mlsanityData to 14 Oct 2026

Anomaly Detection and Failure Root Cause Analysis in (Micro)Service-Based Cloud Applications: A Survey

arXiv 2105.12378 ↗First posted 26 May 2021cs.SEcited by 48

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Abstract (arXiv)

The momentum gained by microservices and cloud-native software architecture pushed nowadays enterprise IT towards multi-service applications. The proliferation of services and service interactions within applications, often consisting of hundreds of interacting services, makes it harder to detect failures and to identify their possible root causes, which is on the other hand crucial to promptly recover and fix applications. Various techniques have been proposed to promptly detect failures based on their symptoms, viz., observing anomalous behaviour in one or more application services, as well as to analyse logs or monitored performance of such services to determine the possible root causes for observed anomalies. The objective of this survey is to provide a structured overview and a qualitative analysis of currently available techniques for anomaly detection and root cause analysis in modern multi-service applications. Some open challenges and research directions stemming out from the analysis are also discussed.

Key facts. Cited by 48 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 May 2021.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 10; 2024: 12; 2025: 7; 2026: 16.

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  1. 2024Exploring LLM-based Agents for Root Cause Analysiscited by 61
  2. 2022A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systemscited by 20
  3. 2025A Survey of AIOps in the Era of Large Language Modelscited by 19
  4. 2023Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localizationcited by 18
  5. 2023AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challengescited by 15
  6. 2024A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trendscited by 10
  7. 2024LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysiscited by 9
  8. 2022A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causescited by 8
  9. 2023Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning for Microservice Systemcited by 7
  10. 2024Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Datasetcited by 7
  11. 2026AgentTrace: Causal Graph Tracing for Root Cause Analysis in Deployed Multi-Agent Systemscited by 7
  12. 2024Multi-modal Causal Structure Learning and Root Cause Analysiscited by 6
36 more
  1. 2023PyRCA: A Library for Metric-based Root Cause Analysiscited by 5
  2. 2023Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Featurescited by 4
  3. 2025Unifying Explainable Anomaly Detection and Root Cause Analysis in Dynamical Systemscited by 4
  4. 2023BALANCE: Bayesian Linear Attribution for Root Cause Localizationcited by 3
  5. 2026MILD: Multi-Intent Learning and Disambiguation for Proactive Failure Prediction in Intent-based Networkingcited by 3
  6. 2022Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanationscited by 2
  7. 2023LogAI: A Library for Log Analytics and Intelligencecited by 2
  8. 2024Generative transformations and patterns in LLM-native approaches for software verification and falsificationcited by 2
  9. 2024Root Cause Localization for Microservice Systems in Cloud-edge Collaborative Environmentscited by 2
  10. 2024CHASE: A Causal Hypergraph based Framework for Root Cause Analysis in Multimodal Microservice Systemscited by 2
  11. 2024PORCA: Root Cause Analysis with Partially Observed Datacited by 2
  12. 2024A Scenario-Oriented Benchmark for Assessing AIOps Algorithms in Microservice Managementcited by 2
  13. 2024LUK: Empowering Log Understanding with Expert Knowledge from Large Language Modelscited by 2
  14. 2026Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operationscited by 2
  15. 2024Online Multi-modal Root Cause Identification in Microservice Systemscited by 1
  16. 2025ClusterRCA: An End-to-End Approach for Network Fault Localization and Classification for HPC Systemcited by 1
  17. 20255G Core Fault Detection and Root Cause Analysis using Machine Learning and Generative AIcited by 1
  18. 2025Graph Neural AI with Temporal Dynamics for Comprehensive Anomaly Detection in Microservicescited by 1
  19. 2026Benchmarking Anomaly Detection Across Heterogeneous Cloud Telemetry Datasetscited by 1
  20. 2026AI-driven Intent-Based Networking Approach for Self-configuration of Next Generation Networkscited by 1
  21. 2026EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systemscited by 1
  22. 2026TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservicescited by 1
  23. 2026UModel: An Agent-Ready Observability Data Modeling Method at Scalecited by 1
  24. 2026Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observationscited by 1
  25. 2026TriCalRAG: A Three-Strategy, Retrieval-Augmented Benchmark for On-Premise LLM-Based Root Cause Analysis in AIOpscited by 1
  26. 2023Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Seriescited by 0
  27. 2023Disentangled Causal Graph Learning for Online Unsupervised Root Cause Analysiscited by 0
  28. 2023RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM considering Token-level informationcited by 0
  29. 2025Autonomic Microservice Management via Agentic AI and MAPE-K Integrationcited by 0
  30. 2025A Comprehensive Forecasting-Based Framework for Time Series Anomaly Detection: Benchmarking on the Numenta Anomaly Benchmark (NAB)cited by 0
  31. 2026PRIM: Meta-Learned Bayesian Root Cause Analysiscited by 0
  32. 2026ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysiscited by 0
  33. 2026StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysiscited by 0
  34. 2026Untangling Co-Drift: Proactive Multi-Intent Failure Prediction and Root-Cause Disambiguation for Self-Driving Networkscited by 0
  35. 2026TriFleetRCA: On-Premise LLM Root Cause Analysis for Kubernetescited by 0
  36. 2026Benchmarking Deep Learning Models for Anomaly Detection in Real-World Cloud Telemetrycited by 0