A Scalable Approach for Packet Classification Using Rule-Base Partition

This paper focuses on a new direction for packet classification, which can substantially improve the performance of a classifier by decreasing the rule-base lookup latency. The classifier partitions the rule-base into smaller independent sub-rule bases by using the hash key of hashing technique. We apply the concept of maximum entropy to select the hash key for optimal partitioning of rule-base. We performed the detailed simulations of our proposed algorithm on synthetic rulebases of size 1K to 200K entries using packet traces. From the simulation results we found that the algorithm significantly outperforms by reducing the size of a rulebase by more than four orders of magnitude with just two-levels of partitioning. Both the space and time complexity of the algorithm exhibit linearity in terms of the size of a rule-bases. The proposed idea suggests a good scalable solution for the packet classification with a large rule-base.