High Performance Parallel Scalable Packet Classification Architecture with Popular Rule Caching

Packet Classification is the enabling function for many Internet functions like QoS and Security. In this paper we propose a Classification engine architecture which exploits parallelism to increase throughput. The architecture also make use of the Temporal locality observed in Internet traffic positively by employing Popular Rule Caching mechanism to increase the classification throughput. We also introduce Rule Splitting mechanism to increase the accuracy of the rule caching mechanism. Simulation results revealed that the architecture is capable of achieving a throughput of more than 200Gbps when lowest amount of temporal locality is present for worst case packet size of 40 bytes.