北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2007, Vol. 30 ›› Issue (4): 60-63.doi: 10.13190/jbupt.200704.60.caot

• 论文 • 上一篇    下一篇

基于参数评估的可调节式分组流分类算法

曹婷 龚向阳   

  1. (北京邮电大学 宽带网研究中心, 北京 100876)
  • 收稿日期:2006-09-11 修回日期:2007-01-19 出版日期:2007-08-30 发布日期:2007-08-30
  • 通讯作者: 曹婷

Adjustable Packet Classification Algorithm Based on Parameter Evaluation

CAO Ting,GONG Xiang-yang   

  1. (Boardband Network Research Center, Beijing University of Posts and Telecommunications, Beijing 100876, China)
  • Received:2006-09-11 Revised:2007-01-19 Online:2007-08-30 Published:2007-08-30
  • Contact: CAO Ting

摘要:

基于决策树的启发式流分类算法目标是建立结点数目尽可能少,树深度尽可能小的数据结构,从而获得较优的时空性能。本文提出的基于参数评估的可调节式流分类算法(PEA:Parameter Evaluation Adjustable algorithm)一方面沿袭目前主流的决策树类流分类算法思想,一方面引入性能参数的概念,并采取调节参数权值的方式获得性能最佳的数据结构。大量测试结果表明,相同条件下本算法对比同类算法能够获得更优的性能结果。

关键词: 分组流分类, 决策树, 时空性能, 参数评估

Abstract:

Heuristic packet classification algorithms based on decision tree aim to classify packets with minimal time and space requirements. In this paper ,an adjustable algorithm based on parameter evaluation is presented. It follows the idea of popular packet classification algorithms,introduces the conception of performance parameters, and adjusts weights of these parameters to aquire data structure with the best performance . Simulations show that , compared with other algorithms of the same kind ,a good improvement can be obtained when using our new algorithm.

Key words: packet classification, decision tree, time and space performance, parameter evaluation

中图分类号: