Graph-based root cause analysis for service-oriented and microservice architectures
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Graph-based approaches are widely applied to anomaly detection and root-cause identification in microservices, as they capture the complex interdependencies among services and enable structured analysis of propagation patterns.
“Graph-based approaches are widely applied to anomaly detection and root-cause identification in microservices, as they capture the complex interdependencies among services and enable structured analysis of propagation patterns [1].”
From Anomaly detection and root-cause identification in microservices: a survey (2026) · cites this paper as [1]
The Abstract and Discussion describe graph representations of interconnected microservices for root-cause analysis, including anomalies that propagate to other elements. But the framework analyzes anomalies after they are detected; the paper does not establish that graph-based approaches are widely applied to both anomaly detection and root-cause identification in microservices. (LLM, conf=0.85)
Experimental — accuracy not yet measuredWhile ML approaches provide powerful tools for anomaly detection and root-cause analysis, they face challenges of explainability and data availability in fast-evolving microservice environments.
“While they provide powerful tools for anomaly detection and root-cause analysis, they face challenges of explainability and data availability in fast-evolving microservice environments [1].”
From Anomaly detection and root-cause identification in microservices: a survey (2026) · cites this paper as [1]
Graph-based methods capture structural and causal relationships effectively, but may be overly complex for isolated failures where simpler techniques may be more efficient.
“While graph-based methods capture structural and causal relationships effectively, they may be overly complex for isolated failures (e.g., single node anomalies) where simpler techniques may be more efficient [1].”
From Anomaly detection and root-cause identification in microservices: a survey (2026) · cites this paper as [1]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,182 · see citing works
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687 · see citing works
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,726 · see citing works
Proceedings of the 25th international conference on Machine learning - ICML '08
(2008) · doi:10.1145/1390156 · Cited by 6,865 · see citing works
Koller, D., & Friedman, N. (2009) · The MIT Press eBooks · Cited by 6,653 · see citing works
A working guide to boosted regression trees
Elith, J., Leathwick, J. R., & Hastie, T. (2008) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2008.01390.x · Cited by 6,484 · see citing works
(2009) · Cited by 6,153 · see citing works
The Hadoop Distributed File System
Shvachko, K. V., Kuang, H., Radia, S., & Chansler, R. J. (2010) · Digest of papers - IEEE Symposium on Mass Storage Systems · doi:10.1109/msst.2010.5496972 · Cited by 4,911 · see citing works
Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J. H., McCauley, M., Franklin, M. J. M., Shenker, S. J., & Stoica, I. G. (2012) · Cited by 3,622 · see citing works
Docker: lightweight Linux containers for consistent development and deployment
Merkel, D. (2014) · Linux journal · Cited by 3,277 · see citing works
Verbs semantics and lexical selection
Wu, Z., & Palmer, M. S. (1994) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/981732.981751 · Cited by 3,157 · see citing works
Graph based anomaly detection and description: a survey
Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504 · see citing works
A Survey on Software Fault Localization
Wong, W. E., Gao, R., Li, Y., Abreu, R., & Wotawa, F. (2016) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2521368 · Cited by 1,110 · see citing works
Failure diagnosis using discrete-event models
Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. (1996) · IEEE Transactions on Control Systems Technology · doi:10.1109/87.486338 · Cited by 737 · see citing works
Microservices Architecture Enables DevOps: Migration to a Cloud-Native Architecture
Balalaie, A., Heydarnoori, A., & Jamshidi, P. (2016) · IEEE Software · doi:10.1109/ms.2016.64 · Cited by 735 · see citing works
Proceedings of the 32nd annual meeting on Association for Computational Linguistics -
Pustejovsky, J. (1994) · doi:10.3115/981732 · Cited by 732 · see citing works
Marginalized kernels between labeled graphs
Kashima, H., Tsuda, K., & Inokuchi, A. (2003) · Cited by 710 · see citing works
Approximate graph edit distance computation by means of bipartite graph matching
Riesen, K., & Horst Bunke, A. (2008) · Image and Vision Computing · doi:10.1016/j.imavis.2008.04.004 · Cited by 651 · see citing works
Dynamic load balancing on Web-server systems
Cardellini, V., Colajanni, M., & Yu, P. S. (1999) · IEEE Internet Computing · doi:10.1109/4236.769420 · Cited by 562 · see citing works
European Semantic Web Conference 2009
Nielsen, F. Å. (2009) · Cited by 553 · see citing works
Caetano, T. S., McAuley, J., Cheng, L., Le, Q. V., & Smola, A. J. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.28 · Cited by 390 · see citing works
Patterns: Service Oriented Architecture And Web Services
Redbooks, I. (2004) · Cited by 356 · see citing works
Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation
Feamster, N., & Mogul, J. (2013) · Cited by 348 · see citing works
GRAPH MATCHING AND LEARNING IN PATTERN RECOGNITION IN THE LAST 10 YEARS
Foggia, P., Percannella, G., & Vento, M. (2013) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001414500013 · Cited by 341 · see citing works
Adding Virtualization Capabilities to the Grid’5000 Testbed
Balouek‐Thomert, D., Carpen-Amarie, A., Charrier, G., Desprez, F., Jeannot, E., Jeanvoine, E., Lèbre, A., Margery, D., Niclausse, N., Nussbaum, L., et al. (2013) · Communications in computer and information science · doi:10.1007/978-3-319-04519-1_1 · Cited by 320 · see citing works
Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs
Chein, M., & Mugnier, M.-L. (2008) · HAL (Le Centre pour la Communication Scientifique Directe) · doi:10.1007/978-1-84800-286-9 · Cited by 305 · see citing works
Evaluation of Docker as Edge computing platform
Ismail, B. I., Goortani, E. M., Karim, M. B. A., Tat, W. M., Setapa, S., Luke, J. Y., & Hoe, O. H. (2015) · IEEE Conference on Open Systems · doi:10.1109/icos.2015.7377291 · Cited by 259 · see citing works
Efficient matching and indexing of graph models in content-based retrieval
Berretti, S., Bimbo, A. D., & Vicario, E. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.954600 · Cited by 228 · see citing works
A Network-aware Virtual Machine Placement and Migration Approach in Cloud Computing
Piao, J. T., & Yan, J. H. (2010) · doi:10.1109/gcc.2010.29 · Cited by 219 · see citing works
Application of artificial neural networks in process fault diagnosis
Sorsa, T., & Koivo, H. N. (1993) · Automatica · doi:10.1016/0005-1098(93)90090-g · Cited by 192 · see citing works
2009 IEEE International Conference on Cloud Computing
(2009) · doi:10.1109/cloud16188.2009 · Cited by 187 · see citing works
Li, M., Tan, J. R., Wang, Y., Zhang, L., & Salapura, V. (2015) · ACM International Conference on Computing Frontiers (CF) · doi:10.1145/2742854.2747283 · Cited by 183 · see citing works
Gremlin: Systematic Resilience Testing of Microservices
Heorhiadi, V., Rajagopalan, S., Jamjoom, H. T., Reiter, M. K., & Sekar, V. (2016) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2016.11 · Cited by 141 · see citing works
The serverless trilemma: function composition for serverless computing
Baldini, I., Cheng, P., Fink, S. J., Mitchell, N., Muthusamy, V., Rabbah, R., Suter, P., & Tardieu, O. (2017) · ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!) · doi:10.1145/3133850.3133855 · Cited by 130 · see citing works
Serverless computing for container-based architectures
Pérez, A. G., Moltó, G., Caballer, M., & Calatrava, A. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.01.022 · Cited by 130 · see citing works
APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN PROCESS FAULT DIAGNOSIS
Sorsa, T., & Koivo, H. N. (1992) · Elsevier eBooks · doi:10.1016/b978-0-08-041275-7.50065-x · Cited by 128 · see citing works
A Natural Language Query Interface to Structured Information
Tablan, V., Damljanović, D., & Bontcheva, K. (2008) · Springer eBooks · doi:10.1007/978-3-540-68234-9_28 · Cited by 126 · see citing works
Analysis of issues with load balancing algorithms in hosted (cloud) environments
Radojevic, B., & Žagar, M. (2011) · International Convention on Information and Communication Technology, Electronics and Microelectronics · Cited by 105 · see citing works
Graph matching and clustering using spectral partitions
Qiu, H., & Hancock, E. R. (2005) · Pattern Recognition · doi:10.1016/j.patcog.2005.06.014 · Cited by 102 · see citing works
Graph embedding in vector spaces by node attribute statistics
Gibert, J., Valveny, E., & Horst Bunke, A. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.01.009 · Cited by 91 · see citing works
Self-Organizing Maps for Learning the Edit Costs in Graph Matching
Neuhaus, M., & Bunke, H. (2005) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2005.846635 · Cited by 80 · see citing works
Survey on Models and Techniques for Root-Cause Analysis
Solé, M., Muntés-Mulero, V., Rana, A. I., & Estrada, G. (2017) · arXiv · doi:10.48550/arxiv.1701.08546 · Cited by 74 · see citing works
Self-citation0006/2011 - Threats to Validity in Search-based Software Engineering Empirical Studies
Barros, M. D. O., Dias‐Neto, A. C., & Gen, R. (2011) · RelaTe-DIA · Cited by 66 · see citing works
Using Docker: Developing and Deploying Software with Containers
Mouat, A. (2015) · Cited by 65 · see citing works
Web Server Farm in the Cloud: Performance Evaluation and Dynamic Architecture
Liu, H., & Wee, S. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10665-1_34 · Cited by 63 · see citing works
Tseitlin, A. (2013) · Communications of the ACM · doi:10.1145/2492007.2492022 · Cited by 61 · see citing works
A Dashboard for Microservice Monitoring and Management
Mayer, B., & Weinreich, R. (2017) · doi:10.1109/icsaw.2017.44 · Cited by 61 · see citing works
Fault diagnosis on production systems with support vector machine and decision trees algorithms
Demetgül, M. (2012) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-012-4639-5 · Cited by 58 · see citing works
Automatic Model-Driven Recovery in Distributed Systems
Joshi, K., Sanders, W. H., Hiltunen, M. A., & Schlichting, R. D. (2006) · IEEE Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/reldis.2005.11 · Cited by 53 · see citing works
Tseitlin, A. (2013) · Queue · doi:10.1145/2493944.2499552 · Cited by 51 · see citing works
Applying Bayesian networks to fault diagnosis
Kirsch, & Kroschel (1994) · The proceedings of the IEEE Conference on Control Applications/The proceedings of the ... IEEE Conference on Control Applications · doi:10.1109/cca.1994.381203 · Cited by 38 · see citing works
Decision Guidance Models for Microservice Monitoring
Haselböck, S., & Weinreich, R. (2017) · doi:10.1109/icsaw.2017.31 · Cited by 38 · see citing works
Düllmann, T. F., & van Hoorn, A. (2017) · doi:10.1145/3053600.3053627 · Cited by 36 · see citing works
Mining Connection Pathways for Marked Nodes in Large Graphs
Akoglu, L., Chau, D. H., Vreeken, J., Tatti, N., Tong, H., & Faloutsos, C. (2013) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972832.5 · Cited by 35 · see citing works
METRICS FOR ATTRIBUTED GRAPHS BASED ON THE MAXIMAL SIMILARITY COMMON SUBGRAPH
Hidović, D., & Pelillo, M. (2004) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001404003216 · Cited by 31 · see citing works
High efficiency and quality: large graphs matching
Zhu, Y., Qin, L., Yu, J. X., Ke, Y., & Lin, X. (2012) · The VLDB Journal · doi:10.1007/s00778-012-0292-8 · Cited by 30 · see citing works
Root cause analysis in IT infrastructures using ontologies and abduction in Markov Logic Networks
Schoenfisch, J., Meilicke, C., von Stülpnagel, J., Jens Ortmann, A., & Stuckenschmidt, H. (2017) · Information Systems · doi:10.1016/j.is.2017.11.003 · Cited by 23 · see citing works
Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion
Binder, W., Italy, I. '. A. I. C. O. P. E. 2. L., Association for Computing Machinery-Digital Library, ACM Special Interest Group on Measurement and Evaluation, ACM Special Interest Group on Software Engineering, & SIGs, A. (2017) · doi:10.1145/3053600 · Cited by 23 · see citing works
Networking in Containers and Container Clusters
Marmol, V., Jnagal, R., & Hockin, T. (2015) · Cited by 21 · see citing works
Estrada, R., & Ruiz, I. (2016) · Apress eBooks · doi:10.1007/978-1-4842-2175-4 · Cited by 21 · see citing works
Model-Based Debugging using Multiple Abstract ModelsPreprint
Mayer, W., & Stumptner, M. (2003) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0309030 · Cited by 20 · see citing works
Diagnosis of Service Failures by Trace Analysis with Partial Knowledge
Mayer, W., FRIEDRICH, G. E., & Stumptner, M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-17358-5_23 · Cited by 18 · see citing works
Leung, A., Spyker, A., & Bozarth, T. (2018) · Communications of the ACM · doi:10.1145/3152529 · Cited by 15 · see citing works
Cloud Computing and Services Science
Helfert, M., Desprez, F., Ferguson, D., & Leymann, F. (2014) · Communications in computer and information science · doi:10.1007/978-3-319-11561-0 · Cited by 12 · see citing works
Sieve: Actionable Insights from Monitored Metrics in MicroservicesPreprint
Thalheim, J., Rodrigues, A., Akkuş, İ. E., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.06686 · Cited by 11 · see citing works
Performance Anomaly Detection In Microservice Architectures Under Continuous Change
Düllmann, T. F. (2017) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.226060 · Cited by 6 · see citing works
Torlak, E., & SPLASH '17: Conference on Systems, P. (2017) · doi:10.1145/3133850 · Cited by 5 · see citing works
Graph-based Anomaly Detection for IoT Microservices
Aubet, F.-X., Pahl, M., Liebald, S., & Norouzian, M. (2018) · doi:10.13140/rg.2.2.22381.69609 · Cited by 3 · see citing works
Flagged first, then by citations
1 of the 72 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- A. Leung, A. Spyker, T. Bozarth, Titus: introducing containers to the netflix cloud, Communications of the ACM 61 (2) (2018) 38-45.
- M. A. Marvasti, A. V. Poghosyan, A. N. Harutyunyan, N. M. Grigoryan, An anomaly event correlation engine: Identifying root causes, bottlenecks, and black swans in it environments, VMware Technical Journal 2 (1) (2013) 35-45.
- J. Boyan, A. W. Moore, Learning evaluation functions to improve optimization by local search, Journal of Machine Learning Research 1 (Nov) (2000) 77-112.
Cited by
Sorted by citations
Soldani, J., & Brogi, A. (2023) · ACM Computing Surveys · doi:10.1145/3501297 · Cited by 256 · see citing works
MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systems
Liu, D., He, C., Peng, X., Lin, F., Zhang, C., Gong, S., Li, Z., Ou, J., & Wu, Z. (2021) · 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip52600.2021.00043 · Cited by 192 · see citing works
Robust Failure Diagnosis of Microservice System Through Multimodal Data
Zhang, S., Jin, P., Lin, Z., Sun, Y., Zhang, B., Xia, S., Li, Z., Zhong, Z., Ma, M., Jin, W., et al. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3290018 · Cited by 118 · see citing works
Xin, R., Chen, P., & Zhao, Z. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111724 · Cited by 116 · see citing works
Groot: An Event-graph-based Approach for Root Cause Analysis in Industrial Settings
Wang, H., Wu, Z., Jiang, H., Huang, Y., Wang, J., Köprü, S., & Xie, T. (2021) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase51524.2021.9678708 · Cited by 105 · see citing works
A Survey on Network Simulators, Emulators, and Testbeds Used for Research and Education
Gomez, J., Kfoury, E. F., Crichigno, J., & Srivastava, G. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.110054 · Cited by 89 · see citing works
On Microservice Analysis and Architecture Evolution: A Systematic Mapping Study
Bushong, V., Abdelfattah, A. S., Maruf, A. A., Das, D., Lehman, A., Jaroszewski, E., Coffey, M., Cerny, T., Frajták, K., Tisnovsky, P., et al. (2021) · Applied Sciences · doi:10.3390/app11177856 · Cited by 78 · see citing works
Actionable and interpretable fault localization for recurring failures in online service systems
Li, Z., Zhao, N., Li, M., Lu, X., Wang, L., Chang, D., Nie, X., Cao, L., Zhang, W., Sui, K., et al. (2022) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3540250.3549092 · Cited by 75 · see citing works
MicroDiag: Fine-grained Performance Diagnosis for Microservice Systems
Wu, L., Tordsson, J., Bogatinovski, J., Elmroth, E., & Kao, O. (2021) · 2021 IEEE/ACM International Workshop on Cloud Intelligence (CloudIntelligence) · doi:10.1109/cloudintelligence52565.2021.00015 · Cited by 72 · see citing works
Pham, L., Ha, H., & Zhang, H. (2024) · Proceedings of the ACM on software engineering. · doi:10.1145/3660805 · Cited by 60 · see citing works
Failure Diagnosis in Microservice Systems: A Comprehensive Survey and Analysis
Zhang, S., Xia, S., Fan, W., Shi, B., Xiong, X., Zhong, Z., Ma, M., Sun, Y., & Pei, D. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3715005 · Cited by 57 · see citing works
Adaptive performance anomaly detection for online service systems via pattern sketching
Chen, Z., Liu, J., Su, Y., Zhang, H., Ling, X., Yang, Y., & Lyu, M. R. (2022) · Proceedings of the 44th International Conference on Software Engineering · doi:10.1145/3510003.3510085 · Cited by 52 · see citing works
Jin, M., Lv, A., Zhu, Y., Wen, Z., Zhong, Y., Zhao, Z., Wu, J., Li, H., He, H., & Chen, F. (2020) · IEEE Access · doi:10.1109/access.2020.3044610 · Cited by 50 · see citing works
Panahandeh, M., Hamou-Lhadj, A., Hamdaqa, M., & Miller, J. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111917 · Cited by 49 · see citing works
Gu, S., Rong, G., Ren, T., Zhang, H., Shen, H., Yu, Y., Li, X., Ouyang, J., & Chen, C. (2023) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2023.3241299 · Cited by 46 · see citing works
Mining Root Cause Knowledge from Cloud Service Incident Investigations for AIOps
Saha, A., & Hoi, S. C. H. (2022) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1145/3510457.3513030 · Cited by 44 · see citing works
KGroot: A knowledge graph-enhanced method for root cause analysis
Wang, T., Qi, G., & Wu, T. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124679 · Cited by 42 · see citing works
Performance Diagnosis in Cloud Microservices Using Deep Learning
Li, W., Bogatinovski, J., Nedelkoski, S., Tordsson, J., & Kao, O. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-76352-7_13 · Cited by 41 · see citing works
Visualizing Microservice Architecture in the Dynamic Perspective: A Systematic Mapping Study
Gortney, M., Harris, P., Cerny, T., Maruf, A. A., Bureš, M., Taibi, D., & Tisnovsky, P. (2022) · IEEE Access · doi:10.1109/access.2022.3221130 · Cited by 40 · see citing works
Ding, R., Zhang, C., Wang, L., Xu, Y., Ma, M., Wu, X., Zhang, M., Chen, Q., Gao, X., Gao, X., et al. (2023) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3611643.3613864 · Cited by 38 · see citing works
Bhanage, D. A., Pawar, A., & Kotecha, K. V. (2021) · IEEE Access · doi:10.1109/access.2021.3128283 · Cited by 37 · see citing works
Calderón, H., Mendoza, L., Vargas-Lombardo, M., Gomez-Pulido, J. M., Rodrı́guez-Puyol, D., Sención, G., & Polo-Luque, M.-L. (2021) · Applied Sciences · doi:10.3390/app11104350 · Cited by 37 · see citing works
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 34 · see citing works
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 · see citing works
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 · see citing works
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 31 · see citing works
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 27 · see citing works
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 · see citing works
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 · see citing works
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 22 · see citing works
Cinque, M., Corte, R. D., & Pecchia, A. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103515 · Cited by 22 · see citing works
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 · see citing works
Zeng, R., Hou, X., Zhang, L., Li, C., Zheng, W., & Guo, M. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-020-0072-3 · Cited by 21 · see citing works
Chen, Y., Xu, D., Chen, N., & Wu, X. (2022) · Information and Software Technology · doi:10.1016/j.infsof.2022.107083 · Cited by 21 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
XAI-LCS: Explainable AI-Based Fault Diagnosis of Low-Cost Sensors
Sinha, A., & Das, D. (2023) · IEEE Sensors Letters · doi:10.1109/lsens.2023.3330046 · Cited by 14 · see citing works
Diagnosing Performance Issues for Large-Scale Microservice Systems With Heterogeneous Graph
Lei, T., Lu, X., Zhang, S., Luan, J., Li, Y., Li, M., Li, Z., Yu, Q., Xie, H., Xu, R., et al. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3402172 · Cited by 13 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
A Root Cause Analysis Framework for IoT Based on Dynamic Causal Graphs Assisted by LLMs
Tang, L., Kou, E., Wang, W., & Chen, Q. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3578512 · Cited by 11 · see citing works
Ben-Shimol, L., Grolman, E., Elyashar, A., Maimon, I., Mimran, D., Brodt, O., Strassmann, M., Lehmann, H., Elovici, Y., & Shabtai, A. (2025) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2025.3598060 · Cited by 11 · see citing works
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 · see citing works
Intelligent Root Cause Localization in MicroService Systems: A Survey and New Perspectives
Fu, N., Cheng, G., Teng, Y., Dai, G., Yu, S., & Chen, Z. (2025) · ACM Computing Surveys · doi:10.1145/3736755 · Cited by 11 · see citing works
LEKG: A System for Constructing Knowledge Graphs from Log Extraction
Wang, F., Bundy, A., Li, X., Zhu, R., Nuamah, K., Xu, L., Mauceri, S., & Pan, J. Z. (2021) · International Joint Conference on Knowledge Graphs (IJCKG) · doi:10.1145/3502223.3502250 · Cited by 10 · see citing works
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 · see citing works
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 10 · see citing works
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 · see citing works
Towards Future Vehicle Diagnostics in Software-Defined Vehicles
Bickelhaupt, S., Hahn, M., Morozov, A. S., & Weyrich, M. (2024) · SAE International Journal of Advances and Current Practices in Mobility · doi:10.4271/2024-01-2981 · Cited by 8 · see citing works
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 · see citing works
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 8 · see citing works
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 · see citing works
A Systematic Review on Microservice TestingPreprint
Panahande, M., & Miller, J. (2023) · Research Square · doi:10.21203/rs.3.rs-3158138/v1 · Cited by 8 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 2 · see citing works
Ghanbari, H., Terimaa, T., & Koskinen, K. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.112803 · Cited by 2 · see citing works
Chen, B. (2024) · International Conference on Machine Intelligence and Digital Applications (MIDA) · doi:10.1145/3662739.3673686 · Cited by 2 · see citing works
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 · see citing works
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 · see citing works
Dynamic Load Balancing in Distributed Storage Systems using Modified Whale Optimization Techniques
(2023) · Journal of System and Management Sciences · doi:10.33168/jsms.2023.0130 · Cited by 2 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Tadi, G., & Mittal, A. (2025) · Cyber Awareness and Research Symposium (CARS) · doi:10.1109/cars67163.2025.11337561 · Cited by 1 · see citing works
Xiao, M., Cui, S. G., & Wang, L. (2026) · Cyber-Physical Energy Systems · doi:10.1016/j.cpes.2026.01.001 · Cited by 1 · see citing works
Towards Performance Management of Large-Scale Microservices Applications
Somashekar, G. (2023) · IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) · doi:10.1109/acsos-c58168.2023.00028 · Cited by 1 · see citing works
Microservices for Asset Tracking Based on Indoor Positioning System
Sasmita, D., & Kusuma, G. P. (2024) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/160162 · Cited by 1 · see citing works
SLIM: a Scalable Light-weight Root Cause Analysis for Imbalanced Data in Microservice
Ren, R., Yang, J., Yang, L., Gu, X., & Sun, L. (2024) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3639478.3643098 · Cited by 1 · see citing works
SinkFlow: Fast and traceable root-cause localization for multidimensional anomaly events
Hu, Z., Liu, L., Ma, L., & Yu, X. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109582 · Cited by 1 · see citing works
Proactive Fault Prediction in Microservices Applications Using Trace Logs and Monitoring Metrics
Kaushik, N., Kumar, H., Raj, V., & Garg, P. (2024) · International Conference on Progressive Innovations in Intelligent Systems and Data Science (ICPIDS) · doi:10.1109/icpids65698.2024.00070 · Cited by 1 · see citing works
Metadata For Root Cause Analysis
Grusho, A. A., Grusho, N. A., Zabezhailo, M. I., Timonina, E. E., & Senchilo, V. V. (2021) · European Conference on Modelling and Simulation (ECMS) · doi:10.7148/2021-0267 · Cited by 1 · see citing works
Anomaly detection and root-cause identification in microservices: a survey
Barata, L. M., Sequeira, S., Lopes, E., Inácio, P. R. M., & Freire, M. M. (2026) · Cluster Computing · doi:10.1007/s10586-026-06095-9
“While they provide powerful tools for anomaly detection and root-cause analysis, they face challenges of explain - ability and data availability in fast-evolving microservice environments [1].”
InfluentialUse of Contradictions in Data for Finding Implicit Failures in Computer Systems
Grusho, A. A., Grusho, N. A., Zabezhailo, M. I., & Timonina, E. E. (2021) · Automatic Control and Computer Sciences · doi:10.3103/s0146411621080149
Wireless Network Fault Localization via Metric Classification
Han, Y., Du, Q., & Xu, J. (2022) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass56207.2022.00049
Development of a control system for intermodular interaction in a virtual training complex
Nazarova, A., Volkov, A., Obukhov, A. D., & Вехтева, Н. А. (2023) · AIP conference proceedings · doi:10.1063/5.0166592
- Authors
- Brandón, Á., Solé, M., Huélamo, A., Solans, D., Pérez, M. S., & Muntés-Mulero, V.
- Affiliations
- Universidad Politécnica de Madrid (ES)
- Corresponding author
- Brandón, Álvaro
- Venue
- Journal of Systems and Software
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 159
- Pages
- 110432
- ISSN
- 0164-1212 (ISSN-L)
- OpenAlex
- W2980932398
- Cited by
- 121
- References
- 72
- Citations by year
- 2026: 15 · 2025: 17 · 2024: 30 · 2023: 20 · 2022: 16 · 2021: 18 · 2020: 5
- FWCI
- 5.34
- Type
- journal article
- Language
- en
- Open access
- green
- oa.upm.es
- Locations
- Journal of Systems and Software (publishedVersion)
- UPM Digital Archive (Technical University of Madrid) (submittedVersion)
- Funders
- European Commission
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- root cause analysis, service-oriented architecture, microservices, graph representation, anomaly detection, troubleshooting, software observability, graph comparison, distributed systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review