一句话：提出基于图表示学习的 GRLIA 框架，利用级联图与 KPI 数据无监督学习事件表示，实现大规模云故障事件快速聚合。
随着在线服务系统复杂度与规模增长，云故障会因级联效应产生大量来自依赖服务和设备的事件，给事件管理带来巨大压力。本文提出 GRLIA，一种在云故障级联图上进行图表示学习的事件聚合框架。它为每种唯一事件类型以无监督统一方式学习表示向量，同时编码事件间的拓扑与时序关联，并利用细粒度 KPI 监控数据恢复故障级联影响的完整范围，从而支持在线事件聚合。基于华为云大规模在线服务系统的真实事件数据评估表明，GRLIA 有效且优于现有方法，并已成功部署于工业实践。
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.
DOI 原文 ·
@inproceedings{paperbot6563,
title = {Graph-based Incident Aggregation for Large-Scale Online Service Systems},
author = {Zhuangbin Chen and Jinyang Liu and Yuxin Su and Hongyu Zhang and Xuemin Wen and Xiao Ling and Yongqiang Yang and Michael R. Lyu},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678746}
}