This paper investigates the energy-aware joint placement and migration of cloud-native functions (CNFs) in an O-RAN edge cloud with a Mixed-Integer Linear Program 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.
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.
The rapid evolution of beyond-5G and emerging 6G networks is driving the need for flexible, reliable, and cost-efficient virtualized Radio Access Network (vRAN) architectures capable of supporting heterogeneous services such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communication (mMTC). Future disaggregated RAN systems are expected to rely heavily on network slicing, functional split flexibility, and optical x-haul infrastructures to support stringent performance, scalability, and availability requirements. In this paper, we present an integrated framework for reliable, slice-aware, and functional split-aware Virtual Network Function (VNF) placement with lightpath provisioning in disaggregated vRAN environments. The proposed approach maximizes mobile network operators'profit by jointly optimizing function placement and optical resource allocation under latency, processing, bandwidth, and availability constraints. We formulate the problem as an Integer Linear Programming (ILP) model with two variants: one that employs unshared backups and another that uses a more cost-efficient shared backup scheme. To address ILP complexity, we develop a heuristic algorithm and a Genetic Algorithm (GA)-based metaheuristic that yields near-optimal solutions in real time. Extensive evaluations on topologies up to 128 nodes show that shared backup variants yield up to 18% higher profit, while maintaining up to 5-10% lower normalized CPU usage than unshared counterparts.
Mayank Ramnani, S. Dixit, Sushil Yadav et al.· arXiv.org· 0 citations
This paper considers priority-aware partial computation offloading in an uplink mobile edge computing (MEC) network. Devices assigned to different groups occupy orthogonal subbands, whereas devices within each group use power-domain non-orthogonal multiple access (NOMA) with successive interference cancellation. Task-input size determines the transmitted and processed workload, while queue backlog and application urgency determine the service weight. The Gaussian multiple-access-channel rate region is convex, but the complete allocation problem is not jointly convex in the adopted variables because the offloaded workload is coupled with reciprocal transmission rate and reciprocal edge-CPU allocation. A structure-exploiting block-coordinate projected-gradient method is developed. It combines exact finite-candidate offloading updates, an exact edge-CPU allocation bounded below by deadline feasibility and above by local-path saturation, and an analytical projected power step with Armijo backtracking. For eight users at 23 dBm, pairwise group-based NOMA reduces the weighted delay–energy cost and device energy by 6.18% and 23.44%, respectively, relative to orthogonal access. Queue-aware weighting reduces upper-backlog-quartile delay by 2.69 ms (95% confidence half-width: 0.78 ms) while increasing lower-quartile delay by 8.34 ms (half-width: 2.07 ms). In a paired 15-iteration ablation, generic projected block-coordinate updates have a cost ratio of 1.0098 (half-width: 0.0086) relative to the structured method. A hybrid deep deterministic policy-gradient policy, evaluated over five training seeds, has an 11.77% higher cost while requiring 0.84% of the median online decision time. Of 432 allocations, 392 satisfy the residual-qualified stopping tests and 40 are explicitly reported as iteration-safeguard terminations.
Jamil K. J. Bataineh, Ahlam Jawarneh, K. Hayajneh et al.· Italian National Conference...· 0 citations
In the tested replays, the Long Short-Term Memory (LSTM)-assisted configuration shows lower video and sensor delay with 38–48% lower mean per-flow video throughput than the baseline—a configuration-level latency-versus-throughput trade-off; the LSTM-specific effect is not isolated.
DPDK-DU-NS is presented, a DPDK-enabled O-RAN DU that supports per-flow network slicing and bandwidth management in compliance with 3GPP 5G QoS flow and bearer management specifications and is validated as a high-performance and scalable foundation for next-generation O-RAN DU implementations.
Space-air-ground integrated networks (SAGINs) offer seamless three-dimensional coverage and strengthened flexibility, which are recognized as a core network architecture of 6G. Software-defined networking (SDN) and network function virtualization (NFV) are two enabling technologies for SAGINs that can be utilized to sequentially arrange virtual network functions (VNFs) into service function chains (SFCs) to provide users with resource-efficient and delay-optimized multi-source multicast request (MMR) services. However, SAGINs exhibit significant dynamism and heterogeneity, it brings great challenges when dynamically deploying the MMR’s source nodes and SFCs for fulfilling MMR routing. This paper investigates the multi-source multicast SFC embedding problem (MMSEP) considering the determination of the source nodes for MMR, VNFs placement, as well as network resources and delay constraints in the SDN/NFV-enabled SAGIN. Firstly, we define and formulate the MMSEP and demonstrate its NP-hardness. Subsequently, we employ a heuristic algorithm to assign the optimal source nodes for all multicast destination nodes and utilize the markov decision process (MDP) to simulate dynamic transitions in network states. Finally, we propose a deep deterministic policy gradient with attention mechanism (DDPG-AM) to address the MMSEP, aiming to minimize resource consumption costs and delays while maximizing the revenue of the internet service provider. The simulation results demonstrate that the proposed algorithm surpasses the state-of-the-art DDPG algorithm by approximately 27% in network utility, 17% in latency reduction, and 5% in acceptance ratio.
Liang Liu, Yejun He, Yujie Zhang et al.· IEEE Transactions on Network...· 0 citations
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