Unsupervised Anomaly Detection in Cloud-Native Microservices via Cross-Service Temporal Contrastive Learning: Adaptation and Calibration Across Domains

Cross-domain use depends on whether predictions remain calibrated when data sources, populations, and decision thresholds change. This structured evidence review evaluates "Unsupervised Anomaly Detection in Cloud-Native Microservices via Cross-Service Temporal Contrastive Learning" alongside nine author-disjoint, topically matched publications in cloud-native anomaly detection. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through cross-domain adaptation and calibration, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda emphasizes external calibration, domain-specific error analysis, and predefined rules for recalibration or withdrawal.

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3. O’Shea, K., Yan, S., Yu, M., Chen, X., Mauceri, S., Dhariyal, B., Xu, L., O’Connor, N., & Liu, M. (2026). Explainable Graph Ensemble Learning for Multivariate Time Series Anomaly Detection in Cloud Microservice Architectures. IEEE Transactions on Cloud Computing, 14(1), 210-223. https://doi.org/10.1109/tcc.2025.3634737 DOI
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6. Wu, D. (2026). Deep Learning Approach to Structure-Temporal Collaborative Anomaly Detection in Microservice Architectures. . https://doi.org/10.20944/preprints202602.0607.v1 DOI
7. 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 DOI
8. Li, Y., Guo, Y., Chen, Y., Cao, Z., & Liang, S. (2025). Self-Supervised Spatio-Temporal Representation Learning for Microservice Anomaly Detection. 2025 8th World Conference on Computing and Communication Technologies (WCCCT), 278-284. https://doi.org/10.1109/wccct65447.2025.11027933 DOI
9. Kalaiah, U. (2026). Multi-Signal Trust Scoring for Cloud-Native Microservice Security: An eBPF-Based Framework for Stealth Attack Detection Without Sidecar Proxies. International Journal of Science and Research (IJSR), 40-75. https://doi.org/10.21275/sr26629100308 DOI
10. Team, F.-B.-U. (2024). Microservices in the Cloud Native Era. Cloud-Native Application Architecture, 1-25. https://doi.org/10.1007/978-981-19-9782-2_1 DOI