Optimal packet classification applicable tothe OpenFlow context

Abstract

Packet Classification remains a hot research topic, as it is a fundamental function in telecommunication networks, which are now facing new challenges. Due to the emergence of new standards such as OpenFlow, packet classification algorithms have to be reconsidered to support effectively classification over more than 5 fields. In this paper, we analyze the performance offered by EffiCuts in the context of OpenFlow. We extended the EffiCuts algorithm according to OpenFlow's context by proposing three improvements: optimization of the leaf data set size, enhancements to the heuristic used to compute the number of cuts, and utilization of an adaptive grouping factor. These extensions provide gains in many contexts but they were tailored for the OpenFlow context. When used in this context, it is shown using suitable benchmarks that they allow reducing the number of memory accesses by a factor of 2 on average, while decreasing the size of the data structure by about 35%.