A Novel Virtual Network Fault Diagnosis Method Based on Long Short-Term Memory Neural Networks

Network virtualization has emerged as a significant trend to solve the issues caused by ossification of traditional network. Under the circumstance of network virtualization, substrate network and virtual network are inextricably interdepending each other. The substrate network serves many virtual networks. Substrate network faults may lead to different virtual network faults. A service's failure may introduce additional influence on other services. Therefore, it has become a big challenge to predict when and where a fault happens in the network. In this paper, we propose a fault diagnosis method by deep learning to predict the failure of virtual network. Our deep learning model enables the earlier failure prediction by the Long Short-Term Memory (LSTM) network, which discovers the long-term features of network history data. Simulation results show that the proposed method performs well on faults prediction.