Next-generation O-RAN Edge: Energy-aware Joint Placement and Migration of Cloud-Native Functions
2026-08-25 • Information Theory
Information TheoryDistributed, Parallel, and Cluster ComputingEmerging TechnologiesPerformance
AI summaryⓘ
The authors study how to save energy when running cloud-based network functions in Open Radio Access Networks (O-RANs), which are modern cellular network setups. They look at two ways of assigning processing tasks: one where each unit is tied to a single processing center (Single-CU) and another where slices of traffic can be handled by different centers (Multi-CU). They create a mathematical model to minimize energy use while meeting performance limits, and also design a faster method to get near-optimal results. Their findings show the Multi-CU approach saves energy compared to Single-CU, and their faster method performs close to the exact model, making it practical for real use.
Open Radio Access NetworksCloud-Native FunctionsCentralized Unit User PlaneMixed-Integer Linear ProgramEnergy OptimizationEdge CloudNetwork SlicingHeuristic AlgorithmLatency ConstraintsF1 User-Plane Interface
Authors
Nguyen Phuc Tran, Brigitte Jaumard, Oscar Delgado
Abstract
The transition toward Open Radio Access Networks (O-RANs) is reshaping how cellular infrastructure is deployed, managed, and optimized. This paper investigates the energy-aware joint placement and migration of cloud-native functions (CNFs) in an O-RAN edge cloud. We consider both a Single-CU-UP association model and a slice-aware Multi-CU-UP relaxation, in which distinct slice-flow groups of the same distributed unit (DU) may be assigned to different Centralized Unit User Plane (CU-UP) processing targets under one Centralized Unit Control Plane (CU-CP). For brevity, these scenarios are referred to as Single-CU and Multi-CU, respectively; Multi-CU never denotes multiple CU-CP associations. We formulate the problem as a Mixed-Integer Linear Program (MILP) that minimizes server, transmission, wake-up, and migration energy while satisfying server-resource capacities and one-way delay requirements over the F1 user-plane interface (F1-U) between each DU and its selected CU-UP in a fat-tree edge data center. To improve computational scalability, we also develop a deterministic k-means-based heuristic that approximates the MILP decisions without requiring repeated exact optimization. Over the evaluated 24-hour workload, the theoretical Multi-CU relaxation reduces modeled energy consumption by 5.7% relative to the Single-CU baseline. For the Multi-CU case, the proposed heuristic remains within approximately 9.7% of the proposed MILP, demonstrating a favorable trade-off between energy efficiency and computational tractability.