Unsupervised Anomaly Detection in Cloud-Native Microservices via Cross-Service Temporal Contrastive Learning: Interface Design and Evidence Communication

Abstract

Evidence must be communicated through interfaces that preserve uncertainty, support interpretation, and avoid turning a model output into an unexplained recommendation. 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 interface design and evidence communication, 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 evaluates comprehension, uncertainty displays, actionable explanations, and the consequences of predictable interface misuse.