基于FPGA的分组分类优化

Michal Kekely, J. Korenek
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引用次数: 0

摘要

对于许多不同的网络任务,从交换或路由到监视和安全设备(如防火墙或IDS),包分类是一项关键的时间关键操作。实现分组分类的加速体系结构必须满足当前高速网络日益增长的需求。然而,分组分类通常与其他分组处理算法一起使用,这减少了FPGA芯片上可用的硬件资源。P4语言的引入要求数据包分类更加灵活,同时在有限的资源下保持高吞吐量。因此,我们需要灵活和高性能的体系结构来平衡特定类型规则的处理速度和硬件资源。DCFL算法具有高性能和灵活性。因此,我们提出对DCFL算法和整体数据包处理硬件架构进行优化。目标是最大化吞吐量并最小化资源紧张。该方法的主要思想是分析规则集,识别出一些冲突的规则,并将这些规则卸载到其他硬件模块。这种方法使我们能够更快地处理数据包,即使在最坏的情况下也是如此。此外,我们可以在FPGA中安装更多的数据包处理,并对数据包处理体系结构进行微调,以满足特定网络应用的吞吐量和资源需求。通过提出的优化,我们可以将数据包分类的吞吐量提高76%。或者,我们可以实现所需资源减少37%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing Packet Classification on FPGA
Packet classification is a crucial time-critical operation for many different networking tasks ranging from switching or routing to monitoring and security devices like firewalls or IDS. Accelerated architectures implementing packet classification must satisfy the ever-growing demand for current high-speed networks. However, packet classification is generally used together with other packet processing algorithms, which decreases the available hardware resources on the FPGA chip. The introduction of the P4 language requires the packet classification to be even more flexible while maintaining a high throughput with limited resources. Thus, we need flexible and high-performance architectures to balance processing speed and hardware resources for specific types of rules. DCFL algorithm provides high performance and flexibility. Therefore, we propose optimizations to the DCFL algorithm and overall packet processing hardware architecture. The goal is to maximize the throughput and minimize the resource strain. The main idea of the approach is to analyze the ruleset, identify some conflicting rules and offload these rules to other hardware modules. This approach allows us to process packets faster, even in the worst-case scenarios. Moreover, we can fit more packet processing into the FPGA and fine-tune the packet processing architecture to meet a specific network application’s throughput and resource demands. With the proposed optimizations we can achieve up to a 76 % increase in the throughput of the packet classification. Alternatively, we can achieve up to a 37 % decrease in resources needed.
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