Taming Large Classifiers with Rule Reference Locality

An important aspect of packet classification problem on which little light has been shed so far is the rule reference dynamics. In this paper, we argue that for any given classifier, there is likely a significant skew in the rule reference pattern. We term such phenomenon rule reference locality, which we believe stems from biased traffic pattern and/or the existence of “super-rules” that cover a large subset of the rule hyperspace. Based on the observation, we propose an adaptive classification approach that dynamically accommodates the skewed and possibly time-varying reference pattern. It is not a new classification method per se, but it can effectively enhance existing packet classification schemes, especially for large classifiers. As an instance, we present a new classification method called segmented RFC with dynamic rule base reconfiguration (SRFC+DR). When driven by several large real-life packet traces, it yields a several-fold speedup for 5-field 100K-rule classification as compared with another scalable method ABV. In general, we believe exploiting the rule reference locality is a key to scaling to a very large number of rules in future packet classifiers.