A Ternary Unification Framework for Optimizing TCAM-Based Packet Classification Systems - 2018


Packet classification is that the key mechanism for enabling many NetWorking and security services. Ternary content addressable memory (TCAM) has been the commercial customary for implementing high-speed packet classification because of its constant classification time. However, TCAM chips have small capability, high power consumption, high heat generation, and large space-size. This Project focuses on the TCAM-based mostly classifier compression drawback: given a classifier C , we have a tendency to need to construct the littlest potential list of TCAM entries T that implement C . In this Project, we tend to propose the ternary unification framework (TUF) for this compression downside and three concrete compression algorithms within this framework. The framework allows us to seek out a lot of optimization opportunities and style new TCAM-based classifier compression algorithms. Our experimental results show that the TUF will speed up the prior algorithm TCAM Razor by 20 times or additional and results in new algorithms that improve compression performance over previous algorithms by a mean of on our largest real-life classifiers. The experimental results show that our algorithms can improve both the runtime and also the compression ratio over previous work.

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