Experimental Results and Artifacts for: Predictively Controlling the Computing Continuum with Distributed Energy-Aware Orchestration (ARAPO)

Distributing computation across the Computing Continuum reduces latency and preserves data locality but introduces management complexity on heterogeneous, resource-constrained edge nodes. Traditional reactive orchestration triggers only after saturation occurs. Under bursty or high-density workloads, this latency leads to service degradation, instability, and inefficient energy usage. To address this, the Adaptive Resource-Aware Predictive Orchestrator (ARAPO) couples per-service local forecasting with calibrated node-level aggregation. It employs a dual-threshold policy based on predicted and observed load to trigger migrations, mapping CPU forecasts to power for energy-aware placement without external instrumentation. ARAPO is evaluated in a realistic hospital reference scenario under high-stress and high-density conditions. Results demonstrate that the system anticipates saturation in balanced workloads and provides a robust hybrid response under shock loads. Node-level forecasting achieves R² up to 0.86, while the power model tracks consumption with a mean absolute error as low as 0.40 W, validating its suitability as a lightweight, energy-efficient controller.