Preprint / Version 1

Energy Efficiency in Network Slicing: Survey and Taxonomy

##article.authors##

  • JOBERTO S. B. MARTINS Universidade Salvador (UNIFACS) https://orcid.org/0000-0003-1310-9366
  • ADNEI WILLIAN DONATTI Universidade de São Paulo (USP)
  • MARCIA CRISTINA MACHADO 1Universidade de São Paulo (USP)
  • MARVIN ALEXANDER LOPEZ MARTINEZ 2Universidade Federal de Pernambuco (UFPE)
  • SABINO ROGÉRIO S. ANTUNES Universidade Federal de Pernambuco (UFPE)
  • ELI CARLOS FIGUEIREDO SOUZA Universidade Federal de Pernambuco (UFPE)
  • SAND CORREA Universidade Federal de Goiás (UFG)
  • TIAGO FERRETO Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS)
  • JOSÉ AUGUSTO SURUAGY Universidade Federal de Pernambuco (UFPE)
  • TEREZA CRISTINA CARVALHO Universidade de São Paulo (USP)

DOI:

https://doi.org/10.31224/6449

Keywords:

Artificial Intelligence, Energy-Efficient Slicing, Energy-Efficient Slicing Strategy, Energy Efficiency, Network Slicing, Taxonomy

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.

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Posted

2026-02-10