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Scaling Regular Expression Matching Performance in Parallel Systems through Sampling Techniques

机译:通过采样技术扩展并行系统中的正则表达式匹配性能

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Modern network devices need to perform deep packet inspection at high speed for security and application- specific services. For this purpose, regular expressions are used, due to their high expressive power, and Deterministic Finite Automata (DFAs) are adopted to match them. Many works have been proposed to improve DFAs, especially in terms of memory consumption and speed. Instead, we address another issue: the scalability of DFAs to parallel systems and their buffer requirements. To our knowledge, a single attempt to parallelize DFA walk on regular multicore systems (which ex- ploits speculation with limited efficiency) has been proposed in literature. We propose a solution in which a number of processing units are committed to walk in parallel a DFA for the same packet; at this aim, sampling techniques on both text and regular expressions are adopted. This scheme is the first in literature that proposes effective parallelization of DFA walk, hence allowing for packet processing time reduction and less memory for reordering buffers. The result is that speed scales as the number of processing units.
机译:现代网络设备需要高速执行深度数据包检查,以实现安全性和特定于应用程序的服务。为此,正则表达式由于具有较高的表达能力而被使用,并且采用确定性有限自动机(DFA)来匹配它们。已经提出了许多改进DFA的工作,特别是在内存消耗和速度方面。相反,我们解决了另一个问题:DFA对并行系统的可伸缩性及其缓冲区要求。据我们所知,文献中已经提出了一种在常规多核系统上并行化DFA遍历的尝试(这种方法可以以有限的效率进行推测)。我们提出了一种解决方案,其中多个处理单元被承诺对同一数据包并行地在DFA中移动;为此,采用了文本和正则表达式的采样技术。该方案是文献中第一个提出有效并行化DFA遍历的方案,因此可以减少数据包处理时间,并减少用于重新排序缓冲区的内存。结果是速度随着处理单元的数量而扩展。

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