A SDN-based intelligent prediction approach to power traffic identification and monitoring for smart network access

Nowadays, more and more electric power services are carried on the power information communication network (PICN) including power grid production and scheduling, communication, and environment sensing, in the form of data, voice and video. To improve the resource utilization efficiency, it is necessary to carry out traffic prediction approach in PICN. However, the accessing businesses have diversified characteristics, which are reflected to different types of traffic flow in PICN. Moreover, the traditional PICN is a distributed network and cannot be controlled flexibly, which leads to the poor accuracy of traffic prediction algorithm. To address these problems, we combine the Software Defined Networking (SDN) architecture and Radial Basis Function neural network (RBFNN) for traffic intelligent prediction in PICN. The SDN controller can acquire global knowledge of PICN in each time slot to guide the data sampling process. Further, the complex nonlinear relationships of large-scale network traffics are analyzed by RBFNN model to realize high-precision traffic identification. The proposed scheme is evaluated based on by POX and Mininet platforms. Simulation results show that the proposed SDN-based intelligent prediction scheme can accurately forecast the change trend of each traffic flow and has better performance and lower prediction error than current schemes.