This dataset contains synthetic infrastructure monitoring metrics collected from a Kubernetes cluster environment. The dataset was produced using a CODECO Synthetic Data Generator (CSDG) that periodically extracts metrics that simulates the behavior of the data derived from the different CODECO components of the CODECO platform.The dataset integrates metrics originating from multiple platform components:ACM — node-level infrastructure metrics.MDM — data governance metrics.NetMa — overlay and underlay network topology metrics.Metrics are generated via custom made functions mimicing the behavior of the workload data derived from the CODECO components, periodically collected from the defined Kubernetes cluster and exported into a time-series CSV dataset, where each row represents the state of a node at a specific timestamp.The dataset is intended to simulate realistic cloud-native orchestration environments, allowing experimentation with infrastructure monitoring, orchestration intelligence, and machine learning-based optimisation.Use Cases and Intended AudienceThis dataset is primarily intended for:Training and evaluating Machine Learning (ML) models for infrastructure optimisation.Research on cognitive orchestration systems.Anomaly detection in distributed edge-cloud continum infrastructures.Development of self-adaptive distributed systems.Benchmarking algorithms for edge-cloud orchestration platforms.Research on autonomous infrastructure management.Data Origin and Generation MethodologyOrigin: Fully synthetic dataset generated using the CODECO Synthetic Data Generator.Platform: Kubernetes-based infrastructure environment.