Discrete Bit Selection: Towards a Bit-Level Heuristic Framework for Multi-Dimensional Packet Classification

Baohua Yang, Yaxuan Qi, Fei He, Y. Xue, Jun Li
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引用次数: 4

Abstract

Packet classification is still a challenging problem in practice under large number of classification rules and constant growth of performance requirement. Most of the existing algorithms try to solve the problem heuristically by leveraging on the inherent field-level characteristics of the rules. This paper proposes a bit-level heuristic framework: Discrete Bit Selection (DBS) for multi-dimensional packet classification. Preliminary experimental results show that DBS-based algorithm gains much better performance both in search time and memory requirement than the well-known field-level algorithms with various real-life rule sets.
离散位选择:面向多维数据包分类的位级启发式框架
在大量的分类规则和不断增长的性能要求下,包分类在实践中仍然是一个具有挑战性的问题。现有的大多数算法都试图利用规则固有的字段级特征来启发式地解决问题。本文提出了一种用于多维数据包分类的位级启发式框架:离散位选择(DBS)。初步的实验结果表明,基于数据库的搜索算法在搜索时间和内存需求方面都比现实生活中各种规则集的知名字段级算法有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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