AI-Driven Scalable Framework for Energy-Efficient SDN Configurations
This work proposes a Machine Learning (ML)-based algorithm that uses clustering and classification techniques to find energy-efficient configurations in Software-Defined Networks, achieving up to 53% energy savings while reducing execution time by up to 8,000 times.