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
A non-collision hash trie-tree based fast IP classification algorithm
Journal of Computer Science and Technology
Packet classification using multidimensional cutting
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Fast and scalable conflict detection for packet classifiers
Computer Networks: The International Journal of Computer and Telecommunications Networking
Scalable packet classification
IEEE/ACM Transactions on Networking (TON)
ClassBench: a packet classification benchmark
IEEE/ACM Transactions on Networking (TON)
Algorithms for packet classification
IEEE Network: The Magazine of Global Internetworking
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Packet classification on multi-fields is a fundamental mechanism in network equipments, and various classification solutions have been proposed. Because of inherent difficulties, many of these solutions scale poorly in either time or space as rule sets grow in size. Recursive Flow Classification (RFC) is an algorithm with a very high classifying speed. However, its preprocessing complexity and memory requirement are rather high. In this paper, we propose an enhanced RFC (ERFC) algorithm, in which a hash-based aggregated bit vector scheme is exploited to speed up its preprocessing procedure. A compressed and cacheable data structure is also introduced to decrease total memory requirement and improve its searching performance. Evaluation results show that ERFC provides a great improvement over RFC in both space requirement and preprocessing time. The search time complexity of ERFC is equivalent to that of RFC in the worst case; and its average classifying speed is improved by about 100%.