Packet classification plays a critical role in improving the performance of many network devices including routers, firewalls and intrusion detection systems. Due to the increasing number of classification rules, high traffic volume and high bandwidth network links, des More
Packet classification plays a critical role in improving the performance of many network devices including routers, firewalls and intrusion detection systems. Due to the increasing number of classification rules, high traffic volume and high bandwidth network links, designing an efficient packet classifier becomes more challenging. Packet classification algorithms that use static data structure do not consider the pattern of the incoming traffic in optimizing their search mechanism. Therefore, we use some statistical characteristics of the incoming traffic to propose a traffic aware data structure. Since most Internet traffic volume belong to long-live flows, the majority of the packets are matched to the rules in a few sub trees. To take the advantage of this feature, AVL tree data structure is served for storing classification rules where the upper and lower limits of the rule-set are used as nodes. Our evaluation have shown that with increasing the skewness of data packets, the average number of memory accesses are significantly decreased compared to the basic case. Finally, evaluation results show that the traffic-aware packet classification with high frequency rules can decrease more than 40% of the average number of memory accesses and consequently the lookup time.
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