DOT: Towards Fast Decision Tree Packet Classification by Optimizing Rule Partitions

Packet classification is a crucial component of modern networks. Existing decision tree-based algorithms alleviate the rule replication problem caused by overlapping rules in the ruleset via rule partitioning. They partition the ruleset into multiple subsets based on rule characteristics to reduce rule overlaps. However, existing algorithms fail to address the overlap between rules in the same set, seriously decreasing speed and memory performance. In this paper, we propose DOT, a framework for optimizing rule partitions before constructing decision trees. Its key idea is to migrate rules in subsets based on rule overlaps and the features of heuristics used to construct trees, as well as reorganize rules aided by tuples. DOT finds out the migrated rule candidates using rule dependency graphs and heuristic features, then transforms the rule migration problem into an integer linear programming problem and solves for the optimal migration strategy. Further, we employ a tuple-assisted approach to accelerate rule matching. Experiments show that DOT enhances existing decision tree-based algorithms, improving lookup speed by 1.69 ×, reducing average 24.85% memory consumption and 31.03% decision tree depth.