CutSplit: A Decision-Tree Combining Cutting And Splitting For Scalable Packet Classification

Wenjun Li Peking University, P.R. China
Xianfeng Li Peking University, P.R. China
Hui Li Peking University Shenzhen Graduate School, P.R. China
Gaogang Xie Institute of Computing Technology, Chinese Academy of Sciences, P.R. China


Efficient algorithmic solutions for multi-field packet classification have been a challenging problem for many years. This problem is becoming even worse in the era of Software Defined Network (SDN), where flow tables with increasing complexities are playing a central role in the forwarding plane of SDN. In this paper, we first conduct an unprecedented in-depth reasoning on issues that led to the unsuccess of the major quests for scalable algorithmic solutions. With the insights obtained, we propose a practical framework called CutSplit, which can exploit the benefits of cutting and splitting techniques adaptively. By addressing the central problem caused by uncontrollable rule replications suffered by the major efforts, CutSplit not only pushes the performance of algorithmic packet classification more closely to hardware-based solutions, but also reduces the memory consumption to a practical level. Moreover, our work achieves low pre-processing time for rule updates, a problem that has long been ignored by previous decision-trees, but is becoming more relevant in the context of SDN due to frequent updates of rules. Experimental results show that using ClassBench, CutSplit achieves a memory reduction over 10 times, as well as 3x improvement on performance in terms of the number of memory access on average.

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