Generic and robust root cause localization for multi-dimensional data in online service systems

Abstract

Localizing root causes for multi-dimensional data is critical to ensure online service systems’ reliability. When a fault occurs, only the measure values within specific attribute combinations (e.g., Province = Beijing) are abnormal. Such attribute combinations are substantial clues to the underlying root causes and thus are called root causes of multi-dimensional data. This paper proposes a generic and robust root cause localization approach for multi-dimensional data, PSqueeze. We propose a generic property of root cause for multi-dimensional data, generalized ripple effect (GRE). Based on it, we propose a novel probabilistic cluster method and a robust heuristic search method. Moreover, we identify the importance of determining external root causes and propose an effective method for the first time in literature. Our experiments on two real-world datasets with 5400 faults show that the F1-score of PSqueeze outperforms baselines by 32.89%, while the localization time is around 10 s across all cases. The F1-score in determining external root causes of PSqueeze achieves 0.90. Furthermore, case studies in several production systems demonstrate that PSqueeze is helpful to fault diagnosis in the real world.