Packet classification using multidimensional cutting
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Survey and taxonomy of packet classification techniques
ACM Computing Surveys (CSUR)
Fast packet classification using bloom filters
Proceedings of the 2006 ACM/IEEE symposium on Architecture for networking and communications systems
ClassBench: a packet classification benchmark
IEEE/ACM Transactions on Networking (TON)
EffiCuts: optimizing packet classification for memory and throughput
Proceedings of the ACM SIGCOMM 2010 conference
Experiences in Co-designing a Packet Classification Algorithm and a Flexible Hardware Platform
Proceedings of the 2011 ACM/IEEE Seventh Symposium on Architectures for Networking and Communications Systems
Layer Partitioned Search Tree for Packet Classification
AINA '12 Proceedings of the 2012 IEEE 26th International Conference on Advanced Information Networking and Applications
Algorithms for packet classification
IEEE Network: The Magazine of Global Internetworking
ParaSplit: A Scalable Architecture on FPGA for Terabit Packet Classification
HOTI '12 Proceedings of the 2012 IEEE 20th Annual Symposium on High-Performance Interconnects
Scalable packet classification on FPGA
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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Packet Classification remains a hot research topic, as it is a fundamental function in telecommunication networks, which are now facing new challenges. Due to the emergence of new standards such as OpenFlow, packet classification algorithms have to be reconsidered to support effectively classification over more than 5 fields. In this paper, we analyze the performance offered by EffiCuts in the context of OpenFlow. We extended the EffiCuts algorithm according to OpenFlow's context by proposing three improvements: optimization of the leaf data set size, enhancements to the heuristic used to compute the number of cuts, and utilization of an adaptive grouping factor. These extensions provide gains in many contexts but they were tailored for the OpenFlow context. When used in this context, it is shown using suitable benchmarks that they allow reducing the number of memory accesses by a factor of 2 on average, while decreasing the size of the data structure by about 35%.