The Complexity of Some Problems on Subsequences and Supersequences
Journal of the ACM (JACM)
Network Processors
Packet Classification Using Extended TCAMs
ICNP '03 Proceedings of the 11th IEEE International Conference on Network Protocols
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
Compressing rectilinear pictures and minimizing access control lists
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Complete Redundancy Removal for Packet Classifiers in TCAMs
IEEE Transactions on Parallel and Distributed Systems
TCAM Razor: a systematic approach towards minimizing packet classifiers in TCAMs
IEEE/ACM Transactions on Networking (TON)
PETCAM—A Power Efficient TCAM Architecture for Forwarding Tables
IEEE Transactions on Computers
Complete redundancy detection in firewalls
DBSec'05 Proceedings of the 19th annual IFIP WG 11.3 working conference on Data and Applications Security
Bit weaving: a non-prefix approach to compressing packet classifiers in TCAMs
IEEE/ACM Transactions on Networking (TON)
Exact Worst Case TCAM Rule Expansion
IEEE Transactions on Computers
Hi-index | 0.00 |
Packet classification is the key mechanism for enabling many networking and security services. Ternary Content Addressable Memory (TCAM) has been the industrial standard for implementing high-speed packet classification because of its constant classification time. However, TCAM chips have small capacity, high power consumption, high heat generation, and large area size. This paper focuses on the TCAM-based Classifier Compression problem: given a classifier C, we want to construct the smallest possible list of TCAM entries T that implement C. In this paper, we propose the Ternary Unification Framework (TUF) for this compression problem and three concrete compression algorithms within this framework. The framework allows us to find more optimization opportunities and design new TCAM-based classifier compression algorithms. Our experimental results show that the TUF can speed up the prior algorithm TCAM Razor by twenty times or more and leads to new algorithms that improve compression performance over prior algorithms by an average of 13.7% on our largest real life classifiers.