HD-NRC: Network Route Calculation Based on High-dimensional Features Knowledge

Faultless and cost-saving route calculation plays a fundamental role in the network traffic engineering. The existing route calculation methods mostly are data-driven and lack the interpretability, which make less use of complex network information. Knowledge graph has the ability to convert complex network data into interrelated knowledge, so the interpretability can be enhanced. In this paper, we propose a novel network route calculation solution based on the knowledge graph, which utilizes the link prediction to handle the high-dimensional features of network. By improving the path-based link prediction framework NBFNet, the defects caused by lack of interpretability are made up. The simulation results show that the proposed method outperforms Bellman-Ford and TransH in terms of packet loss and delay and maintains the network load balance with various network topologies.