Unsupervised Anomaly Detection in Cloud-Native Microservices via Cross-Service Temporal Contrastive Learning: An Evidence Synthesis on Resource Efficiency And Performance Trade-Offs

This evidence-synthesis article examines cloud-native anomaly detection through the focal contribution “Unsupervised Anomaly Detection in Cloud-Native Microservices via Cross-Service Temporal Contrastive Learning” and nine author-disjoint, topically matched studies. The analysis is organized around resource efficiency and performance trade-offs. Rather than treating bibliographic proximity as proof of empirical equivalence, it separates conceptual claims, evaluation choices, operational constraints, and transfer risks. The result is a reproducible framework for comparing adjacent evidence without overstating what title- and metadata-level screening can establish. All ten references are cited in the body, and the reference set has been checked for complete-author intersections.

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