Multi-Controller Resource Management for Software-Defined Wireless Networks

Given decoupling the control layer and the infrastructure layer, the software-defined wireless networks (SDWNs) is beneficial in terms of providing both low-latency and low-energy consumption services for mobile users, where multicontroller placement and resource management become a pair of bottlenecks. In this letter, we propose an energy-aware multicontroller placement scheme as well as a latency-aware resource management model for the SDWN. Moreover, the particle swarm optimization is invoked for solving the multi-controller placement problem, and a deep reinforcement learning algorithm-aided resource allocation strategy is conceived. Finally, experimental results show that our proposed schemes are conducive to reducing both the execution time and the energy consumption of each task.