To ensure the efficient operation of large-scale networks, the flow scheduling in the software defined network (SDN) requires the matching time and memory overhead of rule matching to be as low as possible. To meet the requirement, we solve the rule matching problem by integrating machine learning methods, including recurrent neural networks, reinforcement learning, and decision trees. We first describe the SDN rule matching problem and transform it into a heterogeneous integrated learning problem. Then, we design and implement an SDN flow forwarding rule matching algorithm based on heterogeneous integrated learning, referred to as RMHIL. Finally, we compare RMHIL with two existing algorithms, and the comparative experimental results show that RMHIL has advantages in matching time and memory overhead.
Keywords: SDN; flow forwarding; heterogeneous integrated learning; rule matching.