Graph-based root cause analysis for service-oriented and microservice architectures

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MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systems

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Robust Failure Diagnosis of Microservice System Through Multimodal Data

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CausalRCA: Causal inference based precise fine-grained root cause localization for microservice applications

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Groot: An Event-graph-based Approach for Root Cause Analysis in Industrial Settings

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A Survey on Network Simulators, Emulators, and Testbeds Used for Research and Education

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On Microservice Analysis and Architecture Evolution: A Systematic Mapping Study

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Actionable and interpretable fault localization for recurring failures in online service systems

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MicroDiag: Fine-grained Performance Diagnosis for Microservice Systems

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BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection

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Failure Diagnosis in Microservice Systems: A Comprehensive Survey and Analysis

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Adaptive performance anomaly detection for online service systems via pattern sketching

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An Anomaly Detection Algorithm for Microservice Architecture Based on Robust Principal Component Analysis

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ServiceAnomaly: An anomaly detection approach in microservices using distributed traces and profiling metrics

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TrinityRCL: Multi-Granular and Code-Level Root Cause Localization Using Multiple Types of Telemetry Data in Microservice Systems

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Mining Root Cause Knowledge from Cloud Service Incident Investigations for AIOps

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KGroot: A knowledge graph-enhanced method for root cause analysis

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Performance Diagnosis in Cloud Microservices Using Deep Learning

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Visualizing Microservice Architecture in the Dynamic Perspective: A Systematic Mapping Study

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TraceDiag: Adaptive, Interpretable, and Efficient Root Cause Analysis on Large-Scale Microservice Systems

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IT Infrastructure Anomaly Detection and Failure Handling: A Systematic Literature Review Focusing on Datasets, Log Preprocessing, Machine & Deep Learning Approaches and Automated Tool

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Evaluating Service-Oriented and Microservice Architecture Patterns to Deploy eHealth Applications in Cloud Computing Environment

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Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

Calderón, H., Mendoza, L., Vargas-Lombardo, M., Gomez-Pulido, J. M., Castillo, J. L., Sanz, J., & Sención, G. (2020) · IEEE Access · doi:10.1109/access.2020.3005638 · Cited by 38

Interdependent Causal Networks for Root Cause Localization

Wang, D., Chen, Z., Ni, J., Tong, L., Wang, Z., Fu, Y., & Chen, H. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599849 · Cited by 32

The design and application of landslide monitoring and early warning system based on microservice architecture

Bai, D., Tang, J., Lu, G., Zhu, Z., Liu, T., & Ji, F. (2020) · Geomatics Natural Hazards and Risk · doi:10.1080/19475705.2020.1766580 · Cited by 32

Incremental Causal Graph Learning for Online Root Cause Analysis

Wang, D., Chen, Z., Fu, Y., Liu, Y., & Chen, H. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599392 · Cited by 42

Causal Inference Techniques for Microservice Performance Diagnosis: Evaluation and Guiding Recommendations

Li, W., Tordsson, J., Elmroth, E., & Kao, O. (2021) · IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) · doi:10.1109/acsos52086.2021.00029 · Cited by 28

A comprehensive survey of Network Digital Twin architecture, capabilities, challenges, and requirements for Edge–Cloud Continuum

Raza, S. M., Minerva, R., Crespi, N., Alvi, M., Herath, M., & Dutta, H. (2025) · Computer Communications · doi:10.1016/j.comcom.2025.108144 · Cited by 27

CausIL: Causal Graph for Instance Level Microservice Data

Chakraborty, S., Garg, S., Agarwal, S., Chauhan, A., & Saini, S. K. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583274 · Cited by 24

MicroIRC: Instance-level Root Cause Localization for Microservice Systems

Zhu, Y., Wang, J., Li, B., Zhao, Y., Zhang, Z., Xiong, Y., & Chen, S. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112145 · Cited by 23

Micro2vec: Anomaly detection in microservices systems by mining numeric representations of computer logs

Cinque, M., Corte, R. D., & Pecchia, A. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103515 · Cited by 23

Chain-of-event: interpretable root cause analysis for microservices through automatically learning weighted event causal graph

Yao, Z., Pei, C., Chen, W., Wang, H., Su, L., Jiang, H., Xie, Z., Nie, X., & Pei, D. (2024) · Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering · doi:10.1145/3663529.3663827 · Cited by 21

Performance optimization for cloud computing systems in the microservice era: state-of-the-art and research opportunities

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FRL-MFPG: Propagation-aware fault root cause location for microservice intelligent operation and maintenance

Chen, Y., Xu, D., Chen, N., & Wu, X. (2022) · Information and Software Technology · doi:10.1016/j.infsof.2022.107083 · Cited by 21

Information system development for restricting access to software tool built on microservice architecture

Safaryan, O. A., Pinevich, E., Roshchina, E., Cherckesova, L. V., & Kolennikova, N. (2020) · E3S Web of Conferences · doi:10.1051/e3sconf/202022401041 · Cited by 19

Root Cause Analysis in 5G/6G Networks

Canastro, D. R. G. S., Rocha, R., Antunes, M., Gomes, D., & Aguiar, R. L. (2021) · International Conference on Future Internet of Things and Cloud (FiCloud) · doi:10.1109/ficloud49777.2021.00039 · Cited by 17

MicroCBR: Case-Based Reasoning on Spatio-temporal Fault Knowledge Graph for Microservices Troubleshooting

Liu, F., Wang, Y., Li, Z., Ren, R., Guan, H., Yu, X., Chen, X., & Xie, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-14923-8_15 · Cited by 17

Change impact analysis in microservice systems: A systematic literature review

Lelovic, L., Huizinga, A., Goulis, G., Kaur, A., Boone, R., Muzrapov, U., Abdelfattah, A. S., & Cerny, T. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112241 · Cited by 17

Holistic Root Cause Analysis for Failures in Cloud-Native Systems Through Observability Data

Han, Y., Du, Q., Huang, Y., Li, P., Shi, X., Wu, J., Fang, P., Tian, F., & He, C. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3478759 · Cited by 16

Heterogeneous Data-Driven Failure Diagnosis for Microservice-Based Industrial Clouds Toward Consumer Digital Ecosystems

Xu, Y., Qiu, Z., Gao, H., Zhao, X., Wang, L., & Li, R. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3337351 · Cited by 16

XAI-LCS: Explainable AI-Based Fault Diagnosis of Low-Cost Sensors

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Diagnosing Performance Issues for Large-Scale Microservice Systems With Heterogeneous Graph

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Root Cause Analysis for Cloud-Native Applications

Bartosz, Z., & Zieliński, K. (2024) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2024.3358823 · Cited by 12

Explaining Microservices' Cascading Failures From Their Logs

Soldani, J., Forti, S., Roveroni, L., & Antonio Brogi, A. (2024) · Software Practice and Experience · doi:10.1002/spe.3400 · Cited by 12

Improved data-driven root cause analysis in fog computing environment

Bulla, C. M., & Birje, M. N. (2021) · Journal of Reliable Intelligent Environments · doi:10.1007/s40860-021-00158-x · Cited by 12

What Went Wrong? Explaining Cascading Failures in Microservice-Based Applications

Soldani, J., Montesano, G., & Antonio Brogi, A. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-87568-8_9 · Cited by 12

MicroEGRCL: An Edge-Attention-Based Graph Neural Network Approach for Root Cause Localization in Microservice Systems

Chen, R., Ren, J., Wang, L., Pu, Y., Yang, K., & Wu, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20984-0_18 · Cited by 12

A Root Cause Analysis Framework for IoT Based on Dynamic Causal Graphs Assisted by LLMs

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Observability and Incident Response in Managed Serverless Environments Using Ontology-Based Log Monitoring

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AAMR: Automated Anomalous Microservice Ranking in Cloud-Native Environment

Zhang, Z. (2021) · International Conferences on Software Engineering and Knowledge Engineering · doi:10.18293/seke2021-091 · Cited by 10

Intelligent Root Cause Localization in MicroService Systems: A Survey and New Perspectives

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LEKG: A System for Constructing Knowledge Graphs from Log Extraction

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Improved Data-Driven Root Cause Analysis in a Fog Computing Environment

Bulla, C. M., & Birje, M. N. (2022) · International Journal of Intelligent Information Technologies · doi:10.4018/ijiit.296238 · Cited by 10

An effective failure detection method for microservice-based systems using distributed tracing data

Purfallah, Z., & Rasoolzadegan, A. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108558 · Cited by 11

MTG_CD: Multi-scale learnable transformation graph for fault classification and diagnosis in microservices

Chen, J., Zhang, R., Chen, P., Ren, J., Wu, Z., Wang, Y., Li, X., & Xiong, L. (2024) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-024-00666-0 · Cited by 9

Towards Future Vehicle Diagnostics in Software-Defined Vehicles

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Graph Based Root Cause Analysis in Cloud Data Center

Dandona, D., Demir, M. A., & Prevost, J. J. (2020) · International Conference on System of Systems Engineering · doi:10.1109/sose50414.2020.9130526 · Cited by 8

Semi-automated Cross-Component Issue Management and Impact Analysis

Speth, S. (2021) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase51524.2021.9678830 · Cited by 10

Root Cause Analysis of Anomalies Based on Graph Convolutional Neural Network

Li, Z., Tu, Y., & Ma, Z. (2022) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s0218194022500395 · Cited by 8

A Systematic Review on Microservice TestingPreprint

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MicroNet: Operation Aware Root Cause Identification of Microservice System Anomalies

Yang, J., Guo, Y., Chen, Y., & Zhao, Y. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3387552 · Cited by 7

Causality Enhanced Graph Representation Learning for Alert-Based Root Cause Analysis

Yu, Z., Ouyang, Q., Pei, C., Wang, X., Chen, W., Su, L., Jiang, H., Wang, X., Li, J., & Pei, D. (2024) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid59990.2024.00018 · Cited by 7

Microservice logs analysis employing AI: A systematic literature review

Uddin, A., Weerasinghe, S., Gajewski, D., Akbarsharifi, M., Akbarsharifi, R., Stoner, C., Cerny, T., & He, S. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.112786 · Cited by 7

Making Fault Localization in Online Service Systems More Actionable and Interpretable

Xv, K., Guo, S., Li, H., Li, C., Chen, R., Li, X., & Jiang, H. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3714466 · Cited by 7

Failure Root Cause Analysis for Microservices, Explained

Soldani, J., Forti, S., & Antonio Brogi, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16092-9_6 · Cited by 7

Centrality Change Proneness: An Early Indicator of Microservice Architectural Degradation

Bakhtin, A., Esposito, M., Lenarduzzi, V., & Taibi, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-02138-0_16 · Cited by 6

Unsupervised root-cause identification of software bugs in 5G RAN

Sundqvist, T., Bhuyan, M. H., & Elmroth, E. (2022) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc49033.2022.9700501 · Cited by 5

Conceptual and Comparative Analysis of Application Metrics in Microservices

Pulcinelli, L. E. G., Pedroso, D. F., & Bruschi, S. M. (2023) · International Symposium on Computer Architecture and High Performance Computing Workshops (SBAC-PADW) · doi:10.1109/sbac-padw60351.2023.00028 · Cited by 5

An Approach of Automated Anomalous Microservice Ranking in Cloud-Native Environments

Zhang, Z., Li, B., Wang, J., & Liu, Y. (2021) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s0218194021400167 · Cited by 4

Architecture-Based Cross-Component Issue Management and Propagation Analysis

Speth, S. (2024) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3639478.3639814 · Cited by 4

Evidence-Gated Search: Controlling Operational Search Explosion in LLM-Driven Incident Response

Venkata, S. B. (2026) · Journal of Computer Science and Technology Studies · doi:10.32996/jcsts.2026.8.5.8 · Cited by 3

LLM4MDG: Leveraging Large Language Model to Construct Microservices Dependency Graph

Hu, J., Li, Y., Xiang, Z., Ma, L., Jia, X., & Huang, Q. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00128 · Cited by 3

Localization of the Root Cause of the Anomaly

Grusho, A. A., Grusho, N. A., Zabezhailo, M. I., & Timonina, E. E. (2021) · Automatic Control and Computer Sciences · doi:10.3103/s0146411621080137 · Cited by 3

RPCover: Recovering gRPC Dependency in Multilingual Projects

Fang, A., Zhou, R., Tang, X., & He, P. (2023) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00108 · Cited by 3

Network Analysis of Microservices: A Case Study on Alibaba Production Clusters

Khodabandeh, G., Ezaz, A., & Ezzati‐Jivan, N. (2024) · ACM/SPEC International Conference on Performance Engineering (ICPE) · doi:10.1145/3629527.3651842 · Cited by 2

SLIM: a Scalable and Interpretable Light-weight Fault Localization Algorithm for Imbalanced Data in Microservice

Ren, R., Yang, J., Yang, L., Gu, X., & Sun, L. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3694984 · Cited by 2

Dynamic QoS-Driven Framework for Co-Scheduling of Distributed Long-Running Applications on Shared Clusters

Zhu, J., Wang, H., Su, P., Wang, Y., & Pan, W. (2025) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2025.3571098 · Cited by 2

AmocRCA: At Most One Change Segmentation and Relative Correlation Ranking for Root Cause Analysis

Altenbernd, A., Zhi-yuan, W., & Kao, O. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3731612 · Cited by 2

ReconRCA: Root Cause Analysis in Microservices with Incomplete Metrics

Zhang, Z., Wang, J., Li, B., & Zhang, L. (2025) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws67624.2025.00024 · Cited by 3

Using development environment as code for enhancing developer experience: An action design research study

Ghanbari, H., Terimaa, T., & Koskinen, K. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.112803 · Cited by 2

Research on the architecture of digital enabling detection of data anomalies in intelligent distribution network

Chen, B. (2024) · International Conference on Machine Intelligence and Digital Applications (MIDA) · doi:10.1145/3662739.3673686 · Cited by 2

MicroHFRCL: A History Faults Based Root Cause Localization Framework in Microservice Systems

Zhang, L., Shi, Y., Qi, K., Wu, D. F., Wang, X., Yan, Z., & Chen, Z. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650929 · Cited by 2

Design and Implement of AIOps System Based on Knowledge Graph

Zhao, Y., Zheng, Y., Luo, H., Wei, D., Liu, C., & Chen, K. (2023) · International Conference on Electronics and Communication, Network and Computer Technology (ECNCT) · doi:10.1109/ecnct59757.2023.10281148 · Cited by 2

Dynamic Load Balancing in Distributed Storage Systems using Modified Whale Optimization Techniques

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Graph-Based Ensemble Learning for Enhanced Fault Localization in Microservices

Chen, R., Peng, F., Ji, X., Xiang, N., Lou, Y., Zhang, K., Pu, Y., & Wu, W. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831257 · Cited by 1

NetEventCause: Event-Driven Root Cause Analysis for Large Network System Without Topology

Yuan, Z., Ma, L., Wei, W., Zhu, X., Sun, M., Chen, D., & Ban, X. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3574316 · Cited by 1

Classification of Anomalies in Microservices Using an XGBoost-Based Approach With Data Balancing and Hyperparameter Tuning

Barata, L., Lopes, E., Inácio, P. R. M., & Freire, M. M. (2025) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2025.3622532 · Cited by 1

Root Cause Analysis Method Based on Large Language Models with Anchor-Guided Hierarchical Fusion

Zhou, L., Liu, A., Liu, H., He, M., Zhang, H., & Li, J. (2026) · International Conference on Communications, Information System and Computer Engineering (CISCE) · doi:10.1109/cisce69494.2026.11504885 · Cited by 1

AI-Enabled Infrastructure Management: A Comprehensive Framework and Empirical Analysis from Platform Engineering Perspective

Tadi, G., & Mittal, A. (2025) · Cyber Awareness and Research Symposium (CARS) · doi:10.1109/cars67163.2025.11337561 · Cited by 1

Digital twin-based integrated modeling and maintenance framework in the power grid data center scenario

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