{"ID":2892477,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.16077","arxiv_id":"2507.16077","title":"AI-driven Orchestration at Scale: Estimating Service Metrics on National-Wide Testbeds","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.","short_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 enab...","url_abs":"https://arxiv.org/abs/2507.16077","url_pdf":"https://arxiv.org/pdf/2507.16077v1","authors":"[\"Rodrigo Moreira\",\"Rafael Pasquini\",\"Joberto S. B. Martins\",\"Tereza C. Carvalho\",\"Flávio de Oliveira Silva\"]","published":"2025-07-21T21:24:40Z","proceeding":"cs.ET","tasks":"[\"cs.ET\",\"cs.AI\",\"cs.LG\",\"cs.MA\",\"cs.NI\"]","methods":"[]","has_code":false}