Generic and Robust Root Cause Localization for Multi-Dimensional Data in
  Online Service Systems
Zeyan Li Junjie Chen Yihao Chen Chengyang Luo Yiwei Zhao Yongqian Sun Kaixin Sui Xiping Wang Dapeng Liu Xing Jin Qi Wang Dan Pei
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 are abnormal. Such attribute
combinations are substantial clues to the underlying root causes and thus are
called root causes of multidimensional 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 seconds 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.