Software-defined Power Communication Network Routing Control Strategy Based on Graph Convolution Network
Min Xiang, Huayang Rao, Jinjin Zhang, Mengxin Chen · 2021
Transmission delay and packet loss rate are critical issues in reliable transmission of power communication services. A minimum path selection routing control strategy for software-defined power communication networks is proposed. Combining the characteristics of the centralized control structure of the software-defined power communication network, a Link Bandwidth Occupancy Predictive model based on Graph Convolutional Network (LBOP-GCN) is built to analyze the route paths bandwidth occupancy in the next period. The selectivity (Q) of different transmission paths from the source node is calculated to the destination node is calculated by using Triangle Modular Operator (TMO) to fuse the transmission delay of the path, the path bandwidth occupancy at the current moment and the path bandwidth occupancy at the next moment. Then the path with the lowest Q value is used as the flow table of the OpenFlow switch delivered by the Software Defined Network (SDN) controller. Experiments show that the proposed routing control strategy can effectively reduce service transmission delay and packet loss rate.