Energy efficiency in network slicing: survey and taxonomy
| dc.creator | Donatti , Adnei Willian | |
| dc.creator | Machado, Marcia Cristina | |
| dc.creator | López Martínez, Marvin Alexander | |
| dc.creator | Antunes, Sabino Rogério da Silva | |
| dc.creator | Souza, Eli Carlos Figueiredo | |
| dc.creator | Correa, Sand Luz | |
| dc.creator | Ferreto, Tiago Coelho | |
| dc.creator | Monteiro, José Augusto Suruagy | |
| dc.creator | Martins, Joberto Sérgio Barbosa | |
| dc.creator | Carvalho, Tereza Cristina Melo de Brito | |
| dc.date.accessioned | 2026-03-03T21:08:02Z | |
| dc.date.available | 2026-03-03T21:08:02Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Network Slicing (NS) is a fundamental feature of 5G, 6G, and future mobile networks, enabling logically isolated virtual networks over shared infrastructure. As data demand increases and services diversify, ensuring Energy Efficiency (EE) in NS is vital (not only for operational cost savings but also to reduce the Information and Communication Technology (ICT) sector’s environmental footprint). This survey addresses the need for a comprehensive and holistic perspective on energy-efficient NS by reviewing and classifying recent strategies across the NS life cycle. Our contributions are threefold: (i) a thorough review of state-of-the-art techniques aimed at reducing energy consumption in NS; (ii) a novel taxonomy that organizes strategies into infrastructure, path/route, and slice operation levels; and (iii) the identification of open challenges and research directions, with a focus on systemic, cross-layer, and AI-driven approaches. By consolidating insights from recent developments, our work bridges existing gaps in the literature, offering a structured foundation for researchers and practitioners to design, evaluate, and improve energy-efficient network slicing systems. | |
| dc.identifier.citation | DONATTI, Adnei Willian et al. Energy efficiency in network slicing: survey and taxonomy. IEEE Access, [s. l.], v. 13, p. 134570-134589, 2025. DOI: 10.1109/ACCESS.2025.3590365. Disponível em: https://ieeexplore.ieee.org/document/11084777/. Acesso em: 24 fev. 2026. | |
| dc.identifier.doi | 10.1109/ACCESS.2025.3590365 | |
| dc.identifier.issn | e- 2169-3536 | |
| dc.identifier.uri | https://repositorio.bc.ufg.br//handle/ri/29822 | |
| dc.language.iso | eng | |
| dc.publisher.country | Estados unidos | |
| dc.publisher.department | Instituto de Informática - INF (RMG) | |
| dc.rights | Acesso Aberto | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Artificial intelligence | |
| dc.subject | Energy-efficient slicing | |
| dc.subject | Energy-efficient slicing strategy | |
| dc.subject | Energy efficiency | |
| dc.subject | Network slicing | |
| dc.subject | Taxonomy | |
| dc.title | Energy efficiency in network slicing: survey and taxonomy | |
| dc.type | Artigo |