Packet classification by multilevel cutting of the classification space: an algorithmic-architectural solution for IP packet classification in next generation networks

Authors:
Motasem Aldiab;Emi Garcia-Palacios;Danny Crookes;Sakir Sezer
Affiliations:
Institute of Electronics, Communications and Information Technology, Queen's University Belfast, Belfast, UK;Institute of Electronics, Communications and Information Technology, Queen's University Belfast, Belfast, UK;Institute of Electronics, Communications and Information Technology, Queen's University Belfast, Belfast, UK;Institute of Electronics, Communications and Information Technology, Queen's University Belfast, Belfast, UK
Venue:
Journal of Computer Systems, Networks, and Communications
Year:
2008

Citing 17
Cited 0

Computational geometry: an introduction

Computational geometry: an introduction
Content-Addressable and Associative Memory: Alternatives to the Ubiquitous RAM

Computer
Range searching and point location among fat objects

Journal of Algorithms
Scalable high speed IP routing lookups

SIGCOMM '97 Proceedings of the ACM SIGCOMM '97 conference on Applications, technologies, architectures, and protocols for computer communication
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
Router plugins: a software architecture for next generation routers

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
Contentaddressable Memories

Contentaddressable Memories
Associative Processors and Memories: A Survey

IEEE Micro
Packet classification using multidimensional cutting

Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Scalable packet classification

IEEE/ACM Transactions on Networking (TON)
DPPC-RE: TCAM-Based Distributed Parallel Packet Classification with Range Encoding

IEEE Transactions on Computers
Efficient multi-match packet classification with TCAM

HOTI '04 Proceedings of the High Performance Interconnects, 2004. on Proceedings. 12th Annual IEEE Symposium
ClassBench: a packet classification benchmark

IEEE/ACM Transactions on Networking (TON)
Fast and scalable packet classification

IEEE Journal on Selected Areas in Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Traditionally, the Internet provides only a "best-effort" service, treating all packets going to the same destination equally. However, providing differentiated services for different users based on their quality requirements is increasingly becoming a demanding issue. For this, routers need to have the capability to distinguish and isolate traffic belonging to different flows. This ability to determine the flow each packet belongs to is called packet classification. Technology vendors are reluctant to support algorithmic solutions for classification due to their nondeterministic performance. Although content addressable memories (CAMs) are favoured by technology vendors due to their deterministic high-lookup rates, they suffer from the problems of high-power consumption and high-silicon cost. This paper provides a new algorithmic-architectural solution for packet classification that mixes CAMs with algorithms based on multilevel cutting of the classification space into smaller spaces. The provided solution utilizes the geometrical distribution of rules in the classification space. It provides the deterministic performance of CAMs, support for dynamic updates, and added flexibility for system designers.