Aiming at the local overload of multi-controller deployment in software-defined networks, a load balancing mechanism of SDN controller based on reinforcement learning is designed. The initial paired migrate-out domain and migrate-in domain are obtained by calculating the load ratio deviation between the controllers, a preliminary migration triplet, contains migration domain mentioned above and a group of switches which are subordinated to the migrate-out domain, makes the migration efficiency reach the local optimum. Under the constraint of the best efficiency of migration in the whole and without migration conflict, selecting multiple sets of triples based on reinforcement learning, as the final migration of this round to attain the global optimal controller load balancing with minimum cost. The experimental results illustrate that the mechanism can make full use of the controllers' resources, quickly balance the load between controllers, reduce unnecessary migration overhead and get a faster response rate of the packet-in request.

翻译：针对软件界定的网络中多控制器部署的当地超负荷问题,设计了一个基于强化学习的SDN控制器负荷平衡机制。最初配对的迁移域和迁移域是通过计算控制器、一个初步迁移三者、包含上述移徙域和一组从属于迁移网的开关之间的负载比率偏差获得的。使移徙效率达到当地最佳水平。在整个移徙效率和没有移民冲突的情况下,由于限制整个移徙的最佳效率,选择了基于强化学习的三套三套组合,作为这一轮的最后迁移,以达到全球最佳控制器负载与最低成本的平衡。实验结果表明,这一机制可以充分利用控制器的资源,迅速平衡控制器之间的负载,减少不必要的迁移间接费用,并加快申请包的响应率。