Unsupervised Anomaly Detection in Cloud-Native Microservices via Cross-Service Temporal Contrastive Learning: Monitoring Performance and Model Drift

A result that is credible at launch may degrade as inputs, workflows, and populations change, making longitudinal monitoring part of the evidence rather than an afterthought. 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 longitudinal monitoring and model drift, 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 defines drift indicators, review intervals, alert thresholds, and criteria for recalibration, retraining, or retirement.

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