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
Scalable packet classification
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
Packet classification using multidimensional cutting
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
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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Decision tree based schemes are widely used in designing high-speed packet classification algorithms. The primary objective is to construct a decision tree with minimal storage and searching time complexity. In this paper, we proposed a novel decision tree packet classification algorithm based on Efficient Multiple Bit Selection (EMBS). In the proposed algorithm, prefix fields are transformed to a set of independent bits; and multiple arbitrary bits are selected to cut nodes when building the decision tree. A Performance Estimate Function (PEF) and an efficient bit selecting algorithm are exploited to determine which bits should be selected to obtain an efficient decision tree with high performance. The proposed EMBS algorithm is capable of handling range match fields, and it is suitable for IPv6 packet classification as well as IPv4. Evaluation results show that EMBS provides a great improvement over recent decision tree based algorithms in both space requirement and searching performance.