Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.16077 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911069427990528 |
|---|---|
| author | Moreira, Rodrigo Pasquini, Rafael Martins, Joberto S. B. Carvalho, Tereza C. Silva, Flávio de Oliveira |
| author_facet | Moreira, Rodrigo Pasquini, Rafael Martins, Joberto S. B. Carvalho, Tereza C. Silva, Flávio de Oliveira |
| contents | Network Slicing (NS) realization requires AI-native orchestration architectures to efficiently and intelligently handle heterogeneous user requirements. To achieve this, network slicing is evolving towards a more user-centric digital transformation, focusing on architectures that incorporate native intelligence to enable self-managed connectivity in an integrated and isolated manner. However, these initiatives face the challenge of validating their results in production environments, particularly those utilizing ML-enabled orchestration, as they are often tested in local networks or laboratory simulations. This paper proposes a large-scale validation method using a network slicing prediction model to forecast latency using Deep Neural Networks (DNNs) and basic ML algorithms embedded within an NS architecture, evaluated in real large-scale production testbeds. It measures and compares the performance of different DNNs and ML algorithms, considering a distributed database application deployed as a network slice over two large-scale production testbeds. The investigation highlights how AI-based prediction models can enhance network slicing orchestration architectures and presents a seamless, production-ready validation method as an alternative to fully controlled simulations or laboratory setups. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16077 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-driven Orchestration at Scale: Estimating Service Metrics on National-Wide Testbeds Moreira, Rodrigo Pasquini, Rafael Martins, Joberto S. B. Carvalho, Tereza C. Silva, Flávio de Oliveira Emerging Technologies Artificial Intelligence Machine Learning Multiagent Systems Networking and Internet Architecture Network Slicing (NS) realization requires AI-native orchestration architectures to efficiently and intelligently handle heterogeneous user requirements. To achieve this, network slicing is evolving towards a more user-centric digital transformation, focusing on architectures that incorporate native intelligence to enable self-managed connectivity in an integrated and isolated manner. However, these initiatives face the challenge of validating their results in production environments, particularly those utilizing ML-enabled orchestration, as they are often tested in local networks or laboratory simulations. This paper proposes a large-scale validation method using a network slicing prediction model to forecast latency using Deep Neural Networks (DNNs) and basic ML algorithms embedded within an NS architecture, evaluated in real large-scale production testbeds. It measures and compares the performance of different DNNs and ML algorithms, considering a distributed database application deployed as a network slice over two large-scale production testbeds. The investigation highlights how AI-based prediction models can enhance network slicing orchestration architectures and presents a seamless, production-ready validation method as an alternative to fully controlled simulations or laboratory setups. |
| title | AI-driven Orchestration at Scale: Estimating Service Metrics on National-Wide Testbeds |
| topic | Emerging Technologies Artificial Intelligence Machine Learning Multiagent Systems Networking and Internet Architecture |
| url | https://arxiv.org/abs/2507.16077 |