基于监控数据的MySQL异常检测算法

MySQL Outlier Detection Algorithm Based on Monitoring Data

随着互联网数据规模的增长,服务器集群的规模快速扩大,对大规模的集群进行监控和分析成为互联网行业运维的难点。为此,根据监控统计数据剧烈波动的特点,提出一种My SQL异常检测分析算法,采用基于模式的异常检测方法,无须设置阈值,分段取模式特征值,计算异常点、异常区间和异常程度。实验结果表明,该算法对于抖动剧烈监控数据的时序序列可以较好地提取数据特征,与基于均值方差的异常检测算法相比,具有更高的精准度,对监测数据的适用性较强。

With the explosive growth of the data on the Internet, the scale How to carry out large-scale cluster monitoring and analysis becomes a of the server cluster is rapidly expanding difficult problem in the Internet industry Therefore,this paper presents a new method for detection and analysis of the monitoring data according to the monitoring jittering data. It adopts pattern-based outlier detection method without setting a threshold, takes the eigenvalues, calculaties the outliers,and obtains the abnormal range and degrees. Experimental results show that the algorithm can extract data features for time sequence of jittering data, and has a higher precision and better applicability than the outlier detection algorithm based on mean-variance.