This paper presents a telemetry dataset capturing resource utilization and power consumption metrics across the ENACT edge-cloud continuum. The dataset contains empirical telemetry collected in real-time for both infrastructure nodes and application workloads. More specifically, a distributed weather forecasting scenario has been emulated, comprising five pods: two different weather data sources, two forecasting services (one per node/computing layer) and one long-term storage pool. A cloud-based machine and an edge device belonging to the same Kubernetes cluster have been considered for the deployment of the application pods, corresponding to heterogeneous computing tiers. Data acquisition was performed using ENACT’s Telemetry Data Collector and Monitoring Engine which measures telemetry and energy metrics at node and pod levels in real-time. The resulting dataset provides time-series records including CPU, memory and disk utilization, network throughput, and energy consumption for the cloud node, the edge node and the five application pods. Telemetry data was collected during two distinct phases: for a period with application workloads running normally and for a baseline period when applications were removed from the cluster. This allows for assessing the impact of the applications activity in terms of resource usage and energy consumption. This dataset offers valuable insights for the research community in distributed systems, the edge-cloud continuum and cognitive computing, wherein datasets on real-world data, especially reflecting both infrastructure-level and application-level telemetry, are currently very limited. It is particularly useful for developers and research scientists that require such data for tasks such as training and fine-tuning time-series forecasting models, benchmarking anomaly detection models and validating scheduling algorithms and energy-aware strategies, to name a few.
Kapetanidou et al. (Sun,) studied this question.