EESDN: Energy-efficient routing based on traffic classification in software-defined networks

Modern network infrastructures are becoming more concerned about energy consumption, especially with software-defined networks (SDNs). Because SDN separates the control plane from the data plane, it allows for programmability in networks, which is not possible with conventional networks. Conventional routing techniques usually lead to excessive power consumption because they handle traffic. However, numerous frameworks have been presented to address this, with a focus on utility-based and machine learning techniques for traffic-aware energy optimization in SDN. These methods, however, often overlook the combined effect of dynamic routing and intelligent traffic classification for energy savings. The research paper proposes a new framework called EESDN that presents an energy-efficient routing (EER) algorithm that uses intelligent traffic classification to allocate paths in an energy-efficient manner. The objective is to maintain network performance while reducing energy consumption. For the SDN energy-saving opportunity identification issue, we propose an integer linear programming (ILP) approach. Using real-world traffic traces with the Mininet emulator and POX controller, the Abilene network architecture is examined. Our model shows a potential reduction in active network components, which, under idealized conditions, could translate to power savings of 8 to 25 W in our emulation and decreased average path length under high-traffic conditions. The emulation results demonstrate that the framework reduces energy consumption per bit by approximately 10% compared with existing methods.