标题： MER-SDN：基于机器学习的SDN中流量感知能效路由框架 显示英文标题

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

摘要： 软件定义网络（SDN）通过分离控制平面和数据平面实现网络的可编程性。 它使网络管理和控制更加灵活。 能源效率是具有经济和环境影响的全球性挑战问题之一。 在基于SDN的网络控制器中会生成大量信息。 机器学习赋予计算机从数据中逐步学习的能力，而无需编写特定指令。 在这项工作中，我们提出了MER-SDN：一种用于SDN中流量感知节能路由的机器学习框架。 特征提取、训练和测试是学习机器的三个主要阶段。 实验使用SNDlib中的现实网络拓扑和动态流量轨迹，在Mininet和POX控制器上进行。 结果显示，我们的方法实现了超过65%的特征尺寸减少，高效启发式算法参数预测的准确率超过70%，并且我们的预测细化启发式方法相比暴力破解法可以将预测值收敛到最优参数值的速度提高多达25倍。 显示英文摘要

摘要： 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 network. 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 real-world network topology and dynamic traffic traces 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.