Deep Learning-Based Traffic Prediction for Energy Efficiency Optimization in Software-Defined Networking

Designed a real-time traffic prediction mechanism based on Gated Recurrent Unit (GRU) neural network of deep learning to capture the temporal characteristics of network traffic, and proposed a heuristic algorithm for energy efficiency optimization to balance flow demand and energy consumption and to achieve dynamic load balancing and energy saving.