AAMR: Automated Anomalous Microservice Ranking in Cloud-Native Environment
Open accessAbstract
Recently, it has become a trend for developers to build applications using the microservice architecture.The functionality of each application is divided into multiple independent microservices, which are interconnected to others.With the emergence of cloud-native technologies, such as Docker and Kubernetes, developers can achieve a consistent and scalable delivery for complex software applications.However, it is challenging to diagnose performance issues in microservices due to the complex runtime environments and the numerous metrics.In this paper, we propose a novel root cause analysis approach named AAMR.AAMR firstly constructs a service dependency graph based on real-time metrics.Next, it updates the anomaly weight of each microservice automatically.Finally, a PageRankbased random walk is applied for further ranking root causes, i.e., ranking potential problematic services.Experiments conducted on Kubernetes clusters show that the proposed approach achieves a good analysis result, which outperforms several state-of-the-art methods.
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.
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?
BIRCH clustering has been integrated into MicroDiag, MicroRCA, and AAMR, where it is applied to response times and service dependencies to identify anomalies and propagation paths.
“BIRCH clustering has been integrated into MicroDiag [97], MicroRCA [98], and AAMR [103], where it is applied to response times and service dependencies to identify anomalies and propagation paths.”
From Anomaly detection and root-cause identification in microservices: a survey (2026) · cites this paper as [103]
The Anomaly Detection section says AAMR uses BIRCH to cluster each microservice’s response times and identify anomalous nodes. The Service Dependency Graph Construction section says a separate graph, built from network connections, represents anomaly propagation; it does not say BIRCH is applied to service dependencies or identifies propagation paths. No page numbers are available in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredRecent studies highlight the importance of correlating system-level and application-level metrics, enabling earlier anomaly identification and more accurate root-cause localization.
“Recent studies also highlight the importance of correlating system-level and application-level metrics [103,104], enabling earlier anomaly identification and more accurate root-cause localization.”
From Anomaly detection and root-cause identification in microservices: a survey (2026) · cites this paper as [103]
The Introduction says AAMR uses both system-utilization and application-level metrics, and the Conclusion reports more accurate root-cause localization than comparison methods. The Localization Time Comparison reports faster root-cause localization, but the paper does not show that combining those metrics enables earlier anomaly identification; its Anomaly Detection section describes detecting anomalies from response time. Page numbers were not available in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredAAMR ranked anomalies in Service Dependency Graphs (SDGs) with personalized PageRank.
“AAMR [103] ranked anomalies in Service Dependency Graphs (SDGs) with personalized PageRank.”
From Anomaly detection and root-cause identification in microservices: a survey (2026) · cites this paper as [103]
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
Detecting large-scale system problems by mining console logs
Xu, W., Huang, L., Fox, A., Patterson, D., & Jordan, M. I. (2009) · Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles · doi:10.1145/1629575.1629587 · Cited by 1,332 · see citing works
Pinpoint: problem determination in large, dynamic Internet services
Chen, M. Y., Kıcıman, E., Fratkin, E., Fox, A., & Brewer, E. (2003) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2002.1029005 · Cited by 846 · see citing works
Newman, S. (2015) · CERN Document Server (European Organization for Nuclear Research) · Cited by 768 · see citing works
Dapper, a Large-Scale Distributed Systems Tracing Infrastructure
Sigelman, B. H., Barroso, L. A., Burrows, M., Stephenson, P., Plakal, M., Beaver, D., Jaspan, S., & Shanbhag, C. (2010) · Cited by 477 · 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
Microscope: Pinpoint Performance Issues with Causal Graphs in Micro-service Environments
Lin, J., Chen, P., & Zheng, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03596-9_1 · Cited by 204 · see citing works
CloudRanger: Root Cause Identification for Cloud Native Systems
Wang, P., Xu, J., Ma, M., Lin, W., Pan, D., Wang, Y., & Chen, P. (2018) · 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) · doi:10.1109/ccgrid.2018.00076 · Cited by 181 · see citing works
Localizing Failure Root Causes in a Microservice through Causality Inference
Meng, Y., Zhang, S., Sun, Y., Zhang, R., Hu, Z., Zhang, Y., Jia, C., Wang, Z., & Pei, D. (2020) · International Workshop on Quality of Service · doi:10.1109/iwqos49365.2020.9213058 · Cited by 173 · see citing works
Ziegel, E. R., Wilson, P. W., Dell, L. D., & Anderson, G. (1994) · Technometrics · doi:10.2307/1270255 · Cited by 155 · 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
Thalheim, J., Rodrigues, A., Akkuş, İ. E., Bhatotia, P., Chen, R., Viswanath, B., Jiao, L., & Fetzer, C. W. (2017) · International Middleware Conference · doi:10.1145/3135974.3135977 · Cited by 106 · see citing works
Using Service Dependency Graph to Analyze and Test Microservices
Ma, S., Fan, C.-Y., Chuang, Y., Lee, W.-T., Lee, S.-J., & Hsueh, N. (2018) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2018.10207 · Cited by 88 · see citing works
An Approach for Anomaly Diagnosis Based on Hybrid Graph Model with Logs for Distributed Services
Jia, T., Chen, P., Yang, L., Li, Y., Meng, F., & Xu, J. (2017) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws.2017.12 · Cited by 87 · see citing works
Root Cause Analysis of Anomalies of Multitier Services in Public Clouds
Weng, J., Wang, J. H., Yang, J., & Yang, Y. (2018) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2018.2843805 · Cited by 79 · see citing works
Root cause detection in a service-oriented architecture
Kim, M., Sumbaly, R., & Shah, S. (2013) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/2465529.2465753 · Cited by 74 · see citing works
Anomaly Detection and Diagnosis for Container-Based Microservices with Performance Monitoring
Du, Q., Xie, T., & He, Y. T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-05063-4_42 · Cited by 71 · 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
Microservices Monitoring with Event Logs and Black Box Execution Tracing
Cinque, M., Corte, R. D., & Pecchia, A. (2019) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2019.2940009 · Cited by 63 · see citing works
MS-Rank: Multi-Metric and Self-Adaptive Root Cause Diagnosis for Microservice Applications
Ma, M., Lin, W., Pan, D., & Wang, P. (2019) · 2019 IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws.2019.00022 · Cited by 58 · see citing works
Liu, P., Chen, Y., Nie, X., Zhu, J., Zhang, S., Sui, K., Zhang, M., & Pei, D. (2019) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre.2019.00014 · Cited by 49 · see citing works
Detecting Anomalous Behavior of Black-Box Services Modeled with Distance-Based Online Clustering
Gulenko, A., Schmidt, F., Acker, A., Wallschläger, M., Kao, O., & Liu, F. (2018) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2018.00134 · Cited by 36 · see citing works
Wang, H., Nguyen, P., Li, J., Köprü, S., Zhang, G., Katariya, S., & Ben-Romdhane, S. (2019) · Proceedings of the VLDB Endowment · doi:10.14778/3352063.3352105 · Cited by 34 · see citing works
A Real-Time Trace-Level Root-Cause Diagnosis System in Alibaba Datacenters
Cai, Z., Li, W., Zhu, W., Liu, L., & Yang, B. (2019) · IEEE Access · doi:10.1109/access.2019.2944456 · Cited by 20 · see citing works
Ranking Metric Anomaly in Invariant Networks
Ge, Y., Jiang, G., Ding, M., & Xiong, H. (2014) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2601436 · Cited by 19 · see citing works
Root cause analysis of anomalies of multitier services in public clouds
Weng, J., Wang, J. H., Yang, J., & Yang, Y. (2017) · International Workshop on Quality of Service · doi:10.1109/iwqos.2017.7969155 · Cited by 12 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
PBScaler: A Bottleneck-Aware Autoscaling Framework for Microservice-Based Applications
Xie, S., Wang, J., Li, B., Zhang, Z., Li, D., & Hung, P. C. K. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3376202 · Cited by 48 · see citing works
Self-citationMicroIRC: 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
Self-citationChange 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
Xie, S., Wang, J., He, H., Wang, Z., Zhao, Y., Zhang, N., & Li, B. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3734868 · Cited by 22 · 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
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
Self-citationA Decentralized Root Cause Localization Approach for Edge Computing Environments
Fernando, D., Rodríguez, M. V. R., & Buyya, R. (2026) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2026.3668228 · Cited by 2 · see citing works
Xu, D., Wu, X., Chen, N., & Liu, C. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5672-8_8 · 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
“BIRCH clustering has been integrated into MicroDiag [97], MicroRCA [98], and AAMR [103], where it is applied to response times and service dependencies to identify anomalies and propagation paths.”
InfluentialBindox: An Efficient and Secure Cross-System IPC Mechanism for Multi-Platform Containers
Xiang, Y., Deng, B., Zhang, H., Xu, R., Mao, M., Wang, Y., & Qi, Z. (2023) · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · doi:10.18293/seke2023-124
- Authors
- Zhang, Z.
- Affiliations
- Wuhan University (CN)
- Corresponding author
- Zhang, Zekun
- ORCID
- Zhang, Zekun
- Venue
- International Conferences on Software Engineering and Knowledge Engineering
- Publisher
- KSI Research Inc.
- Published
- 2021-07-02
- Volume
- 2021
- Pages
- 86-91
- ISSN
- 2325-9000 (ISSN-L)
- OpenAlex
- W3197415297
- Cited by
- 10
- References
- 25
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 4 · 2023: 1
- FWCI
- 1.15
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering (publishedVersion)
- Funders
- National Natural Science Foundation of China (2018YFB1402800, 62032016, 61832014)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- microservices, root cause analysis, anomaly detection, service dependency graph, PageRank, random walks, performance diagnosis, Kubernetes, cloud-native applications, real-time metrics
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
