Unsupervised Anomaly Detection in Cloud-Native Microservices via Cross-Service Temporal Contrastive Learning: Resource Efficiency and Performance Trade-offs

Abstract

Efficiency claims should state which resources are saved, what performance is exchanged, and whether the trade-off remains acceptable at operational scale. 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 resource efficiency and performance trade-offs, 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 reports compute, memory, energy, latency, and maintenance costs beside task performance at realistic scale.