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
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
Packet Classification Using Extended TCAMs
ICNP '03 Proceedings of the 11th IEEE International Conference on Network Protocols
Packet classification in large ISPs: design and evaluation of decision tree classifiers
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Algorithms for advanced packet classification with ternary CAMs
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Chisel: A Storage-efficient, Collision-free Hash-based Network Processing Architecture
Proceedings of the 33rd annual international symposium on Computer Architecture
ClassBench: a packet classification benchmark
IEEE/ACM Transactions on Networking (TON)
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
400 Gb/s Programmable Packet Parsing on a Single FPGA
Proceedings of the 2011 ACM/IEEE Seventh Symposium on Architectures for Networking and Communications Systems
TreeCAM: decoupling updates and lookups in packet classification
Proceedings of the Seventh COnference on emerging Networking EXperiments and Technologies
Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication
ACM SIGCOMM Computer Communication Review - Special october issue SIGCOMM '12
Structural compression of packet classification trees
Proceedings of the eighth ACM/IEEE symposium on Architectures for networking and communications systems
LEAP: latency- energy- and area-optimized lookup pipeline
Proceedings of the eighth ACM/IEEE symposium on Architectures for networking and communications systems
Optimal packet classification applicable tothe OpenFlow context
Proceedings of the first edition workshop on High performance and programmable networking
Scalable rule management for data centers
nsdi'13 Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation
Replication free rule grouping for packet classification
Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
Optimizing the "one big switch" abstraction in software-defined networks
Proceedings of the ninth ACM conference on Emerging networking experiments and technologies
SWSL: software synthesis for network lookup
ANCS '13 Proceedings of the ninth ACM/IEEE symposium on Architectures for networking and communications systems
| Hi-index | 0.00 |
Packet Classification is a key functionality provided by modern routers. Previous decision-tree algorithms, HiCuts and HyperCuts, cut the multi-dimensional rule space to separate a classifier's rules. Despite their optimizations, the algorithms incur considerable memory overhead due to two issues: (1) Many rules in a classifier overlap and the overlapping rules vary vastly in size, causing the algorithms' fine cuts for separating the small rules to replicate the large rules. (2) Because a classifier's rule-space density varies significantly, the algorithms' equi-sized cuts for separating the dense parts needlessly partition the sparse parts, resulting in many ineffectual nodes that hold only a few rules. We propose EffiCuts which employs four novel ideas: (1) Separable trees: To eliminate overlap among small and large rules, we separate all small and large rules. We define a subset of rules to be separable if all the rules are either small or large in each dimension. We build a distinct tree for each such subset where each dimension can be cut coarsely to separate the large rules, or finely to separate the small rules without incurring replication. (2) Selective tree merging: To reduce the multiple trees' extra accesses which degrade throughput, we selectively merge separable trees mixing rules that may be small or large in at most one dimension. (3) Equi-dense cuts: We employ unequal cuts which distribute a node's rules evenly among the children, avoiding ineffectual nodes at the cost of a small processing overhead in the tree traversal. (4) Node Co-location: To achieve fewer accesses per node than HiCuts and HyperCuts, we co-locate parts of a node and its children. Using ClassBench, we show that for similar throughput EffiCuts needs factors of 57 less memory than HyperCuts and of 4-8 less power than TCAM.