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Graph-based root cause analysis for service-oriented and microservice architectures
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“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]
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“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]
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“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]
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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 22,191
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,730
Proceedings of the 25th international conference on Machine learning - ICML '08
(2008) · doi:10.1145/1390156 · Cited by 6,865
Koller, D., & Friedman, N. (2009) · The MIT Press eBooks · Cited by 6,653
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
(2009) · Cited by 6,153
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
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
Verbs semantics and lexical selection
Wu, Z., & Palmer, M. S. (1994) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/981732.981751 · Cited by 3,305
Docker: lightweight Linux containers for consistent development and deployment
Merkel, D. (2014) · Linux journal · Cited by 3,191
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
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
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
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
Proceedings of the 32nd annual meeting on Association for Computational Linguistics -
Pustejovsky, J. (1994) · doi:10.3115/981732 · Cited by 732
Marginalized kernels between labeled graphs
Kashima, H., Tsuda, K., & Inokuchi, A. (2003) · Cited by 710
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
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
European Semantic Web Conference 2009
Nielsen, F. Å. (2009) · Cited by 553
Proceedings of the 2012 SIAM International Conference on Data Mining
(2012) · doi:10.1137/1.9781611972825 · Cited by 421
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
Patterns: Service Oriented Architecture And Web Services
Redbooks, I. (2004) · Cited by 356
Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation
Feamster, N., & Mogul, J. (2013) · Cited by 355
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
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
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
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
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
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
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
2009 IEEE International Conference on Cloud Computing
(2009) · doi:10.1109/cloud16188.2009 · Cited by 187
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
Proceedings of the 2004 SIAM International Conference on Data Mining
(2004) · doi:10.1137/1.9781611972740 · Cited by 149
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
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
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
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
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
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
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
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
Self-Organizing Maps for Learning the Edit Costs in Graph Matching
Neuhaus, M., & Horst Bunke, A. (2005) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2005.846635 · Cited by 80
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
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
Using Docker: Developing and Deploying Software with Containers
Mouat, A. (2015) · Cited by 65
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
Tseitlin, A. (2013) · Communications of the ACM · doi:10.1145/2492007.2492022 · Cited by 61
A Dashboard for Microservice Monitoring and Management
Mayer, B., & Weinreich, R. (2017) · doi:10.1109/icsaw.2017.44 · Cited by 61
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 59
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
Tseitlin, A. (2013) · Queue · doi:10.1145/2493944.2499552 · Cited by 51
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
Decision Guidance Models for Microservice Monitoring
Haselböck, S., & Weinreich, R. (2017) · doi:10.1109/icsaw.2017.31 · Cited by 38
Düllmann, T. F., & van Hoorn, A. (2017) · doi:10.1145/3053600.3053627 · Cited by 36
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
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
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
Root cause analysis in IT infrastructures using ontologies and abduction in Markov Logic Networks
Schoenfisch, J., Meilicke, C., von Stülpnagel, J., Ortmann, J., & Stuckenschmidt, H. (2017) · Information Systems · doi:10.1016/j.is.2017.11.003 · Cited by 24
Model-Based Debugging using Multiple Abstract ModelsPreprint
Mayer, W., & Stumptner, M. (2003) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0309030 · Cited by 24
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
Networking in Containers and Container Clusters
Marmol, V., Jnagal, R., & Hockin, T. (2015) · Cited by 21
Estrada, R., & Ruiz, I. (2016) · Apress eBooks · doi:10.1007/978-1-4842-2175-4 · Cited by 21
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
Leung, A., Spyker, A., & Bozarth, T. (2018) · Communications of the ACM · doi:10.1145/3152529 · Cited by 15
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
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
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
Torlak, E., & SPLASH '17: Conference on Systems, P. (2017) · doi:10.1145/3133850 · Cited by 5
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
Research works printed but not linked: 3
- Boyan, 2000, Learning evaluation functions to improve optimization by local search, J. Mach. Learn. Res., 1, 77
- Marvasti, 2013, An anomaly event correlation engine: identifying root causes, bottlenecks, and black swans in it environments, VMware Techn. J., 2, 35
- Russell, 2003, vol. 2
Cited by
Sorted by citations
Soldani, J., & Brogi, A. (2023) · ACM Computing Surveys · doi:10.1145/3501297 · Cited by 256
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
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
Xin, R., Chen, P., & Zhao, Z. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111724 · Cited by 116
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
Metadata
- 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 › Software › 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
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