Toward Predictable Performance in Decision Tree based Packet Classification Algorithms

Packet classification has been studied extensively in the past decade. While many efficient algorithms have been proposed, the lack of deterministic performance has hindered the adoption and deployment of these algorithms: the expensive and power-hungry TCAM is still the de facto standard solution for packet classification. In this work, in contrast to proposing yet another new packet classification algorithm, we present the first steps to understand this unpredictability in performance for the existing algorithms. We focus on decision-tree based algorithms in this paper. In order to achieve the predictability, we firstly revisit the classical and many state-of-art packet classification algorithms. Through a detailed analysis, we conclude that two features of ruleset usually dominate the performance results: 1) the uniformity of the range distribution in different dimensions of the rules; 2) the existence and the number of "orthogonal structure" and wildcard rules in the ruleset. We conduct experiments to show the correctness of these observations, and discribe some potential applications for those results. Our work provides some insight to make the packet classification algorithms a credible alternative to the TCAM-only solutions.