Designing Storage Efficient Decision Trees
IEEE Transactions on Computers
Range searching and point location among fat objects
Journal of Algorithms
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 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
Tree bitmap: hardware/software IP lookups with incremental updates
ACM SIGCOMM Computer Communication Review
Shape Shifting Tries for Faster IP Route Lookup
ICNP '05 Proceedings of the 13TH IEEE International Conference on Network Protocols
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Decision tree-based packet classification algorithms are easy to implement and allow the tradeoff between storage and throughput. However, the memory consumption of these algorithms remains quite high when high throughput is required. The Adaptive Binary Cuttings (ABC) algorithm exploits another degree of freedom to make the decision tree adapt to the geometric distribution of the filters. The three variations of the adaptive cutting procedure produce a set of different-sized cuts at each decision step, with the goal to balance the distribution of filters and to reduce the filter duplication effect. The ABC algorithm uses stronger and more straightforward criteria for decision tree construction. Coupled with an efficient node encoding scheme, it enables a smaller, shorter, and well-balanced decision tree. The hardware-oriented implementation of each variation is proposed and evaluated extensively to demonstrate its scalability and sensitivity to different configurations. The results show that the ABC algorithm significantly outperforms the other decision tree-based algorithms. It can sustain more than 10-Gb/s throughput and is the only algorithm among the existing well-known packet classification algorithms that can compete with TCAMs in terms of the storage efficiency.