Root Cause Location Based on Prophet and Kernel Density Estimation

When an online service, such as an online Web service, encounters an abnormality, operators need to analyse many abnormal monitoring indicators on the affected machine and rapidly determine the root cause indicators, which can quickly locate specific problems. Then, the abnormality is isolated until the cascading effects caused by the anomalies are eliminated. This paper proposes a root cause indicator location algorithm named ProphetKdeRCL. First, the improved Prophet algorithm detects many abnormal time-series indicators. Then, the abnormal deviation degree algorithm based on kernel density estimation is used to measure the fluctuation of each abnormal indicator for sorting. The analyses of the delay causal factor dependence are combined with the time window. Finally, a root cause location list is generated to assist operators in quick troubleshooting operations. This paper uses public datasets to evaluate the overall effectiveness of the algorithm. The results show that compared to other algorithms, the ProphetKdeRCL algorithm has higher accuracy on evaluation indicator AC@1 and superior accuracy on the other commonly used indicators, namely, AC@2 and AC@3.