Energy Efficiency in Network Slicing: Survey and Taxonomy
DOI:
https://doi.org/10.31224/6449Keywords:
Artificial Intelligence, Energy-Efficient Slicing, Energy-Efficient Slicing Strategy, Energy Efficiency, Network Slicing, TaxonomyAbstract
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.
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Copyright (c) 2026 JOBERTO S. B. MARTINS, ADNEI WILLIAN DONATTI, MARCIA CRISTINA MACHADO, MARVIN ALEXANDER LOPEZ MARTINEZ, SABINO ROGÉRIO S. ANTUNES, ELI CARLOS FIGUEIREDO SOUZA, SAND CORREA, TIAGO FERRETO, JOSÉ AUGUSTO SURUAGY, TEREZA CRISTINA CARVALHO

This work is licensed under a Creative Commons Attribution 4.0 International License.