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Packet classification using diagonal-based tuple space search

机译:使用基于对角线的元组空间搜索进行数据包分类

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Multidimensional packet classification has attracted considerable research interests in the past few years due to the increasing demand on policy based packet forwarding and security services. These network services typically involve determining the action to take on packets according to a set of rules. As the number of rules increases, time for determining the best matched rule for an incoming IP packet will increase and subsequently incur long processing delay. To address this issue, in this paper we propose a two-dimensional packet classification algorithm which focuses on reducing time for classification while keeping reasonable memory requirement in practice. Our approach extends the tuple space framework and then allows performing binary search on the tuple space. To our knowledge, the proposed scheme is the first binary search scheme on two-dimensional tuples. With the proposed scheme, given a filter set with n two-dimensional filters, it requires only O(log(w)) hash operations to determine the best matched filter, where w is the maximum prefix length of filters. The proposed scheme achieves fast packet classification, and according to our experimental results, it does not require huge memory space. This makes it useful for network applications that require high speed packet classification.
机译:由于对基于策略的数据包转发和安全服务的需求不断增长,多维数据包分类在过去几年中吸引了相当多的研究兴趣。这些网络服务通常涉及根据一组规则确定对数据包采取的操作。随着规则数量的增加,为传入IP数据包确定最佳匹配规则的时间将增加,并随后导致较长的处理延迟。为了解决这个问题,本文提出了一种二维包分类算法,该算法着重于减少分类时间,同时在实践中保持合理的存储需求。我们的方法扩展了元组空间框架,然后允许在元组空间上执行二进制搜索。据我们所知,提出的方案是第一个基于二维元组的二进制搜索方案。对于提出的方案,给定一个具有n个二维过滤器的过滤器集,它仅需要O(log(w))个哈希操作即可确定最佳匹配的过滤器,其中w是过滤器的最大前缀长度。所提出的方案实现了快速的分组分类,并且根据我们的实验结果,它不需要巨大的存储空间。这对于需要高速数据包分类的网络应用程序很有用。

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