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Towards High-Performance Pattern Matching on Multi-Core Network Processing Platforms

机译:迈向多核网络处理平台上的高性能模式匹配

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With the continual growth of network speed and the increasing sophistication of network applications, keeping network operations efficient and secure becomes more challenging. Pattern matching is one of the key technologies for content-ware network processing, such as traffic classification, application identification and intrusion prevention. In this paper, we propose a hybrid pattern matching algorithm optimized for multi-core network processing platforms. As a system-level solution, our scheme focuses on both performance stability and hardware/software co-design. To verify the effectiveness of our design, the proposed algorithm is implemented on a state-of-art 16-MIPS-core network processing platform and evaluated with real-life data sets. Experimental results show that, when compared with the traditional Aho-Corasick algorithm, our hybrid solution saves 60~95% memory space while guarantees stable performance on large pattern sets and against adverse test traffic.
机译:随着网络速度的不断提高和网络应用程序的日益成熟,保持网络运行的高效性和安全性变得越来越具有挑战性。模式匹配是内容软件网络处理的关键技术之一,例如流量分类,应用程序标识和入侵防御。在本文中,我们提出了一种针对多核网络处理平台优化的混合模式匹配算法。作为系统级解决方案,我们的方案同时注重性能稳定性和硬件/软件协同设计。为了验证我们设计的有效性,所提出的算法是在最新的16-MIPS核心网络处理平台上实施的,并通过实际数据集进行了评估。实验结果表明,与传统的Aho-Corasick算法相比,我们的混合解决方案节省了60%至95%的内存空间,同时保证了在大型模式集上的稳定性能以及对不利测试流量的抵御能力。

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