Workload-Aware Edge Node Orchestration and Dynamic Resource Scaling in MEC

The emergence of edge computing introduces significant opportunities to improve real-time responsiveness and reduce latency by deploying computational resources closer to end users, at the edge, compared to traditional centralized cloud computing. However, stochastic and fluctuating workloads pose challenges in maintaining Quality of Service, often leading to resource fragmentation, service node saturation, and energy inefficiencies. In addition, imbalances in service node utilization, arising from either under-utilization or over-utilization, degrade the overall system performance and lead to unnecessary operational costs. Furthermore, finding an optimal balance between total latency cost and load balancing in different network topologies remains a significant challenge. In this research, we propose and evaluate a workload-aware orchestration framework that integrates short-term workload forecasting with dynamic resource scaling to efficiently manage edge node infrastructure under dynamic processing demands. The framework employs heuristic schemes that consider both workload distribution and service proximity throughout the edge network to optimize the distribution of edge users’ service requests across service nodes. Simulation results on grid and irregular edge network topologies, utilizing both synthetic and real-world dataset, demonstrate that the proposed framework and the integrated heuristics outperform other benchmark approaches. Specifically, our framework achieves up to 20% lower load imbalance variance, maintains high resource utilization, decreases system reconfigurations and increases service reliability, providing a robust, low-overhead and adaptive solution for dynamic orchestration in edge computing environments and infrastructures.