Distributing microservices 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. It maps CPU forecasts to power for energy-aware placement without external instrumentation. ARAPO is evaluated in a realistic hospital reference scenario against a reactive-only baseline. Results demonstrate that the system anticipates saturation and prevents control plane congestion. It significantly improves stability in oscillating workloads. Overload time drops from 28.4% to 4.5%. Consequently, energy usage during overload falls to 14.9% of the reactive baseline. Node-level forecasting achieves R 2 up to 0.86. The power model tracks consumption with a mean absolute error as low as 0 . 40 W . This validates its suitability as a lightweight, energy-efficient controller.
Rodríguez et al. (Mon,) studied this question.