基于网络内容的无阻塞近似流分类的并行建模  

Parallel Modeling for Line Speed Approximate Content-Based Packet Classification

作　　者：李旭东[1] 徐扬[1] 李竞[1] 刘斌[1] 

机构地区：[1]清华大学计算机科学与技术系,北京100084

出　　处：《计算机研究与发展》2005年第6期938-944,共7页Journal of Computer Research and Development

基　　金：国家自然科学基金项目(60173009;60373007);国家"八六三"高技术研究发展计划基金项目(8633000199)

摘　　要：针对大字符集语言的特点,提出一种并行硬件模型实现基于网络内容的近似流分类.由于采用并行设计和流水线设计,该模型在大规则库下仍有较好的性能,并可适用于高速网络.该并行模型有如下特点:①通过采用不同的规则组合器可完成插入、删除、替代和交换错误的近似匹配;②通过配置参数,可灵活控制近似匹配的程度;③可直接应用于大字符集语言下的网络内容流分类;④针对中文环境做了概率建模,分析了并行硬件模型对网络分组的匹配概率,证明该模型在一般情况下具有较好的可应用性.A parallel and pipeline hardware scheme is proposed for approximate content-based packet classification, which is scalable for large rule set and high-speed network rate. With the employment of configurable window unit, the error level of approximate matching can be flexibly adjusted. Furthermore, various kinds of approximate matching errors (insertion, deletion, substitution, transposition) can be detected with different structures of rule combination unit. A probability model of packet matched is also proposed for large alphabet (Chinese char) environment, which proves that the hardware scheme is practicable.

关 键 词：基于内容的流分类 近似字符串匹配 并行建模 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]