ETSI GR ZSM 005 V1.1.1 (2020-05)

Zero-touch network and Service Management (ZSM); Means of Automation

DGR/ZSM-005ed111_Autom

General Information

• Status: Not Published
• Technical Committee: ZSM - Zero-touch network and Service Management
• Current Stage: 12 - Completion
• Due Date: 16-May-2020
• Completion Date: 19-May-2020

Standards Content (Sample)

GROUP REPORT
Zero-touch network and Service Management (ZSM);
Means of Automation
Disclaimer
The present document has been produced and approved by the Zero touch network and Service Management (ZSM) ETSI
Industry Specification Group (ISG) and represents the views of those members who participated in this ISG.
It does not necessarily represent the views of the entire ETSI membership.

2 ETSI GR ZSM 005 V1.1.1 (2020-05)

Reference
DGR/ZSM-005ed111_Autom
Keywords
automation, management, model, network,
orchestration, service
ETSI
650 Route des Lucioles
F-06921 Sophia Antipolis Cedex - FRANCE

Tel.: [phone]  Fax: [phone]

Siret N° 348 623 562 00017 - NAF 742 C
Association à but non lucratif enregistrée à la
Sous-Préfecture de Grasse (06) N° 7803/88

Important notice
The present document can be downloaded from:
http://www.etsi.org/standards-search
The present document may be made available in electronic versions and/or in print. The content of any electronic and/or
print versions of the present document shall not be modified without the prior written authorization of ETSI. In case of any
existing or perceived difference in contents between such versions and/or in print, the prevailing version of an ETSI
deliverable is the one made publicly available in PDF format at www.etsi.org/deliver.
Users of the present document should be aware that the document may be subject to revision or change of status.
Information on the current status of this and other ETSI documents is available at
https://portal.etsi.org/TB/ETSIDeliverableStatus.aspx
If you find errors in the present document, please send your comment to one of the following services:
https://portal.etsi.org/People/CommiteeSupportStaff.aspx
Copyright Notification
No part may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying
and microfilm except as authorized by written permission of ETSI.
The content of the PDF version shall not be modified without the written authorization of ETSI.
The copyright and the foregoing restriction extend to reproduction in all media.

© ETSI 2020.
All rights reserved.
DECT™, PLUGTESTS™, UMTS™ and the ETSI logo are trademarks of ETSI registered for the benefit of its Members.

3GPP™ and LTE™ are trademarks of ETSI registered for the benefit of its Members and
of the 3GPP Organizational Partners.
oneM2M™ logo is a trademark of ETSI registered for the benefit of its Members and
of the oneM2M Partners. ®
GSM and the GSM logo are trademarks registered and owned by the GSM Association.
ETSI
3 ETSI GR ZSM 005 V1.1.1 (2020-05)
Contents
Intellectual Property Rights . 6
Foreword . 6
Modal verbs terminology . 6
Introduction . 6
1 Scope . 9
2 References . 9
2.1 Normative references . 9
2.2 Informative references . 9
3 Definition of terms, symbols and abbreviations . 13
3.1 Terms . 13
3.2 Symbols . 13
3.3 Abbreviations . 14
4 Means of Automation . 14
4.1 Overview . 14
4.2 Means of Automation: Policy Driven Automation . 15
4.2.1 Motivation. 15
4.2.2 Problems to be solved . 16
4.2.3 Solution Principles and Concepts . 16
4.2.4 Implications . 17
4.2.5 Proof of concept . 17
4.2.6 Relevance for ZSM . 17
4.3 Means of Automation: "Intent Based" . 18
4.3.0 Introduction. 18
4.3.1 Motivation. 18
4.3.2 Problems to be solved . 19
4.3.3 Principles and Concepts . 19
4.3.3.0 Differentiating Intent from Policy . 19
4.3.3.1 Policy-based management . 19
4.3.3.2 Intent-based management . 20
4.3.3.3 Intent-based modelling . 20
4.3.4 Implications . 22
4.3.4.0 Notions of "Intent-based" . 22
4.3.4.1 Intent API . 23
4.3.4.2 Intent-based networking systems . 23
4.3.4.3 Intent-based Network Service orchestration . 23
4.3.4.4 Intent-based Service orchestration . 24
4.3.5 Proof of concept . 24
4.3.5.0 "Intent-Based" is proven . 24
4.3.5.1 Intent API . 24
4.3.5.2 Intent based networking system . 25
4.3.5.3 Intent based Network Service orchestration . 25
4.3.5.4 Intent-based Service orchestration . 26
4.3.6 Relevance for ZSM . 27
4.4 Means of Automation: Intent Based Service Orchestration . 27
4.4.0 Introduction. 27
4.4.1 Motivation. 28
4.4.1.1 Business Goals . 28
4.4.1.2 Inhibitors of agility . 28
4.4.1.3 Cope with complexity . 28
4.4.1.4 Simplify automation . 29
4.4.2 Problems to be solved . 29
4.4.2.1 Aspects to consider per service . 29
4.4.2.2 Behaviour is independent from service model . 30
4.4.2.3 Models are independent from software . 30
ETSI
4 ETSI GR ZSM 005 V1.1.1 (2020-05)
4.4.2.4 Organization and vendor independence . 30
4.4.2.5 New modelling approach . 30
4.4.3 Principles and Concepts . 31
4.4.3.1 Intent based modelling . 31
4.4.3.2 Modelling language: Dynamic Service Descriptors (DSD) . 31
4.4.3.3 Modularization and composition of services . 33
4.4.3.4 The generic orchestration engine . 33
4.4.3.5 Automate integrations and Service marketplace . 34
4.4.4 Implications . 34
4.4.4.1 Applicable languages and necessary extensions . 34
4.4.4.2 Architecture simplification . 35
4.4.5 Proof of concept . 36
4.4.6 Relevance for ZSM . 37
4.4.6.0 Several implications become relevant . 37
4.4.6.1 Define Everything as service. 38
4.4.6.2 Operators landscape is a service . 38
4.4.6.3 Closed Loop is a service . 38
4.4.6.4 Support brownfield and classic services . 38
4.4.6.5 Support unexpected future services . 38
4.4.6.6 Revisit modelling languages . 38
4.4.6.7 Define an architecture framework . 38
4.5 Means of Automation for Network Governance . 39
4.5.1 Motivation. 39
4.5.2 Problems to be solved . 39
4.5.3 Solution Principles and Concepts . 40
4.5.3.0 Functions to differentiate . 40
4.5.3.1 The Human-to-Network Function . 41
4.5.3.2 The Policy Derivation and Management Function . 43
4.5.3.3 The AF Management Function . 45
4.5.3.4 The Enforcement Function . 45
4.5.3.5 Examples of Governance Mechanisms . 46
4.5.3.5.1 Translation Mechanisms . 46
4.5.3.5.2 Semantic-based Approach for Policy Conflict Detection . 47
4.5.4 Implications . 48
4.5.5 Proof of concept . 49
4.5.6 Relevance for ZSM . 49
4.6 Means of Automation for Network Stability . 49
4.6.1 Motivation. 49
4.6.2 Problems to be solved . 49
4.6.3 Solution Principles and Concepts . 53
4.6.3.1 Times of the Identification of Interactions between AFs . 53
4.6.3.2 Algorithms to Insure Coordination . 54
4.6.3.3 Coordination Mechanisms to Control AFs . 56
4.6.4 Implications . 56
4.6.5 Proof of concept . 57
4.6.6 Relevance for ZSM . 57
4.7 Means of Automation: Reinforcement Learning . 58
4.7.1 Motivation. 58
4.7.2 Problems to be solved . 58
4.7.3 Solution Principles and Concepts . 59
4.7.4 Implications . 60
4.7.5 Proof of concept . 61
4.7.6 Relevance for ZSM . 61
4.8 Means of Automation: Transfer Learning . 61
4.8.1 Motivation. 61
4.8.2 Problems to be solved . 62
4.8.3 Solution Principles and Concepts . 62
4.8.3.1 The Deep Reinforcement Learning framework . 62
4.8.3.2 Self-Transfer Optimization Network. 63
4.8.3.2.1 Deep Reinforcement Learning Framework . 63
4.8.3.2.2 Transfer Learning Approach . 65
4.8.4 Implications . 67
ETSI
5 ETSI GR ZSM 005 V1.1.1 (2020-05)
4.8.5 Proof of concept . 68
4.8.6 Relevance for ZSM . 68
Annex A: Change History . 69
History . 70

ETSI
6 ETSI GR ZSM 005 V1.1.1 (2020-05)
Intellectual Property Rights
Essential patents
IPRs essential or potentially essential to normative deliverables may have been declared to ETSI. The information
pertaining to these essential IPRs, if any, is publicly available for ETSI members and non-members, and can be found
in ETSI SR 000 314: "Intellectual Property Rights (IPRs); Essential, or potentially Essential, IPRs notified to ETSI in
respect of ETSI standards", which is available from the ETSI Secretariat. Latest updates are available on the ETSI Web
server (https://ipr.etsi.org/).
Pursuant to the ETSI IPR Policy, no investigation, including IPR searches, has been carried out by ETSI. No guarantee
can be given as to the existence of other IPRs not referenced in ETSI SR 000 314 (or the updates on the ETSI Web
server) which are, or may be, or may become, essential to the present document.
Trademarks
The present document may include trademarks and/or tradenames which are asserted and/or registered by their owners.
ETSI claims no ownership of these except for any which are indicated as being the property of ETSI, and conveys no
right to use or reproduce any trademark and/or tradename. Mention of those trademarks in the present document does
not constitute an endorsement by ETSI of products, services or organizations associated with those trademarks.
Foreword
This Group Report (GR) has been produced by ETSI Industry Specification Group (ISG) Zero touch network and
Service Management (ZSM).
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.
Introduction
The automation of network management and service delivery is becoming critical. All major industries are rapidly
digitizing and automating their businesses, relying on state-of-the-art cloud platforms and connectivity services
supporting a similar level of business agility and flexibility. CSP (Communications Service Provider) service delivery
and network management automation is thus becoming critical for handling the increase in overall complexity and scale
of operations created by the transformation of networks into a programmable, software-driven, service-based
architecture. Going forward, unprecedented operational agility will be required to support new business opportunities
enabled by technologies, such as network slicing and artificial intelligence.
The goal is to have all operational processes and tasks (e.g. service creation, fulfilment, assurance, and optimization)
executed automatically and enabled at scale and at a required TCO.
The present document explores and introduces different means and approaches to automate particular aspects of these
functionalities, operational processes and tasks.
Towards the automation continuum
In its simplest form, automation is the action of making a task executable without human intervention. It is realized by
introducing new automatic functions or by replacing, modifying or augmenting manual functions with automation
artifacts (e.g. a script executing a series of commands).
ETSI
7 ETSI GR ZSM 005 V1.1.1 (2020-05)
Automation applies to different granularities: from tasks (or function) and processes up to the entire management and
operation of digital infrastructures, i.e. to the entire Life Cycle Management (LCM) of networks and services.
Communication networks and services are already well but fragmentary automated systems.
Individual functions usually exhibit high-level automation. For example, Interior Gateway Protocols (IGP) such as
OSPF or IS-IS can automatically discover peers, advertise capabilities, share topology information, compute routing
paths and react to failures, independently of external control or human supervision [i.1].
Automation also applies to the network or service life cycle management covering phases such as installation,
configuration, provisioning, and termination; and coping with additional level of flexibility introduced by the
decoupling of control and data planes and virtualisation techniques. The essential challenge arising from this advanced
capability is the ability to develop integrated solutions (or systems) out of the composable components with the right
levels of performance, robustness, extensibility, reconfigurability; stressing even further the need for standardized
interfaces, models and mechanisms.
Yet, building a comprehensive automation solution remains an open problem. A comprehensive automation solution
consists in chaining automated functions, with the following properties:
• Vertically end-to-end, i.e. across the protocol stack or from the service-layer to the physical-layer.
• Horizontally end-to-end, i.e. across different technologies or administrative domains.
• Repeatable and reusable in different contexts, i.e. relies on standardized or best current practices for interfaces
and models.
• Provisioning dynamically, customizable "control or touch points" in the end-to-end automation loop for human
supervision.
Therefore, a comprehensive automation provides an approach for combining function automation with process
automation; in line with the approach of Continuous Provisioning (CP), as an additional step of the Continuous
Integration (CI)/Continuous Delivery (CD) model.
Automation challenges
Realizing the automation continuum faces certain challenges.
Automation pros: machine-based automation is useful for repetitive, intensive and/or error-prone tasks. Where a human
will fatigue and introduce errors, a program will run relentlessly and without departing from the normative work flow of
its operations. There is no contest that in front of repetitive and intensive actions, machines outperform human
capabilities by far. Automation can be applied virtually anywhere in the digital infrastructure chain of operation, from
functional level to inter-functions, to processes and life-cycle.
Automation cons: Pure automation techniques show rapidly their limitations limits in front of heterogeneity, changing
context/conditions of the problem at hand, i.e. they are not adaptive. These techniques will require:
• either advanced level of customization or fine tuning, which will make the automation solution specialized and
lower the automation gain;
• or human intervention to adapt to the new context (technology, domain, operation), which works against the
initial goal of automating tasks.
Tackling the automation challenge is necessary but not sufficient. Automation alone can only adapt within the function
pre-defined scope and settings. Higher levels of autonomy can be reached by combining the automatic, aware and
adaptive properties [i.2].
ETSI
8 ETSI GR ZSM 005 V1.1.1 (2020-05)

Figure 1: From Automatic to Autonomous - the "AAAA properties"
Collectively, the four properties qualify an autonomous system. Ultimately, this boils down to the essential coupling of
automation with the intelligence that will drive it towards cognitive operation.
From automation to autonomous and cognitive management
Cognitive management automatically solves complex problems under uncertain (sometimes hostile) conditions to adjust
and produce effective (re)action plans [i.3]. Cognitive management covers:
• Automation: ability to perform according to predetermined set of instructions.
• Complex: involve learning, inference and reasoning, causality analysis, expert knowledge.
• Uncertainty: epistemic (lack of knowledge) or aleatory (randomness/variability).
• Closed-loop: mainly adaptive (less often model-reference/predictive).

Figure 2: Legacy versus Cognitive Management - enabling a Closed Loop
Autonomous or cognitive systems relies on closed control loops for their base operation. Several types of closed control
loops exist such as MAPE-K, OODA, MRACL [i.4] for the most used and well-known ones. Both individual functions
and systems composed of functions can realize closed control loop operations. In the case of systems, it is the
combination or chaining of the functions that collectively achieve the closed loop operation. It is therefore important to
consider not only the functional-level design of the system but also the external properties of its functions or the system
behaviour to assess whether the systems is autonomous (i.e. exhibits the AAAA properties).
Beyond the different types of control loops, 3 main pillars characterize cognitive system management: measure, learn,
decide.
There is a gradient of autonomy levels for the combined management and managed systems. The share of autonomy of
each part of the system can vary from case to case, from function to function and from domain to domain.
To reach a certain autonomy level, the composing parts of the system interact with each other: they discover
capabilities, requirements and constraints supported by each, eventually negotiate or trade responsibilities and
implications (e.g. areas of application, separation of concerns), define conditions of their interactions (escalation,
delegation and coordination) to support the targeted autonomy level. This trading is dynamic. Most of the negotiation
can be set at instantiation, but it should be possible to change the negotiated parameters over the life of the system. Such
considerations and levels of flexibility should be considered in the design goals.

ETSI
9 ETSI GR ZSM 005 V1.1.1 (2020-05)
1 Scope
The present document explores different existing means or approaches to achieve automation. Such means may exist at
different level of management and managed systems, e.g. at service and network management level, at managed
network function level and at managed network level for autonomous optimization. All these means can have a value to
achieve the ZSM goals.
The present document does not address a systematic survey of all standardization activities, open-source or other means
to achieve automation. Rather it evaluates selected existing and proven means of automation at different levels of
managed and management systems, provided by members of the ISG. That comprises for example:
• Alternatives for classic modelling such as intent vs. imperative vs. declarative modelling.
• Lessons learnt from 'model driven automation' of service and policy orchestration, including closed loop.
• Framework for self-managed VNFs based on cloud native network functions and implications.
• Delineate service modelling against 'machine learning'-inspired 'closed loop automation' modelling.
• Autonomous management of networks.
Means of automation may be single elements or composition of elements which enable service and network automation.
They are made visible by this report, and may be considered by the ISG as elements in other work items for
specifications.
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long term validity.
The following referenced documents are not necessary for the application of the present document but they assist the
user with regard to a particular subject area.
[i.1] OSPF for Implementing Self-Adaptive Routing in Autonomic Networks: A Case Study, Rétvári G.
et al.
[i.2] Towards Autonomic Networks, S. Schmid et al.
[i.3] Interactive Self Driving Networks, P. Dimitriou.
[i.4] The Use of Control Loops in Autonomic Networking, Strassner J. et al.
[i.5] IETF RFC 7575 (June 2015): "Autonomic Networking: Definitions and Design Goals",
M.Behringer et al.
NOTE: Available at https://tools.ietf.org/html/rfc7575.
[i.6] ETSI GR NFV-IFA 023: "Network Functions Virtualisation (NFV); Management and
Orchestration; Report on Policy Management in MANO; Release 3".
ETSI
10 ETSI GR ZSM 005 V1.1.1 (2020-05)
[i.7] OPNFV Wiki, copper project.
NOTE: Available at https://wiki.opnfv.org/display/copper.
[i.8] OpenStack Wiki, congress project.
NOTE: Available at https://wiki.openstack.org/wiki/Congress.
[i.9] ONAP Wiki, Policy Framework.
NOTE: Available at https://wiki.onap.org/display/DW/The+ONAP+Policy+Framework.
[i.10] IETF RFC 3060 (February 2001): "Policy Core Information Model -- Version 1 Specification", B.
Moore, E. Ellesson, J. Strassner, A. Westerinen.
NOTE: Available at https://tools.ietf.org/html/rfc3060.
[i.11] IETF RFC 3198 (November 2001): "Terminology for Policy-Based Management", A. Westerinen
and al.
NOTE: Available at https://tools.ietf.org/html/rfc3198.
[i.12] Morris Sloman: "Policy driven management for distributed systems", Journal of Network and
Systems Management, Plenum Press, Vol. 2, No. 4, 1994, pp. 333-360.
NOTE: Available at https://core.ac.uk/download/pdf/1587309.pdf.
[i.13] John Strassner: "Intent-based Policy management".
NOTE: Available at https://datatracker.ietf.org/meeting/95/materials/slides-95-sdnrg-1.
[i.14] A. K. Bandara, E. C. Lupu, J. Moffett and A. Russo, "A goal-based approach to policy
refinement," Proceedings. Fifth IEEE International Workshop on Policies for Distributed Systems
and Networks, 2004. POLICY 2004., Yorktown Heights, NY, USA, 2004, pp. 229-239.
NOTE: Available at https://www.researchgate.net/publication/4079236_A_goal-
based_approach_to_policy_refinement.
[i.15] Network World Jun 2017.
NOTE: Available at https://www.networkworld.com/article/3202699/lan-wan/what-is-Intent-based-
networking.html.
[i.16] Dave Lenrow: "Intent: Don't Tell Me What to Do! (Tell Me What You Want)".
NOTE: Available at https://www.sdxcentral.com/articles/contributed/network-intent-summit-perspective-david-
lenrow/2015/02/.
[i.17] ONF TR-523 (October 2016): "Intent NBI - Definition and Principles".
NOTE: Available at https://www.opennetworking.org/images/stories/downloads/sdn-resources/technical-
reports/TR-523_Intent_Definition_Principles.pdf.
[i.18] The Nemo Project.
NOTE: Available at http://nemo-project.net/.
[i.19] SDXcentral report from Cisco: "intent based networking explained".
NOTE: Available at https://www.sdxcentral.com/networking/sdn/intent-based/definitions/what-is-intent-based-
networking.
[i.20] HPE service and network automation web page and white paper: "Reimagining OSS for service
agility in hybrid networks".
NOTE: Available at https://h20195.www2.hpe.com/V2/GetDocument.aspx?docname=4AA6-3832ENW
ETSI
11 ETSI GR ZSM 005 V1.1.1 (2020-05)
[i.21] Light Reading Webinar: "Making the case for NFV: it's all about the Service Model".
NOTE: Available at https://www.lightreading.com/webinar.asp?webinar_id=641.
[i.22] TMF Catalyst 2016 about policy driven: "Operations Defined Automation", based on the described
intent based orchestration engine.
NOTE: Available at https://inform.tmforum.org/features-and-analysis/2016/12/will-operations-defined-
automation-get-real-virtuality/?_ga=2.39738019.1872407733.1527684564-1401621168.1525266880.
[i.23] SDX central webinar, A.Krichel, R.Eberhardt, R.Chua: "Intent Based Orchestration - Enabling &
st
Automating Next-Generation Networks", Nov 1 2016.
NOTE: Available at https://www.hpe.com/h22228/video-gallery/us/en/700000804/EN/US/aec78df2-4a88-4fea-
9b88-4b75e175755a/intent-based-orchestration-_-enabling--automating-next_generation-networks/video.
[i.24] TM Forum ZOOM project, with references to ODA and HIP.
NOTE: Available at https://www.tmforum.org/collaboration/zoom-project/.
[i.25] TM Forum HIP: "TMF070 Implementation and Deployment Blueprints for Hybrid Environments
R17.5.1".
NOTE: Available at https://www.tmforum.org/resources/specification/tmf070-implementation-and-deployment-
blueprints-for-hybrid-environments-r17-5-0/.
[i.26] MEF Lifecycle-Service orchestration.
NOTE: Available at https://www.mef.net/lso/lifecycle-service-orchestration.
[i.27] López, J. A. L., Muñoz, J. M. G., & Morilla, J. (2007). A Telco Approach to Autonomic
Infrastructure Management. In Advanced Autonomic Networking and Communication (pp. 27 42).
Birkhäuser Basel.
[i.28] EU FP7 Project EFIPSANS: "Exposing the Features in IP version Six protocols that can be
exploited/extended for the purposes of designing/building Autonomic Networks and Services
(2008-2011)".
NOTE: Available at https://cordis.europa.eu/project/rcn/85542/factsheet/en.
[i.29] ETSI GS AFI 002 (V1.1.1): "Autonomic network engineering for the self-managing Future
Internet (AFI); Generic Autonomic Network Architecture (An Architectural Reference Model for
Autonomic Networking, Cognitive Networking and Self-Management)".
NOTE: Available at http://www.etsi.org/deliver/etsi_gs/AFI/001_099/002/01.01.01_60/gs_afi002v010101p.pdf.
[i.30] J. Strassner, N. Agoulmine, and E. Lehtihet, "FOCALE - A novel autonomic networking
architecture," in International Transactions on Systems, Science, and Applications (ITSSA)
Journal, Vol. 3, No. 1, pp. 64-79, May 2007.
[i.31] Tsagkaris, K., Nguengang, G., Peloso, P., Fuentes, B., Mamatas, L., Georgoulas, S. &
Smirnov, M. (2013). Unified Management Framework (UMF) Specifications - Release 3 -
UniverSelf Deliverable D2.4.
NOTE: Available at http://www.univerself-project.eu/technical-reports.
[i.32] EuCNC conference, Unified Management for Converged Networks, 2013.
NOTE: Available at https://www.eucnc.eu/history/.
[i.33] M. Behringer, B. Carpenter, T. Eckert, L. Ciavaglia, J. Nobre (2016). A Reference Model for
Autonomic Networking (work in progress).
NOTE: Available at https://datatracker.ietf.org/doc/draft-ietf-anima-reference-model/.
ETSI
12 ETSI GR ZSM 005 V1.1.1 (2020-05)
[i.34] The Linux Foundation, Open Network Automation Platform (ONAP) Project (March 2017).
NOTE: Available at https://www.onap.org/.
[i.35] M.S. Sloman: "Policy Driven Management for Distributed Systems," Journal of Network and
Systems Management, Vol. 2, No. 4, pp. 333-360, Plenoum Press, December 1994.
[i.36] Davy, S., Jennings, B., & Strassner, J. (2007). The policy continuum-a formal model. In
Proceedings of the Second IEEE International Workshop on Modelling Autonomic
Communications Environments (pp. 65-79).
[i.37] Recommendation ITU-T M.3050.1 (2007): "Enhanced Telecom Operations Map (eTOM) - The
business process framework".
[i.38] Antoniou, G., & Van Harmelen, F. (2009). Web ontology language: OWL. In Handbook on
ontologies (pp. 91-110). Springer, Berlin, Heidelberg.
[i.39] Telemanagement Forum - TMF (2018). GB922 Information Framework R17.5 (SID). Parsippany,
NJ.
[i.40] ETSI TS 132 500 (V11.1.0): "Universal Mobile Telecommunications System (UMTS); LTE;
Telecommunication management; Self-Organizing Networks (SON); Concepts and requirements
(3GPP TS 32.500 version 11.1.0 Release 11)".
[i.41] IFIP/IEEE International Symposium (2013): "Conflict free coordination of SON functions in a
Unified Management Framework: Demonstration of a proof of concept prototyping platform", pp.
1092-1093, Koutsouris, N., Tsagkaris, K., Demestichas, P., Altman, Z., Combes, R., Peloso, P. &
Galis, A.
[i.42] Ciavaglia, L., Ghamri-Doudane, S., Smirnov, M., Demestichas, P., Stavroulaki, V. A., Bantouna,
A., & Sayrac, B. (2012). Unifying management of future networks with trust. Bell Labs Technical
Journal, 17(3), 193-212.
[i.43] IEEE 7th International ICST Conference (2012): "A mathematical model for joint optimization of
coverage and capacity in self-organizing network in centralized manner", pp. 622-626, Luo, W.,
Zeng, J., Su, X., Li, J., & Xiao, L.
[i.44] IEEE 11th International Symposium - In Modeling & Optimization in Mobile, Ad Hoc & Wireless
Networks (WiOpt) (2013): "Coordination of autonomic functionalities in communications
networks", pp. 364-371, Combes, R., Altman, Z., & Altman, E.
[i.45] Iacoboaiea, O. C., Sayrac, B., Ben Jemaa, S., & Bianchi, P. (2014). SON conflict resolution using
reinforcement learning with state aggregation. In Proceedings of the 4th workshop on All things
cellular: operations, applications, & challenges (pp. 15-20). ACM.
[i.46] H. Lipson: "Robots on the run," Nature, 28 March 2019.
NOTE: Available: https://www.nature.com/articles/d41586-019-00999-w, accessed 1 August 2019.
[i.47] K. Goldberg: "Robots and the return to collaborative intelligence" Nature Machine Intelligence,
vol. 1, pp. 2-4, 2019.
[i.48] X. Zhao, L. Xia, T. Jiliang and Y. Dawei: "Deep Reinforcement Learning for Search,
Recommendation, and Online Advertising: A Survey," ACM SIGWEB Newsletter, 2019.
[i.49] V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu and Veness, "Human-level control through deep
reinforcement learning," Nature, vol. 518, pp. 529-533, 2015.
[i.50] D. Silver, A. Huang, C. J. Maddison, A. Guez and L. Sifre: "Mastering the game of Go with deep
neural networks and tree search," Nature, vol. 529, pp. 484-489, 2016.
[i.51] P. Henderson, R. Islam, P. Bachman, J. Pineau, D. Precup and D. Meger: "Deep reinforcement
learning that matters," in Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
ETSI
13 ETSI GR ZSM 005 V1.1.1 (2020-05)
[i.52] IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2018):
"Training Deep Networks With Synthetic Data: Bridging the Reality Gap by Domain
Randomization", J. Tremblay, A. Prakash, D. Acuna, M. Brophy, V. Jampani, C. Anil, T. To, E.
Cameracci, S. Boochoon and S. Birchfield.
[i.53] R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction, MIT press, 2018.
[i.54] "Machine intelligence addresses increasing network complexity", 27 February 2018.
NOTE: Available at https://www.ericsson.com/en/press-releases/2018/2/machine-intelligence-addresses-
increasing-network-complexity.
[i.55] V. Yajnanarayana, H. Rydén, L. Hévizi, A. Jauhari and M. Cirkic: "5G handover using
Reinforcement Learning", arXiv:1904.02572, 2019.
[i.56] Bao, Y., Li, Y., Huang, S., Zhang, L., Zamir, A. R., & Guibas, L. (2019): "An Information-
Theoretic Metric of Transferability for Task Transfer Learning. ICIP".
[i.57] Berger, S., Simsek, M., & et al. (2016): "Joint downlink and uplink tilt-based self-organization of
coverage and capac
...