AI 中文总结
针对O-RAN边缘云，本文研究云原生功能的节能联合部署与迁移，提出MILP模型与k-means启发式算法，Multi-CU场景可降5.7%能耗，启发式算法能效接近MILP且计算更高效。
AI 中文摘要
开放无线接入网（O-RAN）的转型正在重塑蜂窝基础设施的部署、管理与优化方式。本文研究O-RAN边缘云中云原生功能（CNF）的节能型联合部署与迁移问题，考虑两种场景：单CU-UP关联模型，以及面向切片的多CU-UP松弛模型——同一分布式单元（DU）的不同切片流组可被分配至同一集中式单元控制平面（CU-CP）下的不同集中式单元用户平面（CU-UP）处理目标；为简化表述，上述场景分别称为Single-CU和Multi-CU，其中Multi-CU绝不表示多个CU-CP关联。我们将该问题建模为混合整数线性规划（MILP），目标是最小化服务器、传输、唤醒及迁移能耗，同时满足服务器资源容量要求，以及胖树边缘数据中心中各DU与所选CU-UP间F1用户平面接口（F1-U）的单向延迟要求。为提升计算可扩展性，我们还开发了一种基于确定性k-means的启发式算法，可在无需重复精确优化的情况下近似MILP的决策。在评估的24小时工作负载下，与Single-CU基线相比，理论上的Multi-CU松弛模型可降低5.7%的建模能耗；对于Multi-CU场景，所提启发式算法的能耗与MILP的差距约为9.7%，展现出能效与计算可处理性间的良好权衡。
英文摘要
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.
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