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Scaling Regular Expression Matching Performance in Parallel Systems through Sampling Techniques

机译:通过采样技术缩放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,以进行安全性和应用特定服务。为此目的,使用正则表达式,由于它们的高富有效力功率,并且采用了确定性有限自动机(DFA)来匹配它们。已经提出了许多作品来改善DFA,特别是在记忆消耗和速度方面。相反,我们解决了另一个问题:DFA的可扩展性与并行系统及其缓冲区要求。据我们所知,在文献中提出了一种尝试在常规多核系统上并将DFA步行平行于DFA步行(具有有限效率的猜测)。我们提出了一种解决方案,其中许多处理单元致力于为同一数据包并行地行走DFA;在此目的,采用文本和正则表达式的采样技术。该方案是第一个在文献中,提出了DFA步行的有效并行化的文献中,因此允许分组处理时间减少和用于重新排序缓冲区的存储器较少。结果是速度尺度作为处理单元的数量。

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