Multi-fusion algorithm root cause location model based on causal failure dependency graph

In order to make the root cause location model more fully capture the long-term dependencies and complex temporal characteristics of metric data, and to add directed edges in the failure dependency graph to more clearly indicate the dependencies between metrics, a root cause localization model CFMRCL that integrates causal discovery algorithms, Recurrent Neural Networks (RNN), and Variational Autoencoders (VAE) is proposed. Use the causal discovery algorithm to construct a causal failure dependency graph (CFDG) to represent the dependence between metrics, and add directed edges to represent the causal relationship between failures, thereby more accurately reflecting the interconnection and propagation direction of failures. Secondly, for the feature extraction of metric data, RNN is used to better capture and model the long-term dependencies and temporal characteristics in the time series, and VAE is combined to learn the potential distribution of the data, thereby more comprehensively mining important features in metric data, improving the expressive ability and data modeling ability of feature extraction. The collaboration between each module improves the accuracy of CFMRCL in locating root causes. The proposed CFMRCL model is compared with the existing DejaVu model and other baseline models. Experiments on four data sets show that the CFMRCL model has greatly improved accuracy and efficiency in locating the root cause of failures. The CFMRCL model has a maximum accuracy of 97.87% in locating the top five root causes. The accuracy is up to 8% higher than the DejaVu model. The efficiency is on average 16% higher than the DejaVu model and 70% higher than the baseline model.