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.

本文提出了一个名为 PSqueeze 的多维数据通用且稳健的根因定位方法，其中基于广义波及效应的属性，建立了一个根因的通用特征，并提出了一个新的概率聚类方法和鲁棒的启发式搜索方法，实现了对多维数据中根因的定位。实验结果表明，PSqueeze 相对于基准方法的 F1 得分提高了 32.89％，对于外部根因的定位 F1 得分达到了 0.90，并在几个生产系统的案例中体现出对故障诊断的实际帮助。