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中文摘要：
随着网络功能日益多样化,分组分类技术对匹配域数量、表项深度等需求不断提高,加剧了硬件存储压力。为保证查表效率和硬件资源利用率,提出基于信息熵的匹配域裁剪算法。通过分析匹配域冗余信息,提出匹配域裁剪模型;通过分组头部信息熵的映射建模,将匹配域裁剪算法复杂度从NP难降为线性复杂度。实验结果表明,较现有方案,所提方案所需三态内容寻址存储器（TCAM,ternary content-addressable memory）存储空间能够进一步减少40%以上,或随着流表规模增长,所提案能够明显减少算法运行时间。
英文摘要：
With the increasing diversity of network functions, packet classification had a higher demand on the number of match fields and depth of match table, which placed a severe burden on the storage capacity of hardware. To ensure the efficiency of matching process while at the same time improve the usage of storage devices, an information entropy based cutting algorithm on match fields was proposed. By the analysis on the redundancy of match fields and distribution pat- tern in a rule set, a match field cutting model was proposed. With the mapping of matching process to the process of en- tropy reduction, the complexity of optimal match field cutting was reduced from NP-hard to linear complexity. Experi- ment results show that compared to existing schemes, this scheme can need 40% less TCAM storage space, and on the other side, with the growing of table size, the time complexity of this algorithm is also far less than other algorithms.
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