的实验，以比较这两种方法。

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