一句话：提出LiDAR——基于深度学习的关联事件识别方法，利用文本描述与历史链接结构提升大规模在线服务系统事件关联效果。
大规模在线服务系统中事件频繁发生，可能由软硬件更新或运行环境变化等引起，并显著影响可用性与用户满意度。部分事件因重复或相互关联而存在链接，这些关联事件有助于值班工程师寻找缓解方案并定位根因。本文研究真实事件管理（IcM）系统中的事件及其链接，基于识别出的关联指标，提出LiDAR（Linked Incident identification with DAta-driven Representation）深度学习方法。该方法融合事件文本描述与从历史关联事件中提取的结构信息，在大规模事件中识别可能链接。在真实IcM系统上评估，LiDAR优于其他先进方法。
In large-scale online service systems, incidents occur frequently due to a variety of causes, from updates of software and hardware to changes in operation environment. These incidents could significantly degrade system’s availability and customers’ satisfaction. Some incidents are linked because they are duplicate or inter-related. The linked incidents can greatly help on-call engineers find mitigation solutions and identify the root causes. In this work, we investigate the incidents and their links in a representative real-world incident management (IcM) system. Based on the identified indicators of linked incidents, we further propose LiDAR (Linked Incident identification with DAta-driven Representation), a deep learning based approach to incident linking. More specifically, we incorporate the textual description of incidents and structural information extracted from historical linked incidents to identify possible links among a large number of incidents. To show the effectiveness of our method, we apply our method to a real-world IcM system and find that our method outperforms other state-of-the-art methods.
DOI 原文 ·
@inproceedings{paperbot5765,
title = {Identifying linked incidents in large-scale online service systems},
author = {Yujun Chen and Xian Yang and Hang Dong and Xiaoting He and Hongyu Zhang and Qingwei Lin and Junjie Chen and Pu Zhao and Yu Kang and Feng Gao and Zhangwei Xu and Dongmei Zhang},
booktitle = {FSE},
year = {2020},
doi = {10.1145/3368089.3409768}
}