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
Efficient Mapping of Range Classifier into Ternary-CAM
HOTI '02 Proceedings of the 10th Symposium on High Performance Interconnects HOT Interconnects
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
Firewall Design: Consistency, Completeness, and Compactness
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
DSN '04 Proceedings of the 2004 International Conference on Dependable Systems and Networks
Algorithms for advanced packet classification with ternary CAMs
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
SSA: a power and memory efficient scheme to multi-match packet classification
Proceedings of the 2005 ACM symposium on Architecture for networking and communications systems
Survey and taxonomy of packet classification techniques
ACM Computing Surveys (CSUR)
Packet classifiers in ternary CAMs can be smaller
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
DPPC-RE: TCAM-Based Distributed Parallel Packet Classification with Range Encoding
IEEE Transactions on Computers
Efficient packet classification using TCAMs
Computer Networks: The International Journal of Computer and Telecommunications Networking
Compressing rectilinear pictures and minimizing access control lists
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
DRES: Dynamic Range Encoding Scheme for TCAM Coprocessors
IEEE Transactions on Computers
Complete Redundancy Removal for Packet Classifiers in TCAMs
IEEE Transactions on Parallel and Distributed Systems
Complete redundancy detection in firewalls
DBSec'05 Proceedings of the 19th annual IFIP WG 11.3 working conference on Data and Applications Security
Fast and scalable packet classification
IEEE Journal on Selected Areas in Communications
Algorithms for packet classification
IEEE Network: The Magazine of Global Internetworking
Hardware accelerators targeting a novel group based packet classification algorithm
International Journal of Reconfigurable Computing
An impulse-c hardware accelerator for packet classification based on fine/coarse grain optimization
International Journal of Reconfigurable Computing
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Several range reencoding schemes have been proposed to mitigate the effect of range expansion and the limitations of small capacity, large power consumption, and high heat generation of ternary content addressable memory (TCAM)-based packet classification systems. However, they all disregard the semantics of classifiers and therefore miss significant opportunities for space compression. In this paper, we propose new approaches to range reencoding by taking into account classifier semantics. Fundamentally different from prior work, we view reencoding as a topological transformation process from one colored hyperrectangle to another, where the color is the decision associated with a given packet. Stated another way, we reencode the entire classifier by considering the classifier's decisions rather than reencode only ranges in the classifier ignoring the classifier's decisions as prior work does. We present two orthogonal, yet composable, reencoding approaches: domain compression and prefix alignment. Our techniques significantly outperform all previous reencoding techniques. In comparison to prior art, our experimental results show that our techniques achieve at least five times more space reduction in terms of TCAM space for an encoded classifier and at least three times more space reduction in terms of TCAM space for a reencoded classifier and its transformers. This, in turn, leads to improved throughput and decreased power consumption.