Enhanced Multidimensional Root Cause Localization with JSqueeze in AIOps (S)

In modern microservices systems, identifying the root causes of faults in multivariate datasets is crucial.However, traditional methods that rely on statistical counts to detect anomalies can lead to excessive prediction errors due to too many attribute combinations without effectively utilizing the distribution characteristics of outliers.To address this, this paper introduce an improved multivariate root cause localization algorithm called JSqueeze.JSqueeze integrates Core Distance Bound Clustering method, a novel search algorithm, and an improved stopping strategy and introduces Jensen-Shannon divergence to assess the distribution properties of root causes.Based on this, a new two-phase algorithm framework is proposed.In the clustering phase, a new density clustering method is introduced, and in the root cause identification phase, a new robust heuristic search method is used.Experiments on a dataset with 6300 faults show that JSqueeze significantly improves the F1-score and time efficiency.Compared to existing state-of-the-art algorithms, our improved method increases the average F1-score by 5.8% and saves time by 14.5%.Moreover, JSqueeze shows superior performance in more complex datasets (i.e., those with multiple root causes), providing a new, robust, and efficient solution for anomaly localization in complex microservice systems.Code is available at https://github.com/DYamIK/JSqueeze.