一种基于神经网络的alarm2vec告警压缩算法

An Alarm2vec Alarm Compression Algorithm Based on Neural Network

告警压缩是在网络故障告警的相关性分析中,发现告警间的关联性,从而减少告警数量,获取有用信息,根据已建立的告警知识库快速准确地对其进行分析推理,获取根源故障,从而确定网络设备或网络功能故障,完成故障定位。针对该场景,提出了一种基于神经网络的alarm2vec算法,并结合极大团图挖掘告警间的关联规则,实际网络告警数据实验结果表明,该方法相对原始数据,实现39.85%的告警压缩率,对告警数据信息进行了有效压缩。

Alarm compression in network fault alarm correlation analysis need find the correlation in alarms from mass alarms,to reduce the number of alarm and obtain useful warning information,according to the established warning knowledge base,rapidly and accurately analyze its reasoning,get root cause failure,so as to determine the fault network device or function and complete the fault location.This paper proposes an alarm association rule mining method based on neural network alarm2VEc algorithm combined with maximal clip graph mining.Experimental results of actual network alarm data show that this method achieves 39.85%alarm compression rate compared with original data and can effectively compress alarm data.