Unsupervised Anomaly Detection in Cloud-Native Microservices via Cross-Service Temporal Contrastive Learning: Attributing Performance to Model Components

Abstract

Component-level evidence is needed to distinguish genuine contributions from gains produced by the surrounding pipeline. 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 ablation logic and component attribution, 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 calls for controlled ablations, interaction tests, and reporting of uncertainty around each component's incremental contribution.