Proactive Scaling and Energy Optimization for 5G O-RAN: Experimental Insights

Abstract

The RAN is responsible for approximately 70% of the total power consumption of 5G systems. The disaggregation and virtualization of the 5G RAN specified in the O-RAN architecture introduce an unprecedented flexibility for dynamic resource management, enabling operations that enhance the energy-efficiency strategies of the overall network. In this paper, we investigate how traffic-aware scaling of the CU-UP component can be used to reduce the operational power and energy consumption of a fully disaggregated 5G O-RAN network. We design two AI-driven xApps running on the near-RT RIC that implement proactive CU-UP scaling based on machine-learning traffic forecasting. The first xApp implements a vertical scaling strategy by dynamically adjusting the CPU resources allocated to a CU-UP instance to minimize the 5G O-RAN power consumption, while the second performs latency-aware horizontal scaling by instantiating and placing multiple CU-UP instances across the infrastructure to jointly optimize energy consumption and midhaul latency. The proposed approaches are evaluated on a 5G O-RAN testbed using traffic traces from a real-world dataset. The dataset follows a real UE behavior rather than artificially prolonged low-load conditions that would act in favour of our contributions. Experimental results shed light on the applicability of scaling for the CU-UP component. Results demonstrate that vertical scaling maximizes energy efficiency, achieving up to 23% energy savings during underutilized regimes, 11% during sub-peak periods, and 8% over the full execution compared to a baseline static provisioning. In contrast, horizontal scaling delivers a complementary trade-off, achieving up to 16% savings during underutilized regimes, 7% during non-peak periods, and 4.7% for the whole execution, while significantly reducing end-to-end latency by optimally placing CU-UP instances. The results highlight that ML-driven CU-UP scaling is a practical and effective mechanism to reduce the power consumption, improve the energy efficiency, and the performance of the O-RAN 5G networks.