日志信息驱动的计算机网络节点故障预测研究

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

针对计算机网络中节点故障对正常业务运行的影响,提出了一种以日志信息为驱动的故障预测方法,通过构建高效的深度学习模型,并引入校正机制,对计算机网络中的节点故障进行预测和诊断,支持网络运维的需求。首先收集计算机网络中各节点产生的日志信息,获得各节点的状态向量和所有节点的状态矩阵,然后通过状态填补原则补充数据集,最后将故障预测问题转换成时间序列预测问题。在公开的小规模运维数据集GAIA中进行性能评估。实验结果表明,与其他算法相比,所提模型在局部网络场景下预测效果良好,预测有效性得到了验证,为计算机网络故障预测研究提供了一定的参考价值。

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 cer‐tain reference value for computer network fault prediction research.