Next-generation O-RAN Edge: Energy-aware Joint Placement and Migration of Cloud-Native Functions

2026-08-25

AI Summary

本文研究了O-RAN边缘云中云原生功能的能量感知联合放置与迁移问题，通过MILP模型和k-means启发式方法来优化能耗和服务质量。

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.

Problem

• Open Radio Access Networks
• cloud-native functions
• energy-aware
• joint placement and migration
• edge cloud

Innovation

• Energy-aware
• Joint Placement and Migration
• Cloud-Native Functions
• O-RAN Edge Cloud
• k-means-based Heuristic