A TCAM-based Caching Architecture Framework for Packet Classification

Abstract

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).