AIOps根因定位（二）：微服务架构的异常检测与根因定位
1 背景
本文主要从基于日志、基于trace和基于监控指标这三个方面，初步罗列了微服务架构的异常检测和根因定位的相关论文。
2 基于日志的异常检测与根因定位
2.1 异常检测
Anomaly Detection Using Program Control Flow Graph Mining From Execution Logs.
An Approach for Anomaly Diagnosis Based on Hybrid Graph Model with Logs for Distributed Services.
LogSed: Anomaly Diagnosis through Mining Time-Weighted Control Flow Graph in Logs.
2.2 根因定位
Localization of Operational Faults in Cloud Applications by Mining Causal Dependencies in Logs using Golden Signals.
3 基于trace的异常检测与根因定位
3.1 异常检测
3.1.1 无监督检测
Unsupervised Detection of Microservice Trace Anomalies through Service-Level Deep Bayesian Networks.
Anomaly Detection from System Tracing Data Using Multi-modal Deep Learning.
An Anomaly Detection Algorithm for Microservice Architecture Based on Robust Principal Component Analysis.
3.1.2 有监督检测
Anomaly Detection and Classification using Distributed Tracing and Deep Learning.
Seer: Leveraging Big Data to Navigate the Complexity of Performance Debugging in Cloud Microservices.
Self-SupervisedAnomalyDetectionfrom Distributed Traces.
Latent Error Prediction and Fault Localization for Microservice Applications by Learning from System Trace Logs.
3.1.3 trace比对
Workflow-Aware Automatic Fault Diagnosis for Microservice-Based Applications With Statistics.
Detecting anomalies in microservices with execution trace comparison.
A Framework of Virtual War Room and Matrix Sketch-Based Streaming Anomaly Detection for Microservice Systems.
3.2 根因定位
3.2.1 基于可视化的分析
Graph-Based Trace Analysis for Microservice Architecture Understanding and Problem Diagnosis.
Fault Analysis and Debugging of Microservice Systems: Industrial Survey, Benchmark System, and Empirical Study.
3.2.2 直接分析（Direct Analysis）
Toward Fine-Grained, Unsupervised, Scalable Performance Diagnosis for Production Cloud Computing Systems.
Unsupervised Detection of Microservice Trace Anomalies through Service-Level Deep Bayesian Networks.
3.2.3 基于拓扑图的分析
MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systems.
Root Cause Detection in a Service-Oriented Architecture.
4 基于监控指标的异常检测
4.1 异常检测
4.1.1 无监督检测
Detecting Anomalous Behavior of Black-Box Services Modeled with Distance-Based Online Clustering.
Localizing Faults in Cloud Systems.
DLA: Detecting and Localizing Anomalies in Containerized Microservice Architectures Using Markov Models.
Performance Diagnosis in Cloud Microservices using Deep Learning.
MicroRCA: Root Cause Localization of Performance Issues in Microservices.
4.1.2 有监督检测
Predicting failures in multi-tier distributed systems.
Anomaly Detection and Diagnosis for Container-Based Microservices with Performance Monitoring.
4.1.3 SLO Check（Service Level Objective）
CauseInfer: Automated End-to-End Performance Diagnosis with Hierarchical Causality Graph in Cloud Environment.
CauseInfer: Automatic and distributed performance diagnosis with hierarchical causality graph in large distributed systems.
On Anomaly Detection and Root Cause Analysis of Microservice Systems.
Microscope: Pinpoint Performance Issues with Causal Graphs in Micro-service Environments.
4.2 根因分析
4.2.1 直接分析（Direct Analysis）
-Diagnosis: Unsupervised and Real-Time Diagnosis of Small-Window Long-Tail Latency in Large-Scale Microservice Platforms.
Root-Cause Metric Location for Microservice Systems via Log Anomaly Detection.
PAL: Propagation-Aware Anomaly Localization for Cloud Hosted Distributed Applications.
FChain: Toward Black-Box Online Fault Localization for Cloud Systems.
4.2.2 基于拓扑图的分析
Graph-based root cause analysis for service-oriented and microservice architectures.
Sieve: Actionable Insights from Monitored Metrics in Distributed Systems.
Performance Diagnosis in Cloud Microservices using Deep Learning.
MicroRCA: Root Cause Localization of Performance Issues in Microservices.
DLA: Detecting and Localizing Anomalies in Containerized Microservice Architectures Using Markov Models.
4.2.3 基于因果图的分析
CauseInfer: Automated End-to-End Performance Diagnosis with Hierarchical Causality Graph in Cloud Environment.
CauseInfer: Automatic and distributed performance diagnosis with hierarchical causality graph in large distributed systems.
On Anomaly Detection and Root Cause Analysis of Microservice Systems.
Microscope: Pinpoint Performance Issues with Causal Graphs in Micro-service Environments.
FacGraph: Frequent Anomaly Correlation Graph Mining for Root Cause Diagnose in Micro-Service Architecture.
MS-Rank: Multi-Metric and Self-Adaptive Root Cause Diagnosis for Microservice Applications.
Self-Adaptive Root Cause Diagnosis for Large-Scale Microservice Architecture.
AutoMAP: Diagnose Your Microservice-Based Web Applications Automatically.
CloudRanger: Root Cause Identification for Cloud Native Systems.
Localizing Failure Root Causes in a Microservice through Causality Inference.
Localizing Faults in Cloud Systems.