标题： 大规模的AI驱动编排：在国家级测试床上的服务指标估算 显示英文标题

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

摘要： 网络切片（NS）的实现需要AI原生的编排架构，以高效且智能地处理异构用户需求。 为了实现这一点，网络切片正在向更加以用户为中心的数字化转型演进，重点关注集成并隔离方式下实现自管理连接的原生智能架构。 然而，这些举措在生产环境中验证其结果时面临挑战，特别是那些利用机器学习（ML）启用的编排系统，因为它们通常在本地网络或实验室仿真中进行测试。 本文提出了一种大规模验证方法，使用网络切片预测模型，通过嵌入在网络切片架构中的深度神经网络（DNN）和基本机器学习算法来预测延迟，并在真实的大型生产测试平台中进行了评估。 它测量并比较了不同DNN和机器学习算法的性能，考虑了一个分布式数据库应用作为网络切片部署在两个大型生产测试平台之上。 这项研究强调了基于人工智能的预测模型如何增强网络切片编排架构，并提供了一种无缝的、可投入生产的验证方法，作为完全受控仿真或实验室设置的替代方案。 显示英文摘要

摘要： 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.