# Enhancing Network Slicing Architectures with Machine Learning, Security, Sustainability and Experimental Networks Integration

arXiv: https://arxiv.org/abs/2307.09151 · HTML: https://mlsanity.com/paper/2307.09151/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 75 references that are in the map.
- First posted on arXiv 18 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [AI-Native Network Slicing for 6G Networks](https://mlsanity.com/paper/2105.08576/) (2021)
- [5G network slicing using SDN and NFV- A survey of taxonomy, architectures and future challenges](https://mlsanity.com/paper/1912.02802/) (2019)
- [Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles](https://mlsanity.com/paper/2201.11295/) (2022)
- [Customized Slicing for 6G: Enforcing Artificial Intelligence on Resource Management](https://mlsanity.com/paper/2102.10498/) (2021)
- [Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction](https://mlsanity.com/paper/2207.01019/) (2022)

## Built on by (most cited first, 5 of 5)

- [Intelligent Data-Driven Architectural Features Orchestration for Network Slicing](https://mlsanity.com/paper/2401.06538/) (2024)
- [An Intelligent Native Network Slicing Security Architecture Empowered by Federated Learning](https://mlsanity.com/paper/2410.05312/) (2024)
- [Towards Sustainability in 6G Network Slicing with Energy-Saving and Optimization Methods](https://mlsanity.com/paper/2505.12132/) (2025)
- [AI-driven Orchestration at Scale: Estimating Service Metrics on National-Wide Testbeds](https://mlsanity.com/paper/2507.16077/) (2025)
- [Performance Evaluation and Threat Mitigation in Large-scale 5G Core Deployment](https://mlsanity.com/paper/2507.17850/) (2025)

## Abstract (arXiv)

Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet of Vehicles and industrial IoT, among others. NS is foreseen as one of the leading enablers for 6G futuristic and highly demanding applications since it allows the optimization and customization of scarce and disputed resources among dynamic, demanding clients with highly distinct application requirements. Various standardization organizations, like 3GPP's proposal for new generation networks and state-of-the-art 5G/6G research projects, are proposing new NS architectures. However, new NS architectures have to deal with an extensive range of requirements that inherently result in having NS architecture proposals typically fulfilling the needs of specific sets of domains with commonalities. The Slicing Future Internet Infrastructures (SFI2) architecture proposal explores the gap resulting from the diversity of NS architectures target domains by proposing a new NS reference architecture with a defined focus on integrating experimental networks and enhancing the NS architecture with Machine Learning (ML) native optimizations, energy-efficient slicing, and slicing-tailored security functionalities. The SFI2 architectural main contribution includes the utilization of the slice-as-a-service paradigm for end-to-end orchestration of resources across multi-domains and multi-technology experimental networks. In addition, the SFI2 reference architecture instantiations will enhance the multi-domain and multi-technology integrated experimental network deployment with native ML optimization, energy-efficient aware slicing, and slicing-tailored security functionalities for the practical domain.