一句话：提出 MID 框架，将多维问题报告中的故障事件识别编码为组合优化，并用定制元启发式搜索快速发现指示事件的属性组合。
大规模云系统中，非计划的服务中断会严重降低可用性，且故障事件可能突发出现，因此快速准确识别事件对运维至关重要。实践中通常通过分析监控产生的问题报告来检测事件，但问题报告具有大量多维类别属性，难以确定指示事件的具体属性组合；现有剪枝式搜索在高维数据上耗时，不适用于大规模云系统。本文提出 MID 框架，将事件识别编码为组合优化问题，并设计针对性的元启发式搜索方法，以快速识别指示事件的属性组合。作者使用合成数据和大规模生产云系统的真实数据进行广泛实验。结果表明，MID 在有效性和效率上显著优于当前最先进方法，并已成功应用于 Microsoft 云系统，显著减少人工维护工作。
In large-scale cloud systems, unplanned service interruptions and outages may cause severe degradation of service availability. Such incidents can occur in a bursty manner, which will deteriorate user satisfaction. Identifying incidents rapidly and accurately is critical to the operation and maintenance of a cloud system. In industrial practice, incidents are typically detected through analyzing the issue reports, which are generated over time by monitoring cloud services. Identifying incidents in a large number of issue reports is quite challenging. An issue report is typically multi-dimensional: it has many categorical attributes. It is difficult to identify a specific attribute combination that indicates an incident. Existing methods generally rely on pruning-based search, which is time-consuming given high-dimensional data, thus not practical to incident detection in large-scale cloud systems. In this paper, we propose MID (Multi-dimensional Incident Detection), a novel framework for identifying incidents from large-amount, multi-dimensional issue reports effectively and efficiently. Key to the MID design is encoding the problem into a combinatorial optimization problem. Then a specific-tailored meta-heuristic search method is designed, which can rapidly identify attribute combinations that indicate incidents. We evaluate MID with extensive experiments using both synthetic data and real-world data collected from a large-scale production cloud system. The experimental results show that MID significantly outperforms the current state-of-the-art methods in terms of effectiveness and efficiency. Additionally, MID has been successfully applied to Microsoft's cloud systems and helped greatly reduce manual maintenance effort.
DOI 原文 ·
@inproceedings{paperbot5705,
title = {Efficient incident identification from multi-dimensional issue reports via meta-heuristic search},
author = {Jiazhen Gu and Chuan Luo and Si Qin and Bo Qiao and Qingwei Lin and Hongyu Zhang and Ze Li and Yingnong Dang and Shaowei Cai and Wei Wu and Yangfan Zhou and Murali Chintalapati and Dongmei Zhang},
booktitle = {FSE},
year = {2020},
doi = {10.1145/3368089.3409741}
}