A sorted partitioning approach to high-speed and fast-update OpenFlow classification

Abstract

OpenFlow packet classification needs to satisfy two requirements: high speed and fast updates. Although packet classification is a well-studied problem, no existing solution satisfies both requirements. Decision tree methods, such as HyperCuts, EffiCuts, and SmartSplit can achieve high-speed packet classification but not fast updates. The Tuple Space Search (TSS) algorithm used in Open vSwitch achieves fast updates but not high-speed packet classification. In this paper, we propose a hybrid approach, PartitionSort, that combines the benefits of both TSS and decision trees achieving both high-speed packet classification and fast updates. A key to PartitionSort is a novel notion of ruleset sortability that provides two key benefits. First, it results in far fewer partitions than TSS. Second, it allows the use of Multi-dimensional Interval Trees to achieve logarithmic classification and update time for each sortable ruleset partition. Our extensive experimental results show that PartitionSort is an order of magnitude faster than TSS in classifying packets while achieving comparable update time. PartitionSort is a few orders of magnitude faster in construction time than SmartSplit, a state-of-the-art decision tree classifier, while maintaining competitive classification time. Finally, PartitionSort is scalable to an arbitrary number of fields.