Packet Classification is the enabling function for performing many networking applications like Integrated Services, Differentiated Services, Access Control/Firewalls, and Intrusion Detection. To cope with high-speed links and ever-increasing bandwidth requirements, time-efficient solutions are needed for which Ternary Content Addressable Memories (TCAMs) are popularly used. However, high cost, heavy power consumption, and poor scalability limit their use in many commercial switches. In this work, an efficient framework for caching the packet classification rules on TCAMs in accordance with traffic characteristics is proposed. The proposed design will have a two-level classification engine in which level-1 is a TCAM classifier with a smaller rule capacity and level-2 is a software classifier. The classifiers are assisted by a rule update engine that monitors the rule temporal behavior and performs timely updates of the rules onto level-1. Crucial challenges with respect to the proposed framework design are defined and addressed effectively in this work. Simulation results shows that the architecture can achieve a throughput of 250 Gbps on average by caching only 10% of the total rules for rule databases of sizes 10,000. The proposed architecture, to the best of our knowledge, is the only traffic-aware architecture using TCAMs that provides a completely deployable framework and also can scale for speeds beyond 250 Gbps (OC-1920 and beyond).

中文翻译：

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一种基于 TCAM 的数据包分类缓存架构框架

数据包分类是执行许多网络应用程序的启用功能，例如集成服务、差异化服务、访问控制/防火墙和入侵检测。为了应对高速链路和不断增加的带宽需求，需要时间高效的解决方案，三元内容可寻址存储器 (TCAM) 被普遍使用。然而，成本高、功耗大、可扩展性差等限制了它们在许多商用交换机中的应用。在这项工作中，提出了一种根据流量特征在 TCAM 上缓存数据包分类规则的有效框架。提议的设计将有一个两级分类引擎，其中1级是具有较小规则容量的 TCAM 分类器，并且2级是一个软件分类器。分类器由规则更新引擎辅助，该引擎监控规则时间行为并及时将规则更新到级别 1。在这项工作中，定义并有效解决了与拟议框架设计相关的关键挑战。仿真结果表明，该架构通过仅缓存 10,000 大小的规则数据库的总规则的 10% 即可实现平均 250 Gbps 的吞吐量。据我们所知，所提议的架构是唯一使用 TCAM 的流量感知架构，它提供了一个完全可部署的框架，并且还可以扩展到超过 250 Gbps（OC-1920 及更高）的速度。