Research on fault prediction of computer network nodes driven by log information

Abstract

A fault prediction method driven by log information was proposed to address the impact of node failures on normal business operations in computer networks. By constructing an efficient deep learning model and introducing a correction mechanism, node failures in computer networks were predicted and diagnosed to meet the needs of network operation and maintenance. Firstly, the log information generated by each node in the computer network was collected, the state vectors of each node and the state matrices of all nodes were obtained, then the dataset through the state filling principle was supplemented, and finally the fault prediction problem into a time series prediction problem was transformed. The performance evaluation is conducted on the publicly available small-scale operation and maintenance dataset GAIA, and the experimental results show that compared with other algorithms, the proposed model has good predictive performance in local network scenarios, and its predictive effectiveness is verified, providing a certain reference value for computer network fault prediction research.

Keywords

• log;computer network;node failure;failure prediction;deep learning;correction mechanism;time series