Energy efficiency in network slicing: survey and taxonomy

dc.creatorDonatti , Adnei Willian
dc.creatorMachado, Marcia Cristina
dc.creatorLópez Martínez, Marvin Alexander
dc.creatorAntunes, Sabino Rogério da Silva
dc.creatorSouza, Eli Carlos Figueiredo
dc.creatorCorrea, Sand Luz
dc.creatorFerreto, Tiago Coelho
dc.creatorMonteiro, José Augusto Suruagy
dc.creatorMartins, Joberto Sérgio Barbosa
dc.creatorCarvalho, Tereza Cristina Melo de Brito
dc.date.accessioned2026-03-03T21:08:02Z
dc.date.available2026-03-03T21:08:02Z
dc.date.issued2025
dc.description.abstractNetwork 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.citationDONATTI, 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.doi10.1109/ACCESS.2025.3590365
dc.identifier.issne- 2169-3536
dc.identifier.urihttps://repositorio.bc.ufg.br//handle/ri/29822
dc.language.isoeng
dc.publisher.countryEstados unidos
dc.publisher.departmentInstituto de Informática - INF (RMG)
dc.rightsAcesso Aberto
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectArtificial intelligence
dc.subjectEnergy-efficient slicing
dc.subjectEnergy-efficient slicing strategy
dc.subjectEnergy efficiency
dc.subjectNetwork slicing
dc.subjectTaxonomy
dc.titleEnergy efficiency in network slicing: survey and taxonomy
dc.typeArtigo

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