Research on Fault Prediction of Computer Network Nodes Based on Log information

Aiming at the impact of node faults on normal business operation in computer networks, a log information-driven fault prediction method is proposed. By constructing an efficient deep learning model and introducing a correction mechanism, node faults in computer networks are predicted and diagnosed to support network operation and maintenance requirements. Firstly, the log information generated by each node in the computer network is collected, the state vector of each node and the state matrix of all nodes are obtained, and then the data set is supplemented by the state filling principle. Finally, the fault prediction problem is transformed into a time series prediction problem. The performance was evaluated in GAIA, a public smallscale operation and maintenance data set. The experimental results show that compared with other algorithms, the proposed model has a good prediction effect in local network scenarios, and the prediction effectiveness has been verified, which provides a certain reference value for computer network fault prediction research.