As online service systems continue to grow in terms of complexity and volume, how service incidents are managed will significantly impact company revenue and user trust. Due to the cascading effect, cloud failures often come with an overwhelming number of incidents from dependent services and devices. To pursue efficient incident management, related incidents should be quickly aggregated to narrow down the problem scope. To this end, in this paper, we propose GRLIA, an incident aggregation framework based on graph representation learning over the cascading graph of cloud failures. A representation vector is learned for each unique type of incident in an unsupervised and unified manner, which is able to simultaneously encode the topological and temporal correlations among incidents. Thus, it can be easily employed for online incident aggregation. In particular, to learn the correlations more accurately, we try to recover the complete scope of failures' cascading impact by leveraging fine-grained system monitoring data, i.e., Key Performance Indicators (KPIs). The proposed framework is evaluated with real-world incident data collected from a large-scale online service system of Huawei Cloud. The experimental results demonstrate that GRLIA is effective and outperforms existing methods. Furthermore, our framework has been successfully deployed in industrial practice.

翻译：随着在线服务系统在复杂程度和数量方面继续增长,如何管理服务事故将极大地影响公司收入和用户信任。由于分层效应,云层失灵往往带来依赖性服务和装置造成的大量事件。为了追求高效事件管理,应迅速将相关事件汇总,缩小问题范围。为此,我们在本文件中提议GRIA,一个基于在云层失灵层层图中进行图表表达学习的事故汇总框架。以不受监督和统一的方式,为每个独特的类型事件学习一个代表矢量,能够同时对事件之间的表面和时间相关性进行编码。因此,它很容易用于在线事件汇总。特别是,为了更准确地了解相关关系,我们试图通过利用微缩的系统监测数据,即关键性能指标(KPIs),来恢复失灵层影响的完整范围。拟议框架以一个大型的Huawewweu Cloud在线服务系统收集到的真实世界事件数据进行评估。实验结果显示,GRIA是有效的,超越了我们现有的工业框架。