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Related Experiment Video

Updated: Apr 30, 2026

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

A telemetry dataset on resource utilisation and power consumption in the edge-cloud continuum.

Ioanna Angeliki Kapetanidou¹, Thanasis Kotsiopoulos¹, Giorgos Thanasoulis¹

¹Information Technologies Institute, Centre for Research & Technology Hellas, P.O.Box 60361, 6th km Harilaou, Thermi, GR 57001, Thessaloniki, Greece.

|April 21, 2026

Summary

This study introduces a new dataset on edge-cloud continuum resource usage and energy consumption. It enables research in distributed systems and AI model training with real-world telemetry data.

Keywords:

Computing continuum Edge device Energy Infrastructure monitoring Kubernetes Performance Workloads

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Last Updated: Apr 30, 2026

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Area of Science:

Distributed Systems
Edge Computing
Cloud Computing
Cognitive Computing

Background:

Real-world telemetry datasets for the edge-cloud continuum, encompassing both infrastructure and application levels, are scarce.
Understanding resource utilization and power consumption is critical for optimizing distributed systems.

Purpose of the Study:

To present a novel telemetry dataset for the ENACT edge-cloud continuum.
To provide empirical data on resource utilization and power consumption for infrastructure nodes and application workloads.
To facilitate research in distributed systems, edge-cloud continuum, and cognitive computing.

Main Methods:

Collected real-time telemetry data using ENACT's Telemetry Data Collector and Monitoring Engine.
Emulated a distributed weather forecasting scenario with five pods deployed on heterogeneous cloud and edge nodes within a Kubernetes cluster.
Acquired time-series metrics including CPU, memory, disk utilization, network throughput, and energy consumption for nodes and pods during normal operation and baseline periods.

Main Results:

The dataset contains detailed time-series records of resource utilization and energy consumption at both node and pod levels.
Data was collected across different computing tiers (cloud and edge) within a unified Kubernetes cluster.
The dataset captures metrics during application workloads and a baseline period, enabling impact assessment.

Conclusions:

The presented dataset offers valuable insights for researchers in distributed systems, edge-cloud, and cognitive computing.
It is particularly useful for training/fine-tuning time-series forecasting models, benchmarking anomaly detection, and validating scheduling/energy-aware strategies.
This resource addresses the limited availability of real-world, granular telemetry data in the edge-cloud domain.