AutoML-based QoS-Aware Energy Management Framework for Digital Twin empowered Software Defined Wireless Networks

Recently, the energy consumption of WiFi in smart buildings has reached extreme levels. Here, SDN leads intelligent energy management by decoupling the control and physical layers without touching network equipment. DT also enables continuous monitoring and synchronized management across the topology by combining real and virtual data in the central controller. DT-enabled SDN is proposed to optimize timeliness while predicting access point states to minimize energy efficiency in time series analysis. There are many ML models for time-series problems in the literature, but they do not solve the trade-off between prediction performance and model training time. Therefore, we've proposed an Autogluon-based energy management framework to determine the best ML model in a short DT-based SDN period. To execute it, a novel energy efficiency metric is defined in an optimization formula. To solve this optimization with low cost, we proposes AP state prediction and QoS-aware energy management algorithms that determine AP state configurations and embed OpenFlow rules. According to the results, the proposed method outperforms the conventional approaches with an energy gain of 48.54%.