Abstract: The routing and forwarding algorithm using a specific mathematical model struggles to meet the diversified quality of service required by users. The intelligent routing scheme based on deep learning has become the development direction of routing decision-making because of its advantages of accuracy, efficiency, and universality. However, currently, most intelligent routing algorithms need to be retrained when the network topology changes dynamically, resulting in untimely route updates and difficult-to-handle dynamic changes of network topology. In this study, an intelligent routing algorithm based on the Graph Convolutional Neural Network(GCN) framework is proposed. When offline, it uses the network information collected in advance to train the GCN intelligent routing model according to the routing overhead label. Then, the single hop routing overhead is output through the model. When online, it collects real-time information, adjusts the network layer routing protocol according to the routing overhead results output by the model, calculates the network routing path with the minimum routing overhead, and realizes automatic adaptation to network updates. The algorithm uses the graph data structure of GCN to deal with irregular network topologies and automatically extracts the features through the graph convolution operator to solve the problem of multi-attribute parameter extraction of the routing network. Meanwhile, the Fuzzy C-Means(FCM) algorithm is introduced to discretize the network state and generate labels for the data set to effectively supervise the training of GCN model. Experimental results demonstrate that the algorithm attains better routing performance than those of ECMP, DRL-TE, and SmartRoute algorithms. Furthermore, its average packet loss rate, delay, and throughput are the best among the compared algorithms, and it has stronger generalization ability compared to a single traffic mode.
Graph Convolutional Neural Network(GCN),
Fuzzy C-Means(FCM) clustering,