Towards high-performance flow-level packet processing on multi-core network processors
Proceedings of the 3rd ACM/IEEE Symposium on Architecture for networking and communications systems
EffiCuts: optimizing packet classification for memory and throughput
Proceedings of the ACM SIGCOMM 2010 conference
ParaSplit: A Scalable Architecture on FPGA for Terabit Packet Classification
HOTI '12 Proceedings of the 2012 IEEE 20th Annual Symposium on High-Performance Interconnects
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Most of state-of-the-art packet classification algorithms employ heuristics to trade off between classification speed and memory usage. However, intelligent heuristics often result in complex data structures in algorithm implementation. This brings difficulties to the deployment and optimization of packet classification algorithms. In this poster, a structural compression approach is presented for decision tree based packet classification algorithms. This approach exploits the similarity in real-life filter sets to achieve high compression ratio without loss of tree semantics.