AI-driven orchestration at scale: Estimating service metrics on national-wide testbeds

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...

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Vydáno v:Future generation computer systems Ročník 174; s. 107971
Hlavní autoři: Moreira, Rodrigo, Pasquini, Rafael, Martins, Joberto S.B., Carvalho, Tereza C., de Oliveira Silva, Flávio
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.01.2026
Témata:
Deep Neural Networks
Distributed database
Machine learning
Network slicing
Service-level agreement
Network slicing
Service-level agreement
Deep Neural Networks
Distributed database
Machine learning
ISSN:0167-739X
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Shrnutí: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. •Forecasting behavior in production-ready network slicing architectures.•Architectural study of embedded DNNs and basic ML for slicing SLA conformance.•Evaluation of hyperparameter tuning for AI-native network slices on testbeds.•Creation of a dataset workflow for realistic slicing application workloads;
ISSN:0167-739X
DOI:10.1016/j.future.2025.107971