一句话：提出基于元数据的解决方案检索，通过整合异构数据并自动构造查询，提升 AIOps 事件解决文档推荐相关性。
在云服务运维中，事件发生后站点可靠性工程师通常通过人工构造的简短文本查询检索相似历史事件，但这类查询往往不充分，影响解决文档获取。本文提出面向 AIOps 解决建议的基于元数据检索方法：将分散在异构、孤立数据集中的信息整合为可直接使用的知识库，并利用历史问题上下文训练元数据预测模型；运行时，该模型根据日志异常检测模块发现的异常自动构造查询，再在元数据索引而非文本索引上检索。作者在部署于云上的 Web 应用服务器应用上开展实验，表明该方法相比文本检索返回更精准的结果，且有 60% 的查询中相关解决文档出现在前 3 位。
For a cloud service provider, the goal is to proactively identify signals that can help reduce outages and/or reduce the mean-time-to-detect and mean-time-to-resolve. After an incident is reported, the Site Reliability Engineers diagnose the fault and search for a resolution by formulating a textual query to find similar historical incidents - this approach is called text-based retrieval. However, it has been observed that the formulated queries are inadequate and short. An alternate approach, presented in this paper, integrates information spread across heterogeneous and siloed datasets, as a ready-to-use knowledge base for metadata-based resolution retrieval. Additionally, it exploits historical problem context for building metadata prediction models which are used at run-time for automatically formulating queries from log anomalies detected by the Log Anomaly Detection module. The query, thus formed, is run against the metadata-based index, unlike the text-based index in text retrieval, resulting in superior performance, in terms of relevancy of the resolution documents retrieved. Through experiments on web application server applications deployed on the cloud, we show the efficacy of metadata-based retrieval, which not only returns targeted results as compared to text-based retrieval but also the relevant resolution document appear amongst the top 3 positions for 60% of the queries.
DOI 原文 ·
@inproceedings{paperbot7099,
title = {Metadata-based retrieval for resolution recommendation in AIOps},
author = {Harshit Kumar and Ruchi Mahindru and Debanjana Kar},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3558964}
}