基于告警日志的网络故障预测

Network failure prediction based on alarm log

收集整理某城域网络14个月的网络告警日志作为网络故障预测研究的数据集并提出一种基于告警日志的网络故障预测研究方法:首先以基于两级时间窗口的特征提取方法构建特征表征网络运行状态,并通过大量实验来选择构建特征所需的最佳参数组合,然后设计并实现了一种基于分类学习方法的自适应故障预测模型。大量的数据实验表明:对于整个网络未来6小时是否出现故障的预测准确率可以达到70%以上,明显好于基于威布尔分布的预测模型;在对网络设备故障进行预测时,分类预测的结果仍然优于基于威布尔分布的预测模型。初步研究结果表明,网络中大部分故障可通过网络运行日志数据进行预测,证明该方法具有较好的预测效果。

This paper researched the network failure prediction upon 14 months＇ network alarm logs collected from a metropolitan area network.The research method is shown as below：firstly,construct features to represent network characteristics by the means of the feature construction method which is based on two levels time windows;secondly,select optimal parameter combination to create the feature files through multiple experiments;thirdly,design and build adaptive failure prediction model according to classification learning methods.Numbers of experiments show that the accuracy of predicting whether the network failure takes place in 6 hours is up to 70%,is better than the prediction result of Weibull distribution model obviously;the results of classification prediction for network equipment failure are slightly better than Weibull distribution model.Preliminary research results show that most network failures can be predicted through analyzing previous network running logs and the method proposed in this paper is verified to be with good prediction effect.