Jul 2026· International Mediterranean Conference on Communications and Networking· pp. 1-6· 0 citations· 20 references
Abstract
We study the problem of Distributed Unit (DU) & Centralized Unit (CU) placement in Open RAN for reducing the energy footprint of the network, under explicit distance & bandwidth constraints on real-world Radio Unit (RU) topologies. We show that the DU / CU placement can be formulated as a minimum dominating set (MDS) problem on graphs derived from latency & bandwidth constraints, enabling exact solutions that minimize the number of deployed nodes. To further refine the placement while preserving this minimum deployment cardinality, we propose a sequential distance-weighted MDS approach that selects, among all minimum-cardinality solutions, the one reducing the load transport cost. We evaluate the proposed method on a real-world node topology using a detailed energy model capturing both computational and transport costs. The results show that the MDS formulation significantly reduces infrastructure footprint compared to a clustering-based baseline, leading to a global RAN energy gain of around 14%, while the sequential refinement provides additional gains reducing latency and transport energy cost.
This letter develops a radio access network (RAN) framework for mixed discrete-continuous optimization problems that arise in user-centric cell=free massive multiple-antenna networks. The novel framework exploits the structural decomposition between discrete clustering decisions and continuous resource allocation variables by modeling the space of feasible serving states as a graph with Hamming-topology neighborhoods. A serving-state graph abstraction is introduced to enable topology-aware search-and-evaluate optimization procedures and a graph-based search-and-evaluate (GBSE) algorithm is devised along with their complexity analysis. Energy efficiency maximization at the RAN level is presented as an application of considered alongside the proposed framework and GBSE algorithm. Numerical results show that minimal Hamming neighborhoods offer an attractive trade-off between scalability and exploration capability in grap-based optimization and GBSE outperforms existing techniques.
Mobile edge networks delivers low-latency, high-quality services by deploying Virtual Network Functions (VNFs) on resource-constrained edge nodes. However, sequential VNF processing incurs significant latency, while reusing existing VNFs under heavy demand may force requests onto longer paths, increasing bandwidth consumption. Parallelization combined with efficient VNF placement is therefore essential, yet these decisions are inherently coupled and may lead to suboptimal deployments if optimized separately. To address this challenge, this paper proposes a unified framework that jointly optimizes VNF parallelization and placement to minimize latency and resource consumption. The framework constructs a Dependency-Deployment Graph (DPG) that integrates VNF functional dependencies with Mobile Edge Networks topology. By assigning latency and resource weights to DPG nodes and edges, the framework captures the interplay between transmission delay, resource usage, parallelization, and deployment decisions. To efficiently explore the solution space, iterative algorithms progressively refine candidate configurations by pruning inferior solutions and focusing on promising regions of the search space. Experimental evaluations across diverse network configurations demonstrate that the proposed framework achieves promising improvements in reducing service latency and resource consumption compared with representative baseline methods. The results further indicate the effectiveness and robustness of the proposed joint parallelization and placement strategy under the tested heterogeneous edge computing scenarios.
Yuhao Xie, Zhen Zhang, Yuhui Deng et al.· IEEE Transactions on Network...· 0 citations
The connected p-median problem is defined as a variant of the classical p-median problem when the facility nodes induce a connected subgraph. In this paper, we introduce the weighted version of the above problem when the weight of the facility connection in the objective function is defined by the minimum weight spanning tree of the facility nodes. This approach is motivated by the sink node selection in distributed sensor networks, in which the collected information is shared among the sink nodes through the minimum spanning tree. The weights of the graph determining the network topology of the candidate sink nodes as connection costs are distinguished from the standard access costs of the p-median problem. The fixed deployment costs for the setup of facilities are also considered. The objective is to minimize the overall cost as the sum of deployment cost, access cost and connection cost. We show that the problem is NP-hard and propose three mixed-integer linear programming (MILP) formulations adapted from the traveling salesperson problem literature. Since these formulations are poorly scalable with respect to network size, we develop a four-phase matheuristic method based on linear programming rounding. We conduct an extensive computational study to evaluate the performance of the MILP formulations and 22 variants of the matheuristic under different parameter settings. The results indicate that the MILP models perform effectively on small instances but struggle to solve medium- and large-scale instances within a two-hour time limit. In contrast, several matheuristic variants consistently produce high-quality solutions within minutes. Finally, we analyze the impact of network structure, size, density, and the parameter $p$ on solution quality, providing further insights for network design.
Murat Elhuseyni, Burak Kocuk, Miklós Krész· 0 citations
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.
Nguyen Phuc Tran, B. Jaumard, Oscar Delgado· 0 citations
This paper proposes a multi-objective Integer Linear Programming (ILP) formulation for optimal virtual Content Delivery Network (vCDN) placement in fixed broadband networks. The proposed framework jointly minimizes backhaul traffic and end-to-end latency across a six-tier topology spanning OLT, Tier 2/Tier 1 aggregation, Provider Edge, Transport Backbone, and International Gateway nodes. Key contributions include a partial caching model with ratio α***(0,1] that increases cache diversity by 1/α (Theorem 1), a view-time threshold mechanism with effective utilization factor ηⱼ, and a placement efficiency metric Φ that quantifies performance gain per unit deployment cost. Proof-of-concept simulation confirms 81.2% average RTT reduction (5.704 ms → 1.075 ms), directly validating the latency component of the ILP objective.
Yohana Jayanti Aruan, R. Munadi, S. Hertiana et al.· International Conference on...· 0 citations
The digitalization of university campuses has intensified the need for reliable, energy-efficient, and sustainable network infrastructures. While Wi-Fi systems offer flexibility and ease of deployment, their long-term energy footprint often exceeds that of wired alternatives. Conversely, Ethernet networks, though more stable and efficient, require careful optimization to reduce installation costs and material use. This study develops a two-stage framework that combines graph-theoretic optimization with empirical device-level measurements to inform sustainable campus network design.In the first phase, the physical network layout was modeled as a Minimum Spanning Tree (MST) using the Manhattan distance metric to reflect realistic in-building cable routing. The MST was solved via Kruskal’s algorithm and validated through a mixed-integer programming model implemented in Gurobi, yielding a total cable length of 1,435.69 metres and a 63.4% reduction in total cable length—implying comparable savings in material and installation costs—compared with a naive layout. In the second phase, an experimental analysis examined the energy consumption of Wi-Fi, native Ethernet, and Ethernet-adapter connections across multiple devices. Although the paired t-tests revealed no statistically significant differences at the 95% confidence level, the results indicate that device-specific characteristics play a crucial role in determining actual energy efficiency.Overall, the findings underscore the importance of integrating optimization and empirical evaluation in sustainable network planning. The proposed framework not only supports cost-efficient infrastructure decisions but also contributes to the broader goal of reducing the carbon footprint of digital campuses.
Irmak Uzun Bayar, Ceyda Ceylan· Ankara Hacı Bayram Veli Üniv...· 0 citations
