AI-driven Orchestration at Scale: Estimating Service Metrics on National-Wide Testbeds

TL;DR

This paper presents a large-scale validation method using AI-driven prediction models to improve network slicing orchestration in real production environments, moving beyond traditional simulation-based testing.

Contribution

It introduces a scalable validation approach employing DNNs and ML algorithms for network slicing performance prediction in real-world testbeds, demonstrating practical deployment.

Findings

AI models effectively predict latency in large-scale networks

Validation method outperforms traditional simulation approaches

Enhanced orchestration through AI-driven insights

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.