On the Integration of AI/ML-based scaling operations in the 5Growth platform

J. Baranda, J. Mangues‐Bafalluy, E. Zeydan, L. Vettori, R. Martínez, Xi Li, Andres Garcia-Saavedra, C. Chiasserini, C. Casetti, Konstantin Tomakh, O. Kolodiazhnyi, C. Bernardos
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引用次数: 122

Abstract

The automated assurance of vertical service level agreements (SLA) is a challenge in 5G networks. The EU 5Growth project designs and develops a 5G End-to-End service platform that integrates Artificial Intelligence (AI) and Machine Learning (ML) techniques for any decision-making process in the management and orchestration (MANO) stack. This paper presents the detailed architecture and first prototype of the 5Growth platform taking AI/ML-based network service auto-scaling decisions. This also includes the modification of the ETSI network service descriptors for requesting AI/ML-based decisions for orchestration problems and the integration of a data engineering pipeline for real-time data gathering and model execution. Our evaluation shows that AI/ML-related service handling operations (1–2 s.) are well below instantiation/termination procedures (80/60 s., respectively). Furthermore, online classification can be performed in the order of hundreds of milliseconds (600 ms).
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关于基于AI/ ml的扩展操作在5Growth平台的整合
垂直服务水平协议(SLA)的自动化保障是5G网络面临的挑战。EU 5Growth项目设计并开发了一个5G端到端服务平台,该平台集成了人工智能(AI)和机器学习(ML)技术,可用于管理和编排(MANO)堆栈中的任何决策过程。本文介绍了采用基于AI/ ml的网络服务自动扩展决策的5Growth平台的详细架构和第一个原型。这还包括对ETSI网络服务描述符的修改,用于请求基于AI/ ml的编排问题决策,以及集成用于实时数据收集和模型执行的数据工程管道。我们的评估表明,与AI/ ml相关的服务处理操作(1-2秒)远低于实例化/终止程序(分别为80/60秒)。此外,在线分类可以以数百毫秒(600毫秒)的顺序执行。
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