Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Machine Learning and Cybernetics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.