ByteCuts: Fast Packet Classification By Interior Bit Extraction

James Daly Michigan State University, USA
Eric Torng Michigan State University, USA


Many networking devices, such as firewalls and routing tables, rely upon packet classifiers to define their behavior for various kinds of network traffic. Since these devices have real-time constraints, it is important for packet classification to be as fast as possible. We present a new method, ByteCuts, which includes two major improvements over existing tree-based packet classifiers. First, it introduces a new cutting method that is more efficient than existing methods. Second, ByteCuts intelligently partitions the rule list into multiple trees in a way that supports the cutting method and reduces rule replication. We compare ByteCuts to several existing methods such as HyperCuts, HyperSplit, and SmartSplit. We find that ByteCuts outperforms SmartSplit, the previous fastest classifier, in every metric; specifically, ByteCuts is able to classify packets 58% faster, can be constructed in seconds rather than minutes, and uses orders of magnitude less memory.

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