On Optimizing Energy Efficiency in SDN Networks Through ML-Driven Configuration

The rapid evolution of 5G and 6G technologies, coupled with growing environmental concerns, underscores the critical need for energy-efficient computer networks. To this end, a key challenge would be to minimize energy consumption by dynamically adjusting the number of active network devices based on the traffic demand and matrix. This requires an efficient mapping of the traffic matrix, which represents the amount of data traffic exchanged between different nodes over a given period, onto the network to ensure a minimal number of active while meeting performance requirements. Traditional approaches, based on Integer Linear Programming and heuristic algorithms, face significant limitations in scalability and computational efficiency, particularly for large-scale networks. To address these challenges, this work proposes a Machine Learning (ML)-based algorithm that leverages clustering techniques to identify near-optimal mappings of traffic matrices in Software-Defined Networks. Simulations on realistic network topologies demonstrate that our solution achieves substantial energy savings, up to 53 %, outperforms heuristic methods in execution time by orders of magnitude, and delivers near-optimal performance. These results highlight the potential of ML-driven approaches to enable scalable and energy-efficient network management.