PERFORMANCE EVALUATION OF IPV6 PACKET CLASSFICATION WITH CACHING

Abstract

Packet classification can be applied in network security, QoS, routing, network load balancing, bandwidth sharing etc. Algorithms of packet classification are categorized into either hardware-based or software-based solutions. Nowadays packet classification implementations are inefficient in IPv6 network environment because much longer address fields have to be processed. In this paper, we propose schemes that use cache memory to improve the performance of IPv6 packet classification. We evaluate the performance of our schemes through simulation under different cache sizes, architectures, and replacement policies. We use real world IPv6 traffic flows for the experiment, and the numerical results show that our schemes achieve higher than 90% hit rate when cache size is no less than 1024 entries in 4-way associative cache memory architecture, this significantly improves the performance of IPv6 packet classification.