Multi-Resource Orchestration and Energy-Aware VNF Placement for Open RAN

This paper summarizes the PhD research work conducted on resource orchestration in disaggregated Open RAN (O-RAN) architectures. In O-RAN, the gNB radio protocol stack is split into virtualized functions- Centralized Unit (CU), Distributed Unit (DU), and Radio Unit (RU)-that are dynamically deployed across edge and regional cloud infrastructures. This architectural transformation introduces new challenges in managing computing, radio, and transport resources while satisfying diverse Quality of Service (QoS) requirements. Our research is structured into three major contributions. First, the problem of CU/DU placement is addressed under fixed User Equipment (UE)-to-RU associations through an Integer Linear Programming (ILP) formulation and a Bi-directional Long Short-Term Memory (Bi-LSTM)-based Recurrent Neural Network (RNN) heuristic for scalable inference. The second contribution extends the model to jointly optimize CU/DU placement and UE-to-RU association, with a decomposition heuristic and an enhanced RNN-based solution. The proposed models significantly outperform conventional baselines in terms of user admittance and runtime. Finally, an energy-aware extension is introduced to minimize total system power consumption by consolidating workloads and managing radio and computing resources activation aiming to enhance the sustainability of <tex>$\mathbf{O}$</tex> RAN orchestration while maintaining QoS guarantees.