Stress-testing dependency-aware scoring for microservice anomaly detection under 18% telemetry delay: a factor ablation
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Keywords

microservice anomaly detection
dependency-aware scoring
telemetry delay
paired simulation
reproducibility

Abstract

We evaluated dependency-aware scoring for microservice anomaly detection under 18% telemetry delay. A deterministic paired simulation generated 64 cases and preserved a boundary-condition stratum. Mean early-warning F1 changed from 0.559 to 0.600; the paired difference was +0.041 (95% interval +0.038 to +0.043). The result is limited to the stated simulation and is reported with a reproducible result artifact.

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References

Zhang, Z., Liu, W., Tao, J., Zhu, H., Li, S., & Xiao, Y. (2025). Unsupervised Anomaly Detection in Cloud-Native Microservices via Cross-Service Temporal Contrastive Learning. 2025 5th International Symposium on Artificial Intelligence and Big Data (AIBDF), 221-226. https://doi.org/10.1109/aibdf67964.2025.11440805

Natalino, C., Manso, C., Gifre, L., Muñoz, R., Vilalta, R., Furdek, M., & Monti, P. (2022). Microservice-Based Unsupervised Anomaly Detection Loop for Optical Networks. Optical Fiber Communication Conference (OFC) 2022, Th3D.4. https://doi.org/10.1364/ofc.2022.th3d.4

La Cruz, E. D., Prakash, C., & Rana, S. (2026). AI-Driven Anomaly Detection and Zero Trust Frameworks. Cybersecurity for Industrial IoT in Hybrid Cloud Environments, 297-350. https://doi.org/10.4018/979-8-3373-4581-9.ch009

Vilkhivska, O. V., Brynza, N. O., & Bulakh, M. I. (2026). How to Integrate Internet Marketing into a Cloud Storage System: Developing a Microservice for Customer Acquisition. Business Inform, 2(577), 442-455. https://doi.org/10.32983/2222-4459-2026-2-442-455

Gayathri, S., & Surendran, D. (2024). Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks. Journal of Cloud Computing, 13(1), 49. https://doi.org/10.1186/s13677-024-00595-y

Zhang, J., & Yang, H. (2026). CPU-Only Spatiotemporal Anomaly Detection in Microservice Systems via Dynamic Graph Neural Networks and LSTM. Symmetry, 18(1), 87. https://doi.org/10.3390/sym18010087

Liu, X., Zhu, S., Yang, F., & Liang, S. (2022). Research on unsupervised anomaly data detection method based on improved automatic encoder and Gaussian mixture model. Journal of Cloud Computing, 11(1), 58. https://doi.org/10.1186/s13677-022-00328-z

Kalivoshko, N. (2025). Explainable Anomaly Detection in Multimodal Telemetry of Banking Microservices. Universal Library of Business and Economics, 2(4), 136-144. https://doi.org/10.70315/uloap.ulbec.2025.0204015

Liu, Y. (2026). Graph-Based Contrastive Representation Learning for Predicting Performance Anomalies in Cloud and Microservice Platforms. https://doi.org/10.20944/preprints202602.0559.v1

Shuai Fang (2024). Research on Anomaly Detection in Microservice Based on Graph Neural Networks. Computer Fraud and Security, 44-56. https://doi.org/10.52710/cfs.88