Nowadays smart cities towards software defined network (SDN) approach will become better flexibility and manageability. A stronger, more dynamic network is an SDN network, which is precisely what a smart city network must be if it wants to be viable on a real-world scale. SDN architecture is developed to implement a learning framework for network optimization. The proposed method is called mixed-integer and reinforcement learned network optimization (MI-RLNO) for SDN monitoring. In the first phase, mixed-integer programming formulation is used as an optimization formulation for latency and convergence time. In the second phase, a reinforced Q Learning model is designed that uses communication and computation time as input state vector. Optimization formulation is used as the actions and strategies to be followed during the design and operation of communication networks, therefore contributing fairness and throughput. Simulation results improved the efficiency of the MI-RLNO method.

如今，智慧城市朝着软件定义网络（SDN）的方法将变得更好的灵活性和可管理性。SDN网络是一个更强大，更动态的网络，如果要在现实世界中可行，这正是智能城市网络所必须具备的。SDN架构的开发旨在实现用于网络优化的学习框架。所提出的方法称为用于SDN监视的混合整数和强化学习网络优化（MI-RLNO）。在第一阶段，混合整数编程公式被用作等待时间和收敛时间的优化公式。在第二阶段，设计了一个强化的Q学习模型，该模型使用通信和计算时间作为输入状态向量。优化公式被用作通信网络的设计和操作过程中要遵循的动作和策略，因此有助于公平性和吞吐量。仿真结果提高了MI-RLNO方法的效率。