Fast and scalable layer four switching
Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication
High-speed policy-based packet forwarding using efficient multi-dimensional range matching
Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication
Packet classification using tuple space search
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Packet classification on multiple fields
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Building Internet firewalls (2nd ed.)
Building Internet firewalls (2nd ed.)
Scalable packet classification
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
Space Decomposition Techniques for Fast Layer-4 Switching
PfHSN '99 Proceedings of the IFIP TC6 WG6.1 & WG6.4 / IEEE ComSoc TC on on Gigabit Networking Sixth International Workshop on Protocols for High Speed Networks VI
Efficient Mapping of Range Classifier into Ternary-CAM
HOTI '02 Proceedings of the 10th Symposium on High Performance Interconnects HOT Interconnects
ISCC '06 Proceedings of the 11th IEEE Symposium on Computers and Communications
Fast and scalable packet classification
IEEE Journal on Selected Areas in Communications
An evaluation of the key design criteria to achieve high update rates in packet classifiers
IEEE Network: The Magazine of Global Internetworking
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In this paper, we introduce a new packet classification algorithm, which can substantially improve the performance of a classifier. The algorithm is built on the observation that a given packet matches only a few rules even in large classifiers, which suggests that most of rules are independent in any given rulebase. The algorithm hierarchically partitions the rulebase into smaller independent subrulebases based on hashing. By using the same hash key used in the partitioning a classifier only needs to look up the relevant subrulebase to which an incoming packet belongs. For an optimal partitioning of rulebases, we apply the notion of maximum entropy to the hash key selection.We performed the detailed simulations of our proposed algorithm on synthetic rulebases of size 1 K to 500 K entries using real-life packet traces. The results show that the algorithm can significantly outperform existing classifiers by reducing the size of a rulebase by more than four orders of magnitude with just two-levels of partitioning. Both the time complexity and the space complexity of the algorithm exhibit linearity in terms of the size of a rulebase. This suggests that the algorithm can be a good scalable solution for medium to large rulebases.