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

arXiv: https://arxiv.org/abs/2105.12378 · HTML: https://mlsanity.com/paper/2105.12378/

## Key facts

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

## Built on by (most cited first, 30 of 48)

- [Exploring LLM-based Agents for Root Cause Analysis](https://mlsanity.com/paper/2403.04123/) (2024)
- [A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems](https://mlsanity.com/paper/2206.04149/) (2022)
- [A Survey of AIOps in the Era of Large Language Models](https://mlsanity.com/paper/2507.12472/) (2025)
- [Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization](https://mlsanity.com/paper/2302.01987/) (2023)
- [AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges](https://mlsanity.com/paper/2304.04661/) (2023)
- [A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends](https://mlsanity.com/paper/2408.00803/) (2024)
- [LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis](https://mlsanity.com/paper/2406.05375/) (2024)
- [A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes](https://mlsanity.com/paper/2206.15033/) (2022)
- [Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System](https://mlsanity.com/paper/2310.04701/) (2023)
- [Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset](https://mlsanity.com/paper/2411.09047/) (2024)
- [AgentTrace: Causal Graph Tracing for Root Cause Analysis in Deployed Multi-Agent Systems](https://mlsanity.com/paper/2603.14688/) (2026)
- [Multi-modal Causal Structure Learning and Root Cause Analysis](https://mlsanity.com/paper/2402.02357/) (2024)
- [PyRCA: A Library for Metric-based Root Cause Analysis](https://mlsanity.com/paper/2306.11417/) (2023)
- [Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features](https://mlsanity.com/paper/2307.10869/) (2023)
- [Unifying Explainable Anomaly Detection and Root Cause Analysis in Dynamical Systems](https://mlsanity.com/paper/2502.12086/) (2025)
- [BALANCE: Bayesian Linear Attribution for Root Cause Localization](https://mlsanity.com/paper/2301.13572/) (2023)
- [MILD: Multi-Intent Learning and Disambiguation for Proactive Failure Prediction in Intent-based Networking](https://mlsanity.com/paper/2602.14283/) (2026)
- [Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations](https://mlsanity.com/paper/2210.04145/) (2022)
- [LogAI: A Library for Log Analytics and Intelligence](https://mlsanity.com/paper/2301.13415/) (2023)
- [Generative transformations and patterns in LLM-native approaches for software verification and falsification](https://mlsanity.com/paper/2404.09384/) (2024)
- [Root Cause Localization for Microservice Systems in Cloud-edge Collaborative Environments](https://mlsanity.com/paper/2406.13604/) (2024)
- [CHASE: A Causal Hypergraph based Framework for Root Cause Analysis in Multimodal Microservice Systems](https://mlsanity.com/paper/2406.19711/) (2024)
- [PORCA: Root Cause Analysis with Partially Observed Data](https://mlsanity.com/paper/2407.05869/) (2024)
- [A Scenario-Oriented Benchmark for Assessing AIOps Algorithms in Microservice Management](https://mlsanity.com/paper/2407.14532/) (2024)
- [LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models](https://mlsanity.com/paper/2409.01909/) (2024)
- [Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operations](https://mlsanity.com/paper/2604.26805/) (2026)
- [Online Multi-modal Root Cause Identification in Microservice Systems](https://mlsanity.com/paper/2410.10021/) (2024)
- [ClusterRCA: An End-to-End Approach for Network Fault Localization and Classification for HPC System](https://mlsanity.com/paper/2506.20673/) (2025)
- [5G Core Fault Detection and Root Cause Analysis using Machine Learning and Generative AI](https://mlsanity.com/paper/2508.09152/) (2025)
- [Graph Neural AI with Temporal Dynamics for Comprehensive Anomaly Detection in Microservices](https://mlsanity.com/paper/2511.03285/) (2025)

## 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.