Experimental Dataset for Control-Aware Autoscaling of Shared Controllers in Edge Kubernetes Systems

This dataset provides a comprehensive experimental benchmark for the study of control-aware autoscaling of shared controllers in edge computing environments based on Kubernetes. It contains aggregated measurements from a large set of controlled experiments in which multiple Industrial Internet of Things (IIoT) devices share a limited number of fuzzy control services deployed as scalable Kubernetes pods. The experiments systematically vary the number of devices, the number of shared controllers, and the operating regime of the controlled systems, enabling the analysis of trade-offs between control performance, temporal stability, and computational resource usage.

The dataset covers three representative classes of control systems with distinct dynamics: a DC motor (fast and delay-sensitive dynamics), a level control tank (slow and naturally damped dynamics), and a vehicle speed control system (high-inertia dynamics). For each plant, multiple operating setpoints were evaluated, and for each setpoint the number of active IIoT devices and shared controllers was varied across a wide range. The dataset includes key metrics such as the integrated absolute error (IAE), the 90th percentile of control loop time and request delay, CPU usage of the control services, and network traffic statistics. Each row in the dataset corresponds to a complete experimental scenario, fully characterized by its control, computational, and networking context.

This dataset is intended to support reproducible research in edge control systems, autoscaling strategies, and data-driven resource management. It can be used for empirical performance evaluation, comparative analysis of autoscaling policies, and as a benchmark for machine learning methods such as risk-aware regression, quantile prediction, and offline reinforcement learning. By providing a unified and well-documented experimental dataset, this work aims to facilitate further research on scalable control architectures and intelligent resource management in edge computing environments.