A Ternary Unification Framework for Optimizing TCAM-Based Packet Classification Systems

Abstract

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 20 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. The experimental results show that our algorithms can improve both the runtime and the compression ratio over prior work.