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引用次数: 0
摘要
报文分类在网络功能中起着至关重要的作用。传统的分类算法假设所有字段值都是可用且有效的。然而,随着网络变得越来越复杂,这样的前提正在受到挑战。缺少字段的场景给包分类器带来了巨大的挑战。现有的方法只能列出这种情况下的所有可能情况,从而成倍地增加了工作量。在我们之前的工作中,RFC算法被证明对这个问题有帮助,但它的空间性能很差。本文提出了一种利用元组空间搜索(Tuple Space Search, TSS)处理缺失字段的分类方案。我们重新设计了哈希计算方法,并提出了一种新的数据结构来恢复丢失字段的数据包。实验表明,RTSS将内存消耗和构建时间降低了几个数量级。同时,RTSS在支持快速更新的同时,具有更好的分类性能。
RTSS: Robust Tuple Space Search for Packet Classification
Packet classification shows an essential role in net-work functions. Traditional classification algorithms assume that all field values are available and valid. However, such a premise is being challenged as networks become more complex now. Scenarios with field-missing poses great challenges to packet classifiers. Existing approaches can only list all possible situations in such cases, increasing the workload exponentially. RFC algorithm is proved to be helpful for this issue in our previous work, but its spacial performance is much poor. In this paper, we propose a novel classification scheme using Tuple Space Search (TSS) to deal with missing fields. We redesign the hash calculation method and raise a new data structure to recover field-missing packets. The experiment shows that RTSS reduce the memory consumption and construction time by several orders of magnitude. At the same time, RTSS has better classification performance than previous work, while supporting fast updates.