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Software Defined Networking (SDN) achieves programmability of a network through separation of the control and data planes. It enables flexibility in network management and control. Energy efficiency is one of the challenging global problems which has both economic and environmental impact. A massive amount of information is generated in the controller of an SDN based networks. Machine learning gives the ability to computers to progressively learn from data without having to write specific instructions. In this work, we propose MER-SDN, a machine learning framework for traffic aware energy efficient routing in SDN. Feature extraction, training, and testing are the three main stages of the learning machine. Experiments are conducted on Mininet and POX controller using a real-world network topology and dynamic traffic trace from SNDlib. Results show that our approach achieves more than 65% feature size reduction, more than 70% accuracy in parameter prediction of an energy efficient heuristics algorithm, also our prediction refine heuristics converges the predicted value to the optimal parameters values with up to 25X speedup as compared to the brute force method.

2018 ieee 16th intl conf on dependable, autonomic and secure computing, 16th intl conf on pervasive intelligence and computing, 4th intl conf on big data intelligence and computing and cyber science and technology congress(dasc/picom/datacom/cyberscitech)

MER-SDN: Machine Learning Framework for Traffic Aware Energy Efficient Routing in SDN