The rapid densification of fifth generation radio access networks and the growing demand for low-latency services have significantly increased the energy consumption of mobile infrastructures, raising critical concerns regarding operational cost and environmental sustainability. Multi-access edge computing has been introduced as a key architectural paradigm to support stringent latency requirements by deploying computing resources closer to base stations. However, the deployment of edge computing does not inherently guarantee energy efficiency, as edge platforms may consume substantial baseline power under low utilization if orchestration and task placement are not energy-aware. This paper proposes an energy-efficient edge computing architecture for 5G networks that integrates real-time energy monitoring with load-aware task scheduling at the edge layer. The proposed architecture is aligned with standardized 5G edge deployment frameworks and is evaluated using real operational base station data, including traffic load, computing utilization, and power consumption measurements. By leveraging real data rather than synthetic workloads, the proposed approach enables a realistic assessment of energy efficiency under practical operating conditions. Experimental results demonstrate that the proposed architecture achieves approximately 30% improvement in energy efficiency compared with a conventional edge computing deployment without energy-aware scheduling, while maintaining comparable latency performance. The findings indicate that data-driven energy-aware orchestration at the network edge can deliver measurable energy savings in commercial 5G environments. This work provides practical insights for mobile network operators seeking to reduce the energy footprint of 5G infrastructures and contributes a deployable architectural framework for energy-efficient edge computing in next-generation mobile networks.

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