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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| Vydané v: | Future generation computer systems Ročník 174; s. 107971 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.01.2026
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| ISSN: | 0167-739X |
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| Abstract | 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; |
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| AbstractList | 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; |
| ArticleNumber | 107971 |
| Author | Carvalho, Tereza C. Martins, Joberto S.B. de Oliveira Silva, Flávio Pasquini, Rafael Moreira, Rodrigo |
| Author_xml | – sequence: 1 givenname: Rodrigo orcidid: 0000-0002-9328-8618 surname: Moreira fullname: Moreira, Rodrigo email: rodrigo@ufv.br organization: Federal University of Viçosa (UFV), Rio Paranaíba, Minas Gerais, Brazil – sequence: 2 givenname: Rafael orcidid: 0000-0002-8781-3914 surname: Pasquini fullname: Pasquini, Rafael email: rafael.pasquini@ufu.br organization: Federal University of Uberlândia (UFU), Uberlândia, Brazil – sequence: 3 givenname: Joberto S.B. orcidid: 0000-0003-1310-9366 surname: Martins fullname: Martins, Joberto S.B. email: joberto.martins@animaeducacao.com.br organization: Salvador University (UNIFACS), Salvador, Bahia, Brazil – sequence: 4 givenname: Tereza C. orcidid: 0000-0002-0821-0614 surname: Carvalho fullname: Carvalho, Tereza C. email: terezacarvalho@usp.br organization: University of São Paulo (USP), São Paulo, Brazil – sequence: 5 givenname: Flávio surname: de Oliveira Silva fullname: de Oliveira Silva, Flávio email: flavio@di.uminho.pt organization: University of Minho (UMinho), Braga, Portugal |
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| Keywords | Network slicing Service-level agreement Deep Neural Networks Distributed database Machine learning |
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