日志信息驱动的计算机网络节点故障预测研究

王雨晞; 叶庆卫; 周鹏; 李冰; 王晓东

doi:10.11959/j.issn.1000-0801.2024168

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日志信息驱动的计算机网络节点故障预测研究

研究与开发 | 更新时间：2024-09-23

- 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
- 电信科学 2024年40卷第8期页码：11-22
- 作者机构：
  
  宁波大学信息科学与工程学院，浙江宁波 315211
- 作者简介：
  
  [ "王雨晞（1999- ），男，宁波大学信息科学与工程学院硕士生，主要研究方向为通信网智能运维。" ]
  [ "叶庆卫（1970- ），男，宁波大学信息科学与工程学院教授、硕士生导师，主要研究方向为通信信号处理、信号传输与检测、分类器与机器学习等。" ]
  周鹏（1999- ），男，宁波大学信息科学与工程学院硕士生，主要研究方向为脑电信号处理。
  李冰（2001- ），女，宁波大学信息科学与工程学院硕士生，主要研究方向为通信网智能运维。
  王晓东（1970- ），男，宁波大学信息科学与工程学院教授、硕士生导师，主要研究方向为多媒体信号处理、图像处理等。
- 基金信息：
  
  浙江省产学研合作项目(062400020);大型横向项目网络运维平台研发项目(HK2022000189)
- DOI：10.11959/j.issn.1000-0801.2024168
  中图分类号： TN915
- 收稿：2024-02-21，
  
  修回：2024-05-09，
  
  纸质出版：2024-08-20
- 稿件说明：
移动端阅览
王雨晞,叶庆卫,周鹏等.日志信息驱动的计算机网络节点故障预测研究[J].电信科学,2024,40(08):11-22.

WANG Yuxi,YE Qingwei,ZHOU Peng,et al.Research on fault prediction of computer network nodes driven by log information[J].Telecommunications Science,2024,40(08):11-22.
王雨晞,叶庆卫,周鹏等.日志信息驱动的计算机网络节点故障预测研究[J].电信科学,2024,40(08):11-22. DOI： 10.11959/j.issn.1000-0801.2024168.

WANG Yuxi,YE Qingwei,ZHOU Peng,et al.Research on fault prediction of computer network nodes driven by log information[J].Telecommunications Science,2024,40(08):11-22. DOI： 10.11959/j.issn.1000-0801.2024168.

摘要

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。

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.

关键词

Keywords

references

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