XJ-AIOps

Materials about Artificial Intelligence for IT Operations (AIOps).

• Researchers
• Industrial Materials
  • Competitions
  • White Papers
  • Blogs & Tutorials & Magazines
  • Companies
• Academic Materials
  • Talks
  • Workshops
• Papers
  • Survey & Empirical Study
  • Benchmarks
  • Knowledge Graph for AIOps
  • Microservices and Serverless
  • Dependency and Tracing
  • Anomaly and Failure Detection
  • Incident and Alarm Management
  • Node, Disk, and Storage
  • VM Analysis and Management
  • Deployment
• Competitions
  • Tsinghua 清华 CCF 国际AIOps大赛
  • Alibaba-智能运维大赛
  • 华为通信智能运维大赛
• Datasets
  • CUHK 香港中文大学
  • Microsoft 微软
  • Tsinghua 清华 CCF 国际AIOps大赛
  • Google 谷歌
  • Backblaze 云备份和存储供应商
  • Alibaba-智能运维大赛
  • AIOps2022通信网络智能运维大赛数据集
  • GAIA-DataSet
  • 华为网络运维数据集
• Tools, Models and Systems
• Community
• Internet articles
• Others
  • Courses
• Reference

一.Researchers

China (& HK SAR)
Michael R. Lyu, CUHK	Dongmei Zhang, Microsoft	Pengfei Chen, SYSU	Dan Pei, Tsinghua
Pengfei Chen, SYSU	Xin Peng, Fudan	Qingwei Lin, Microsoft
USA
Ryan Huang, JHU	Yingnong Dang, Microsoft	Christina Delimitrou, MIT EECS
Europe
Odej Kao, TU Berlin
Australia
Hongyu Zhang, UON

Dan Pei-Tsinghua(裴丹-清华)

• 个人主页
• 谷歌主页(镜像)
• NetMan实验室官网
• 实验室Github

Pengfei Chen(陈鹏飞-中山)

• 个人主页
• 谷歌主页(镜像)
• 实验室主页
• 实验室Github

Qingwei Lin(林庆维-微软)

• 个人主页
• 谷歌主页(镜像)

Pinjia He(贺品嘉-港中文)

• 个人主页
• 谷歌主页(镜像)

Shenglin Zhang(张圣林-南开)

• 个人主页
• 谷歌主页(镜像)
• AIOps@NKU官网
• Publications

Hanzhang Wang(王含璋-eBay)

• 个人主页
• 谷歌主页(镜像)

二. Industrial Materials

White Papers

• [VMware] Proactive Incident and Problem Management
• [GREATOPS 高效运维社区] 《企业级 AIOps 实施建议》白皮书
• [Awesome Open Source] Aiops Handbook

Blogs & Tutorials & Magazines

• [Tsinghua University] 清华裴丹：AIOps落地的15条原则
• [Tsinghua University] 清华裴丹：AIOps效果落地最后一公里
• [Alibaba Cloud] 基于大数据的智能网络分析-齐天
• [Tsinghua University] 清华裴丹：AIOps效果落地最后一公里
• [Moogsoft] What is AIOps?
• [Microsoft] Advancing Azure service quality with artificial intelligence: AIOps

Companies

• Datadog: A monitoring and security platform for cloud applications
• 必示 bizseer
• 听云 TINGYUN: 端到端的全平台应用性能管理系统
• Loom Systems

三. Academic Materials

Talks

• [Michael R. Lyu] Reliability-Driven AIOps for Cloud Resilience (Keynote talk at ICSE '21)

Workshops

• ICSE21 Workshop on Cloud Intelligence
• AAAI-20 Workshop on Cloud Intelligence
• AIOPS 2020 (International Workshop on Artificial Intelligence for IT Operations)

四. Papers

Survey & Empirical Study

• [arXiv '21] Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection
• [CSUR '21] A Survey on Automated Log Analysis for Reliability Engineering
• [ESEC/FSE '20] Towards intelligent incident management: why we need it and how we make it
• [arXiv '20] A Systematic Mapping Study in AIOps
• [ICSE '19] AIOps: Real-World Challenges and Research Innovations
• [ISSRE '16] Experience Report: System Log Analysis for Anomaly Detection
• [ASE '13] Software analytics for incident management of online services: An experience report

The benchmarks

• [arXiv '22] Constructing Large-Scale Real-World Benchmark Datasets for AIOps
• [ASPLOS '19] An Open-Source Benchmark Suite for Microservices and Their Hardware-Software Implications for Cloud and Edge Systems

Knowledge Graph for AIOps

• [ICSE-SEIP '22] Mining Root Cause Knowledge from Cloud Service Incident Investigations for AIOps
• [ICSE-SEIP '21] Neural knowledge extraction from cloud service incidents
• [arXiv '21] SoftNER: Mining Knowledge Graphs From Cloud Incidents
• [APPLSCI '20] A Causality Mining and Knowledge Graph Based Method of Root Cause Diagnosis for Performance Anomaly in Cloud Applications

Microservices and Serverless

• [ASPLOS '21] Sage: Practical & Scalable ML-Driven Performance Debugging in Microservices
• [ICDCS '21] Defuse: A Dependency-Guided Function Scheduler to Mitigate Cold Starts on FaaS Platforms
• [FSE '20] Graph-based trace analysis for microservice architecture understanding and problem diagnosis
• [OSDI '20] FIRM: An Intelligent Fine-grained Resource Management Framework for SLO-Oriented Microservices
• [ESEC/FSE '19] Latent Error Prediction and Fault Localization for Microservice Applications by Learning from System Trace Logs
• [TSE '18] Fault Analysis and Debugging of Microservice Systems: Industrial Survey, Benchmark System, and Empirical Study

Dependency and Tracing

• [ASE '21] AID: Efficient Prediction of Aggregated Intensity of Dependency in Large-scale Cloud Systems [code]
• [NSDI '07] X-Trace: A Pervasive Network Tracing Framework
• [HotNets '06] Discovering Dependencies for Network Management

Anomaly and Failure Detection

• [ICSE '22] Adaptive Performance Anomaly Detection for Online Service Systems via Pattern Sketching [code]
• [KDD '19] Time-Series Anomaly Detection Service at Microsoft
• [OSDI '18] Capturing and Enhancing In Situ System Observability for Failure Detection
• [ESEC/FSE '18] Identifying Impactful Service System Problems via Log Analysis
• [CCS '17] DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning

Incident and Alarm Management

• [DSN '22] Characterizing and Mitigating Anti-patterns of Alerts in Industrial Cloud Systems
• [USENIX ATC '21] Fighting the Fog of War: Automated Incident Detection for Cloud Systems
• [ASE '21] Graph-based Incident Aggregation for Large-Scale Online Service Systems
• [ASE '21] Groot: An Event-graph-based Approach for Root Cause Analysis in Industrial Settings
• [SIGCOMM '20] Scouts: Improving the Diagnosis Process Through Domain-customized Incident Routing
• [ASE '20] How Incidental are the Incidents?: Characterizing and Prioritizing Incidents for Large-Scale Online Service Systems
• [ESEC/FSE '20] Identifying linked incidents in large-scale online service systems
• [ESEC/FSE '20] Efficient incident identification from multi-dimensional issue reports via meta-heuristic search
• [ESEC/FSE '20] Real-time incident prediction for online service systems
• [ESEC/FSE '20] How to mitigate the incident? an effective troubleshooting guide recommendation technique for online service systems
• [ICSE '20] Understanding and Handling Alert Storm for Online Service Systems
• [HotOS '19] What bugs cause production cloud incidents?
• [ASE '19] Continuous Incident Triage for Large-Scale Online Service Systems
• [ICSE '19] An empirical investigation of incident triage for online service systems
• [WWW '19] Outage Prediction and Diagnosis for Cloud Service Systems
• [KDD '14] Correlating Events with Time Series for Incident Diagnosis

Node, Disk, and Storage

• [DSN '21] General Feature Selection for Failure Prediction in Large-scale SSD Deployment
• [TOSEM '20] Predicting Node Failures in an Ultra-Large-Scale Cloud Computing Platform: An AIOps Solution
• [ICDCS '20] Toward Adaptive Disk Failure Prediction via Stream Mining
• [VLDB '20] Diagnosing root causes of intermittent slow queries in cloud databases
• [USENIX ATC '19] IASO: A Fail-Slow Detection and Mitigation Framework for Distributed Storage Services
• [NSDI '18] Deepview: Virtual Disk Failure Diagnosis and Pattern Detection for Azure
• [ESEC/FSE '18] Predicting Node Failure in Cloud Service Systems
• [USENIX ATC '18] Improving Service Availability of Cloud Systems by Predicting Disk Error

VM Analysis and Management

• [NSDI '22] CloudCluster: Unearthing the Functional Structure of a Cloud Service
• [OSDI '20] Predictive and Adaptive Failure Mitigation to Avert Production Cloud VM Interruptions

Deployment

• [SOSP '21] Understanding and Detecting Software Upgrade Failures in Distributed Systems
• [NSDI '20] Gandalf: An Intelligent, End-To-End Analytics Service for Safe Deployment in Large-Scale Cloud Infrastructure

五. Competitions

CCF国际AIOps挑战赛

• [AIOps Challenge] A series of AIOps competitions hosted by Tsinghua University

阿里巴巴

• 第三届阿里云磐久智维算法大赛
• PAKDD2021 第二届阿里云智能运维算法大赛
• PAKDD2020 阿里巴巴智能运维算法大赛

华为

• AIOps 2022 通信网络智能运维大赛

六. Datasets

CUHK 香港中文大学

• [CUHK] Loghub

Microsoft 微软

• [Microsoft Azure] Azure Public Dataset

Tsinghua 清华

• [Tsinghua] 2018 AIOps Challenge Dataset
• [Tsinghua] 2019 AIOps Challenge Dataset
• [Tsinghua] 2020 AIOps Challenge Dataset

Google 谷歌

• [Google] Cluster Traces

Backblaze 云备份和存储供应商

• [Backblaze] Hard Drive Dataset

Alibaba 智能运维大赛数据集

• [Alibaba] SMART Dataset of PAKDD CUP 2020
• [Alibaba] SSD SMART logs and failure data

Ceph Drive Telemetry Data

• [Ceph Drive] kaggle

GAIA-DataSet

• GAIA

七. Tools、Models and Systems

• [Log Analytics] LogPAI
• [AI for Cloud Operation] OpsPAI
• [Outlier Detection] PyOD
• [Anomaly Detection] ADTK
• [Anomaly Detection] PySAD
• [Online Machine Learning] River
• [Online Machine Learning] scikit-multiflow
• [Fault Injection] Chaos Mesh
• [Fault Injection] ChaosBlade
• [Container Monitoring] cAdvisor
• [Performance Monitoring] Netdata
• [Anomaly Detection Labeling Tool] Microsoft TagAnomaly
• [Serverless App Dev. Framework] AWS Serverless Application Model (AWS SAM)
• [Fudan] Train Ticket (A Benchmark Microservice System) 该项目是一个基于微服务架构的火车票预订系统，包含41个微服务。
• [Weaveworks] Sock Shop (A Microservices Demo Application) 袜子店模拟了一个销售袜子的电子商务网站中面向用户的部分。它的目的是帮助演示和测试微服务和云原生技术。

八. Community

• [云智慧] CloudWise-OpenSource

九. Internet articles

• 基于深度学习的运维日志领域新进展
• 从实验室到用户桌面，深度解析AI落地实践之路
• 智能化软件开发微访谈·第二十一期：可观测性与智能化运维

十. Others

Courses

• [Coursera] Cloud-Based Network Design & Management Techniques
• [Tsinghua] AIOps Course of Tsinghua

十一. Reference

• [OpsPAI] https://github.com/WeibinMeng/log-anomaly-detection
• [WeibinMeng] https://github.com/WeibinMeng/log-anomaly-detection
• [awesome-AIOps] https://github.com/linjinjin123/awesome-AIOps