Postmortem analysis is essential in the management of incidents within cloud
systems, which provides valuable insights to improve system's reliability and
robustness. At CloudA, fault pattern profiling is performed during the
postmortem phase, which involves the classification of incidents' faults into
unique categories, referred to as fault pattern. By aggregating and analyzing
these fault patterns, engineers can discern common faults, vulnerable
components and emerging fault trends. However, this process is currently
conducted by manual labeling, which has inherent drawbacks. On the one hand,
the sheer volume of incidents means only the most severe ones are analyzed,
causing a skewed overview of fault patterns. On the other hand, the complexity
of the task demands extensive domain knowledge, which leads to errors and
inconsistencies. To address these limitations, we propose an automated
approach, named FaultProfIT, for Fault pattern Profiling of Incident Tickets.
It leverages hierarchy-guided contrastive learning to train a hierarchy-aware
incident encoder and predicts fault patterns with enhanced incident
representations. We evaluate FaultProfIT using the production incidents from
CloudA. The results demonstrate that FaultProfIT outperforms state-of-the-art
methods. Our ablation study and analysis also verify the effectiveness of
hierarchy-guided contrastive learning. Additionally, we have deployed
FaultProfIT at CloudA for six months. To date, FaultProfIT has analyzed 10,000+
incidents from 30+ cloud services, successfully revealing several fault trends
that have informed system improvements.

自动化方法 FaultProfIT 利用层次引导对比学习对云系统的事故模式进行分析，能够准确预测故障模式并提供系统改进的有关信息。

FaultProfIT: 大规模云系统中故障事件票据的层次化故障分析

FaultProfIT: Hierarchical Fault Profiling of Incident Tickets in  Large-scale Cloud Systems

Hierarchical text classification is a challenging subtask of multi-label
classification due to its complex label hierarchy. Existing methods encode text
and label hierarchy separately and mix their representations for
classification, where the hierarchy remains unchanged for all input text.
Instead of modeling them separately, in this work, we propose Hierarchy-guided
Contrastive Learning (HGCLR) to directly embed the hierarchy into a text
encoder. During training, HGCLR constructs positive samples for input text
under the guidance of the label hierarchy. By pulling together the input text
and its positive sample, the text encoder can learn to generate the
hierarchy-aware text representation independently. Therefore, after training,
the HGCLR enhanced text encoder can dispense with the redundant hierarchy.
Extensive experiments on three benchmark datasets verify the effectiveness of
HGCLR.

本文提出了一种将层级结构直接嵌入文本编码器的层次引导对比学习（HGCLR）方法，通过此方法，将正样本进行层次指导，以学习生成具备层次感知的文本表示，在三个基准数据集上的实验证明了 HGCLR 的有效性。