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Towards Sustainability in 6G Network Slicing with Energy-Saving and Optimization Methods

Rodrigo Moreira, Tereza C. M. Carvalho, Flávio de Oliveira Silva, Nazim Agoulmine, Joberto S. B. Martins

TL;DR

This work addresses the sustainability challenge of 6G network slicing by proposing ML-native energy-aware orchestration within the SFI2 NS architecture to reduce energy consumption during slice preparation, deployment, and operation. It introduces three ML-enabled optimization modes (SRO, NRO, SDO) and leverages contrastive learning to forecast and allocate resources efficiently. A core contribution is a contrastive-learning-based anomaly detector for unlabeled energy-time series, demonstrating robustness to noise versus conventional methods on a datacenter energy dataset. The findings point to practical avenues for lowering the telecom sector's carbon footprint through energy-aware NS design and real-time energy monitoring.

Abstract

The 6G mobile network is the next evolutionary step after 5G, with a prediction of an explosive surge in mobile traffic. It provides ultra-low latency, higher data rates, high device density, and ubiquitous coverage, positively impacting services in various areas. Energy saving is a major concern for new systems in the telecommunications sector because all players are expected to reduce their carbon footprints to contribute to mitigating climate change. Network slicing is a fundamental enabler for 6G/5G mobile networks and various other new systems, such as the Internet of Things (IoT), Internet of Vehicles (IoV), and Industrial IoT (IIoT). However, energy-saving methods embedded in network slicing architectures are still a research gap. This paper discusses how to embed energy-saving methods in network-slicing architectures that are a fundamental enabler for nearly all new innovative systems being deployed worldwide. This paper's main contribution is a proposal to save energy in network slicing. That is achieved by deploying ML-native agents in NS architectures to dynamically orchestrate and optimize resources based on user demands. The SFI2 network slicing reference architecture is the concrete use case scenario in which contrastive learning improves energy saving for resource allocation.

Towards Sustainability in 6G Network Slicing with Energy-Saving and Optimization Methods

TL;DR

This work addresses the sustainability challenge of 6G network slicing by proposing ML-native energy-aware orchestration within the SFI2 NS architecture to reduce energy consumption during slice preparation, deployment, and operation. It introduces three ML-enabled optimization modes (SRO, NRO, SDO) and leverages contrastive learning to forecast and allocate resources efficiently. A core contribution is a contrastive-learning-based anomaly detector for unlabeled energy-time series, demonstrating robustness to noise versus conventional methods on a datacenter energy dataset. The findings point to practical avenues for lowering the telecom sector's carbon footprint through energy-aware NS design and real-time energy monitoring.

Abstract

The 6G mobile network is the next evolutionary step after 5G, with a prediction of an explosive surge in mobile traffic. It provides ultra-low latency, higher data rates, high device density, and ubiquitous coverage, positively impacting services in various areas. Energy saving is a major concern for new systems in the telecommunications sector because all players are expected to reduce their carbon footprints to contribute to mitigating climate change. Network slicing is a fundamental enabler for 6G/5G mobile networks and various other new systems, such as the Internet of Things (IoT), Internet of Vehicles (IoV), and Industrial IoT (IIoT). However, energy-saving methods embedded in network slicing architectures are still a research gap. This paper discusses how to embed energy-saving methods in network-slicing architectures that are a fundamental enabler for nearly all new innovative systems being deployed worldwide. This paper's main contribution is a proposal to save energy in network slicing. That is achieved by deploying ML-native agents in NS architectures to dynamically orchestrate and optimize resources based on user demands. The SFI2 network slicing reference architecture is the concrete use case scenario in which contrastive learning improves energy saving for resource allocation.
Paper Structure (6 sections, 5 equations, 6 figures, 1 table, 1 algorithm)

This paper contains 6 sections, 5 equations, 6 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. 6G Slicing with Energy-Saving for Sustainability
  3. 6G Resource Orchestration and Energy-Saving in SFI2 Network Slicing Architecture
  4. Proposed Solution: Network Slicing Energy Saving using Contrastive Learning
  5. Contrastive Learning for Detecting Anomalies in Unlabeled Energy Consumption Time Series
  6. Final Considerations

Figures (6)

  • Figure 1: 6G Telecommunication Sector - Sustainability.
  • Figure 2: The Slicing Future Internet Infrastructures (SFI2) Network Slicing Reference Architecture martins_enhancing_2023.
  • Figure 3: 6G Resource Orchestration and Optimization towards Energy Saving in SFI2 Architecture.
  • Figure 4: A LSTM model for Contrastive Learning.
  • Figure 5: Losses over training time.
  • ...and 1 more figures