TCAM Razor: A Systematic Approach Towards Minimizing Packet Classifiers in TCAMs

Packet classification is the core mechanism that enables many networking services on the Internet such as firewall packet filtering and traffic accounting. Using ternary content addressable memories (TCAMs) to perform high-speed packet classification has become the
de facto
standard in industry. TCAMs classify packets in constant time by comparing a packet with all classification rules of ternary encoding in parallel. Despite their high speed, TCAMs suffer from the well-known range expansion problem. As packet classification rules usually have fields specified as ranges, converting such rules to TCAM-compatible rules may result in an explosive increase in the number of rules. This is not a problem if TCAMs have large capacities. Unfortunately, TCAMs have very limited capacity, and more rules mean more power consumption and more heat generation for TCAMs. Even worse, the number of rules in packet classifiers has been increasing rapidly with the growing number of services deployed on the Internet. In this paper, we consider the following problem: given a packet classifier, how can we generate another semantically equivalent packet classifier that requires the least number of TCAM entries? In this paper, we propose a systematic approach, the
TCAM Razor
, that is effective, efficient, and practical. In terms of effectiveness, TCAM Razor achieves a total compression ratio of 29.0%, which is significantly better than the previously published best result of 54%. In terms of efficiency, our TCAM Razor prototype runs in seconds, even for large packet classifiers. Finally, in terms of practicality, our TCAM Razor approach can be easily deployed as it does not require any modification to existing packet classification systems, unlike many previous range encoding schemes.