HyMER: A Hybrid Machine Learning Framework for Energy Efficient Routing in SDN

Software-defined networks (SDN) with programmable data plane and machine learning for discovering patterns are utilized in security, traffic classification, quality of services prediction, and network performance, that has increasing research attention. Addressing the significance of energy efficiency in networks, we propose a novel hybrid machine learning-based framework named HyMER that combines the capabilities of SDN and machine learning for traffic-aware energy efficient routing. To the best of our knowledge, HyMER is the first that utilizes a hybrid machine learning solution with supervised and reinforcement learning components for energy efficiency and network performance in SDN. The supervised learning component consists of feature extraction, training, and testing. The reinforcement learning component learns from existing data or from scratch by iteratively interacting with the network environment. The HyMER framework is developed on POX controller and is evaluated on Mininet using real-world topologies and dynamic traffic traces. Experimental results show that the supervised component achieves up to 70% feature size reduction and more than 80\% accuracy in parameter prediction. We demonstrate that combining the supervised and reinforcement methods not only does capture the dynamic change more efficiently but also increases the convergence speed. As compared to state-of-the-art utility based energy saving approaches, HyMER heuristics has shown up to 50% link saving, and also exhibits up to 14.7 watts less power consumption for realistic network topology and traffic traces.Comment: Double column 12 pages, 13 figures, 6 table