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 using tuple space search
Proceedings of the 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
A Two-Stage Packet Classification Algorithm
AINA '03 Proceedings of the 17th International Conference on Advanced Information Networking and Applications
Packet classification using multidimensional cutting
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Packet Classification Using Extended TCAMs
ICNP '03 Proceedings of the 11th IEEE International Conference on Network Protocols
Deep Packet Filter with Dedicated Logic and Read Only Memories
FCCM '04 Proceedings of the 12th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
Efficient packet classification for network intrusion detection using FPGA
Proceedings of the 2005 ACM/SIGDA 13th international symposium on Field-programmable gate arrays
Algorithms for advanced packet classification with ternary CAMs
Proceedings of the 2005 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
Wire speed packet classification without tcams: a few more registers (and a bit of logic) are enough
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Storage-Efficient Architecture for Routing Tables via Prefix Transformation
LCN '07 Proceedings of the 32nd IEEE Conference on Local Computer Networks
Scalable packet classification using interpreting: a cross-platform multi-core solution
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
Swift: a fast dynamic packet filter
NSDI'08 Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation
Multi-Engine Packet Classification Hardware Accelerator
ICCCN '09 Proceedings of the 2009 Proceedings of 18th International Conference on Computer Communications and Networks
Packet classification using diagonal-based tuple space search
Computer Networks: The International Journal of Computer and Telecommunications Networking
Fast and scalable packet classification
IEEE Journal on Selected Areas in Communications
Design and implementation of a fast dynamic packet filter
IEEE/ACM Transactions on Networking (TON)
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Packet classification is complex due to multiple fields present in each filter rule, easily manifesting itself as a router performance bottleneck. Most known classification approaches involve either hardware support or optimization steps (to add precomputed markers and insert rules in the search data structures). Unfortunately, an approach with hardware support is expensive and has limited scalability, whereas one with optimization fails to handle incremental rule updates effectively. This work treats a rapid packet classification mechanism, realized by hashing round-down prefixes (HaRP) in a way that the source and the destination IP prefixes specified in a rule are rounded down to "designated prefix lengths" (DPL) for indexing into hash sets. Utilizing the first ζ bits of an IP prefix with l bits (for ζ ≤ l, ζ ∈ DPL) as the key to the hash function (instead of using the original IP prefix), HaRP exhibits superb hash storage utilization, able to not only outperform those earlier software-oriented classification techniques but also well accommodate dynamic creation and deletion of rules. HaRP makes it possible to hold all its search data structures in the local cache of each core within a contemporary processor, dramatically elevating its classification performance. Empirical results measured on our Broadcom BCM-1480 multicore platform under nine filter datasets obtained from a public source unveil that HaRP enjoys up to some 5× (or 10×) throughput improvement when compared with well-known HyperCuts (or Tuple Space Search).