ServiceAnomaly: An anomaly detection approach in microservices using distributed traces and profiling metrics
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
State-of-the-art anomaly detection solutions for microservice applications assume the application topology remains static over time and fail to account for dynamic changes in the application and its infrastructure.
“Nevertheless, state-of-the-art solutions assume the topology of the monitored application to remain static over time and fail to account for the dynamic changes the application, and the infrastructure it is deployed on, undergoes.”
From Toward Context-Aware Anomaly Detection for AIOps in Microservices Using Dynamic Knowledge Graphs (2026) · cites this paper as [29]
All related work in the domain evaluates their methodologies under varying user load, but the topology of the application is always assumed to be static.
“As explicitly pointed out by ServiceAnomaly [29], all related work in the domain evaluates their methodologies under varying user load, but the topology of the application is always assumed to be static.”
From Toward Context-Aware Anomaly Detection for AIOps in Microservices Using Dynamic Knowledge Graphs (2026) · cites this paper as [29]
ServiceAnomaly combines distributed traces and resource metric monitoring data into a Control Flow Graph, but this method is intrusive, and the constructed CFGs are limited to application layer dependencies and do not encompass metrics captured at the infrastructure layer.
“ServiceAnomaly [29] tackles this by combining distributed traces and resource metric monitoring data into a CFG. This method, however, is once again intrusive. Furthermore, the constructed CFGs are limited to the application layer dependencies and do not encompass metrics captured at the infrastructure layer.”
From Toward Context-Aware Anomaly Detection for AIOps in Microservices Using Dynamic Knowledge Graphs (2026) · cites this paper as [29]
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Comparing community structure identification
Danon, L., Dı́az-Guilera, A., Duch, J., & Arenas, À. (2005) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/2005/09/p09008 · Cited by 2,888 · see citing works
Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing
Vapnik, V. N., Golowich, S. E., & Smola, A. J. (1996) · Cited by 2,776 · see citing works
DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning
Du, M., Li, F., Zheng, G., & Srikumar, V. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134015 · Cited by 1,835 · see citing works
Mean Absolute Percentage Error for regression models
Myttenaere, A. D., Golden, B., Grand, B. L., & Fabrice Rossi, A. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.12.114 · Cited by 1,541 · see citing works
A (sub)graph isomorphism algorithm for matching large graphs
Cordella, L. P., Foggia, P., Sansone, C., & Vento, M. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.75 · Cited by 1,486 · see citing works
An online algorithm for segmenting time series
Keogh, E., Chu, S., Hart, D., & Pazzani, M. (2002) · Proceedings 2001 IEEE International Conference on Data Mining · doi:10.1109/icdm.2001.989531 · Cited by 1,149 · see citing works
Proceedings 2001 IEEE International Conference on Data Mining
(2001) · Proceedings · doi:10.1109/icdm.2001.989492 · Cited by 776 · 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
Isomorphism of graphs of bounded valence can be tested in polynomial time
Luks, E. M. (1982) · Journal of Computer and System Sciences · doi:10.1016/0022-0000(82)90009-5 · Cited by 660 · see citing works
Hands-On Machine Learning with R
Boehmke, B., & Greenwell, B. (2019) · Chapman & Hall/CRC eBooks · doi:10.1201/9780367816377 · Cited by 609 · see citing works
(1997) · SZTAKI Publication Repository (Hungarian Academy of Sciences) · Cited by 598 · see citing works
Yerushalmy, J. (1947) · Public Health Reports (1896-1970) · doi:10.2307/4586294 · Cited by 465 · see citing works
Comparison of co-expression measures: mutual information, correlation, and model based indices
Song, L., Langfelder, P., & Horvath, S. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-328 · Cited by 460 · see citing works
Containerization and the PaaS Cloud
Pahl, C. (2015) · IEEE Cloud Computing · doi:10.1109/mcc.2015.51 · Cited by 457 · see citing works
The pains and gains of microservices: A Systematic grey literature review
Soldani, J., Tamburri, D. A., & van den Heuvel, W. (2018) · Journal of Systems and Software · doi:10.1016/j.jss.2018.09.082 · Cited by 438 · see citing works
Zhou, X., Peng, X., Xie, T., Sun, J., Ji, C., Li, W., & Ding, D. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2018.2887384 · Cited by 410 · see citing works
Proceedings of the 44th International Conference on Software Engineering
(2022) · doi:10.1145/3510003 · Cited by 327 · see citing works
Kratzke, N., & Quint, P.-C. (2017) · Journal of Systems and Software · doi:10.1016/j.jss.2017.01.001 · Cited by 281 · see citing works
MicroRCA: Root Cause Localization of Performance Issues in Microservices
Wu, L., Tordsson, J., Elmroth, E., & Kao, O. (2020) · NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium · doi:10.1109/noms47738.2020.9110353 · Cited by 265 · see citing works
Unsupervised Detection of Microservice Trace Anomalies through Service-Level Deep Bayesian Networks
Liu, P., Xu, H., Ouyang, Q., Jiao, R., Chen, Z., Zhang, S., Yang, J., Mo, L., Zeng, J., Xue, W., et al. (2020) · 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) · doi:10.1109/issre5003.2020.00014 · Cited by 252 · see citing works
Zhang, C., Peng, X., Sha, C., Zhang, K., Fu, Z., Wu, X., Lin, Q., & Zhang, D. (2022) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3510003.3510180 · Cited by 214 · see citing works
2016 IEEE International Conference on Mechatronics and Automation
(2016) · doi:10.1109/icma38789.2016 · Cited by 162 · see citing works
von Kistowski, J., Eismann, S., Schmitt, N., et al. (2018) · IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS) · doi:10.1109/mascots.2018.00030 · Cited by 159 · see citing works
A novel feature selection method based on normalized mutual information
Vinh, L. T., Lee, S., Park, Y.-T., et al. (2011) · Applied Intelligence · doi:10.1007/s10489-011-0315-y · Cited by 154 · see citing works
Anomaly Detection Using Program Control Flow Graph Mining From Execution Logs
Nandi, A., Mandal, A., Atreja, S., Dasgupta, G. B., & Bhattacharya, S. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939712 · Cited by 154 · see citing works
A Spatiotemporal Deep Learning Approach for Unsupervised Anomaly Detection in Cloud Systems
He, Z., Chen, P., Li, X., Wang, Y., Yu, G., Chen, C., Li, X., & Zheng, Z. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027736 · Cited by 151 · see citing works
Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data
Lee, C., Yang, T., Chen, Z., Su, Y., & Lyu, M. R. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00150 · Cited by 149 · see citing works
Statistical Models: Theory and Practice
Bammann, K. (2006) · Biometrics · doi:10.1111/j.1541-0420.2006.00588_4.x · Cited by 135 · see citing works
2009 Ninth IEEE International Conference on Data Mining
(2009) · doi:10.1109/icdm16247.2009 · Cited by 131 · see citing works
AutoMAP: Diagnose Your Microservice-based Web Applications Automatically
Ma, M., Xu, J., Wang, Y., Chen, P., Zhang, Z., & Wang, P. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380111 · Cited by 129 · see citing works
Graph-based root cause analysis for service-oriented and microservice architectures
Brandón, Á., Solé, M., Huélamo, A., Solans, D., Pérez, M. S., & Muntés-Mulero, V. (2020) · Journal of Systems and Software · doi:10.1016/j.jss.2019.110432 · Cited by 121 · see citing works
Enjoy your observability: an industrial survey of microservice tracing and analysis
Li, B., Peng, X., Xiang, Q., Wang, H., Xie, T., Sun, J., & Liu, X. (2022) · Empirical Software Engineering · doi:10.1007/s10664-021-10063-9 · Cited by 121 · see citing works
An anomaly detection system based on variable N-gram features and one-class SVM
Khreich, W., Khosravifar, B., Hamou‐Lhadj, A., & Talhi, C. (2017) · Information and Software Technology · doi:10.1016/j.infsof.2017.07.009 · Cited by 115 · see citing works
Self-citationZuo, Y., Wu, Y., Min, G., Huang, C., & Pei, K. (2020) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2020.2966615 · Cited by 94 · see citing works
A mutual information approach to calculating nonlinearity
Smith, R. D. (2015) · Stat · doi:10.1002/sta4.96 · Cited by 83 · 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
Detecting anomalies in microservices with execution trace comparison
Meng, L., Ji, F., Sun, Y., & Wang, T. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.040 · Cited by 70 · see citing works
FD4C: Automatic Fault Diagnosis Framework for Web Applications in Cloud Computing
Wang, T., Zhang, W., Ye, C., Wei, J., Zhong, H., & Huang, T. (2015) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2015.2430834 · Cited by 68 · see citing works
Execution anomaly detection in large-scale systems through console log analysis
Bao, L., Li, Q., Lu, P., Lu, J., Ruan, T., & Zhang, K. (2018) · Journal of Systems and Software · doi:10.1016/j.jss.2018.05.016 · Cited by 67 · see citing works
2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)
Hong Va Leong, S. S. S., Shahriar, H. V. L. A. S. S. S. A. Y. T. A. A. C. A. H. K. A. D. T. A. J.-J. Y. A. H., Leong, H. V., et al. (2022) · doi:10.1109/compsac54236.2022 · Cited by 60 · see citing works
Workflow-Aware Automatic Fault Diagnosis for Microservice-Based Applications With Statistics
Wang, T., Zhang, W., Xu, J., & Gu, Z. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3022028 · Cited by 54 · see citing works
Samir, A., & Pahl, C. (2019) · 2019 7th International Conference on Future Internet of Things and Cloud (FiCloud) · doi:10.1109/ficloud.2019.00036 · Cited by 49 · see citing works
Proceedings 10th International Workshop on Program Comprehension
(2002) · doi:10.1109/wpc.2002 · Cited by 46 · see citing works
Visualizing Distributed System Executions
Beschastnikh, I., Liu, P., Xing, A., Wang, P., Brun, Y., & Ernst, M. D. (2020) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3375633 · Cited by 45 · see citing works
Yu, G., Huang, Z., & Chen, P. (2023) · Journal of Software: Evolution and Process · doi:10.1002/smr.2413 · Cited by 44 · cited 3× · see citing works
“Studies have shown that the analysis of distributed traces is useful for program comprehension [10], [11], fault diagnosis [12], [13], root cause analysis [14], [15], and anomaly detection [16], [17], the focus of this paper.”
BackgroundJiang, M., Munawar, M. A., Reidemeister, T., & Ward, P. A. S. (2011) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2011.16 · Cited by 37 · see citing works
Ayers, A., Schooler, R., Metcalf, C., et al. (2005) · Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation · doi:10.1145/1065010.1065035 · Cited by 35 · see citing works
Jacob, S., Qiao, Y., Ye, Y., et al. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102728 · Cited by 33 · see citing works
Anomaly Detection Techniques Based on Kappa-Pruned Ensembles
Islam, M. S., Khreich, W., & Hamou‐Lhadj, A. (2018) · IEEE Transactions on Reliability · doi:10.1109/tr.2017.2787138 · Cited by 31 · see citing works
Self-citationStratified sampling of execution traces: Execution phases serving as strata
Pirzadeh, H., Shanian, S., Hamou‐Lhadj, A., et al. (2012) · Science of Computer Programming · doi:10.1016/j.scico.2012.11.002 · Cited by 22 · see citing works
Self-citationKale, V. S. (2019) · CRC Press eBooks · doi:10.1201/9781351029148-18 · Cited by 18 · see citing works
Graph Based Representations in Pattern Recognition
Jolion, J.-M., & Kropatsch, W. G. (1998) · Computing. Supplementum · doi:10.1007/978-3-7091-6487-7 · Cited by 14 · see citing works
Trace-based Intelligent Fault Diagnosis for Microservices with Deep Learning
Chen, H., Wei, K., Li, A., Wang, T., & Zhang, W. (2021) · 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC) · doi:10.1109/compsac51774.2021.00121 · Cited by 14 · see citing works
System performance anomaly detection using tracing data analysis
Kohyarnejadfard, I., Shakeri, M., & Aloise, D. (2019) · International Conference on Computer and Technology Applications (ICCTA) · doi:10.1145/3323933.3324085 · Cited by 13 · see citing works
Fault Localization for Microservice Applications with System Logs and Monitoring Metrics
Zhang, Q., Jia, T., Wu, Z., Wu, Q., Jia, L., Li, D., Tao, Y., & Xiao, Y. (2022) · 2022 7th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) · doi:10.1109/icccbda55098.2022.9778893 · Cited by 13 · see citing works
LongTale: Toward Automatic Performance Anomaly Explanation in Microservices
Li, R., Du, M., Wang, Z., Chang, H., Mukherjee, S., & Eide, E. N. (2022) · ACM/SPEC International Conference on Performance Engineering (ICPE) · doi:10.1145/3489525.3511675 · Cited by 12 · see citing works
2012 Sixth International Conference on Research Challenges in Information Science (RCIS)
Rolland, C., Castro, J. B., & Pastor, O. E. (2012) · doi:10.1109/rcis19694.2012 · Cited by 11 · see citing works
Dashuber, V., & Philippsen, M. (2022) · Information and Software Technology · doi:10.1016/j.infsof.2022.106989 · Cited by 9 · see citing works
Simple and efficient tree pattern matching
Feruglio, V., & Alejandro, G. (2000) · Cited by 8 · see citing works
Anomaly Detection and Analysis for Reliability Management Clustered Container Architectures
SAMIR, A., El Ioini, N., Fronza, I., et al. (2019) · Open Repository and Bibliography (University of Luxembourg) · Cited by 6 · see citing works
Loss Data Analysis: The Maximum Entropy Approach
Gzyl, H., Mayoral, S., & Gomes-Gonçalves, E. (2018) · Cited by 2 · see citing works
Graph Neural Network-Based Anomaly Detection for Trace of MicroservicesPreprint
Xu, P., Gao, X., & Zhang, Z. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4111928 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
Toward Context-Aware Anomaly Detection for AIOps in Microservices Using Dynamic Knowledge Graphs
Moens, P., Steenwinckel, B., Ongenae, F., Volckaert, B., & Hoecke, S. V. (2026) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2026.3652304 · Cited by 6 · see citing works
“As explicitly pointed out by ServiceAnomaly [29], all related work in the domain evaluates their methodologies under varying user load, but the topology of the application is always assumed to be static.”
Influential
- Authors
- Panahandeh, M., Hamou-Lhadj, A., Hamdaqa, M., & Miller, J.
- Affiliations
- University of Alberta (CA)
- Polytechnique Montréal (CA)
- Corresponding author
- Panahandeh, Mahsa
- Venue
- Journal of Systems and Software
- Publisher
- Elsevier BV
- Published
- 2024-03
- Volume
- 209
- Pages
- 111917
- ISSN
- 0164-1212 (ISSN-L)
- OpenAlex
- W4389752973
- Cited by
- 49
- References
- 74
- Citations by year
- 2026: 18 · 2025: 24 · 2024: 7
- FWCI
- 5.44
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Systems and Software (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- PolyPublie (École Polytechnique de Montréal) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- anomaly detection, microservices, context propagation
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
