决策树报文分类算法

报文分类是网络的基本功能，研究人员在过去二十年提出了众多解决方案，其中决策树报文分类算法由于吞吐量高、适用于多字段、可流水线化等特点受到了广泛关注和深入研究。本文介绍了决策树算法最新研究成果，阐述了决策树报文分类算法的几何意义、常用技术和测试基准，从节点切割技术和规则集分组技术两个维度对决策树算法进行了系统分析和归纳。针对两类常用的决策树构建技术介绍了其中的典型算法，对比了各种典型算法的设计思路和特点，分析了它们的适用场景。总结并展望了决策树算法的下一步研究方向。

Decision tree algorithm for packet classification

Packet classification is the fundamental function of network, and researchers have proposed many packet classification solutions in the past two decades. Among them, the decision tree algorithm for packet classification has received extensive attention and in-depth research due to its high throughput, suitable for multiple fields, and pipelining. The recent research on the decision tree algorithm for packet classification was introduced, the geometric meaning, common techniques and test benchmarks of the decision tree algorithm were described, and the decision tree algorithm from the two dimensions of node cutting technology and rule set grouping technology were systematically analyzed. The typical algorithms of the two types of common technologies for building decision tree were introduced respectively, the design ideas and characteristics of various algorithms were compared, and their applicable scenarios were given. The conclusion and discuss the future work of decision tree algorithms were stated out.