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。

在线服务系统中通用且稳健的多维数据根本原因定位

Generic and Robust Root Cause Localization for Multi-Dimensional Data in  Online Service Systems

Structured weight pruning is a representative model compression technique of
DNNs to reduce the storage and computation requirements and accelerate
inference. An automatic hyperparameter determination process is necessary due
to the large number of flexible hyperparameters. This work proposes
AutoCompress, an automatic structured pruning framework with the following key
performance improvements: (i) effectively incorporate the combination of
structured pruning schemes in the automatic process; (ii) adopt the
state-of-art ADMM-based structured weight pruning as the core algorithm, and
propose an innovative additional purification step for further weight reduction
without accuracy loss; and (iii) develop effective heuristic search method
enhanced by experience-based guided search, replacing the prior deep
reinforcement learning technique which has underlying incompatibility with the
target pruning problem. Extensive experiments on CIFAR-10 and ImageNet datasets
demonstrate that AutoCompress is the key to achieve ultra-high pruning rates on
the number of weights and FLOPs that cannot be achieved before. As an example,
AutoCompress outperforms the prior work on automatic model compression by up to
33x in pruning rate (120x reduction in the actual parameter count) under the
same accuracy. Significant inference speedup has been observed from the
AutoCompress framework on actual measurements on smartphone. We release all
models of this work at anonymous link: this http URL

本文提出 AutoCompress，一种自动结构化剪枝框架，可实现超高剪枝比率，同时提供自动超参数确定过程，以及采用最先进的 ADMM-based 结构加权剪枝作为核心算法，并提出了一种新的附加净化步骤，无需损失准确性进行更进一步的权重减少，其有效的启发式搜索方法通过基于经验的指导搜索进行增强，代替了具有潜在不兼容性的问题的先前深度强化学习技术。