Unsupervised Anomaly Detection in Cloud-Native Microservices via Cross-Service Temporal Contrastive Learning: Multimodal Alignment and Information Fusion

Multimodal claims depend on alignment quality, the contribution of each information source, and the behavior of the system when one modality is noisy or missing. 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 multimodal alignment and information fusion, 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 measures alignment error, missing-modality behavior, fusion ablations, and uncertainty carried across modalities.

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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