Application-aware QoS routing in SDNs using machine learning techniques

Software Defined Networking has become an efficient and promising means for overcoming the limitations of traditional networks, e.g., by guaranteeing the corresponding Quality of Service (QoS) of various applications. Compared with the inherent distributed characteristics of the traditional network, SDN is logically centralized and can utilize machine learning techniques to keep track of transmission requirements of each application. In this research, we first develop an efficient data dimension reduction approach by considering the correlation coefficients between data items. We classify the traffic data into distinguished categories based on the QoS requirements by a supervised machine learning method. Then, we propose a QoS Aware Routing (QAR) algorithm according to the QoS requirements of each application that finds a path with either the minimum average link occupied times or the maximum average path residual capacity. The accuracy of machine learning model shows that our proposed dimension reduction approach is more effective than other data preprocessing methods, and the results of blocking probability indicate that our QAR algorithm outperforms significantly previous algorithms.