通信网络告警数据特征分析

Alarm Data Feature Analysis of Communication Network

针对通信网络中网络异常原因分析的问题,提出一种基于决策树进行告警数据特征分析的方法.该方法首先针对网络设备产生的告警数据多为无标记数据,基于地域和时间特征关联分析告警数据获得根源信息并添加标记,然后多维化告警数据特征,再采用决策树方法分析各类特征的重要度及对准确率的影响,并通过剪枝缩减时间上的计算负担.各地域的告警数据实验结果表明,预处理及关联分析后告警压缩率分别约为70%和90%,告警对象类型与告警逻辑分类特征对网络异常的影响稳定,重要度在0.25以上,其中根源告警多属于告警逻辑分类中的通信告警.该方法可在一定程度上辅助网络管理人员识别主要的告警数据特征,根据数据特征先一步进行网络恢复.

Aiming at the problem of network anomaly cause analysis in communication networks, we proposed a method of alarm data feature analysis based on decision trees. Firstly, the alarm data generated by network devices were mostly untagged data, and the alarm data was analyzed based on the correlation analysis of geographical and temporal features to obtain root information and add tags, and then the alarm data features were multi-dimensional. Secondly, the importance of various features and their impact on accuracy were analyzed by the decision tree method, and the computational burden on time was reduced by pruning. The experimental results of alarm data in each region show that the alarm compression rate is about 70% and 90% after pre-processing and correlation analysis, respectively. The impact of alarm object type and alarm logic classification features on network anomalies is stable, and the importance degree is above 0.25, among them, the root alarm mostly belongs to communication alarm in the alarm logic classification. This method can help network managers to identify the main alarm data features to a certain extent, and recover the network first according to the data features.