Meta-algorithms for Software-Based Packet Classification

Abstract

We observe that a same rule set can induce very different memory requirement, as well as varying classification performance, when using various well known decision tree based packet classification algorithms. Worse, two similar rule sets, in terms of types and number of rules, can give rise to widely differing performance behaviour for a same classification algorithms. We identify the intrinsic characteristics of rule sets that yield such performance differences, allowing us to understand and predict the performance behaviour of a rule set for various modern packet classification algorithms. Indeed, from our observations, we are able to derive a memory consumption model and an offline algorithm capable of quickly identifying which packet classification is suited to a give rule set. By splitting a large rule set in several subsets and using different packet classification algorithms for different subsets, our Smart Split algorithm is shown to be capable of configuring a multi-component packet classification system that exhibits up to 11 times less memory consumption, as well as up to about 4× faster classification speed, than the state-of-art work [20] for large rule sets. Our Auto PC framework obtains further performance gain by avoiding splitting large rule sets if the memory size of the built decision tree is shown by the memory consumption model to be small.