NNIRSS: neural network-based intelligent routing scheme for SDN

Abstract

With the increasing diversification of network applications, SDN tends to be inefficient to satisfy the diversified application demands. Meanwhile, the continuous update of OpenFlow and flow table expansion causes the efficiency of routing and forwarding ability decreased as well as the storage space of ternary content addressable memory (TCAM) occupied by flow tables increased. In this paper, we present NNIRSS, a novel neural network (NN)-based intelligent routing scheme for SDN, which leverages the centralized controller to achieve transmission patterns of data flow by utilizing NN and replaces flow table with well-trained NN in the form of NN packet. The route of data flow can be predicted based on its application type to meet the quality of service requirements of network applications. Furthermore, we devise a radial basis function neural network-based intelligent routing mechanism. With combining APC-III and K-means algorithm, we propose APC-K-means algorithm to determine radial basis function centers. Finally, the simulation results demonstrate that our proposed NNIRSS is feasible and effective. It can reduce storage space of TCAM and routing time overhead as well as improve routing efficiency.