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首页> 外文期刊>IEEE communications letters >A Quad-Trie Conditionally Merged with a Decision Tree for Packet Classification
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A Quad-Trie Conditionally Merged with a Decision Tree for Packet Classification

机译：有条件合并决策树的Quad-Trie

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摘要

Trie-based algorithms and decision tree-based algorithms are well-known packet classification solutions which show trade-off between throughput performance and memory requirement. The trie-based algorithms require small memory since each rule is stored exactly once, but they do not provide high throughput because of rule comparison at every rule node. The decision tree-based algorithms provide high throughput since the number of rules compared with an input packet can be controlled as a limited number, but they require excessive amount of memory because of high degree of rule replication. This paper proposes to combine these two types of algorithms. The proposed algorithm primarily constructs a trie and then applies a decision tree for nodes having more rules than a threshold value. Simulation results show that the throughput performance is improved by up to 41 times compared with the trie, and the memory requirement is reduced by up to 38 times compared with the decision tree, so that the performance of both is within a tolerable range for practical implementation.
机译：基于Trie的算法和基于决策树的算法是众所周知的数据包分类解决方案，它们显示了吞吐量性能和内存需求之间的权衡。基于三叉戟的算法需要很小的内存，因为每个规则只存储一次，但是由于每个规则节点上的规则比较，它们无法提供高吞吐量。基于决策树的算法可提供较高的吞吐量，因为可以将与输入数据包相比的规则数量控制为有限的数量，但是由于规则复制的程度很高，因此它们需要过多的内存。本文提出将这两种算法结合起来。所提出的算法主要构造树，然后将决策树应用于具有比阈值更多规则的节点。仿真结果表明，与trie相比，吞吐量性能提高了41倍，与决策树相比，内存需求减少了38倍，因此两者的性能在实际实现的可容忍范围内。

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