Fast and scalable layer four switching
Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication
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
Packet classification using multidimensional cutting
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
OC-3072 Packet Classification Using BDDs and Pipelined SRAMs
HOTI '01 Proceedings of the The Ninth Symposium on High Performance Interconnects
Algorithms for routing lookups and packet classification
Algorithms for routing lookups and packet classification
HSM: A Fast Packet Classification Algorithm
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 1
Algorithms for advanced packet classification with ternary CAMs
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
ClassBench: a packet classification benchmark
IEEE/ACM Transactions on Networking (TON)
Scalable packet classification using interpreting: a cross-platform multi-core solution
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
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The ability to classify each incoming packet is called packet classification and is based on an arbitrary number of packet header fields. The role of packet classification is important in special services such as VPNs, firewalls and differentiated services, and influence wire-speed routing. After studding the characteristics of real life classifiers and also requirements of packet classification, it seems that distribution of rules scope is non-uniform and in some sub spaces have more density inside the total space of classifiers. This feature guided us to add "cut point heuristic" to HiCuts, one of the most efficient algorithms. Based on this new heuristic, two new optimized designs for HiCuts have been proposed and their performance are simulated and evaluated. The most specifications of proposed methods are balancing of decision trees and reducing the consumed memory.