一句话：提出一种从多源异构运维语料中主动识别日志异常并检索其解决方案的 AIOps 方法。
云服务运维中，SRE 大量精力消耗在事故的事后分析与误报/漏报排查上，难以主动发现并快速消解故障。本文提出一种 AIOps 方法，主动识别日志异常并检索其解决方案：从历史工单、历史日志与产品文档等多源异构语料中构建增强数据集，完成元数据选择与预测，并在运行时依据日志触发的信号检索上下文相关的解决方案。作者使用生产中间件应用服务器的日志进行早期评估，并由领域专家开展定性评价，元数据预测准确率为 78.57%，解决方案检索准确率为 65.7%。
Based on 2020 SRE report, 80% of SREs work on postmortem analysis of incidents due to lack of provided information and 16% of toil come from investigating false positives/negatives. As a cloud service provider, the desire is to proactively identify signals that can help reduce outages and/or reduce the mean time to resolution. By leveraging AI for Operations (AIOps), this work proposes a novel methodology for proactive identification of log anomalies and its resolutions by sifting through the log lines. Typically, relevant information to retrieve resolutions corresponding to logs is spread across multiple heterogeneous corpora that exist in silos, namely historical ticket data, historical log data, and symptom resolution available in product documentation, for example. In this paper, we focus on augmented dataset preparation from multiple heterogeneous corpora, metadata selection and prediction, and finally, using these elements during run-time to retrieve contextual resolutions for signals triggered via logs. For early evaluation, we used logs from a production middleware application server, predicted log anomalies and their resolutions, and conducted qualitative evaluation with subject matter experts; the accuracy of metadata prediction and resolution retrieval are 78.57% and 65.7%, respectively.
DOI 原文 ·
@inproceedings{paperbot6512,
title = {Log Anomaly to Resolution: AI Based Proactive Incident Remediation},
author = {Ruchi Mahindru and Harshit Kumar and Sahil Bansal},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678815}
}