# AIOps **Repository Path**: gegemeng/aiops ## Basic Information - **Project Name**: AIOps - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2022-03-23 - **Last Updated**: 2023-03-20 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # awesome-AIOps [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) [![知识共享协议（CC协议）](https://img.shields.io/badge/License-Creative%20Commons-DC3D24.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.zh) [![GitHub stars](https://img.shields.io/github/stars/linjinjin123/awesome-AIOps.svg?style=flat&label=Star)](https://github.com/linjinjin123/awesome-AIOps/stargazers) [![GitHub forks](https://img.shields.io/github/forks/linjinjin123/awesome-AIOps.svg?style=flat&label=Fork)](https://github.com/linjinjin123/awesome-AIOps/fork) [![GitHub watchers](https://img.shields.io/github/watchers/linjinjin123/awesome-AIOps.svg?style=flat&label=Watch)](https://github.com/linjinjin123/awesome-AIOps/watchers) - [Awesome AIOps](#awesome-AIOps) - [White Paper](#white-paper) - [Course and Slides](#course-and-slides) - [Industry Practice](#industry-practice) - [Article](#article) - [Tools and Algorithms](#tools-and-algorithms) - [Paper](#paper) - [Dataset](#dataset) - [Useful WeChat Official Accounts](#useful-wechat-official-accounts) ## White Paper * [《企业级 AIOps 实施建议》白皮书](https://www.rizhiyi.com/assets/docs/AIOps.pdf) ## Course and Slides * [Tsinghua-Peidan](http://netman.ai/courses/advanced-network-management-spring2018-syllabus/) - AIOps course in Tsinghua. * [基于机器学习的智能运维](http://netman.ai/wp-content/uploads/2016/12/%E5%9F%BA%E4%BA%8E%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%9A%84%E6%99%BA%E8%83%BD%E8%BF%90%E7%BB%B4v1.6.pdf) ## Industry Practice ------------------------------------------------------------------------------- * [腾讯运维的AI实践](https://myslide.cn/slides/8935) * [AI 时代下腾讯的海量业务智能监控实践](https://cloud.tencent.com/developer/article/1039354) * [织云Metis时间序列异常检测全方位解析](https://ppt.geekbang.org/slide/show?cid=30&pid=1595) * [腾讯织云Metis智能运维学件平台开源代码](https://github.com/Tencent/Metis) ------------------------------------------------------------------------------- * [阿里全链路监控方案](https://mp.weixin.qq.com/s/DJhJKD4TCDgSwyLZbSotKg) * [阿里开源4000台服务器真实数据集](https://github.com/alibaba/clusterdata/tree/v2018) ------------------------------------------------------------------------------- * [百度智能流量监控实战](https://ppt.geekbang.org/slide/show?cid=30&pid=1548) * [异常检测：百度是这样做的](https://mp.weixin.qq.com/s/AXhjawsINKl6cLDV1yf6fw) * [Next Generation of DevOps AIOps in Practice @Baidu](https://www.usenix.org/sites/default/files/conference/protected-files/srecon17asia_slides_qu.pdf) [[video]](https://www.youtube.com/watch?v=5YfqevEtIFw) ------------------------------------------------------------------------------- * [搭建大规模高性能的时间序列大数据平台](https://ppt.geekbang.org/list/assz2018) * [Yahoo大规模时列数据异常检测技术及其高性能可伸缩架构](http://www.infoq.com/cn/articles/automated-time-series-anomaly-detection?utm_source=articles_about_bigdata&utm_medium=link&utm_campaign=bigdata) * [Netflix: Robust PCA](https://medium.com/netflix-techblog/rad-outlier-detection-on-big-data-d6b0494371cc) * [LinkedIn: exponential smoothing](https://github.com/linkedin/luminol) * [Uber: multivariate non-linear model](https://eng.uber.com/argos/) ## Article * [智能运维|AIOps中的四大金刚都是谁？](https://mp.weixin.qq.com/s/NKhQkS59WIGgbIfFKcxonA) * [A Comparison of Mapping Approaches for Distributed Cloud Applications](https://blog.netsil.com/a-comparison-of-mapping-approaches-for-distributed-cloud-applications-52be1f61d293) * [AIOps探索：基于VAE模型的周期性KPI异常检测方法](https://zhuanlan.zhihu.com/p/45400663) ## Tools and Algorithms * [Tools to Monitor and Visualize Microservices Architecture](https://www.programmableweb.com/news/tools-to-monitor-and-visualize-microservices-architecture/analysis/2016/12/14) * [python-fp-growth，挖掘频繁项集](https://github.com/enaeseth/python-fp-growth) * [Anomaly Detection with Twitter in R](https://github.com/twitter/AnomalyDetection) * [百度开源时间序列打标工具：Curve](https://github.com/baidu/Curve) * [Microsoft开源时间序列打标工具: TagAnomaly](https://github.com/Microsoft/TagAnomaly) * [Anomaly Detection Examples](https://github.com/shubhomoydas/ad_examples) * [facebook/prophet, Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.](https://facebook.github.io/prophet) * [google/CausalImpact, An R package for causal inference in time series](https://github.com/google/CausalImpact) * [时间序列分析之ARIMA](https://blog.csdn.net/u010414589/article/details/49622625) * [时间序列特征提取库tsfresh](https://github.com/blue-yonder/tsfresh) * [Yahoo EGADS : A Java package to automatically detect anomalies in large scale time-series data](https://github.com/yahoo/egads) * [Awesome Time Series Analysis and Data Mining](https://github.com/youngdou/awesome-time-series-analysis) ## Paper * [Survey on Models and Techniques for Root-Cause Analysis](https://arxiv.org/pdf/1701.08546.pdf) * [基于机器学习的智能运维](http://netman.ai/wp-content/uploads/2018/04/peidan.pdf) * [HotSpot: Anomaly Localization for Additive KPIs With Multi-Dimensional Attributes](http://netman.aiops.org/wp-content/uploads/2018/03/sunyq_IEEEAccess_HotSpot.pdf) * Chinese：[清华AIOps新作:蒙特卡洛树搜索定位多维指标异常](https://mp.weixin.qq.com/s/Kj309bzifIv4j80nZbGVZw) * [Opprentice: Towards Practical and Automatic Anomaly Detection Through Machine Learning](http://conferences2.sigcomm.org/imc/2015/papers/p211.pdf) * [Robust and Rapid Clustering of KPIs for Large-Scale Anomaly Detection](https://netman.aiops.org/wp-content/uploads/2018/05/PID5338621.pdf) * [KPI-TSAD: A Time-Series Anomaly Detector for KPI Monitoring in Cloud Applications](https://www.mdpi.com/2073-8994/11/11/1350) * [Anomaly Detection Based on Mining Six Local Data Features and BP Neural Network ](https://www.mdpi.com/2073-8994/11/4/571) * [Generic and Robust Localization of Multi-Dimensional Root Causes](https://netman.aiops.org/wp-content/uploads/2019/08/camera_ready.pdf) * [Papers from Tsinghua NetMan Lab](https://netman.aiops.org/publications/) ## Dataset * [Alibaba/clusterdata](https://github.com/alibaba/clusterdata) * [Azure/AzurePublicDataset](https://github.com/Azure/AzurePublicDataset) * [Google/cluster-data](https://github.com/google/cluster-data) * [The Numenta Anomaly Benchmark(NAB)](https://github.com/numenta/NAB) * [Yahoo: A Labeled Anomaly Detection Dataset](https://webscope.sandbox.yahoo.com/catalog.php?datatype=s&did=70) * [港中文loghub数据集](https://github.com/logpai/loghub) * [2018 AIOPS挑战赛预赛测试集](http://iops.ai/dataset_detail/?id=7) [2018 AIOPS挑战赛预赛训练集](http://iops.ai/dataset_detail/?id=6) ## Useful WeChat Official Accounts * 腾讯织云（腾讯的） * 智能运维前沿（清华裴丹团队的） * AIOps智能运维（百度的） * 华为产品可服务能力（华为的）