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Multi-Controller Resource Management for Software-Defined Wireless Networks
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DOI：10.1109/LCOMM.2019.2891527.png)
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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.
Keyword:
Multi-controller placement
resource management
particle swarm algorithm
deep reinforcement learning
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4.4
论文数:
1.3W
被引数:
2.2W
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