一句话：对微软18个在线服务系统的事故进行大规模实证分析，提出基于注意力CNN的DeepIP来提前识别偶发事故并排序。
在线服务系统虽经大量质量保障，仍频繁发生事故，造成用户满意度下降和经济损失。本文首次对微软18个真实在线服务系统的事故进行大规模实证分析，发现许多事故在人工定位根因后并不需要高优先级修复，称为偶发事故，且其数量和成本都很显著。为此提出DeepIP：利用历史事故数据，设计基于注意力机制的卷积神经网络预测事故为偶发事故的概率，并据此排序以优化事故管理。真实数据实验表明，DeepIP的AUC达0.808，显著优于最佳对比方法的平均0.624。
Although tremendous efforts have been devoted to the quality assurance of online service systems, in reality, these systems still come across many incidents (i.e., unplanned interruptions and outages), which can decrease user satisfaction or cause economic loss. To better understand the characteristics of incidents and improve the incident management process, we perform the first large-scale empirical analysis of incidents collected from 18 real-world online service systems in Microsoft. Surprisingly, we find that although a large number of incidents could occur over a short period of time, many of them actually do not matter, i.e., engineers will not fix them with a high priority after manually identifying their root cause. We call these incidents incidental incidents. Our qualitative and quantitative analyses show that incidental incidents are significant in terms of both number and cost. Therefore, it is important to prioritize incidents by identifying incidental incidents in advance to optimize incident management efforts. In particular, we propose an approach, called DeepIP (Deep learning based Incident Prioritization), to prioritizing incidents based on a large amount of historical incident data. More specifically, we design an attention-based Convolutional Neural Network (CNN) to learn a prediction model to identify incidental incidents. We then prioritize all incidents by ranking the predicted probabilities of incidents being incidental. We evaluate the performance of DeepIP using real-world incident data. The experimental results show that DeepIP effectively prioritizes incidents by identifying incidental incidents and significantly outperforms all the compared approaches. For example, the AUC of DeepIP achieves 0.808, while that of the best compared approach is only 0.624 on average.
DOI 原文 ·
@inproceedings{paperbot5894,
title = {How incidental are the incidents?},
author = {Junjie Chen and Shu Zhang and Xiaoting He and Qingwei Lin and Hongyu Zhang and Dan Hao and Yu Kang and Feng Gao and Zhangwei Xu and Yingnong Dang and Dongmei Zhang},
booktitle = {ASE},
year = {2020},
doi = {10.1145/3324884.3416624}
}