└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # AIOps 2 | 原答案由中大小伙伴整理~ 3 | [![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) 4 | [![GitHub stars](https://img.shields.io/github/stars/linjinjin123/awesome-AIOps.svg?style=flat&label=Star)](https://github.com/linjinjin123/awesome-AIOps/stargazers) 5 | [![GitHub forks](https://img.shields.io/github/forks/linjinjin123/awesome-AIOps.svg?style=flat&label=Fork)](https://github.com/linjinjin123/awesome-AIOps/fork) 6 | [![GitHub watchers](https://img.shields.io/github/watchers/linjinjin123/awesome-AIOps.svg?style=flat&label=Watch)](https://github.com/linjinjin123/awesome-AIOps/watchers) 7 | 8 | - [Awesome AIOps](#awesome-AIOps) 9 | - [White Paper](#white-paper) 10 | - [Course and Slides](#course-and-slides) 11 | - [Industry Practice](#industry-practice) 12 | - [Tools and Algorithms](#tools-and-algorithms) 13 | - [Paper](#paper) 14 | - [Dataset](#dataset) 15 | - [Useful WeChat Official Accounts](#useful-wechat-official-accounts) 16 | 17 | ## White Paper 18 | * [《企业级 AIOps 实施建议》白皮书](https://www.rizhiyi.com/assets/docs/AIOps.pdf) 19 | 20 | ## Course and Slides 21 | * [Tsinghua-Peidan](http://netman.ai/courses/advanced-network-management-spring2018-syllabus/) - AIOps course in Tsinghua. 22 | * [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) 23 | 24 | ## Industry Practice 25 | * [阿里全链路监控方案](https://mp.weixin.qq.com/s/DJhJKD4TCDgSwyLZbSotKg) 26 | * [A Comparison of Mapping Approaches for Distributed Cloud Applications](https://blog.netsil.com/a-comparison-of-mapping-approaches-for-distributed-cloud-applications-52be1f61d293). 27 | * [腾讯运维的AI实践](https://myslide.cn/slides/8935) 28 | * [AI 时代下腾讯的海量业务智能监控实践](https://cloud.tencent.com/developer/article/1039354) 29 | * [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) 30 | * [百度智能流量监控实战](https://ppt.geekbang.org/slide/show?cid=30&pid=1548) 31 | * [搭建大规模高性能的时间序列大数据平台](https://ppt.geekbang.org/list/assz2018) 32 | * [异常检测：百度是这样做的](https://mp.weixin.qq.com/s/AXhjawsINKl6cLDV1yf6fw) 33 | * [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) 34 | * [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) 35 | * [Netflix: Robust PCA](https://medium.com/netflix-techblog/rad-outlier-detection-on-big-data-d6b0494371cc) 36 | * [LinkedIn: exponential smoothing](https://github.com/linkedin/luminol) 37 | * [Uber: multivariate non-linear model](https://eng.uber.com/argos/) 38 | 39 | ## Tools and Algorithms 40 | * [Tools to Monitor and Visualize Microservices Architecture](https://www.programmableweb.com/news/tools-to-monitor-and-visualize-microservices-architecture/analysis/2016/12/14) 41 | * [python-fp-growth，挖掘频繁项集](https://github.com/enaeseth/python-fp-growth) 42 | * [Anomaly Detection with Twitter in R](https://github.com/twitter/AnomalyDetection) 43 | * [百度开源时间序列打标工具：Curve](https://github.com/baidu/Curve) 44 | * [Microsoft开源时间序列打标工具: TagAnomaly](https://github.com/Microsoft/TagAnomaly) 45 | * [Anomaly Detection Examples](https://github.com/shubhomoydas/ad_examples) 46 | 47 | ## Paper 48 | * [Survey on Models and Techniques for Root-Cause Analysis](https://arxiv.org/pdf/1701.08546.pdf) 49 | * [基于机器学习的智能运维](http://netman.ai/wp-content/uploads/2018/04/peidan.pdf) 50 | * [HotSpot: Anomaly Localization for Additive KPIs With Multi-Dimensional Attributes](http://netman.ai/wp-content/uploads/2018/03/sunyq_IEEEAccess_HotSpot.pdf) 51 | * 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) 52 | * [Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications](https://arxiv.org/pdf/1802.03903.pdf). [[Slide]](http://netman.ai/wp-content/uploads/2018/05/www2018-slide.pdf) [[Github]](https://github.com/haowen-xu/donut) 53 | * [Opprentice: Towards Practical and Automatic Anomaly Detection Through Machine Learning](http://conferences2.sigcomm.org/imc/2015/papers/p211.pdf) 54 | * [Robust and Rapid Clustering of KPIs for Large-Scale Anomaly Detection](http://netman.ai/~peidan/ANM2018/8.DependencyDiscovery/LectureCoverage/2018IWQOS_ROCKA.pdf) 55 | 56 | ## Dataset 57 | * [Alibaba/clusterdata](https://github.com/alibaba/clusterdata) 58 | * [Azure/AzurePublicDataset](https://github.com/Azure/AzurePublicDataset) 59 | * [Google/cluster-data](https://github.com/google/cluster-data) 60 | * [The Numenta Anomaly Benchmark(NAB)](https://github.com/numenta/NAB) 61 | * [Yahoo: A Labeled Anomaly Detection Dataset](https://webscope.sandbox.yahoo.com/catalog.php?datatype=s&did=70) 62 | * [港中文loghub数据集](https://github.com/logpai/loghub) 63 | 64 | ## Useful WeChat Official Accounts 65 | * 腾讯织云（腾讯的） 66 | * 智能运维前沿（清华裴丹团队的） 67 | * AIOps智能运维（百度的） 68 | * 华为产品可服务能力（华为的） 69 | --------------------------------------------------------------------------------