Automation and Autonomous system
Architecture Framework
by NGMN Alliance
Version:1.0
Date: 28.10.2022
Document Type:Final Deliverable (approved)
Confidentiality Class: P - Public
Authorised Recipients:
(for CR documents only)
Project: Network Automation and Autonomy Based on AI
Editor / Submitter: Sebastian Thalanany (UScellular)
Contributors: Yuhan Zhang (China Mobile Research Institute), Lingli Deng
(China Mobile Research Institute), Tony Verspecht (Cisco),
Roberta Maglione (Cisco), Andreas Volk (HPE), Andreas Krichel
(HPE), Jean Paul Pallois (Huawei), Luigi Licciardi (Huawei),
Paolo Volpato (Huawei), Gary Li (Intel), Sebastian Robitzsch
(Interdigital), Benoit Radier (Orange), Paul Edward Alvarez
(Smart), Sebastian Thalanany (UScellular), Manchang Ju (ZTE),
Liya Yuan (ZTE)
Approved by / Date: <NGMN Board / 28 October 2022 >
(c) 2022 Next Generation Mobile Networks Alliance e.V. All rights reserved. No part of this document may be
reproduced or transmitted in any form or by any means without prior written permission from NGMN
Alliance e.V.
Autonomous System and Network Automation Framework
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The information contained in this document represents the current view held by NGMN Alliance e.V. on the
issues discussed as of the date of publication. This document is provided “as is” with no warranties
whatsoever including any warranty of merchantability, non-infringement, or fitness for any particular
purpose. All liability (including liability for infringement of any property rights) relating to the use of
information in this document is disclaimed. No license, express or implied, to any intellectual property rights
are granted herein. This document is distributed for informational purposes only and is subject to change
without notice. Readers should not design products based on this document.
Autonomous System and Network Automation Framework
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Abstract
This document describes a high-level framework, in terms of entities and functions that
characterise autonomous system capabilities with an E2E (end-to-end) system perspective.
Architectural considerations, associated with autonomous system capabilities, endowed with
Artificial Intelligence/Machine Learning (AI/ML) models of cognition and application are
delineated as high-level requirements. The term "system" is an abstraction, which generalizes
and subsumes details such as specific networks, protocols, and implementations, in terms of
high-level requirements, perspectives, and insights.
The E2E system imbued with autonomous system capabilities consists of a virtualized
environment, with network slicing as a foundational building-block, for the realization of
flexible, granular, and optimized allocation of system resources, such as computing,
networking, and storage, for enabling network automation, without human intervention. The
application of assorted AI/ML models, facilitate autonomous system behaviours to suit
diverse deployment arrangements.
The architectural framework is intended to serve as guidance in the development of inter-
operable and market enabling specifications, for a continuing advancement of the 5G
ecosystem of heterogeneous access, virtualization, forward-looking service enablers, and
emerging usage scenarios.
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Contents
1 Introduction................. 7
2 References ................... 8
3 Definitions ................. 12
4 Automation and Autonomous System Context .......... 13
4.1 Overview of Network Automation ................. 13
4.2 Expected Benefits and Commercial Impact ............... 14
5 Autonomous System Architecture for Automation ........... 15
5.1 Reference Architecture ............... 15
5.2 System Characteristics and Context .............. 19
5.2.1 End-to-End (E2E) Network Slicing .............. 19
5.2.2 Cross-Domain Cooperation ................ 23
5.2.3 Security and Privacy ............... 24
5.2.4 Feedback Control Loop ................. 25
5.2.5 Bearer Plane Programmability ........... 27
5.3 Knowledge Plane .................. 28
5.3.1 Knowledge Management ............. 29
5.4 Management and Orchestration............. 31
5.4.1 Service Based Architecture (SBA) Context ............. 34
5.4.2 Virtualization and Microservices ............... 35
5.4.3 Cloud-Native and Cognitive Model ........... 35
5.4.4 Intelligent Orchestration .............. 39
5.4.5 Intent-based Networking ............. 39
5.5 AI/ML Models .................. 41
5.5.1 Supervised Learning............... 41
5.5.2 Unsupervised Learning ................. 41
5.5.3 Reinforcement Learning ............... 42
5.5.4 Federated Learning ................ 42
5.5.5 Transfer Learning ................... 42
5.5.6 Automated ML ................. 42
5.6 On-boarding and Certification ................. 43
6 Service Scenarios .................... 45
6.1 Common Marketplace ................ 46
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