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

Combining the capabilities of the programmability of networks by SDN and discovering patterns by machine learning are utilized in security, traffic classification, QoS prediction, and network performance and has attracted the attention of researchers. In this work, we propose HyMER: a novel hybrid machine learning framework for traffic aware energy efficient routing in SDN which has supervised and reinforcement learning components. 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 framework is developed on POX controller and is evaluated on Mininet using Abiline, GEANT, and Nobel-Germany 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. The refine heuristics algorithm increases the accuracy of the prediction to 100% with 14X to 25X speedup as compared to the brute force method. The reinforcement learning module converges from 100 to 275 iterations and converges twice faster if applied on top of the supervised component. Moreover, HyMER achieves up to 10 watts per switch power saving, 30% link saving, 2 hops decrease in average path length.