EffiCuts: optimizing packet classification for memory and throughput

Packet Classification is a key functionality provided by modern routers. Previous decision-tree algorithms, HiCuts and HyperCuts, cut the multi-dimensional rule space to separate a classifier's rules. Despite their optimizations, the algorithms incur considerable memory overhead due to two issues: (1) Many rules in a classifier overlap and the overlapping rules vary vastly in size, causing the algorithms' fine cuts for separating the small rules to replicate the large rules. (2) Because a classifier's rule-space density varies significantly, the algorithms' equi-sized cuts for separating the dense parts needlessly partition the sparse parts, resulting in many ineffectual nodes that hold only a few rules. We propose EffiCuts which employs four novel ideas: (1) Separable trees: To eliminate overlap among small and large rules, we separate all small and large rules. We define a subset of rules to be separable if all the rules are either small or large in each dimensio...

• Type: Conference
• Year: 2010
• Where: SIGCOMM
• Authors: Balajee Vamanan, Gwendolyn Voskuilen, T. N. Vijaykumar
• Keywords: Algorithms, Communications, Large Rules, SIGCOMM 2010, Small