Gear up the Classifier: Scalable Packet Classification Optimization Framework via Rule Set Pre-Processing

As one of the critical data path functions for many emerging networking applications, packet classification is gaining more and more concerns nowadays. It is commonly believed that conventional software-based classification algorithms are much more time-consuming than hardware-based solutions, i.e., the costly and power consuming TCAM-based mechanism, and incompetent for future high-end applications. In this paper, we propose an efficient optimization framework which can be applied to "gear up" most exiting software-based packet classification algorithms. Under this framework, the large rule set is pre-partitioned into several small subsets, according to some heuristics and dedicated methods. Then the conventional classification process can be significantly simplified and results in a distinct performance improvement by converging the classification power on only a small portion of the rule set. According to the results of our experiment, in which the framework is applied to one of the best algorithms EGT-PC [2], the memory accesses can even be reduced by up to 70%. This provides a much lower cost and more power-efficient alternative to TCAM-based solutions. Another advantage is that the framework requires no change to the hardware environment and little system cost overhead, making it especially suitable for the modern network processor based network solutions.