Structural compression of packet classification trees

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.