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

中国(内地及港澳台)
Michael R. Lyu, CUHK	Dongmei Zhang, Microsoft
Pengfei Chen, SYSU	Dan Pei, Tsinghua
Pengfei Chen, SYSU	Xin Peng, Fudan
Qingwei Lin, Microsoft	-

美国
Ryan Huang, JHU	Yingnong Dang, Microsoft
Christina Delimitrou, MIT EECS	Manish Shetty, Berkeley
-	-

欧洲
Odej Kao, TU Berlin	-

澳大利亚
Hongyu Zhang, UON	-

Manish-Shetty-University of California,Berkeley

• 个人主页
• 谷歌主页(镜像)
• NetMan实验室官网

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