Energy Efficiency in Network Slicing: Survey and Taxonomy

TL;DR

This survey addresses the challenge of achieving energy efficiency in network slicing (NS) by providing a focused taxonomy that organizes EE strategies across three optimization levels: infrastructure, path/route, and slice operation. It surveys state-of-the-art techniques, maps them to the hierarchical taxonomy, and discusses open challenges, including cross-layer optimization, AI-driven decision making, and sustainability implications. The work highlights the role of NS in enabling energy-aware architectures (e.g., edge/fog, MEC, O-RAN splits) and the trade-offs inherent in ML-based orchestration, such as training energy costs and data-center versus edge deployments. By outlining directions like AI-driven digital twins and federated energy-aware control, the paper lays a structured foundation for researchers and operators to design, evaluate, and deploy energy-efficient NS solutions with end-to-end consideration of environmental impact and operational costs.

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.