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0 | /content/sample_data/Use-case-AN.pdf | I n t e r n a t i o n a l T e l e c o m m u n i c a t i o n U n i o n
ITU-T Technical Specification
TELECOMMUNICATION
STANDARDIZATION SECTOR
OF ITU
(28 October 2021 )
ITU-T Focus Group on Autonomous Networks
Technical Specification
Use cases for Autonomous Networks |
1 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) i Summary
This is a deliverable of the ITU -T Focus Group on Autonomous Networks (FG -AN).
This document analyses use cases for autonomous networks. It provides use case descriptions and
indicates the basic set of possible requirements for each use case. The use cases are divided into
categories, priorities are indicated, and actor -interaction d iagrams are added.
Keywords
Artificial Intelligence, autonomous networks, components, machine learning, requirements, use
cases
Contributors: Abhay Shanker Verma
TEC, Ministry of Communications
India Email: as.verm a@gov.in
Abhishek Dandekar
Fraunhofer HHI
Germany Email: abhishek.girish.dandekar@hhi -
extern.fraunhofer.de
Abhishek Thakur
Institute for Development and Research
in Banking Technology (IDRBT)
India Email: AbhishekT@idrbt.ac.in
Albert Cabellos -Aparicio
Barcelona Neural Networking
Universitat Politècnica de Catalunya
Barcelona, Spain Email: alberto.cabellos@upc.edu
Albert López -Brescó
Barcelona Neural Networking
Universitat Politècnica de Catalunya
Barcelona, Spain Email: alopez@ac.upc.edu
Alfons Mittermaier
Highstreet Technologies GmbH
Germany Email: alfons.mittermaier@highstreet -
technologies.com
Ammar Muthanna
SPbSUT
Russian Federation Email: ammarexpress@gmail.com
Andrey Kucheryavy
ITU-T SG11 Chairman Email: akouch@mail.ru
Ashish S Sharma
Fraunhofer HHI
Germany Email:
ashish.sanjay.sharma@hhi.fraunhofer.de
Bing Qian
China Telecom
China Tel: +8618511588556
Email: qianbing@chinatelecom.cn
Chi Wang
Microsoft Email: wang.chi@microsoft.com |
2 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) ii Cleverson Veloso NAHUM
Federal University of Pará
Brazil Email: cleversonahum@gmail.com
Dan Xu
China Telecom
China Tel: +86 10 5090 2570
Email: xudan6@chinatelecom.cn
Gerhard Wieser
FRINX
Slovakia Email: gwieser@frinx.io
Gokhan Kalem
Turkcell Teknoloji
Turkey Email: gokhan.kalem@turkcell.com.tr
Guillaume Quintard Email: guillaume.quintard@gmail.com
Jiaxin Wei
China Unicom
China Email: weijx29@chinaunicom.cn
Jose Alcaraz Calero
University of the West of Scotland
(UWS)
United Kingdom Email: Jose.Alcaraz -Calero@uws.ac.uk
Julius Schulz -Zander
Fraunhofer HHI
Germany Email: julius.schulz -
zander@hhi.fraunhofer.de
Khakimov Abdukodir
SPbSUT
Russian Federation Email: khakimov -aa@rudn.ru
Laurent Ciavaglia
Rakuten Mobile
Japan Email: laurent.ciavaglia@rakuten.com
Liya Yuan
ZTE Corporation
China Email: yuan.liya@zte.com.cn
Marco Gramaglia
University Carlos III of Madrid
Spain Email: mgramagl@it.uc3m.es
Michaela Blott
Xilinx
United States Email: mblott@xilinx.com
Michele Polese
Northeastern University
United States Email: m.polese@northeastern.edu
Miquel Ferriol -Galmés
Barcelona Neural Networking
Universitat Politècnica de Catalunya
Spain Email: mferriol@ac.upc.edu |
3 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) iii N. V. Narendra Kumar
Institute for Development and Research
in Banking Technology (IDRBT)
India Email: NVNarendra@idrbt.ac.in
Nik Sultana
Illinois Institute of Technology
United States Email: nsultana1@iit.edu
Ozgur Ercetin
Professor, Electronics Engineering
Sabanci University
Turkey Email: oercetin@sabanciuniv.edu
Pål Hermunn Johansen
Varnish Software
Sweden Email: hermunn@varnish -software.com
Paul Harvey
Rakuten Mobile
Japan Email: paul.harvey@rakuten.com
Pradipta Biswas
Indian Institute of Science (IISc)
India Email: pradipta@iisc.ac.in
Qi Sun
China Mobile
China Email: sunqiyjy@chinamobile.com
Qi Wang
University of the West of Scotland
(UWS)
United Kingdom Email: Qi.Wang@uws.ac.uk
Ranjana Sivaram
TEC, Ministry of Communications
India Email: ranjana.sivaram@gov.in
Salih Ergut
Oredata
Turkey Email: salih.ergut@oredata.com
Shabnam Sultana
Highstreet Technologies GmbH
Germany Email: shabnam.sultana@highstreet -
technologies.com
Sirko Straube
DFKI
Germany Email: sirko.straube@dfki.de
V. Udaya Sankar
SRM University -AP
India Email: udayasankar.v@srmap.edu.in
Vijay Kumar Roy
TEC, Ministry of Communications
India Email: vk.roy@gov.in |
4 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) iv Vishnu Ram O.V.
Independent Expert
India Email: vishnu.n@ieee.org
Wei Gao
China Unicom
China Email: gaow25@chinaunicom.cn
Yongsheng Liu
China Unicom
China Email: liuys170@chinaunicom.cn |
5 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) v CONTENTS
Page
1 SCOPE ................................ ................................ ................................ ................................ ................................ ..... 1
2 REFERENCES ................................ ................................ ................................ ................................ ........................ 1
3 TERMS AND DEFINITIONS ................................ ................................ ................................ ............................... 1
3.1 TERMS DEFINED ELSEWHERE ................................ ................................ ................................ ................................ . 1
3.2 TERMS DEFINED HERE ................................ ................................ ................................ ................................ ........... 1
4 ABBREVIATI ONS ................................ ................................ ................................ ................................ ................. 2
5 CONVENTIONS ................................ ................................ ................................ ................................ ..................... 3
6 INTRODUCTION ................................ ................................ ................................ ................................ ................... 3
7 USE CASES ................................ ................................ ................................ ................................ ............................. 4
7.1 IMPORT AND EXPORT OF KNOWLEDGE FOR AUTONOMOUS NETWORK ................................ ................................ .... 4
7.2 CONFIGURING AND DRIVING SIMULATORS FROM AUTONOMOUS COMPONENTS IN THE NETWORK ......................... 6
7.3 PEER-IN-LOOP (INCLUDING HUMANS )................................ ................................ ................................ .................... 8
7.4 CONFIGURING AND DRIVING AUTOMATION LOOPS FROM AUTONOMOUS COMPONENTS IN THE NETWORK ............. 9
7.5 DOMAIN ANALYTICS SERVICES FOR E2E SERVICE MANAGEMENT ................................ ................................ ....... 11
7.6 AUTOMATION AND INTELLIGENT OPERATION , MAINTENANCE AND MANAGEMENT (OAM) OF RADIO NETWORK 13
7.7 INTELLIGENT ENERGY SAVING FOR DATA CENTRES ................................ ................................ ............................. 14
7.8 AUTONOMOUS MASSIVE MIMO ................................ ................................ ................................ .......................... 15
7.9 NETWORK RESOURCE ALLOCATION FOR EMERGENCY MANAGEMENT BASED ON CLOSED LOOP ANALYSIS .......... 16
7.10 INTER -DOMAIN SERVICE AUTOMATION (IDSA) - FOR MICROFINANCE ................................ ............................ 20
7.11 AUTONOMOUS VERTICAL -DRIVEN EDGE SERVICE AND MIDDLE -MILE CONNECTIVITY FOR RURAL FINANCIAL
INCLUSION (FI) ................................ ................................ ................................ ................................ ............................. 23
7.12 SIGNALLING FLOWS FOR AUTONOMOUS IMT-2020 NETWORK ................................ ................................ ....... 26
7.13 PLUG/PLAY OF NETWORK INSTANCE ................................ ................................ ................................ ............... 27
7.14 “GENERATIVE ADVERSARIAL SANDBOX ”: (OR HYBRID CLOSED LOOPS ) ................................ ......................... 28
7.15 OPEN, INTEGRATED , LOG ANALYSIS ................................ ................................ ................................ ............... 29
7.16 COMPOSE -ABLE , HIERARCHICAL CLOSED LOOPS ................................ ................................ ............................ 30 |
6 | /content/sample_data/Use-case-AN.pdf | 7.16 COMPOSE -ABLE , HIERARCHICAL CLOSED LOOPS ................................ ................................ ............................ 30
7.17 QUALITY OF EXPERIENCE (QOE) PREDICTION AS -A-SERVICE (QP AAS) ................................ ......................... 30
7.18 AUTONOMY APPLIED TO CDN S ................................ ................................ ................................ ...................... 33
7.19 ANALYSIS -DRIVEN EVOLUTION IN VIRTUALIZED RAN BASED ON DEVOPS ................................ ..................... 35
7.20 EVOLVING EDGE APPLICATIONS FOR VERTICALS USING PRIVATE 5G ................................ .............................. 37
7.21 EXPERIMENTATION AND “FIRE-DRILLS ” FOR PUBLIC SAFETY NETWORKS ................................ ....................... 39
7.22 MACHINE LEARNING FOR NETWORK AUTOMATION ................................ ................................ ........................ 40
7.23 AUTONOMOUS AGENTS (WITH VARIED COMPETENCE ) IN NETWORKS ................................ ............................. 42
7.24 AUTOMATED , ADAPTIVE ACCELERATION FOR AI @ EDGE ................................ ................................ .............. 44
7.25 ASSISTIVE NETWORKS : ADAPTATION OF COMMUNICATION SYSTEM BASED ON CHANGING USER ACCESSIBILITY
NEEDS ................................ ................................ ................................ ................................ ................................ ....... 45
7.26 EV-AS-A-SERVICE : ACHIEVING ZERO TOUCH EVOLUTION IN A DELEGATED AUTONOMY CASE ....................... 47
7.27 EXPERIMENTATION AS A SERVICE : DIGITAL TWINS AS PLATFORMS FOR EXPERIMENTATION .......................... 50
7.28 EVOLUTION FROM SCENARIO -SPECIFIC , EXPLICIT -COORDINATION TO COORDINATION -FREE INTEROPERABILITY
(ACHIEVED USING DATA -DRIVEN APPROACHES ) ................................ ................................ ................................ ........... 52
7.29 INTELLIGENT MAINTENANCE ASSISTANCE SYSTEM ................................ ................................ ......................... 54
7.30 DEMAND FORECASTING AND LIVE SERVICE MIGRATION METHODS IN EDGE COMPUTING SYSTEMS ................. 57
7.31 OPENCN: AN OPEN REPOSITORY OF INTENTS FOR CONTROLLERS AND MODULES ................................ ........... 59
7.32 AI ENABLED GAME THEORY -BASED MECHANISM FOR RESOURCE ALLOCATION ................................ .............. 61
7.33 SERVICE AUTOMATION USING WORKFLOWS ................................ ................................ ................................ ... 66
7.34 DISAGGREGATION AND PLACEMENT OF IN -NETWORK PROGRAMS ................................ ................................ .. 69
7.35 A FAST AND LIGHTWEIGHT AUTO ML LIBRARY (FLAML) ................................ ................................ .............. 71
7.36 CONNECTED AI (CAI) TESTBED : TESTBED FOR 5G CONNECTED ARTIFICIAL INTELLIGENCE ON VIRTUALIZED
NETWORKS ................................ ................................ ................................ ................................ ................................ .... 73
7.37 NEGOTIATED BOUNDARIES IN AN FOR SEAMLESS NETWORK SHARING ................................ ........................... 75
7.38 AN ENABLED END -TO-END SUPPLY CHAIN ................................ ................................ ................................ ..... 78
7.39 TOWARDS OPENNESS IN AN ................................ ................................ ................................ ........................... 80
7.40 AWARENESS IN AN ................................ ................................ ................................ ................................ ........ 82 |
7 | /content/sample_data/Use-case-AN.pdf | 7.40 AWARENESS IN AN ................................ ................................ ................................ ................................ ........ 82
BIBLIOGRAPHY ................................ ................................ ................................ ................................ ........................... 85 |
8 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 1 Technical Specification
Use cases for Autonomous Networks
1 Scope
This Technical Specification analyses use cases for autonomous networks. It provides use case
descriptions and indicates the basic set of possible requirements for each use case. The use cases are
divided into categories, priorities are indicated, and acto r-interaction diagrams are added.
2 References
[ITU -T Y.3172] ITU-T Recommendation Y.3172 (2019), Architectural framework for
machine learning in future networks including IMT -2020 .
[ITU -T Y.3173] ITU-T Recommendation Y.3173 (2020), Framework for evalua ting
intelligence levels of future networks including IMT -2020 .
[ITU -T Y.3174] ITU-T Recommendation Y.3174 (2020), Framework for data handling to
enable machine learning in future networks including IMT -2020 .
[ITU -T Y.3176] ITU-T Recommendation Y.3 176 (2020), Machine learning marketplace
integration in future networks including IMT -2020 .
[ITU -T Y.3179] ITU-T Recommendation Y.3179 (2021), Architectural framework for
machine learning model serving in future networks including IMT -2020 .
3 Terms and definitions
3.1 Terms defined elsewhere
This Technical Specification uses the following terms defined elsewhere:
3.1.1 application service [b-ITU-T X.1121] : a service like mobile banking, mobile commerce, and
so on.
3.1.2 network service [b-ITU-T Y.3515] : a collection of network functions with a well specified
behaviour.
3.2 Terms defined here
This Technical Specification defines the following terms:
3.2.1 autonomous network (AN) components : logical functions which work together to achieve
autonomous behaviour including evolution, exploration and adaption.
NOTE – Examples of AN components are knowledge base and AN Sandbox. There may be other
components enabling evolution, exploration and adaptation.
3.2.2 autonomous network (AN) configuration : a set of parameters which are input to the AN,
which may control its behaviour.
3.2.3 autonomous network (AN) sandbox : an AN component which hosts simulators (which act
as data generators and provi de action sinks), provide data handling interfaces to real network. This
component is based on ML Sandbox [ITU -T Y.3172], but broader to include all types of AN
experimentation as against only AI/ML model testing and validation. |
9 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 2 3.2.4 industry vertical a pplications : Software realization of a workflow created by application
services [ITU -T X.1121], to serve their customers’ specific needs.
NOTE – e.g., a set of cloud native services which implement loan management in banking industry vertical.
3.2.5 indu stry vertical solution provider : a domain specific business enterprise with specific
customer needs, a part of which may need the services of an ICT network.
NOTE – e.g. A bank which offers financial services to its customers and uses a 5G network to conn ect to its
customers.
3.2.6 knowledge : a collection of resources that helps in solving a specific type of problem.
NOTE – Examples of resources are description of a problem along with the description of corresponding
potential solutions to that type of problem. The descriptions may be in the form of standard metadata.
Resources may include possible causes of the problem, corresponding solutions and their advantages,
disadvantages and optimization approaches etc. Problems may have sub -problems e.g., QoE p roblems may
have sub -problems including coverage problems and/or interference problems.
3.2.7 knowledge base: a logical collection of knowledge that are related in a specific manner.
NOTE – Examples of logical collection are grouping of knowledge resource s with labels specifying their
common relationship, graph of knowledge resources with edges representing the relationships, etc.
Knowledge base may be used to solve problems in a particular domain such as access networks or core
networks, etc.
4 Abbreviation s
AI Artificial Intelligence
AN Autonomous Networks
CI/CD continuous integration and continuous delivery
CN Controller
ER Emergency Response
GNN Graph Neural Networks
GUI Graphical User Interface
IDSA Inter -domain Service Automation
KB Knowledge Base
KPI Key Performance Indicator
LCM Life Cycle Management
MIMO Multiple Input Multiple Output
ML Machine Learning
MLFO Machine Learning Function Orchestrator
mMTC Massive Machine Type Communications
MNO Mobile Network Operator
NF Network Function
OSS Operational Support S ystem
nRT RIC Near Real Time RIC
QoE Quality of Experience |
10 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 3 QoS Quality of Service
QPaaS Quality of Experience (QoE) Prediction as -a-Service
RAN Radio Access Network
RIC RAN intelligent Controller
RSRP Reference Signal Received Power
SINR signal -to-interference -plus-noise ratio
SLA Service Level Agreement
TOSCA Topology and Orchestration Specification for Cloud Applications
URLLC Ultra -reliable low -latency communication
ZSM Zero Touch Service Management
5 Conventions
In this Technical Specification, in alignment with the conventions of [Supplement 55 to ITU -T Y-
series Recommendations] possible requirements which are derived from a given use case, are
classified as follows:
The keywords "it is critical" indicate a poss ible requirement which would be necessary to be
fulfilled (e.g., by an implementation) and enabled to provide the benefits of the use case.
The keywords "it is expected" indicate a possible requirement which would be important but not
absolutely necessary to be fulfilled (e.g., by an implementation). Thus, this possible requirement
would not need to be enabled to provide complete benefits of the use case.
The keywords "it is of added value" indicate a possible requirement which would be optional to be
fulfilled (e.g., by an implementation), without implying any sense of importance regarding its
fulfilment. Thus, this possible requirement would not need to be enabled to provide complete
benefits of the use case.
6 Introduction
As the demand and expectation of communication networks has grown, so have user subscription
and new service expectation. Network operators must find new ways to address these pressures
while at the same time controlling operational cost. Autonomous networks are those that possess the
ability to monitor, operate, recover, heal, protect, optimize, and reconfigure themselves; these are
commonly known as the self -* properties. The impact of autonomy on the network will be in all
areas including planning, security, audit, inventory, optimis ation, orchestration, and quality of
experience. In this context, the main concepts studied by FG AN are exploratory evolution, real -
time responsive experimentation and dynamic adaptation.
Use cases studied in this document are based on contributions and discussions with domain experts
or mentors. There are various types of use cases including those which are directly describing
various autonomous behaviours and those which describe the applications which benefit from them.
Collation of u se cases included specific effort to study the impacts to the key concepts under study
in the FG AN. Effort was made to derive requirements and further to classify the requirements. A
relation to the architecture is provided in the form of guidance to comp onents derived from the use
cases.
The main learnings from this use case analysis are:
• while the use cases for AN are quite varied and requires support from domain experts, a
common refrain has been the application of the key concepts mentioned above.
• use case analysis may need to be continued as the field is still evolving. |
11 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 4 • derivation of architecture concepts and proof of concepts from the use cases is important.
7 Use cases
7.1 Import and export of knowledge for autonomous network
Use case id FG-AN-usecase -001
Use case name Use of knowledge in autonomous network
Base contribution [FGAN -I-12-R1]
Creation date 21/January/2021
Use case context Discussions during ITU webinar on autonomous networks (3 November 2020)
Use case description To satisfy the key concepts of autonomous networks (evolution, experimentation
and adaptation) while minimizing human intervention requires knowledge. This
knowledge may include representation of data about the environment in which the
autonomous system is operating, possibl e actions and consequences, key
configuration options, potential parameter indices and other types of logic. This use
case concerns use of knowledge in the actors in the AN.
Following are related steps in this use case scenario:
1. Knowledge is imported from outside or peer entities of autonomous network
components
2. Knowledge is referred internally in autonomous network components e.g. for
driving evolution, driving exploration, configuration of automation loops etc.
3. Generate report for human cons umption
4. Knowledge is stored and updated within the autonomous network components
5. Knowledge is exported from autonomous network components to outside or
peer entities.
Open issues - Representation mechanisms and transfer protocols for knowledge.
- Capabilities of knowledge base (KB) component that allows storage, query, export,
import and modification of knowledge using standard mechanisms is under study.
- reference points and interfaces between AN and KB is yet to defined.
Use case category Cat 1 : describes a scenario related to core autonomous behaviour itself.
Reference • [b-Clark], [b -AN2020], [b -Jimenez -Ruiz], [b -Myklebust ], [b-Turing]
7.1.1 Use case requirements
Critical requirements
● AN-UC01-REQ -001: It is critical that AN enable exchange of knowledge between the various
components in the AN implementing the various key AN functionalities like evolution, exploration
and adaptation.
● AN-UC01-REQ -002: It is critical that AN enable optimization of knowledge bases.
NOTE – Examples of optimizations applied on the knowledge bases are access policies, granularity of
storage, interconnection between various knowledge bases and relation between problems and solutions,
addition of new knowl edge.
● AN-UC01-REQ -003: It is critical that AN enable creation of reports on the use of knowledge
bases, for consumption of humans and machines.
NOTE – Example of contents of reports are metrics on access by various components in the AN, other
network ser vices in and outside its administrative domains.
● AN-UC01-REQ -004: It is critical that AN enable exchange of knowledge between various
components in the AN, and other network services in the same administrative domain. |
12 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 5 ● AN-UC01-REQ -005: It is critical th at AN use knowledge base for mapping high level use
case description to controller description.
NOTE – Controller description may use languages such as TOSCA, whereas use case descriptions may be
unstructured. The high -level use case description is to be c onverted to a structured controller specification,
also known as Intent, by AN orchestrator. In this process of “conversion”, it may utilize the help of humans
(using GUIs) who can better understand unstructured information, and/or auto -controller generato rs.
Expected requirements
● AN-UC01-REQ -006: It is expected that AN enable exchange of knowledge between various
components in the AN, and entities in other administrative domains.
NOTE – Examples of entities in other administrative domains are network ser vices which are not
implementing AN functionalities.
Added value requirements
● AN-UC01-REQ -007: it is of added value that AN use Auto -controller generators that are
functions which generate controller specifications, using the existing repository in openC N, the
knowledge base and an analytics function aided by AI/ML e.g., GNN, recommendation engine.
7.1.2 Use case specific figures
NOTE 1 – multiple AN components (1 … n) may be present in the system.
Figure 1: actor interaction for Use of knowledge in autonomous network |
13 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 6 NOTE 2 – actor interaction captured in this figure may not reflect the strict time sequence of activities .
7.2 Configuring and driving simulators from autonomous components in the network
Use case id FG-AN-usecase -002
Use case name Configuring and driving simulators from autonomous components in the network
Base contribution [FGAN -I-12-R1]
Creation date 21/January/2021
Use case context Discussions during ITU workshop on autonomous networks, discussions during
review of ITU SG 13 work item on Sandbox, discussions during ITU AI/ML in 5G
Challenge 2020.
Use case description To explore and experiment with various scenarios in autonomous behaviour, the
autonomous network component requires access to simulators. Simulators help
evaluate the outcome of possible options without potential adverse fallouts in the
real network. Long te rm study of simulation results is common by human
researchers to understand the evolutionary needs of the network too. In this respect,
autonomous network components need to interface with, configure and drive the
different simulators.
Following are rel ated steps in this use case scenario:
1. Autonomous network components decide the scenarios for exploration and
experimentation.
2. Autonomous network components interact with the Sandbox to configure
specific simulators which can perform the required experimentation.
3. Sandbox monitors the simulators and reports the completion of simulations.
4. The results are analysed by Autonomous network components and further
actions (like updating knowledge base) are taken.
Open issues 1. Are simulators encapsulated in Sandbox? Or are they open to direct interface
from autonomous network components?
2. There are heterogeneous simulators and uniform interface with simulators do
not exist. This makes their interface and configuration non -standard and difficult
to implement.
3. Additional scenarios like addition of new simulation capabilities, flagging of
new requirement for simulation etc. need to be handled.
Use case category Cat 1: describes a scenario related to core autonomous behaviour itself.
Reference [b-Y.ML -IMT2020 -SANDBOX]
7.2.1 Use case requirements
Critical requirements Figure 2: Component cloud for Use of knowledge in autonomous network |
14 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 7 ● AN-UC02-REQ -001: It is critical that AN components arrive at potential scenarios for
exploration and experimentation.
NOTE – AN components may independently arrive at different scenar ios for exploration and
experimentation based on several factors like the functionalities they implement, current status of their
knowledge, etc. e.g., exploration strategies for access control may be based on game theory approaches or
combinatorial optimization approaches.
● AN-UC02-REQ -002: It is critical that autonomous networks (AN) components trigger
experimentation in AN Sandbox.
NOTE – AN compone nts may independently trigger experimentation by configuring simulators in the AN
Sandbox.
● AN-UC02-REQ -003: It is critical that autonomous networks (AN) Sandbox collate, aggregate
triggers for experimentation to form a coherent, experimentation pipelin e, the execution of which is
monitored and reported by AN Sandbox to AN components.
● AN-UC02-REQ -004: It is critical that autonomous networks (AN) components analyse the
reports from the AN sandbox while considering the steps in AN behaviour.
NOTE – the steps in AN behaviour which depends on the analysis of reports from AN sandbox includes
steps in evolution and update of knowledge
7.2.2 Use case specific figures
Figure 3: actor interaction for Configuring and driving simulators from autonomous components in
the network |
15 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 8
Figure 4: Component cloud for Configuring and driving simulators from autonomous components
in the network
7.3 Peer -in-loop (including humans)
Use case id FG-AN-usecase -003
Use case name Peer-in-loop (including humans)
Base contribution [FGAN -I-12-R1]
Creation date 21/January/2021
Use case context Discussions during ITU workshop on autonomous networks, discussions during
review of ITU SG 13 work item on MLFO, discussions during ITU AI/ML in 5G
Challenge 2020.
Use case description To guide the autonomous behaviour autonomous network component requires
access to peers. Peers include humans and other autonomous entities. Exchange of
information with peers help in taking better decisions. In this respect, autonomous
network components need to interface with, exchange information with various
other autonomous network components and humans.
Following are related steps in this use case scenario:
1. Autonomous network components decide to take guidance from other
autonomous entities (peers like humans).
2. A message exchange with the peer is initiated.
3. The results of the exchange are analysed by Autonomous network components
and further actions (like updating knowledge base) are taken.
Open issues 1. Are some peers more equal than others (e.g. humans)?
2. What are the messages exchanged? e.g. request for comments? report on status?
capability exchange?
Use case category Cat 1: describes a scenario related to core autonomous behaviour itself.
Reference [b-Y.ML -IMT2020 -MLFO]
7.3.1 Use case requirements
Critical requirements
● AN-UC03-REQ -001: It is critical that autonomous networks (AN) components enable
synchronous or asynchronous, interoperable exchange of feedback or information from peers
regarding the dec isions and choices related to AN behaviour.
NOTE 1 – Peers may include humans and machines. Feedback may include exchange of information
regarding AN behaviour such as evolution, experimentation and adaptation. Contents of the information |
16 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 9 exchanged may include capabilities and status of components i n the AN e.g., knowledge base, orchestration,
simulators, etc.
NOTE 2 – Format of information exchange is for future study.
7.3.2 Use case specific figures
Figure 5: actor interaction for Peer -in-loop (including humans)
Figure 6: Component cloud for Peer -in-loop (including humans)
7.4 Configuring and driving automation loops from autonomous components in the network
Use case id FG-AN-usecase -004
Use case name Configuring and driving automation loops from autonomous components in the
network
Base contribution [FGAN -I-12-R1]
Creation date 21/January/2021
Use case context Inspired by discussions on “demand mapping” during Y.3173 and discussions
during ITU AI/ML in 5G Challenge 2020.
Use case description There are different automation loops in various domains of the network, already
proposed by different standards bodies and industry bodies. To reflect the decisions
of autonomous behaviour in the network, the autonomous network component
requires access to automation loops. Automation loops help implement the decisions
taken by the autonomous component in the network. Moreover, it is possible that |
17 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 10 automation loops provide valuable inputs for autonomous components to be
considered for say further experimenta tion.
In this respect, autonomous network components need to interface with, configure
and drive the different automation loops.
Following are related steps in this use case scenario:
1. Autonomous network components decide the configurations of automat ion
loops.
2. Autonomous network components interact with the automation loops to
configure specific scenarios which can perform the required automation.
3. Automation loops monitor the automation and reports the status of automation.
4. The results are analysed by Autonomous network components and further
actions (like updating knowledge base) are taken.
NOTE - Please see [ML5G -I-221] for an example of “influencing analytics service”
for E2E service management. Question -8 discussed the possibility for ZSM
framework consumer to select and provision the type of ML model to be used for
domain specific analytics in ZSM scope.
Open issues 1. Where are the automation loops hosted? Are they open to direct interface from
autonomous network components?
2. There are heterogeneous automation loops and uniform interface do not exist.
This makes their interface and configuration non -standard and difficult to
implement.
3. Additional scenarios like addition of new automation capabilities, flagging of
new requirement for auto mation etc. need to be handled.
Use case category Cat 1: describes a scenario related to core autonomous behaviour itself.
Reference • [ITU -T Y.3173]
7.4.1 Use case requirements
Critical requirements
● AN-UC04-REQ -001: It is critical that autonomous networks (AN) components decide the
type of closed loops and manage the closed loops.
NOTE – AN components may decide the type and structure of closed loops based on their analysis of
reports, monitoring and other information exchanges. Management of closed loops may include instantiating,
deletion, updating , and other operations on closed loops.
● AN-UC04-REQ -002: It is critical that autonomous networks (AN) components consider the
capability and flexibility offered by closed loops to configure them to perform specific automation
tasks.
● AN-UC04-REQ -003: It is critical that closed loops m onitor the specific parameters of
automation tasks and report them to AN components.
NOTE – specific parameters of automation tasks may include data input to automation, analytics used in the
closed loop, actions taken as part of automation. It may also i nclude failures, error logs, etc.
● AN-UC04-REQ -004: It is critical that AN components consider the reports from closed loops
while deciding the AN behaviour.
NOTE – Examples of AN behaviour are evolution, experimentation and adaptation. |
18 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 11 7.4.2 Use case specifi c figures
Figure 7: actor interaction for Configuring and driving automation loops from autonomous
components in the network
Figure 8: Component cloud for Configuring and driving automation loops from autonomous
components in the network
7.5 Domain analytics services for E2E service management
Use case id FG-AN-usecase -005
Use case name Domain analytics services for E2E service management
Base contribution [FGAN -I-12-R1]
Creation date 21/January/2021
Use case context Based on discussions with ETSI ZSM via [ML5G -I-221] |
19 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 12 Based on presentations on [FGAN -I-135].
Use case description Section 6.5.3.2 of [ETSI ZSM ARCH] describes the domain analytics services
which provide domain -specific in sights and generate domain -specific predictions
based on data collected by domain data collection services and other data.
Following are related steps in this use case scenario:
1. Autonomous network components act as a ZSM service consumer.
2. ZSM provides closed loop (CL) management and other domain and cross
domain services (including analytics) to AN components.
3. Discovery of ZSM services is done by AN components
4. ZSM service performs the E2E service management based on the interaction
with autonom ous network component.
a. examples of interactions are: managing subscriptions, configuring
analytics, request analysis results, etc. See Clause 6.5.3.2.1 of
[ETSI ZSM ARCH]
b. Other Examples in the context of Zero Touch provisioning
mentioned in [FGAN -I-135].
Open issues Open issues (refer also those discussed in [ML5G -I-221]).
1. Hierarchies of CL may be deployed in other AN domains and ZSM domains. In
this case interoperability of these deployments may be studied.
2. how to integrate ZSM services as “service -x”?
Use case category Cat 2: describes a scenario related to application of autonomous behaviour in the
network.
Reference [b-ETSI GS ZSM 002]
7.5.1 Use case requirements
Critical requirements
● AN-UC05-REQ -001: It is critical that AN support discovery and consumption of the services
provided by different types of closed loop service automation frameworks.
NOTE – Examples of different types of closed loop service automation frameworks are ETSI ZSM [ETSI
ZSM ARC H] and FRINX machine [FGAN -I-135]. Examples of actions taken by AN after the consumption
of services provided by closed loop service automation frameworks are managing subscriptions, configuring
analytics, request analysis results, etc.
7.5.2 Use case specific f igures
Figure 9: Component cloud for Domain analytics services for E2E service management |
20 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 13 7.6 Automation and intelligent operation, maintenance and management (OAM ) of radio
network
Use case id FG-AN-usecase -006
Use case name Automation and intelligent OAM(operation, maintenance and management)
of radio network
Base contribution [FGAN -I-008]
Description Background : Dynamic radio environment, network structure, user
behaviour, and user distribution drive the network needs to be monitored
and optimised continually. Currently, it consumes experts a lot of time and
labor work to discover problems, analyze root cause, and then formulating
solutions of radio networks. Therefore, AI and big data technology is
necessary to achieve f ull process automation and intelligent management of
wireless network.
Specifically, wireless network autonomous management includes real -time
monitoring of data quality, quasi -real-time diagnosis of abnormal cells, root
cause analysis, recommended solut ions, and evaluation of processing effects.
⚫ Real-time monitoring of data quality
It is needed to firstly collect real -time data from the 4/5G integrated network
management, then compares the consistency of the number of network
elements from the data, and achieves data quality monitoring and warning
through the visualization panel.
⚫ Quasi -real-time diagnosis of abnormal cells
Using several categories of KPI performance indicators, the ML algorithm
(e.g. OneClassSVM ) is used to diagnose network elements in t hese
categories of scenarios such as residential and colleges on a daily/weekly
basis, and distribute them to frontline managers according to the abnormal
probability.
⚫ Root cause analysis and recommended solutions
Through collecting tens of thousands of expert experiences, and radio
network knowledge bases have been established through knowledge graphs
to develop intelligent recommendation algorithms and search engines, and to
directly provide reasons and recommend solutions of each abnormal
network element to first -line experts, thus reducing troubleshooting time and
error rate.
⚫ Evaluation of processing effects
Through a mature evaluation system, the effectiveness of the solution to
each abnormal problem is evaluated after the implementation, and then the
knowledge base and recommendation algorithm are optimized, and the
intelligence level of the entire process is continuously improved.
Category Category 1 - Use case for autonomous behaviour
Reference None
7.6.1 Use case requireme nts
Critical requirements
● AN-UC06-REQ -001: It is critical that autonomous networks enable discovery of problems in
underlay networks, analysis of root cause, and formulation of solutions. |
21 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 14 NOTE – Wireless network autonomous management may include real -time monitoring of data quality,
quasi -real-time diagnosis of abnormal cells, root cause analysis, recommended solutions, and evaluation of
processing effects.
● AN-UC06-REQ -002: It is critical that AN enables data quality and monitoring and
visualization.
NOTE – Data quality may need real -time monitoring, evaluation with respect to openKB, and reporting may
be done using an online GUI or a report to human. Data quality may be u seful to analytics services.
● AN-UC06-REQ -003: It is critical that AN enables capturing and using the knowledge from
domain experts and AI/ML mechanisms for recommendation of solution for root cause analysis.
NOTE – Example of representation formats for knowledge is knowledge graphs.
Expected requirements
● AN-UC06-REQ -004: It is expected that AN uses AI and big data technology to achieve full
process automation and intelligent management of wireless network.
Added value require ments
● AN-UC06-REQ -005: It is of added value that a varying set of KPIs are monitored to identify
faults.
● AN-UC06-REQ -006: It is of added value that AN solutions may be monitored optimized and
continuously improved (themselves).
NOTE – e.g. the OpenKB and recommendation algorithms may be optimized.
7.6.2 Use case specific figures
None .
7.7 Intelligent energy saving for data centres
Use case id FG-AN-usecase -007
Use case name Intelligent Energy Saving for Data Centers
Base contribution [FGAN -I-008]
Description Background : The rapid growth of mobile Internet, cloud computing and
other business drives the need of large -scale data centers. Data centers
consume large amounts of energy to run and maintain their cooling system
and facilities, servers and o ther devices. Traditional methods cannot
efficiently reduce the energy costs of data centers. Therefore, AI
mechanisms are introduced to analyze the monitoring data and adjust the
configurations automatically.
Intelligent energy saving solution include a s eries of autonomous behaviour,
such as automatic data acquisition, AI-based energy consumption modelling
and inference, facilities parameters control policies decision, facilities
adjustment actions implementation, energy saving result evaluation and
contr ol policies continuous optimization.
Category Category 2 - Application of autonomous behaviour
Reference None
7.7.1 Use case requirements
Critical requirements |
22 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 15 ● AN-UC07-REQ -001: It is critical that autonomous networks (AN) support data acquisition,
representation, analysis of collected data and adaptation of configurations in underlays such as data
centers.
NOTE – Data acquisition and data representations may use industry stand ards. Analysis may use ML
techniques. Adaptations may use underlay specific APIs. Adaptations may be arrived at using controllers or
workflows or closed loops .
Expected requirements
● AN-UC07-REQ -002: It is expected that autonomous networks (AN) support re presentation,
autonomous analysis and continuous optimization of policies.
NOTE – Policies may be related to domain specific workflows and decisions e.g. energy usage in data
centers.
7.7.2 Use case specific figures
None.
7.8 Autonomous massive MIMO
Use case id FG-AN-usecase -008
Use case name Autonomous Massive MIMO
Base contribution FGAN -I-30
Creation date 27/January/2021
Use case context
Use case description Massive MIMO is a key technology in 5G, which can effectively improve the
vertical coverage and system capacity in complex scenarios by using large -
scale antenna array and three -dimensional beam -forming.
Compared with traditional antenna, there are more d imensions of parameters
to adjust for massive MIMO large -scale antenna array, including horizontal
lobe width, vertical lobe width, azimuth, dip angle and beam number. Each
dimension can be fine adjusted by setting a reasonable step size and
theoretically and there may be tens of thousands possible combination of
antenna parameter weights in a cell. Therefore, manual optimization and
adjustment based on scenario/service changes can be very hard in
consideration of multi -cell coordination.
The autonomous mas sive MIMO use case is about helping operators quickly
converge and achieve optimal adjustment of antenna parameters with AI
capabilities of multi -dimensional analysis and prediction. The general
workflow is: 5G base station collects position information fr om UE and sends
it to the network management system, which then calculates the distribution
of UE and finds the optimal weight combination with ML algorithms based on
the target RSRP/SINR distribution in the current scenario, so as to maximize
the utilizat ion of system capacity and guarantee the user experience.
Open issues ⚫ How to achieve a balance between coverage, communication traffic and
spectrum efficiency?
⚫ How to ensure a satisfactory speed of parameter adjustment?
Use case category Cat 2: application of autonomous behaviour
Reference |
23 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 16 7.8.1 Use case requirements
Critical requirements
● AN-UC08-REQ -001: It is critical that autonomous networks (AN) support identification of
parameters which can be optimized, including ML parameters, based on the use case.
NOTE – Example of use case is parameter optimization for massive MIMO large -scale antenna array,
including horizontal lobe width, vertical lobe width, azimuth, dip angle and beam number.
● AN-UC08-REQ -002: It is critical that autonomous networks (AN) support identification of
data whi ch can be collected to analyse and infer, based on the use case.
NOTE – Examples of data are distribution of UE, the target RSRP/SINR distribution in the current scenario.
● AN-UC08-REQ -003: It is critical that autonomous networks (AN) support identificat ion of
KPIs which need to be optimized.
NOTE – Examples of KPIs are system capacity and QoE.
● AN-UC08-REQ -004: It is critical that autonomous networks (AN) support optimization of
KPIs in distributed deployments which require multicell coordination.
7.8.2 Use case specific figures
None .
7.9 Network resource allocation for emergency management based on closed loop analysis
Use case id FG-AN-usecase -9
Use case name Network resource allocation for emergency management based on closed loop
analysis
Base contribution FGAN -I-090-R2
Creation date 22/April/2021
Use case context Discussions during [FGAN -I-055-R1], [FGAN -I-054-R1], [FGAN -I-072]
Use case description Telecommunication systems are critical pillar of emergency management. A set
of hierarchical A I/ML based closed loops could be used to intelligently deploy
and manage slice for emergency responders in the affected area. A higher
closed loop in the OSS can be used for detecting which area is affected by the
emergency and deploy a slice for emergency responders to that area. It can then
set a resource arbitration policy for the lower closed loop in RAN. The lower
loop can use this policy to intelligently share RAN resources between the
public and emergency responder slice. It can also intelligently ma nage ML
pipelines across the edge and emergency responder devices by using split
AI/ML models or offloading of inference tasks from the devices to the edge.
Following are related steps in this use case scenario:
1. MNO may instruct OSS to detect certain set o f emergencies and
provide connectivity to emergency responders according to predefined
SLA.
NOTE - e.g. this input may be provided using an operator intent.
2. OSS might deploy a closed loop to achieve this. It might collect data
from sources like network analytics data, social media scraping, input
from emergency responders etc. |
24 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 17 NOTE - e.g. such inputs may be provided from nRT -RICs or other
xNFs in the network.
3. OSS might use AI/ML models to detect emergency and deploy an ER
slice to the location. It might also create high level strategy/policy to
reallocate resources among the slices.
NOTE - e.g. such closed loops may be hosted in non -RT RIC and may
be used for predictive resource allocations to specific edge locations
based on predicted needs, in turn based on detected emergency.
NOTE - the policy to reallocate resources may depend, among other
things, on the type of emergency e.g. a natural disaster, earth quak e, a
law and order situation, traffic accidents, etc.
4. RAN domain might use this high -level strategy/policy and possibly
other inputs from emergency responders to create a closed loop to
arbitrate resources among RAN NSSIs.
NOTE - e.g. such closed loops ma y be hosted nearer to edge e.g. nRT
RIC. The policy input from higher loop may indicate, among other
things, the different sources of data for the lower loop.
5. RAN domain closed loop might also decide to offload inference tasks
from ER devices to the edge or use split AI/ML model to run inference
tasks on edge and ER device. This decision might be taken based on
available network and compute resources.
NOTE - e.g. some layers of the AI/ML model may be hosted in the
wearable devices of the emergency responde rs, which will help in say
locating of persons under distress using various inputs.
Relation with autonomous behavior -
1. Workflows for the closed loops are independent of each other. The
only interaction between closed loops is via high level intents over t he
inter-loop interface.
2. Closed loops can create new closed loops in other network domains
without human intervention.
3. Although loops are deployed in hierarchical fashion, each loop has the
ability to evolve independently. It can use different models and ML
pipelines as required. Each loop may move up or down the autonomy
levels as defined in [ITU -T Y.3173].
4. Closed loops have ability to split and provision AI/ML models to other
closed loops in automated fashion.
5. By making closed loops in edge domain autonomous, we also enable
lesser orchestration delay, better privacy and flexibility for verticals
(e.g., industrial campus networks).
6. Higher loops can use historical knowledge available to them to
optimize and generalize lower loops using high -level intent. This
increases efficiency of lower loops while preserving their autonomy.
(e.g., higher loop might know certain kind of ML models are good for
cyclone emergency management based on previous cyclones.)
NOTE: This use case might be well aligned w ith the use case “Composable,
hierarchical closed loops” in [FGAN -I-072] and others above.
Open issues (as seen by
the proponent) 1. The "propagation" and "escalation" of intents is something which needs
study. |
25 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 18 2. mixing the concept of "declarative" poli cies with "top -level" intent with the
concept of (propagation/escalation) needs study.
3. recursive decomposition coupled with recursive policy mapping
4. The components (“nodes”) of the high -level service are decomposed into
more concrete services (possib ly recursively). Declarative policies must be
“translated” into more concrete declarative policies on the decomposed services
in conjunction. – how to do this?
5. By coupling “event/condition/action” control loops with TOSCA’s
substitution mapping feature, you can make these control loops “cascading”,
i.e. they can propagate down from high -level abstract “intent” statements to
low-level device reconfigurations, and they can escalate back up if necessary.
This is an aspect of TOSCA policies that needs study.
Notes on use case
category Cat 1: describes a scenario related to core autonomous behavior itself.
Notes on priority of the
use case High
has the potential to impact future network architectures and use cases.
Reference [b-ETSI GS ZSM 001]
7.9.1 Use case requirements
Critical requirements
● AN-UC09-REQ -001: it is critical that AN allow interaction between closed loops via high
level intents.
NOTE – Closed loops may create new closed loops in other network domains without human intervention.
● AN-UC09-REQ -002: it is critical that AN allow each loop to evolve independently, using
different analytical, optimization mechanisms including ML models and ML pipelines as required.
NOTE – Each loop may move up or down the autonomy levels as defined in [ITU -T Y.3173]
Expected requirements
● AN-UC09-REQ -003: it is expected that Closed loops have ability to provision or recommend
AI/ML models to other closed loops in automated fashion.
● AN-UC09-REQ -004: it is expected that closed loops in edge domain may be autonomous, in
order to enable lesser orchestration delay, better privacy and flexibility for verticals (e.g., industrial
campus networks).
● AN-UC09-REQ -005: it is expected that higher loops use the knowledge base available to
them to optimize and generalize lower loops using high -level intent.
NOTE –This increases efficiency of lower loops while preserving their autonomy. (e.g., higher loop might
know certa in kind of ML models are good for cyclone emergency management based on previous cyclones.) |
26 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 19 7.9.2 Use case specific figures
Figure 10: actor interaction for Network resource allocation for emergency management based on
closed loop analysis
NOTE 1 – Create a high -level abstract model for closed loops, and then create declarative policies for that
high-level model that express the “intent” of creating ML pipelines. The components (“nodes”) of the high -
level service are decomposed into more concrete ser vices (possibly recursively). Declarative policies must be
“translated” into more concrete declarative policies on the decomposed services in conjunction. For example,
“non -RT” level service may impose certain closed loop requirements on a RIC that impleme nts the ML
pipeline. “nRT” level service may impose some other closed loop requirements on a RIC that implements
that ML pipeline. This recursive decomposition coupled with recursive policy mapping happens all the way
down until service components can get realized on the available resources. At that point, the low -level
declarative policies must be translated somehow into imperative policies (e.g. if jitter exceeds a certain
threshold, re -prioritize the traffic associated with the service).
NOTE 2 – “imperative” policies that use the “event/condition/action” pattern, vs. declarative policies use a
“capabilities/context/constraints” pattern. Declarative policies are more suitable for top -level “intent”
statements, but they need to be translated (by th e orchestrator) into corresponding “imperative” policies in
order to be actionable. The "propagation" and "escalation" of intents: the “event/condition/action” statements
are the control loops you’re referring to that make sure that service components comp ly with desired
behavior at all times. By coupling “event/condition/action” control loops with TOSCA’s substitution
mapping feature, you can make these control loops “cascading”, i.e. they can propagate down from high -
level abstract “intent” statements to low-level device reconfigurations, and they can escalate back up if
necessary.
NOTE 3 – The events are generated (using notifications) by nodes in the service topology model. The
conditions are evaluated based on attribute values of nodes in the service t opology model. The actions are
performed on the service topology model first, and then propagated to the external world (the “resources”) |
27 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 20 7.10 Inter -domain service automation (IDSA) - for microfinance
Use case id FG-AN-usecase -010
Use case name Inter -domain se rvice automation (IDSA) - for microfinance
Base contribution AN-I-060
Creation date 29/March/2021
Use case context Discussions regarding cloud interoperability in 5G use case lab
Use case description Microfinance applications may be hosted by non -experts in 5G or any form of cloud
/ ICT technologies. The end -user requirements are domain -specific e.g. loan
management, banking account/ledger management etc. The main stakeholders who
are enterprises (e.g. banks) may be knowledgeable and would like to focus only in
their business workflows (as against cloud / ICT technologies). The underlying
cloud infrastructure (for that matter the application design) and the network
infrastructure (5G, 6G, or (x+1)G) is immaterial to a bank / finance manager.
However, from a technology perspective the following rewards are desired to be
reaped:
1. Give the best end -user experience: e.g. reduce down -time for services, reduced
latencies for services, security and data priv acy, intelligent services, by exploiting
the best cloud service deployment for the microfinance application. e.g. edge, load
balancing, secure messaging across multi -cloud, hybrid -cloud, AI/ML services via
distributed cloud, etc.
2. Insulate the end -user f rom xG -> (x+1)G migration: by providing interoperable,
standard, backward compatible networking abstraction technologies. Integrating
service lifecycle management pipeline provides agility to service development and
testing.
3. Mitigate the risk of increa sed integration service costs : by using open source
technologies, standards, benchmarking, automating in test beds.
4. Automate: reducing human involvement reduces training costs for banks,
operational costs in networks and brings other benefits like intelligent fault isolation
without depending on 3rd party service providers.
NOTE - devops [ b-ISO/IEC 23167] [ b-ITU-T Y.3515], CI/CD [ b-ITU-T Y.3525] are
exampl es of service lifecycle management pipelines.
Following are related steps in this use case scenario:
1. Intent based cloud service specification
2. processing of intent and development, validation in Sandbox/testbed. Testbed
components (e.g. simulators, data mod els) are selected based on intent.
3. Evaluation and analysis of test results based on key parameter indices (KPI)
specifications in the intent.
4. derivation of optimal configuration, cloud service deployment , management
and orchestration.
5. intent based network service deployment , management and orchestration.
6. Single “cockpit” for monitoring the services
7. Autonomous, Intelligence -guided, technology -agnostic migration of services
from one version of underlying technology to another e.g. xG -> (x+1)G and
edge -> fog.
8. Reports from various parts of the underlying technologies are provided to
humans in regular intervals or event based.
Open issues (as seen
by the proponent) 1. Handling of accounting for such services is not clear. |
28 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 21 Notes on use case
category Cat 2: describes a scenario related to application of autonomous behaviour in the
network.
Notes on priority of
the use case High
- Enables vertical driven applications and network evolution.
Reference
7.10.1 Use case requirements
Critical requirements
● AN-UC10-REQ -001: It is critical that autonomous networks (AN) consider inputs from
industry vertical solution provider regarding the required service characteristics, using an intent -
based mechanism, while deciding the development and deployment options for industry vert ical
applications and network services.
NOTE – AN can autonomously decide the best possible development and deployment option for network
services which can support the verticals. This has to be based on the requirements of the applications [ITU -T
Y.3178] . E.g. for banking applications, service characteristics may include latency on banking transactions,
mean time between service failures, level of privacy of each field in the customer profile, etc. Examples of
deployment options may include edge, core clo ud, enterprise network, using specific hardware etc.
● AN-UC10-REQ -002: It is critical that, AN abstracts the management (creation, deletion and
update) of the industry vertical applications and network services, from the industry vertical
solution provide r.
NOTE – underlying domain orchestration, network specific technologies and APIs are abstracted by AN
towards the industry vertical solution provider. E.g. banking applications may be hosted as web applications
(on popular web frameworks with or without an accompanying mobile component), enterprise applications
(e.g. J2EE based). They may be instantiated as cloud -native applications, may use distributed architecture
across private/public clouds etc. Service management infrastructure supporting the applica tions may include
brokers, workflow managers and schedulers. Irrespective of such deployment and management variance, AN
provides abstracted interfaces to verticals which hides such complexities.
● AN-UC10-REQ -003: It is critical that, AN validates any ch anges to the application and
network services in a sandbox environment before applying it in the network.
NOTE – Autonomous behaviour may result in automated creation, deletion and update of applications and/or
network services. The impact of such modified applications and/or network services has to be studied before
they are applied in the network. This may be done by using a testbed or sandbox with simulators or even a
digital twin-based environment. Specific emphasis may be applied on maintaining compati bility of the
modifications with the applications and network services in the network.
● AN-UC10-REQ -004: It is critical that, AN continuously monitors the application and network
services in the network.
NOTE – monitoring may be done to find erroneous behaviour, faults, gaps in architecture, design, bugs, etc.
Monitoring may identify the gaps in end -to-end service implementations with respect to changing intents of
the verticals. Thus, monitoring may also be use d to find the need for evolution in underlying network
domains.
● AN-UC10-REQ -005: It is critical that, AN produces regular and asynchronous reports for
human consumption.
NOTE – reports to humans may summarize all monitored values, analysis, decision poin ts and explanations
for such decisions by the AN. |
29 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 22 Expected requirements
● AN-UC10-REQ -006: It is expected that, AN provide automated triggers to service lifecycle
management pipeline for management (creation, deletion and update) of application and network
services.
NOTE 1 – devops [ b-ISO/IEC TS 23167] , [b-ITU-T Y.3515], CI/CD [ b-ITU-T Y.3525] are examples of
service lifecycle management pipelines.
NOTE 2 – As part of management of applications, AN may analyse the gaps, faults and issues in the curre nt
design and implementation of end -to-end service. Mitigation of such issues may be triggered to the devops
pipeline. However, the level of automation of the solution may depend on the capabilities of the devops
pipeline.
● AN-UC10-REQ -007: It is expecte d that, the AN configuration includes the set of reference
points which may be used for integration into end -to-end network services and applications.
NOTE – Even though AN exposes abstracted interfaces for application management to verticals, to achieve
the integration of network services, AN may use open interfaces or closed black boxes. The availability of
open interfaces and corresponding components is to be made known to AN via configurations. Such
configurations may be dynamically changing based on a vailability of new components and interfaces.
● AN-UC10-REQ -008: It is expected that, the AN proposes “recipes” of network services and
applications which may satisfy a particular intent from the vertical.
NOTE – Recipes may include a combination of exis ting application components, network service
components, corresponding configuration options, etc.
● AN-UC10-REQ -009: It is expected that, the AN is updated at runtime by the underlying
domain orchestration about the supported set of reference points in th e domain, available set of
network service and application components, which may be used for integration into end -to-end
network services and applications.
NOTE – Runtime changes, triggered by the operator or 3rd parties, in the underlying domains are made aware
to the AN. Such updates may be abstracted and passed by the AN to the verticals, where relevant, for
information or policy decisions.
Added value requirements
● AN-UC10-REQ -010: It is of added value that, the AN proposes a modified “recipe” of
network services and applications which may bridge a gap, fix a fault or solve issues in the current
design and implementation of end -to-end services.
NOTE – Modified recipe may be based on analysis of gaps, issues, or faults encountered while monitoring of
network services and applications. |
30 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 23 7.10.2 Use case specific figures
Figure 12: actor interaction for Inter -domain service automation (IDSA) - for microfinance
7.11 Autonomous vertical -driven edge service and middle -mile connectivity for rural
financial inclusion (FI)
Use case id FG-AN-usecase -011
Use case name Autonomous vertical -driven edge service and middle -mile connectivity for rural
financial inclusion (FI)
Base contribution AN-I-060
Creation date 31/March/2021
Use case context Discussions regarding rural broadband architecture(s) and feedback from survey to
banks during summer 2020
Figure 10: Inter -domain service automation |
31 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 24 Use case description Digital Financial Inclusion in many geographies are limited because of lack of
network availability and low reliability of connection. Frequently bank branches
have to fall back on costly and complex connectivity through satellites. Other than
the capital and operational aspects, the bank staff also needs to handle the link
failover and maintenance activities in case of issues. Furthermore, such solutions do
not allow local community to utilize the network.
Autonomous last hop connectivity both through 4G /5G as well as other non 3GPP
heterogeneous networks need to be seamlessly enabled to operate in an affordable
manner. The solution is likely to provide the following benefits:
1. Based on the requirements from the verticals, provide connectivity for rural FI
sites independent of network providers/large -Telco.
2. Allow the connectivity to be shared with local community to ensure better return
on investments.
3. Edge compute and related infrastructure can enable more compelling deployments
for digital FI as well as other verticals (e.g. short term tele -commute / interviews
etc.).
4. Automate operations and security audits: reducing human involvement in
maintaining and running the edge / last hop reduces training costs for banks,
operational costs in networks and brings other benefits like intelligent fault isolation
without depending on 3rd party service providers, continuous audit of deployed
solution for security etc.
Following are related sub -systems and associated steps in this use case scenario:
1. Deploy mi cro-servers/nano -data-centres for the edge.
2. Enable heterogeneous network connectivity.
3. Autonomous, Intelligence -guided handling of alignment / interference /
mobility related challenges for various last hop approaches.
4. Automate on -boarding of community u sers and community specific apps, their
billing/payments etc.
5. Single “cockpit” for monitoring the services and health of infra to local -bank -
staff/managed -service -provider.
6. Reports from various parts of the underlying technologies are provided to
humans in regular intervals or event based. This includes sharing of usage details
with authorized management systems, These reports may be used for tracking
the usage at a granular level, mapped to the vertical and the tracking the
corresponding benefits from the infrastructure e.g. for the purpose of extending
subsidies to such infrastructure.
Open issues (as seen
by the proponent) 1. Policy framework for such an autonomous community network anchored by a
specific vertical needs to be understood.
Notes on use case
category Cat 2: describes a scenario related to application of autonomous behaviour in the
network.
Notes on priority of
the use case High
- Enables vertical driven applications and network evolution.
Reference
7.11.1 Use case requirements
Critical requirements
● AN-UC11-REQ -001: It is critical that autonomous networks (AN) utilize heterogeneous
network connectivity options at the edge, in the last -mile, including the commissioning, |
32 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 25 provisioning, configuration, integration, main tenance and optimization, in a seamless, real -time and
easy-to-use manner.
NOTE – Especially in rural settings the technology of choice may vary considerably depending on various
factors like availability of technology, ease of deployment, low power consu mption, etc. Currently, in such
deployments, it is invariably upon the industry vertical solution provider to also take on the responsibility of
integrating and maintaining these varied last mile connectivity options. It is important that AN brings
togethe r various such technologies under one umbrella, at the edge, to provide seamless integration and
maintenance. Some of the operations in the lifecycle of the last mile connectivity may need real -time
interventions, some of it may need deep domain expertise and some of it may require training – all of which
may not be possible in certain rural settings.
● AN-UC11-REQ -002: It is critical that autonomous networks (AN) enable sharing of the
various network connectivity options at the edge, across the community driven industry vertical
applications.
NOTE – With an emphasis on providing maximum connectivity and application services to the local
community, the best available option for connectivity need to be chosen, if needed, dynamically.
● AN-UC11-REQ -003: It is critical that autonomous networks (AN) enable onboarding of
industry vertical applications at run -time.
NOTE – Evolution of needs in a local community may result in changing application requirements.
● AN-UC11-REQ -004: It is critical that autonomous netw orks (AN) enable common, open,
interoperable, adaptable mechanisms for managing end -users, based on the needs of the local
community.
NOTE – Onboarding, billing, problem -resolutions and other end -user management functions need to be
agnostic, community -driven at the edge. Based on the use cases, the mechanisms for end -user management
has to adapt. E.g. for low -mobility rural areas, a relevant tariff plan needs to be offered.
● AN-UC11-REQ -005: It is critical that autonomous networks (AN) enable a single -window of
monitoring the heterogeneous underlying technologies.
NOTE – Complexities of monitoring, administering, maintaining the complexities of the underlying
technologies need to be hidden from the industry vertical solution provider as well as the local communities.
7.11.2 Use case specific figures |
33 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 26
Figure 11: Actors in Autonomous vertical -driven edge service
7.12 Signalling flows for autonomous IMT -2020 network
Use case id FG-AN-usecase -012
Use case name Signalling flows for autonomous IMT -2020 network
Base contribution AN-I-064
Creation date 21 March 2021
Use case context Signalling flows between data analysis function and other network functions, to
achieve self -analysis and self -optimization
Use case description Data analysis function (DAF) is introduced in IMT -2020 network [ b-ITU-T
Y.3104]. The signalling flow between DAF and other network functions(e.g. SMF,
PCF, NACF and AF) describes data collection and analysis result providing.
The procedure in Figure 1 is used by DAF to collect data on event (s) related to
SMF by invoking SmfEv entSubscription service.
Figure 1 Signalling flow for Data Collection from SMF
1. The DAF subscribe to or unsubscribe from a (set of) Events(e.g. UE IP
address, UP path change, PDU Session Establishment/ Release, and etc.) by
invoking the SmfEventSubscr iption_Subscribe service operation.
2. The SMF notifies the DAF (e.g. with the event report) by invoking
SmfEventSubscription_Notify service operation.
1. SmfEventSu bscription _Subscribe /
SmfEventSu bscription _Unsubscribe
2. SmfEventSub scription _ NotifyDAF SMF |
34 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 27 The procedure in Figure 2 is used by NF service consumers (e.g. SMF) to request
analytics information from DAF by invoking DafAnalysis_Request service.
Figure 2 Signalling flow for Analytics Subscribe/Unsubscribe from DAF
1. SMF subscribes to or unsubscribe from a (set of) data analytic events by
invoking the DafAnalysisSubscriptions_Subscrib e service operation.
-Subscription requirements of data analytic events may include:
-Load information of UPF
2. The DAF notifies the SMF about analysis events by invoking
DafAnalysisSubscriptions _Notify service operation.
Use case category ▪ Cat 1: aut onomous behaviour in IMT -2020 network
Reference • [b-Q.IMT2020 -PIAS ]
7.12.1 Use case requirements
Critical requirements
● AN-UC012-REQ -001: It is critical that autonomous networks (AN) enable flexible
provisioning and subscription of analysis parameters in network functions.
NOTE – Examples of analysis parameters are events, notifications, corresponding information and event
handing control lers. Network functions may dynamically provision or subscribe to controllers and
corresponding parameters.
7.12.2 Use case specific figures
None.
7.13 Plug/play of network instance
Use case id FG-AN-usecase -013
Use case name Plug/play of network instance
Base contribution AN-I-072
Creation date 13 April 2021
Use case context Discussions regarding FGAN -I-017 and deep dive
Use case description Benefits of open architecture approach include:
*) reducing CAPEX through a prosperous multi -vendor ecosystem with scale
economics. However, more the number of interfaces, more the effort in integration.
This needs to be mitigated using automation.
*) Rich application space enabled using hierarchical controllers. The hierarchical
control loops with varying time criticalities (<10ms (at edge) < 1s (at near edge) <
multi -second (at orchestrator)) were discussed in FGAN -I-017. However,
provisioning of applications at various levels and corresponding coord ination with
capabilities of the network functions is a challenge.
1. DafAnalysisSu bscriptions _Subscribe
2. DafAnalysisSub scriptions _ NotifySMF DAF |
35 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 28
In this context, we introduce the use case “Plug/play of network instance” in open
architecture.
NOTE - The network instance can be network resource, network function, network
slice and net work services [ b-ETSI GS ZSM 001].
Following are related steps in this use case scenario:
9. Addition of SRCs [ITU -T Y.3172]: network instance is plugged into the
network. Data collection functions supported by this new SRCs are analysed.
10. Bottom -up bootstrapping of infrastructure layer (using cloud orchestration),
network as a service (NaaS, using ONAP), services layer (using service
orchestration), based on these new SRCs.
OR
Top down bootstrapping of apps, services, NaaS, infrastructure, based on these
new SRCs.
Open issues E2E automation frameworks for composition of infrastructure, NaaS and services
do not exist.
Use case category Cat 1: describes a scenario related to core autonomous behaviour itself.
Reference
7.13.1 Use case requirements
Critical requirements
● AN-UC013-REQ -001: It is critical that autonomous networks (AN) enable plug and play of
network functions in the underlay and subsequent seamless participation of such network functions
in the AN functions.
NOTE – Examples of AN functions are creation and hosting of controllers. Plug and play may be executed
by manual or autonomous mechanisms.
7.13.2 Use case specific figures
None.
7.14 “Generative adversarial Sandbox”: (or hybrid closed loops)
Use case id FG-AN-usecase -014
Use case name “Generative adversarial Sandbox”: (or hybrid closed loops)
Base contribution FGAN -I-017
Creation date 13/Apr/2021
Use case context Discussions regarding FGAN -I-017 and deep dive
Use case description In addition to open interfaces between various RAN components, a rich ecosystem
of simulators is evolving. This allows implementation of various “hybrid” closed
loops – part of the closed loop (e.g. data generation) is implemented in simulators
whereas res t of the closed loop (e.g. analysis and action) are implemented in another
part of the test network using real network functions (NF).
In this context, we introduce the use case “Generative adversarial Sandbox: or
hybrid closed loops”.
Following are rel ated steps in this use case scenario:
5. Based on the inputs from the NF (e.g. data from SRC) and existing closed loops,
simulator configurations and capabilities are autonomously scripted.
6. Hybrid closed loops are autonomously composed – with parts of the closed loop
in real NF and parts of it in simulators. |
36 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 29 7. Similar to Generative adversarial Networks, hybrid closed loops are evaluated
and tested using 2 -part network – one simulated and another real network
functions.
8. The results are analysed and ranked.
Open issues 4. does the open interfaces extend to simulators?
5. how to rank the experiments?
6. data models may be different in various interfaces with simulators.
Use case category Cat 1: describes a scenario related to core autonomous behaviour itself.
Referen ce
7.14.1 Use case requirements
Critical requirements
● AN-UC014-REQ -001: It is critical that autonomous networks (AN) enable creation of hybrid
closed loops with parts of the closed loops hosted in real network functions as against other parts of
it in simulated network functions.
NOTE – Examples of parts of closed loops are modules which generate data, modules which implement
domain specific functions, modules which provide APIs for implementation of adapting decisions from
controllers .
● AN-UC014-REQ -002: It is critical that autonomous networks (AN) enable testing and
valid ation of closed loops using the parts of hosted in simulated network functions.
NOTE – Examples of such testing are robustness related test scenarios, security and vulnerability testing
scenarios.
7.14.2 Use case specific figures
None.
7.15 Open, integrated, log anal ysis
Use case id FG-AN-usecase -015
Use case name Open, integrated, log analysis
Base Contribution FG AN 2nd virtual meeting
Creation date FGAN -I-017
Use case context Discussions regarding FGAN -I-017 and deep dive
Use case description Fault prediction and isolation based on log analysis is an important existing use case.
Logs are generally implemented in unstructured text with no standard formats. With
a disaggregated network service implementation, correlating logs from various
vendors becomes a challenge. This throws cold water on the fault prediction and
fault isolation algorithms based on logs.
In this context, we introduce the use case “Open, integrated, log analysis”.
Following are related steps in this use case scenario:
4. Collection of logs from various open interfaces and NFs
5. Correlation and Analysis of the collected logs, across various open interfaces
and NFs.
6. Identification of optimization mechanisms based on log analysis.
Open issues
Use case category Cat 2: descri bes a scenario related to application of autonomous behaviour in the
network.
Reference |
37 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 30 7.15.1 Use case requirements
Critical requirements
● AN-UC015-REQ -001: It is critical that autonomous networks (AN) enable correlation and
identification of relevant logs, their access using open interfaces, analysis and resulting optimization
of network underlays to apply specific adaptations.
7.15.2 Use case specific figures
None.
7.16 Compose -able, hierarchical closed loops
Use case id FG-AN-usecase -016
Use case name Compose -able, hierarchical closed loops
Base contribution FGAN -I-017
Creation date 13/Apr/2021
Use case context Discussions regarding FGAN -I-017 and deep dive
Use case description There are different automation loops in different levels of the architecture. High
level use cases (like log -analysis based fault prediction) require access to capabilities
of various network instances. This in turn may be provided by multiple vendors or
opensource providers. Thus, provisioning and management of closed loops should
be driven hierarchically.
In this context, we introduce the use case “Compose -able, hierarchical closed loops”.
Following are related steps in this use cas e scenario:
5. declarative specifications decide the high level aspects of closed loops.
6. they are in turn correlated with declarative specifications for network services.
7. these are in turn used to generate detailed declarative specifications for closed
loops in different parts of the network.
8. orchestrators at various levels generate commands to provision and manage the
closed loops based on these generated declarative specifications.
9. the declarative specifications and/or closed loop components may be
stored/up dated for regeneration of closed loops at any point of time.
Open issues
Use case category Cat 1: describes a scenario related to core autonomous behaviour itself.
Reference
7.16.1 Use case requirements
Critical requirements
● AN-UC016-REQ -001: It is critical that autonomous networks (AN) enable composition of
hierarchical closed loops using declarative specifications.
● AN-UC016-REQ -002: It is critical that autonomous networks (AN) enable derivation of
controllers or closed loops at various levels of the network.
● AN-UC016-REQ -003: It is critical that autonomous networks (AN) enable management of
declarative specifications of closed loops or controllers.
NOTE – Management operations on declarative specifications may include creation, storage, update , delete,
etc.
7.16.2 Use case specific figures
7.17 Quality of Experience (QoE) Prediction as -a-Service (QPaaS)
Use case id FG-AN-usecase -017 |
38 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 31 Use case name Quality of Experience (QoE) Prediction as -a-Service (QPaaS)
Base contribution FGAN -I-110
Creation date 16 June 2021
Use case context "Review of academic papers on QoE Predictions." FGAN -I-110
Use case description Intelligent and autonomous troubleshooting is a crucial enabler for the current 5G
and future 6G networks. Autonomous troubleshooting is chall enging for several
reasons, one of which is the availability of a wide range of applications that future
networks will support.
Traditionally, the methods to gain insight into the delivered quality of service and
the users' experience have been through controlled laboratory experiments, where
users' opinions have been collected. The results are then reported in Mean Opinion
Scores (MOS), corresponding to the average of users' views. These methods are
often referred to as subjective quality assess ment, and there are standardized
methods for conducting them.
In this use case, an application or network service (NS) provider will use a QoE -
Prediction -as-a-Service (QPaaS) autonomous system to conduct and follow -up QoE
measurement and prediction.
Firstly, the autonomous system will conduct subjective tests to measure the user
experience from participating users. The locations and specifications of which users
will be selected and how the users' responses affect the QoE will depend on the
application a nd will be learned by the autonomous system. The autonomous system
will also measure relevant user parameters to map user opinions and application
KPIs.
Secondly, the autonomous system will follow applicable network KPIs and map
network and application KPIs.
Thirdly, based on this mapping, the autonomous system will enable the application
provider to predict the QoE of its users based on network KPIs regardless of their
participation. The autonomous system continuously (or periodically) improve the
prediction accuracy by random subjective tests or user behavior analysis.
Related steps in this use case scenario are:.
• Application or network service (NS) provider demands and deploys QoE
prediction as -a-service (QPaaS) from a third -party server.
o Applicat ion or NS provider provides a mechanism to collect/use
user feedback and network metrics .
• Identify a method of measurement for QoE:
o Perform subjective tests, e.g., video streaming, two -way
communications, etc. User opinions on a scale of 1 -5 or thumbs
up/down.
o Perform user behavior analysis, e.g., gaming, AR/VR, driver
assistance, etc. In a group of gamers connected via various CSP
(communication service providers), if the gamers from a particular
CSP face delays or a specific cell site (geographic area ) is facing
latency, the gaming scores and avatar -behaviour itself leave enough
clues on the QoE. Similarly, on AR/VR, the level of
engagement/interaction, or in assisted driving, the level of
coordination between vehicles, can be measured.
• QPaaS server, c ollects/processes network and application KPIs.
• QPaaS server determines a mapping between application KPIs and
application QoE metric (MoS) using (supervised) machine learning.
o This mapping may be used by the application server for future
objective testing of user QoE. |
39 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 32 • QPaaS server collects/processes relevant network service KPIs and forms a
mapping between network KPI and application KPI (or MoS) using
(supervised) machine learning.
o The NS provider may use this mapping for future objective testing
of netw ork performance given the application.
• Perform periodic verification with subjective tests/user feedback, and
improve learning based on the results.
Due to various applications, QoE measurement and prediction is a significant issue
in future networks. The network should be able to autonomously perform QoE
measurement and mapping of network KPI to QoE metrics.
NOTE - as applications and network services evolve, so do their corresponding KPIs
and the mappings (user satisfaction parameters, to ap plication KPIs, and to network
KPIs). See related open issues below which handles information exchange between
evolving applications and NS and QPaaS.
Open issues (as seen
by the proponent) • How are application KPIs provisioned in the QPaaS server? How is this done
for new applications? Can we transfer knowledge gained from Sandbox in on -
boarding new applications and services and corresponding mappings?
• How do you access relevant network KPIs?
• How are network KPIs provisioned in QPaaS server? How is th is done for new
NS (network services)?
• How do you select users to collect data from?
• How do you ensure the privacy of the user and network service data? E.g.,
federated learning may allow privacy for user data.
• How much training data do you need to colle ct for objective QoE measurement?
• Can you perform reinforcement learning to improve the QoE model?
Notes on use case
category • Cat 2: describes a scenario related to the application of autonomous behavior in
the network.
Notes on priority of
the use case High.
QoE is an essential metric for user satisfaction. Although QoE measurement is well
analyzed in the context of video st reaming, there is no general definition for a wide
variety of applications. This autonomous service will allow future application
developers to model and track the QoE of their applications online. QoE tracking
will enable application developers to updat e network slices dynamically.
Reference [b-Jahromi], [b -Pierucci ], [b-Bouraqia ], [b-Liu]
7.17.1 Use case requirements
Critical requirements
● AN-UC017-REQ -001: It is critical that autonomous networks (AN) use both subjective
information from users and QoE information derived and analysed from network services to arrive
at the application QoE metric.
NOTE – subjective information from users may include user opi nions and subject measures e.g. opinions on
a scale of 1 -5 or thumbs up/down about a streamed video. Examples of QoE information derived and
analysed from network services are the level of engagement/interaction in an online game, or analysis of
gaming sco res and avatar behaviour.
● AN-UC017-REQ -002: It is critical that autonomous networks (AN) learn and update the
process of information collection from users and derivation from network services. |
40 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 33 NOTE 1 – For example the parameters collected, the mechanisms for collecting and the sample set for
collection may be learnt and updated. Also the mapping between the application QoE metric and the
information collected from users and derived from network services may evolve over a period of analysis.
NOTE 2 – the mapping between the application QoE metric and the information collected from users and
derived from network services may be modelled using AI/ML techniques.
● AN-UC017-REQ -003: It is critical that autonomous networks (AN) evolve and update the
mapping between application QoE metric, network KPIs a nd application KPIs.
NOTE – the process of evolution and updation may be triggered by application feature additions, network
service updates or user device updates.
Expected requirements
● AN-UC017-REQ -004: It is expected that autonomous networks (AN) enable the plugin of
QoE prediction algorithms which may be integrated based on abstract APIs exposed from AN,
which are agnostic to the type of application and the specific underlying network technology.
7.17.2 Use case specific figures
Figure 12: actor interaction for Quality of Experience (QoE) Prediction as -a-Service (QPaaS)
7.18 Autonomy applied to CDNs
Use case id FG-AN-usecase -18
Use case name Autonomy applied to CDNs
Base contribution FGAN -I-079, FGAN -I-019
Creation date 15/April/2021
Use case context Discussions regarding AN -I-019, FGAN -I-079
Use case description [AN-I-019] intro duced Autonomous content delivery networks (CDN), especially
looking at a few key aspects of CDN and what makes them unique, focusing on
several of their properties and approaches we can leverage to increase their
autonomy. With increasing bandwidth of ne tworks, proliferation in the connected
devices, increasing demand of content (e.g. live video, cloud gaming, 360 video),
build -your-own approach to CDNs enabled by cloud services, cloud based CDNs |
41 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 34 are attractive but with several challenges. However caching based on data analysis
remains unsolved while CDN providers struggle to provide rich content at high QoE.
[AN-I-019] called out specific aspects that need programmability - routing, caching
and eviction. Importance of logging and metrics were called out on request/response
metadata, timing information, internal logic decisions.
Current implementations of closed loops for managing CDNs are simplistic e.g.
Standard auto scaling in the context of CDN, increasing the stream -per-node
approach by hardware beef -up (better compute, more cores, more memory, L1
cache, networking including PCIe 4.0, cryptographic acceleration)
There exists an opportunity to better QoE using evolution and experimentation
concepts in FG AN.
The following considerations are important to note in the context of autonomous
CDNs:
1) metric for success: CDNs usually define their success based on whether they can
serve the user traffic. But this has a cyclic effect because the ability to attract traffic
depends on how well CDNs process the traffic they currently have. The challenging
part is the metric used to judge the CDN,
examples:
a) incorporating the current number of requests into the score would be useful.
b) if there is anonymised access to user quality of experience (QoE) data, how
fast the page loaded, it would be a useful metric.
c) another option is to measure the response time from the CDN (usually
abridged to the hit ratio)
d) include other overlays in the measurement e.g. control planes, service
management, tiered storage design.
e) going beyond auto scaling to h ealing, load balancing and edge compute,
concurrency,
2) adapting the possible caching strategies:
Memory intensive contents require large memory in CDN.
a) based on the treatment of various type of content and CDN use cases in the
cache. e.g. live vide o that is cached for a brief period of time, VoD, live
transcoding.
To reduce the time to live (TTL) to avoid keeping infrequently popular objects
in cache
b) bypass the cache for large objects, or for certain classes of users, or particular
extensions
c) use the disk, or explicitly forbid it
d) take advantage of flexibility provided by virtual cache
3) as far as open source and standards go: (decouple the components)
a) open -caching is pushing to provide a subset of metrics
b) perhaps an opportunity to der ive “upstream” gaps in standards and lead an
opensource proof of concept (PoC).
c) study of an open, interoperable CDN components – e.g. caching, transcoding,
analytics which can help independent evolution of the CDN pipeline, while
taking advantage of the work in other bodies e.g. encode/decode, AI, graphics.
and hardware evolution e.g. compute/mem/network/acceleration.
d) similarly, take advantage of the software deployment trajectory towards cloud
native.
Following are related steps in this use case sc enario:
1. outer -loop: Represent the “QoS/QoE requirements” in an intent, deployment
considerations (e.g. hardware, cloud) are to be captured in the intent.
software/CDN pipeline considerations are to be captured here too. |
42 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 35 2. based on analysis, derive the cache policy, action: auto scaling/traffic routing,
geographic location (e.g. edge), decide storage configurations, APIs and
concurrency mechanisms.
3. experiment to determine a good combo of KPIs, data measurement, polici es,
action areas (e.g. scaling, positioning)
4. inner -loop: Adapt the CDN and corresponding configurations based on the
above, with tangible, demonstrable benefits in QoE.
5. feedback to intent evolution – to step 1 above.
Open issues (as seen
by the prop onent) 1. Can we capture the app -specific constraints (e.g. hit ratio) in a standard fashion?
(for inputs to perhaps other SDOs)
2. what are the traffic characteristics relevant to CDNs for 6G?
Notes on use case
category Cat 2: describes a scenario relate d to application of autonomous behaviour in the
network.
7.18.1 Use case requirements
Critical requirements
● AN-UC018-REQ -001: it is critical that AN enables representation of QoS/QoE requirements
in an intent, and additionally deployment considerations (e.g. hardware, cloud) and software/CDN
pipeline considerations are to be captured in the intent. are to be captured here too.
● AN-UC018-REQ -002: it is critical that AN enable adaptations based on analysis.
● AN-UC018-REQ -003: it is critical that AN enable experiment to determine a good combo of
KPIs, data measurement, policies, action areas (e.g. scaling, positioning)
● AN-UC018-REQ -004: it is critical that AN enable the tracing of adaptations on
configurations to tangible, demonstrable benefits in QoE.
● AN-UC018-REQ -005: it is critical that AN enable feedback to intent evolution.
NOTE – Feedback may include parameters for representation in the intent, additional deployment
considerations, and adaptations.
7.18.2 Use case specific figures
None.
7.19 Analysis -driven evolution in virtualized RAN based on devops
Use case id FG-AN-usecase -19
Use case name Analysis -driven evolution in virtualized RAN based on devops
Base Contribution AN-I-072
Creation date 22/April/2021
Use case context Discussions in the FG AN meeting during [AN -I-072]
Use case description
Open radio access network (e.g. O -RAN) a rchitectures allow disaggregated
evolution of RAN components. Programmability and interfaces exposed by
RAN components in open RANs allow developers the opportunity to create
applications (e.g. xApps) based on data from RAN.
In parallel, development metho dologies like devops are being applied to
enable rapid introduction of services to networks.
At the same time, technology evolution in the form of 6G is in progress.
This use case links the dev and ops cycle on one side to the programmability
offered by new RAN architectures like O -RAN. |
43 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 36 An analysis of RAN services and applications (e.g. data, messages, interfaces,
logs, etc from xApps and rApps) can provide valuable i nformation regarding
software evolution and technology evolution and deployment evolution.
Following are related steps in this use case scenario (with O -RAN as example
architecture):
1. Analysis of heterogeneous RAN components, corresponding splits,
capabilities, deployment options and interfaces and data models (e.g. E2
nodes and E2AP support).
2. Analyse the information in the near real time RAN intelligent controller
(nRT RIC)
3. Discover the capabilities of various RAN nodes and instantiate (potenti ally
cloud -native versions of ) applications (e.g. xApps) based on RIC SDKs.
4. Provision and analyse the closed loops at near real time RIC.
5. In correlation with the non RT RIC, analyse the devops cycle at the near
real time RIC to provision new types of CNFs in the near real time RIC and
new types of E2 nodes (or new capability -needs in E2 nodes).
6. In the non RT RIC, analyse the devops cycles of near RT RIC, new
capability needs of E2 nodes, arrive at new use cases (e.g. what are the users
not able to do with the current network and why?)
Describe the relation with autonomous behaviour (if any).
- this use case is related to the evolution and experimentation aspects. It
takes advantage of the increased data gathered from RAN via the
open interfaces and the devops style of RIC application development
to automate specific aspects of the evolution process.
Open issues (as seen by
the proponent) - E2AP message structure may need to be predetermined and has to be agreed
with E2 node vendors.
- unified RIC SDK space is needed.
Notes on use case
category Cat 1: describes a scenario related to core autonomous behaviour itself.
Notes on priority of the
use case High
- has the potential to impact future network architectures and use cases.
References • [b-DISH -AWS] , [b-ONF -auto-service]
7.19.1 Use case requirements
Critical requirements
● AN-UC019-REQ -001: it is critical that AN enables analysis of heterogeneous RAN
components, corresponding splits, capabilities, deployment options and interfaces and data models
NOTE – e.g. E2 nodes and E2AP support.
● AN-UC019-REQ -002: it is critical that AN enables discovery of the capabilities of various
RAN nodes and instantiate (potentially cloud -native versions of) applications.
NOTE – Examples of applications are xApps.
● AN-UC019-REQ -003: it is critical that AN enables provisioning and analysis of clo sed loops
at near real time locations.
NOTE – near real time RIC is an example of near real time location.
● AN-UC019-REQ -004: it is critical that AN in correlation with the orchestrator, analyse the
devops cycle at the near real time locations to provisi on new types of network functions in the near
real time locations |
44 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 37 NOTE – Further examples of new types of network functions are new types of E2 nodes (or new capability -
needs in E2 nodes).
7.19.2 Use case specific figures
None.
7.20 Evolving edge applications for verticals using private 5G
Use case id FG-AN-usecase -20
Use case name Evolving Edge applications for verticals using Private 5G
Base contribution [AN-I-065]
Creation date 22/April/2021
Use case context Discussions during [AN-I-065]
Use case description Vertical network applications like corrosion detection and intruder detection
needs to be enabled at the edge using AI/ML. These applications allow :
Inspection and surveillance
services for critical industrial infrastructu res. Multi -domain (core and edge)
e2e deployment of applications, on demand, is needed.
5G orchestration platform allows distributed deployment of applications,
especially in exploiting the capabilities at an edge environment. This allows
network operators to manage the unique KPIs of services at edge sites without
exposing the network architecture.
By providing an environment to develop and deploy edge applications, to
serve specific needs of verticals, network operators are able to create an
ecosystem fo r value creation, especially for domain -focussed small
businesses.
Following are related steps in this use case scenario:
1. enterprises deploy private 5G network slices at edge
2. the applications and KPIs are analysed at the edge
3. network management and optimization approaches are triggered based on
this analysis
4. tailor made applications which are specifically tuned for the needs of the
enterprise are offered to the enterprise.
NOTE - this fits well with the concept of NetApps and network applicati on
orchestrator (NAO) [AN -I-065], decoupling the network operations logic
from service provider logic and providing clear business roles.
6. the process facilitates experimentation and evaluation of candidate
solutions.
5. edge network evolution and adaptation is triggered based on the analysis.
Describe the relation with autonomous behaviour (if any).
- this use case is related to the evolution and experimentation aspects. It
takes advantage of the increased deployment flexibility provided by
private 5G networks. edge -core information exchange is used to
trigger experimentation and adaptation of the edge.
- there is also an expectation of alignment with domain specific
experiments and matching KPIs based on innovations in the verticals.
Open issues (as seen by
the proponent) - lack of standard mechanisms for representing and experimenting with
digital twin -like mechanisms for experimenting.
- lack of standards in edge -core communication for evolution and
adaptation.
- lack of repository for domain -specific 3rd party applications which can
be deployed at the edge.
- lack of inputs and experts from verticals. |
45 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 38 Notes on use case
category Cat 1: describes a scenario related to core autonomous behaviour itself.
Notes on priority of the
use case High
- has the potential to impact future service deployment at the edge
- standards gaps are visible.
- potential collaboration with EU projects.
References •
7.20.1 Use case requirements
Critical requirements
● AN-UC020-REQ -001: It is critical that autonomous networks (AN) interface with network
application orchestration platforms at edge networks to provide both local, vertical -specific,
including real time analytics as well as remote, general, including non -real time analytics .
NOTE – network application orchestration platforms may coordinate with edge analytics and edge service
management to abstract the edge network architecture to the AN.
● AN-UC020-REQ -002: It is critical that autonomous networks (AN) provide both network
management and optimization and application management and optimization services to application
orchestration platforms at the edge.
NOTE – while network management and optimization provides specific inputs to the edge about the network
architecture, appl ication management and optimization services may provide specific inputs on placement,
functionalities and other aspects of applications.
Expected requirements
● AN-UC020-REQ -003: It is expected that autonomous networks (AN), provide tailor -made
recipes f or application management and optimization specific to verticals deployed at the edge.
NOTE – these recipes may be the result of offline, generalized analytics at the AN. These recipes may be
considered by NAO while designing, developing and deploying applications at the edge.
Added value requirements
● AN-UC020-REQ -004: It is of added value that autonomous networks (AN), consider
feedback from NAO, and continuously optimize the tailor -made recipes for application
management and optimization specific to verticals deployed at the edge.
NOTE – the feedback from NAO will not contain the details of net work architecture or user details at the
edge. |
46 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 39 7.20.2 Use case specific figures
Figure 13: actor interaction for Evolving Edge applications for verticals using Private 5G
7.21 Experimentation and “fire-drills” for public safety networks
Use case id FG-AN-usecase -21
Use case name Experimentation and “fire-drills” for public safety networks
Base contribution [AN-I-055-R1]
Creation date 22/April/2021
Use case context Discussions during [AN -I-055-R1]
Use case description Emergency response and public safety needs resilient, on -demand network
setup and management. This will require inputs from verticals including
emergency responders. Experimentation and trial runs may be mandated in
certain regions.
Based on th e new technologies used in evolving the networks, there are
different ways of deploying public safety networks. To validate the readiness
of such networks, experiments need to be designed, even for the rare
scenarios. In fact for public safety networks, th e design of rare scenarios is
more important than the “sunny -day” success scenarios.
It is also important that the experimentation matches step with the evolution
of technologies used for implementing the public safety networks.
Following are related st eps in this use case scenario:
- step 0: continuous analysis of external inputs and creation of strategies for
experiments (experiments are equivalent to "fire drills")
- step 1: closed loops are formed in sandboxes, "fire drills" are conducted and
analyse d.
- step-2: based on the “success” or failure of the rare scenarios, network
optimization may be triggered.
Describe the relation with autonomous behaviour (if any).
- this use case is related to the evolution and experimentation aspects. It
applies the principle similar to GAN for experimenting, validating the
preparedness of public safety networks. |
47 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 40 Open issues (as seen by
the proponent)
- need mechanisms for representing the current state of readiness of the
network to handle emergencies.
- need mechanisms for modelling and automation and validation of
such controlled experiments in the context of public safety networks
Notes on use case
category Cat 1: describes a scenario related to core autonomous behaviour itself.
Notes on priority of the
use case High
- addresses an important end -user scenario
- potential collaboration with other groups working in the area of public
safety networks.
Reference
7.21.1 Use case requirements
Critical requirements
● AN-UC021-REQ -001: It is critical that autonomous networks (AN) enable continuous
analysis of external inputs and creation of strategies for experiments.
● AN-UC021-REQ -002: It is critical that autonomous networks (AN) enable closed loops
formation in sandboxes, where specific tests could be c onducted and analysed on those closed
loops.
● AN-UC021-REQ -003: It is critical that autonomous networks (AN) trigger network
optimizations based on the “success” or failure of the rare scenarios in sandboxes.
7.21.2 Use case specific figures
None.
7.22 Machine learni ng for network automation
Use case id FG-AN-usecase -22
Use case name Machine Learning for Network Automation
Base contribution AN-I-014
Creation date 02/Feb/2021
Use case context Discussions regarding AN -I-014
Use case description [AN-I-014] introduced Machine Learning -enabled network automation and the
required “tailoring” of ML apps for networks.
[AN-I-014] called out specific aspects that need consideration in network while
applying ML: requirements from each domain, specificiti es (time, data and
error) of each domain. Reference architecture was discussed including a ML
orchestration layer. Considerations on algorithm design including trade -offs
were discussed.
The concept of “sub -problems” in networks and the limited role of ML were
discussed too. The challenge in mix -match of data with ML (with respect to
security, location, interoperability, etc) was discussed. A potentially top -down
approach to service optimization was discussed.
Following are the additional considerations in this use case:
1. Domain specific characteristics (called “specificities” in AN-I-14) may not be
known beforehand to the solution designer, especially given the loosely coupled
architecture of future networks and ML.
2. In addition to domain -specificiti es, service based specificities may be
important too. Even in case of multi -domain services, specificities could be
captured per -domain, E2E, at service level. So when we add new tenants, we |
48 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 41 need a way to dynamically capture their specificities + the domai ns which their
corresponding specificities.
3. Agile dev and deployments in future networks may need dynamic discovery
of trade -offs per service. Considering the service life cycle as day -0 (design),
day-1 (deployment), day -2 (monitor), day -3 (optimization ), day -4 (re -design),
day-5 (evolution), feedback loops are important to enable rapid development and
reduce time to market for new services.
4. run -time discovery of “sub -problems”
Following are related steps in this use case scenario:
1. ML pipelines c onfigure policies in the network based on the network QoS
feedback.
2. Service metrics and related policies are provisioned in the ML pipelines
based on the monitoring and analysis of errors.
3. service redesign and optimization is triggered based on “su b-problems” and
“specificities” discovered.
4. network/domain specificities are tracked and similarly optimization
problems are tracked. These are input to the service evolution.
Open issues (as seen by
the proponent) 1. Considering "achieve optimal operation in one domain only is risky" and
"cross -tenant metrics exchange" is needed, and considering the SA5 1:many
relationship with vendors in 28.530, what are the possible feedback from the
CSP and CSCs? Other than the stock "metric feedback" a nd "policy" to the NF,
is there an orthogonal feedback to "ev" to "dev" which can potentially create new
NFs?
2. How do we discover “sub -problems” on the fly?
3. How to do dynamic discovery of trade -offs per service?
4. what tools we have to “design algori thm”?
Notes on use case
category Cat 2: describes a scenario related to application of autonomous behaviour in
the network.
Notes on priority of the
use case High
- has the potential to impact future service development and evolution.
- reuses MLFO and Y.3 172 architecture.
Reference
7.22.1 Use case requirements
Critical requirements
● AN-UC022-REQ -001: It is critical that autonomous networks (AN) enable, in case of multi -
domain services, specificities per -domain, E2E.
NOTE – This includes a way to dynamically, autonomously capture their specificities + the domains which
their corresponding specificiti es.
● AN-UC022-REQ -002: it is critical that AN enable agile dev and deployments in future
networks dynamic discovery of trade -offs per service.
NOTE – Considering the service life cycle as day -0 (design), day -1 (deployment), day -2 (monitor), day -3
(optimi zation), day -4 (re-design), day -5 (evolution), feedback loops are important to enable rapid development
and reduce time to market for new services.
● AN-UC022-REQ -003. it is critical that AN enable run -time discovery of “sub -problems”.
7.22.2 Use case specific fi gures
None. |
49 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 42 7.23 Autonomous agents (with varied competence) in networks
Use case id FG-AN-usecase -23
Use case name Autonomous agents (with varied competence) in networks
Base contribution FGAN -I-052_att
Creation date 04/Mar/2021
Use case context Discussions regarding FGAN -I-052_att
Use case description [AN-I-052] introduced autonomous Systems in hostile environments. Estimation and
judgement of competence as key criteria for det ermining the right level of autonomy was the
focus. Levels of autonomy for unmanned systems were introduced, especially ranging from
“sub-functions” to single functions to single system to teams. A framework for robot autonomy
was discussed with correspond ing guidelines. Based on these, the characteristics for operations
in hostile environments were listed.
[AN-I-052] called out the requirements for operations with autonomous systems. Relation
between the needed level of autonomy depending on environment a nd type of task in contrast
to capabilities of the system in combination with policies. Systems should provide the best
possible support and hence the autonomy level has to be adjusted such that humans only have
to intervene when it is necessary and makes sense. Due to dynamics during missions in hostile
environments, the systems have to adapt their autonomy level at run -time (or humans have to
do it) according to the situation and the corresponding requirements. The existing stage levels
of autonomy have t o be extended by the ability of switching the level in run -time (judgement
by design vs. judgement in run time)
A simple workflow scheme for autonomy with varying autonomy levels was discussed. This
included task specification by human and task understand ing, feasibility check, task planning,
task execution by system. Request for support to human and control by human can be to any of
these workflow steps. Monitoring of performance levels by humans and learning by the system
are added steps.
Autonomy lev el and dependency on competence were discussed. Competence analysis as a
weighted function of capabilities needed, capabilities existing, existing options for actions and
existing constraints was described.
Following are the additional considerations in th is use case:
1. Taking telco service design, development, deployment and operations (ops) as an example -
the levels of autonomy may be applied in follows:
(a) service design is done by designers (100% designer interaction, no ops interaction)
(b) existi ng software development kits (SDKs) and application programming interfaces (APIs)
are exercised to create applications (e.g. rApps or xApps) – (high level designer interaction,
high code but low ops interaction)
(c) configuration of existing or new service s in real time environments (e.g. distributed unit
(DU) – mid level designer interaction, high ops involvement).
(d) service deployment and QoE measurement in customer premises (e.g. VoD – no code, low
designer interaction, collaborative, high ops involve ment).
Thus, it may be relevant to consider the nature of the task in addition to the type of environment.
e.g. for the ops engineers design phase is a “difficult environment” (due to low involvement)
and for the service designers customer premises is a “difficul t environment” (due to constraints
in site visits).
Thus, it may be relevant to consider a multi -agent system where the agents have varied
competences (capabilities + options for actions + constraints).
Following are related steps in this use case sce nario:
1. Problem detected in the network (e.g. video performance degradation for customers) |
50 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 43 - e.g. scenario: ops agents collect debug data. analysis agents with matching capabilities are
deployed at the nRT RIC and triggered to analyse the data.
2. fault isolation (e.g. in CU, DU, user plane, control plane)
- e.g. scenario: collaborative analysis is used to pin -point the cause of failure. this may
involve multi -agent team communication to do the steps in the workflow described in AN -I-
052
3. fault correction (e.g. parameter configuration)
- e.g. scenario: may involve service upgrade or software reconfig.
4. analysis of task performance by agents
- e.g. scenario: collaborative analysis is used to collect data from the agents – including whe re
the human interactions were needed.
5. trigger creation of new agents with new capabilities.
- e.g. scenario: location and capabilities are selected based on the next higher level of
autonomy to reduce human interaction in this scenario.
Open issues (as seen
by the proponent) - need coordination of multi -agent systems to avoid conflicts.
- need mechanisms for adapting autonomy based on competence at run -time and
flagging human (or higher level) assistance
- need multi -domain orchestration to positio n the agents at various network domains.
- In some cases, hardware capability adaptation may be needed which may need new
physical network functions (PNF).
Notes on use case
category Cat 2: describes a scenario related to application of autonomous behaviour in the network.
Notes on priority of
the use case High
- has the potential to impact future service development and evolution.
Reference [b-Beyerer], [b -Hesse]
7.23.1 Use case requirements
Critical requirements
● AN-UC023-REQ -001: It is critical that autonomous networks (AN) enable, autonomous
agents to collect debug data.
NOTE – For example analysis agents with matching capabilities are deployed at the nRT RIC and triggered
to analyse the data.
● AN-UC023-REQ -002: It is critical that autonomous networks (AN) enable, fault isolation
(e.g. in CU, DU, user plane, control plane) using collaborative analysis is used to pin -point the
cause of failure.
NOTE - this may involve multi -agent team communication to do the steps in the workfl ow.
● AN-UC023-REQ -003: It is critical that autonomous networks (AN) enable, fault correction
(e.g. parameter configuration) using service upgrade or software reconfig.
● AN-UC023-REQ -004: It is critical that autonomous networks (AN) enable analysis of tas k
performance by agents
NOTE – Collaborative analysis is used to collect data from the agents – including where the human
interactions were needed.
● AN-UC023-REQ -005: It is critical that autonomous networks (AN) enable creation of new
agents with new capa bilities.
NOTE – Example location and capabilities are selected based on the next higher level of autonomy to reduce
human interaction in this scenario.
7.23.2 Use case specific figures
None. |
51 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 44 7.24 Automated, adaptive acceleration for AI @ edge
Use case id FG-AN-usecase -24
Use case name Automated, adaptive acceleration for AI @ edge
Base contribution FGAN -I-046
Creation date 04/Mar/2021
Use case context Discussions regarding FGAN -I-046
Use case description [AN-I-046] introduced Spatial Architectures whi ch scale performance &
resources to meet the application requirements and Scaling to fit into available
resources – in the context of DNN. It also discussed that reduced precision can
be highly effective to reach
communication requirements. Spatial archite ctures can exploit custom
arithmetic at a greater degree. Further, it discussed topologies fully co designed
for hardware architecture, where the Circuit is the DNN [ b-Umuroglu ]. Adjust
the parameters of DNN (=lookup table (LUT) contents) while iterating o n
training dataset until accuracy.
[AN-I-046] showed results (with an example of intrusion detection) that spatial
processing, customized arithmetic and learned circuits can help scale to
communication throughput and latency requirements.
I-046 also talked about [FINN -R] and Providing tool s and platforms for
exploration of DNN compute architectures. ML engineers can create specialized
hardware architectures on an FPGA with spatial architectures and custom
precision. Design and runtime software tools (e.g. FINN) for DNN to FPGA
development starting with training or learning reduced precision DNNs, using
ONNX based intermediate representation, perform optimization on this
intermediate representation, to create a DNN hardware IP, was discussed.
Thus, it may be relevant to consider the follow ing aspects for this specific use
case:
1) AI -enabled applications are increasingly being deployed at the edge. Low
latency, low power consumption and small footprint are considerations for AI
applications at the edge. Accelerated, AI -enabled applications at the edge are
important enablers for future networks.
2) As AI technology evolves, AI models evolve, the acceleration platform must
also be adaptable and at the same time satisfying the requirements above. Also,
reduced time to market, development time and cost to reach production
readiness, are important factors influencing deployment decisions by network
operators. fully customized circuit board is developed for each application may
not fit this bill.
3) pluggable solutions into a larger edge applicati on, providing both the
flexibility of a custom implementation with the ease -of-use and reduced time -
to-market of an off -the-shelf solution, are needed.
4) Adaptive computing includes hardware that can be highly optimized for
specific applications such as Field Programmable Gate Arrays (FPGAs). In
addition to FPGAs, new types of adaptive hardware such as adaptive System -
on-Chip (SoC) which contains FPGA fabric, coupled with one or more
embedded CPU subsystems, have been introduced recen tly.
5) prebuilt platforms and APIs, software tools enable full customization of the
adaptive hardware, enabling even more flexibility and optimization. This can be
used to design highly flexible, yet efficient systems at the edge.
6) exploiting the develo pment and adoption of standards in interface and
protocols at the edge, different AI -enabled edge applications can use similar
hardware components.
Following are related steps in this use case scenario: |
52 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 45 1. Given an AI/ML model layered architecture, the following considerations
needs to be applied – (a) concurrency in processing of layers, (b)
fragmentation/buffering between layers vs. offloading of layers into compute
(c) precision vs. performance and energy efficiency.
2. given the specific goals and co nstraints of the AI/ML model, consider the
target platform architecture and “sacrificable” precision to explore the model
architecture and layer compositions.
3. transformation of a AI/ML model -> intermediate representation ->
optimization -> hardware imp lementation -> evaluation -> back to
training/modelling.
4. derive feedback for hardware adaptation and design.
Open issues (as seen by
the proponent) - the role played by MLFO is not clear (need further study).
- the intersection with Y.3179 has to be studied further.
- dependencies with specific hardware architectures and specific types of
neural network (NN) topologies need further study.
- the role played by training techniques need further study
- the role played by cycle -faithful simulators like [scalesi m] has to be
studied further.
Notes on use case
category Cat 2: describes a scenario related to application of autonomous behaviour in
the network.
Notes on priority of the
use case High
- has the potential to impact AI/ML operations and optimization in
relation with hardware evolution.
Reference [b-Blott ], [b-Xilinx], [b-Umuroglu ], [b-SCALE -Sim]
7.24.1 Use case requirements
Critical requirements
● AN-UC024-REQ -001: It is critical that autonomous networks (AN) enable, analysis of
concurrency in processing of layers in a DNN, fragmentation/buffering between layers vs.
offloading of layers into compute and analysis of precision vs. performance and energy efficiency.
● AN-UC024-REQ -002: It is critical that autonomous networks (AN) consider the target
platform architecture and “sacrificable” precision to explore the ML model architecture and layer
compositions.
● AN-UC024-REQ -003: It is critical that autonomous networks (AN) enable transformation of
a AI/ML model -> intermediate representation -> optimization -> hardware implementation ->
evaluation -> back to training/modelling.
● AN-UC024-REQ -004: It is critical that autonomous networks (AN) derive feedback for
hardware adaptation and design.
7.24.2 Use case specific figures
None.
7.25 Assistive networks : Adaptation of communication system based on changing user
accessibility needs
Use case id FG-AN-usecase -25
Use case name Assistive networks: Adaptation of communication system based on changing user
accessibility needs.
Base contribution Editor, Discussions about common user profile (CUP)
Creation date 10/May/2021
Use case context Discussions about common user profile (CUP) |
53 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 46 Use case description
This contribution attempts to describe a use case where:
For both disabled and the able-bodied, as the accessibility requirements evolve,
adaptations need to be applied on the network, device and user profiles need to
align with the changing needs of the user.
The scope of assistive technologies needs to be broadened to “assistive networ ks”.
Assistive networks can be thought of E2E network slices that include assistive,
adaptive, and rehabilitative connectivity for persons with specific needs. It also
includes the automated mechanisms used in selecting, locating, using and
customizing the networks. Assistive networks promote greater independence by
enabling people to connect to the devices and network more autonomously.
Environmental models exist for surroundings but building on top of such
environment models to adapt the connectivity to t he user with specific
requirements is the need of the hour.
User model and simulations are needed to provide inputs to AN.
Development of standard definitions are needed for application model, assistive
network, context modelling, environmental model and c ommon user profile,
metadata, simulation and virtual instance in the context of AN.
Individualization needs to be added as an important dimension of AN for future
networks.
Reuse of common user profile (CUP) to automate the collection, analysis and
adaptat ion of the network and applications is proposed here.
Following are related steps in this use case scenario:
1. Environment model including the network environment is built for the user.
e.g. radio propagation models, signal strengths with respect areas, mobility
prediction models.
2. User model is accessed and updated.
e.g. user specific constraints, user inside a car wearing seat belt has limited
mobility within the car. Similarly for elderly and children, persons under
emergency needs.
3. Simulations are used (offline and/or real time) to determine the changes and
adaptations needed in the network to satisfy the needs of the user.
e.g. digital twins which include environment simulations and user specific criteria.
4. Adaptations are applie d to the network and the context.
e.g. drone based coverage is provided, reconfigurable intelligent surface (RIS)
configurations or beam configurations to provide better coverage.
5. generalizations and evolutions are studied for applicability in a larger context.
e.g. continuous update of models, transfer of model parameters across domains for
easy learning, evolution of network simulators and context for new encountered
scenarios.
Describe the relation with autonomous behaviour (if any).
- this use case i s related to adding environment sensing and adaptation to
include inclusivity in the evolution and experimentation aspects. It takes
advantage of the existing studies in Q11/9.
Open issues (as seen
by the proponent) - standard mechanisms for capturing envir onment in the context of network
requires further study.
- simulations and experiments in relation to user model, specific user needs
and network characteristics needs further study. |
54 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 47 Notes on use case
category Cat 2: describes a scenario related to application of autonomous behaviour in the
network.
Notes on priority of
the use case High
- has the potential to evolve assistive future network architectures and
further enable new use cases.
References [b-J.acc -us-prof] , [b-Biswas] , [b-AVA -1], [b-AVA -2], [b-ISO/IEC 24756] , [b-ISO
9241 -129], [b-KUKA ]
7.25.1 Use case requirements
Critical requirements
● AN-UC025-REQ -001: It is critical that autonomous networks (AN) enable, creation and
representation of environment model including the network environment for the user with assistive
needs.
NOTE – e.g. radio propagation models, signal strengths with respect areas, mobility prediction models.
● AN-UC025-REQ -002: It is critical that autonomous networks (AN) enable updation of user
model.
NOTE – e.g. user specific constraints, user inside a car wearing seat belt has limited mobility within
the car. Similarly for elderly and ch ildren, persons under emergency needs.
● AN-UC025-REQ -003: It is critical that autonomous networks (AN) enable Simulations
(offline and/or real time) to determine the changes and adaptations needed in the network to satisfy
the needs of the user.
NOTE – e.g. digital twins which include environment simulations and user specific criteria.
● AN-UC025-REQ -004: It is critical that autonomous networks (AN) enable Adaptations
applied to the network and the context.
NOTE – e.g. drone based coverage is provided, rec onfigurable intelligent surface (RIS) configurations or
beam configurations to provide better coverage.
● AN-UC025-REQ -005: It is critical that autonomous networks (AN) enable generalizations
and evolutions which are studied for applicability in a larger c ontext.
NOTE – e.g. continuous update of models, transfer of model parameters across domains for easy learning,
evolution of network simulators and context for new encountered scenarios.
7.25.2 Use case specific figures
None.
7.26 Ev-as-a-service: Achieving zero touch evolution in a delegated autonomy case
Use case id FG-AN-usecase -26
Use case name Ev-as-a-service: Achieving zero touch evolution in a delegated autonomy case.
Created by Editor
Mentor Laurent Ciavaglia
Creation date 10/May/2021
Use case context Discussions during and after FGAN -I-005
Use case description
This contribution attempts to describe a use case where:
- A multi -domain architecture is assumed. Each domain may have its
own orchestrator. |
55 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 48 NOTE - Example in FG -AN-usecase -20, NAO is mentioned.
MANO/NFVO is used in the NFV domain, service orchestrator may be
used similar to ONAP in the communication domain.
- Closed loops are assumed in each domain, managed by the
corresponding orchestrators.
- (For the purposes of this use case), it is assumed that each closed loop
enables autonomous behaviour in that domain for specific use cases
e.g. resource scaling based on load.
NOTE - The autonomous behaviour enabled by use case specific closed
loops and managed by domain orchestrators can be extended to any
number of management domains.
- Current frameworks [ITU -T Y.3172 ], [ITU -T Y.3179 ], [b-ETSI ZSM
002] assume offline development and provi sioning of services which
form the closed loops. e.g. AI/ML model training based on data from
the network, followed by model serving in the network.
- This use case introduces an evolution (Ev) function which analyses the
inputs from the closed loops (and ot her context information in the
domain orchestrator) to trigger creation of new services which can
cater to the evolving needs of the domains.
- The triggers may be input to devops pipeline. This may result in
creation of new framework services or new applica tions or new VNFs
or new configurations or new AI/ML models etc
- These may then be tested and evaluated in an experimental setup (e.g.
Digital twins, Sandbox, etc) and deployed in corresponding domains
using the domain orchestrators.
In summary, the use ca se proposes to monitor, identify the need for ev,
generate new f() to support this need, “(re -)inject” that function through devops
pipeline into the closed loops. Note that this may require multi -domain
coordination to modify the closed loops and may be c hallenging from an
implementation perspective. Implementation may depend on the capabilities
provided by the underlying closed loop frameworks e.g. ZSM.
Levels of “mutation” of CL:
There can be a spectrum of adaptation changes to the closed loops (CL):
a) no adaptation at all – same input to the CL, always leads to the same output.
b) limited adaptation – CL improves utility over time, so same input may not
lead to the same output (after improvement).
c) full -fledged evolution, involving development and i njection of new
functions.
The capability of underlying closed loop frameworks may be factor in deciding
the level of adaptation possible in the AN.
Division of responsibility between the controller and the CL:
1) “dumb” vs. “intelligent” closed loops: dumb CL may allow full fledged re -
configurations and re -injections of functions which may allow overall mutation
of its functionality over time. Whereas an “intelligent” closed loop may use its
intelligence to limit external influence by the controller. I n any case, the
domain orchestrator should know the mutation -capabilities of the CL. In case
of limitations encountered for adaptations, the CL (or domain orchestrator)
should be able to escalate the requirement to higher domains.
2) timescale of Ev has to be agreed between the CL and the orchestrator.
Following are related (example) steps in this use case scenario:
1. Enterprise/vertical provides intent for application/service
2. A corresponding slice is created by NAO. |
56 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 49 3. Corresponding resources are allocated by NFV MANO
4. NSaaS may be instantiated using ONAP/SO.
5. use case specific closed loops are instantiated in each domain e.g. power
optimization, interference management, resource utilization, self -x.
6. Ev as a Service is instanti ated in the zero touch framework.
7. Based on the analysis of inputs from use case specific closed loops and
domain orchestrators, Ev triggers configurations, updates, service instantiation,
new closed loops, even new service development (using triggers to Devops
pipeline).
8. After testing and validation, such updates are reflected in the domains and
use case specific closed loops.
Describe the relation with autonomous behaviour (if any).
- This use case proposes Ev -as-a-service scenario in relation to zero -
touch frameworks.
Open issues (as seen by
the proponent) - interfaces between NAO and other orchestrators are to be studied.
- interface with devops pipeline is not automated (as of now).
- Human interaction in the process is to be studied
- Whet her AI/ML services need special treatment in this process?
- what supporting enablers are needed to enable Ev? How do we evaluate
whether the proposed Ev meets the KPIs?
Notes on use case
category Cat 2: describes a scenario related to application of autono mous behaviour in
the network.
Notes on priority of the
use case High
- enables integration and interaction with zero touch frameworks.
References [b-ETSI GS ZSM 001] , [b-ETSI GS ZSM 002] , [b-ETSI GR ZSM 009 -3], [b-
Ciavaglia -1], [b-ETSI GS ZSM 013] , [ITU -T Y.3172] , [ITU -T Y.3179]
7.26.1 Use case requirements
Critical requirements
● AN-UC026-REQ -001: It is critical that evolution function (Ev) in autonomous networks (AN)
analyses the inputs from domain specific closed loops (and other context information in the domain
orchestrator) to trigger management (creation, update and delete) of network services, which may in
turn participate in the closed loops.
NOTE – The management (creation, update and delete) of network services, over a number of iterations,
may result in evolution.
● AN-UC026-REQ -002: it is critical that the modifications to network services and applications
may be tested and evaluated in an experimental setup and deployed in corresponding domains using
the domain orchestrators.
NOTE – Examples o f experimental setups used for testing and evaluation of network services and
applications are Digital twins, Sandbox, etc.
Expected requirements
● AN-UC026-REQ -003: it is expected that management of network services or applications or
VNFs or configurations or AI/ML models is done at runtime in coordination with devops pipelines.
● AN-UC026-REQ -004: it is expected that domain specific closed loops allow management of
network services or applications or VNFs or configurations or AI/ML models in coordination with
AN components.
NOTE – There can be a spectrum of adaptation changes (levels of “mutation”) of network services:
a) no adaptation at all |
57 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 50 b) limited adaptation – improves utility over time
c) full -fledged evolution, involving development and injection of new functions.
The capability of underlying closed loop frameworks may be factor in deciding the level of evolution and
adaptation possible in the AN.
Added value requirements
● AN-UC026-REQ -005: It is of added value that Ev function in autonomous networks (AN) act
as consumer of mutation functions provided by underlying service management frameworks and in
turn provides evolution service to underlying service management frameworks.
NOTE – Example of a service management framework is ETSI ZSM [ b-ETSI GS ZSM 001].
7.26.2 Use case specific figures
None.
7.27 Experimentation as a service: Digital twins as platforms for experimentation
Use case id FG-AN-usecase -27
Use case name Experimentation as a service: Digital twins as platforms for experimentation.
Base contribution FGAN -I-058
Creation date 10/May/2021
Use case context Discussions d uring presentation of FGAN -I-058
Use case description
[FGAN -I-058] described a digital twin as a representation of a physical and/ or
logical object. The contribution proposed to build Digital Twins of Computer
Network infrastructures. Some examples of the (hypothetical) questions which
could be answered using digital twins were l isted as: Which is the best network
upgrade given a budget? Which is the best link upgrade to accommodate a new
customer? Can we support a new customer SLA with the current network
capacity? etc.
The impact of digital twins in Network Planning and Upgradin g,
Troubleshooting and Performance Analysis, What -if Analysis were described
in [FGAN -I-058]. This makes digital twins a perfect environment for
experimentation in the context of autonomous networks.
[FGAN -I-058] took the approach of using neural networks (NN) to build digital
twins. The approach using graph ne ural networks (GNN) was described. It
generalizes to unseen topologies, routings and traffics. Specific example of
Routenet was described. RouteNet can generalize to unseen topologies,
routings and traffic matrices.
DRL+GNN looks as a promising technique for real -time network optimization
was introduced in [ FGAN -I-058].
Autonomous networks (AN) aim to remove the human from the control loop.
This poses hard ch allenges to offer 100% guarantees once the AN products are
deployed in networks. In order to achieve mature solutions for autonomous
network control, it will be essential for AN vendors to validate in advance that
their products will operate successfully i n the target customer networks, before
they are actually deployed.
A DT can be used to estimate accurately the resulting network performance of
an experimentation approach and the effect after applying the actions produced
by the AN, thus determining what network scenarios are well -supported by the
product. After a comprehensive validation test, the vendor can apply the
adaptations to the network. |
58 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 51 Following are examples of scenarios in this use case:
Scenario -1: preparation of DT: import network configurations (may include
closed loops?) into digital twin. This sets the stage for preparation of
simulations in the DT.
Scenario -2: trigger of DT for simulations : update of network configurations
in digital twin (if any, by engineer), followed by simulations in digital twin and
generation of asynchronous events. These events are consumed by AN engine
and may in turn result in experimental configurations/updates from the AN
engine towards the DT. This cycle may continue based on the sequ ence of
simulations and scenarios in the DT. The validation of KPIs in the DT as a
result of experimentation and adaptations by the AN engine is an important
step.
Scenario -3: trigger of AN engine by operator : update of network
policy/configurations by en gineer, which triggers AN engine to corresponding
experiments or configurations towards the digital twin. Experimentation may
be configured in the digital twin and corresponding events and KPIs may be
used to evaluation the result of the experimentation. T his may result in
selecting the best possible sequence of actions or adaptations towards the
network. The validation of the AN engine actions is an important step here.
Scenario -4: trigger based on evolution : Other triggers for experiments in AN
engine ma y include inputs from evolution functionality. Experimentation and
evaluation of actions or adaptations towards the network are same as above.
Following are related steps in this use case scenario:
1. Import environment into DT, trigger simulations in DT and validate the
results, especially the use case specific closed loops.
2. AN -triggered experiments and adaptations are tested using corresponding
simulator settings in DT and evaluating the impact in simulations.
Describe the relation with autonomous beh aviour (if any).
- this use case is related to the concept of experimentation.
Open issues (as seen by
the proponent) - what are the features of the digital twin? what are the APIs that the
digital twin expose?
- how to configure closed loops in DT?
- we discussed “a marketplace of DTs and you choose the one that fits
your particular needs” – this needs further study.
- If a DT is trained with certain capabilities, can we transfer the training
using p2p interface to new DT?
- How to take advantage of GNNs for generalisability and
explainability?
- Are there standard interfaces for integrating AI/ML with digital twin?
Is Y.3172 applicable here?
Notes on use case
category Cat 2: describes a scenario related to application of autonomous behaviour in
the network.
Notes on priority of the
use case High
References [b-Rusek] , [b-Almasan] |
59 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 52 7.27.1 Use case requirements
Critical requirements
● AN-UC027-REQ -001: It is critical that AN enable import of simulation environment into DT,
trigger simulations in DT and validate the results, especially the use case specific closed loops.
● AN-UC027-REQ -002: It is critical that AN -triggered experiments and adaptations are tested
using corresponding simulator settings in DT and the impact in simulated environment is evaluated.
7.27.2 Use case specific figures
Figure 14: Experimentation as a service
7.28 Evolution from scenario -specific, explicit -coordination to coor dination -free
interoperability (achieved using data -driven approaches)
Use case id FG-AN-usecase -28
Use case name Evolution from scenario -specific, explicit -coordination to coordination -free
interoperability (achieved using data -driven approaches)
Base contributions [FGAN -I-091], [FGAN -I-029]
Creation date 27/May/2021
Use case context Discussions during [FGAN -I-091], [FGAN -I-029]
Use case description Deep learning is being used to address challenging problems in wireless
communications such as modulation recognition, radio fingerprinting and many
other scenarios. The advantages of this approach include the capability to
address wide -range of scenarios, where a mathematical model is difficult to
make (e.g. channel estimation, beam management for future networks).
Existing solutions mostly rely on explicit coordination between the transmitter
(TX) and the receiver (RX), introducing problems of interopera bility and
necessity for standards. Such signalling messages eat into the costly spectrum
and complicate protocol design.
A data -driven approach based on neural networks (NN) is an alternative to
achieve coordination -free interoperability. |
60 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 53 Example -1 for the use case:
Millimeter wave (mmWave) communication with large antenna arrays is a
promising technique to enable extremely high data rates. Because of their
highly directional transmissions, radios operating at millimeter wave
(mmWave) frequencies need to perform beam management to establish and
maintain reliable mmWave links. Transmitter (TX) and the receiver (RX) need
to coordinate to select the beam pair that yields the highest beamforming gain.
Currently 5G NR defines the stages of exhaustive beam swe ep (EBS) as: initial
access (IA) and beam tracking. For both IA and beam tracking, the 3rd
Generation Partnership Project (3GPP) NR standard for 5G communications
utilizes synchronization signal blocks (SSBs).
Beam management for the IA procedure in 3GPP NR involves:
1) beam sweep: base station transmits directional Synchronization Signals
(SSs) to cover all the TXBs of a certain codebook. Each beam is swept with an
SSB, which is a group of 4 OFDM symbols and 240 subcarriers in frequency.
2) beam measureme nt: the User Equipment (UE) itself, if configured for
directional reception, performs a directional scan, measuring the quality of each
beam pair
3) beam decision: the UE selects the beam to be used to perform initial access
4) beam reporting: During the next SSB in the selected direction, the UE
acquires information on the time and frequency resources in which the base
station will be in receive mode for the random access message using the same
TXB
I-091 explained receiver can associate Signal -to-Nois e-Ratio (SNR) levels to
beams without explicit coordination with the transmitter. pilot -less estimation
technique. the RX infers the Angle of Arrival (AoA) and the TXB by passively
eavesdropping on data transmissions to other users in the network. leveragi ng a
data-driven approach based on convolutional neural networks (CNNs) to
achieve coordination -free beam management in mmWave networks. based on a
unique “signature” of the beam from the impairments .
inferring (i) the Angle of Arrival (AoA) of the beam an d (ii) the actual beam
being used by the transmitter through waveform -level deep learning on ongoing
transmissions between the TX to other receivers.
Experimentation: experimental data collection campaign with two software -
defined radio testbeds, and by us ing multiple antennas, codebooks, gains and
locations (iv) 3 different AoAs; (v) multiple TX and RX locations.
Evaluation criteria: An upper bound on the expected search time of the
proposed algorithm. The proposed technique reduces latency by up to 7x wit h
respect to the 5G NR initial beam sweep in a default configuration and with a
12-beam codebook.
Following are related steps in this use case scenario:
1. Based on the analysis of data from the network, reference points are selected
by Ev() where data -driven NN based approaches can be applied to reduce
signalling. Ev() should cherry -pick the reference points which has the best
trade -offs in terms of benefits (e.g. spectral efficiency, latency, etc) as against
the cost of training. Ev() should also help in understanding the experimentation
approaches to follow.
2. Based on the scenario under study (for evolution), experimentation is setup
and data sources are provisioned and ML pipelines are setup [Y.3172]. |
61 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 54 3. Based on the evaluation of the AI/ML models in the sandbox, they are
injected into the network functions (NFs).
4. Control and data flows are modified according to the evolved netwo rk.
Relation with autonomous behaviour -
Open issues (as seen by
the proponent)
Notes on use case
category Cat 1: describes a scenario related to core autonomous behavior itself.
Notes on priority of the
use case High
has the potential to impact fut ure network architectures and use cases.
Reference [b-O’Shea -1], [b-O’Shea -2], [b-Jagannath] , [b-Mao] , [b-AI4Good -1], [b-
AI4Good -2], [b-AI4Good -3]
7.28.1 Use case requirements
Critical requirements
● AN-UC028-REQ -001: It is critical that AN enable selection of reference points are based on
evolution where data -driven NN based approaches can be applied to reduce signalling.
NOTE – Ev() should cherry -pick the reference points which has the best trade -offs in terms of b enefits (e.g.
spectral efficiency, latency, etc) as against the cost of training. Ev() should also help in understanding the
experimentation approaches to follow.
● AN-UC028-REQ -002: It is critical that AN enable, based on the scenario under study (for
evolution), experimentation setup and data sources provisioning and ML pipelines setup [ ITU-T
Y.3172].
● AN-UC028-REQ -003: It is critical that AN enable, injection of ML models into the network
functions (NFs), Based on the evaluation of the AI/ML models in the sandbox,
● AN-UC028-REQ -004: It is critical that AN enable, modification of Control and data flows
according to the evolved network.
7.28.2 Use case specific figures
None.
7.29 Intelligent maintenance assistance system
Use case id FG-AN-usecase -029
Use case name Intelligent Maintenance Assistance System
Base contribution FGAN -I-108
Creation date 24/June/2021
Use case context Discussions in the weekly meeting during presentation of FGAN -I-108
Use case description The intelligent maintenance assistance system is an intelligent service system for
network operation and maintenance. The system combines AI algorithms and AR
capabilities to provide intelligent assistance for front line staff of operators in aspects
of networ k operation and maintenance.
The system includes backstage support system and AR glasses app. The backstage
support system is deployed in cloud servers in the form of micro -service, and is
connected with the network management system of operators to exchan ge data. The
AR glasses app is used for staff's on -site work.
The backstage support system provides the following functions.
1. AI algorithms and AR capabilities for system. Developers can also use these
algorithms to develop applications. |
62 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 55 1) AI algorith ms include Bar code / QR code recognition, OCR, device port
recognition.
2) AR capabilities include image recognition and tracking, 3D object recognition
and tracking, visual simultaneous localization and mapping (SLAM).
2. The data management function can help implement equipment data
transmission, storage, and management, including network resource data,
equipment status data, equipment operation data, etc.
3. The system management function includes user management, authority
management, system operation management, parameters configuration and other
functions to ensure the stable and reliable operation of the system.
The AR glasses app provides the following functions.
1. Information collection. Staff can collect pictures through the camera of AR
glasses and upload them to the backstage support system. The AI
algorithms in the backstage support system can recognize the collected
pictures and save the recognition results. Here, the staff can collect the text
information of the labels of devices to supplement a nd update the
information in the backstage support system.
2. Data visualization. The data managed in the backstage support system can
be displayed on the AR glasses. This function can provide the display of
alarm information, base station information, equipm ent information,
electricity consumption and network status, to assist the network operation
and maintenance of staff.
3. Remote guidance of experts. Through the camera of AR glasses, experts
get the situation of the work site and provide remote guidance.
The steps in this use case are as below:
1. Using AR glasses (and other external sensors), collect data about the
environment, which includes equipment label, port, electricity consumption etc.
This step may include barcode / QR code recognition, OCR, Device port
recognition etc.
2. AI based co gnition analysis, perception visualization and other analysis
algorithms are applied on the collected data to create a virtual model of planning
and design to real environment to assist network designers. This step may include
application of Image recognit ion and tracking, 3D object recognition and tracking,
Visual SLAM.
3. This model is then used in conjunction with real data for maintenance and
optimization by intelligence maintenance assistance system. This step may involve
query of the virtual model, a nalysis of real alarms, cell data, along with the virtual
model, to create intelligence assistance for frontline workers. This step may use
network data management, system management and core algorithms for the whole
system. The output from this step may i nclude 3D models which can be rendered
in AR glasses, AI processed network information for display, real -time remote
guidance information.
4. As the network services evolve and new network functions are plugged in
(virtual or physical), the following evolution steps are applied:
a. AR app is updated to collect new data, including new equipment data, and
new sensors and new environment information.
b. backstage support system is updated with new data management systems,
core algorithms etc |
63 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 56
5. Perio dic or asynchronous reports are produced for human consumption
regarding the operation of intelligent maintenance assistant system.
6. A software development kit (SDK) may be exposed to 3rd party developers who
may develop new applications to analyse the AR-collected data. This may in turn
help operators to provide new value -added applications in the intelligent
maintenance assistant system.
Open issues (as seen
by the proponent) 1. How autonomy can be applied to simply this process or enhance to enable more?
2. The bidirectional relationship: how the (real world) AR+AI system feeds the
(digital world) network management & ops (databases) + how the network supports
the AI+AR system, e.g. by providing inventory and processes information and
access to controls？
Notes on use case
category Cat 2: describes a scenario related to application of autonomous behaviour in the
network.
Notes on priority of
the use case High
has the potential to impact future service development and evolution.
Reference
7.29.1 Use case requirements
Critical requirements
● AN-UC029-REQ -001: It is critical that autonomous networks (AN) enable collection of
environment data related to network operation and maintenance using automated techniques such as
augmented reality (AR) glasses.
● AN-UC029-REQ -002: It is critical that autonomous network s (AN) enable analysis of
environment data related to network operation and maintenance using cloud and AI techniques.
● AN-UC029-REQ -003: It is critical that autonomous networks (AN) provide intelligent
assistance, rendered using automated techniques such as AR, for network operation and
maintenance.
NOTE – The intelligent assistance may be produced using analysis by AI/ML on the collected data from AR .
● AN-UC029-REQ -004: It is critical that autonomous networks (AN) update the data collection
mechanisms and data analysis mechanisms along with the result rendering mechanisms based on
the analysis by AI/ML on the collected data from AR and the evolution of the underlay networks.
● AN-UC029-REQ -005: It is critical that autonomous networks (AN) pro vide periodic and/or
asynchronous updates to humans about the operation of the intelligent assistant system.
Expected requirements
● AN-UC029-REQ -006: It is expected that autonomous networks (AN) enable exposure of
programming capabilities to 3rd party dev elopers for creation of novel applications which can help
automated operation and maintenance of network, including evolution and adaptation of network
functions.
NOTE – Such novel applications may analyse the data collected using AR, suggest new data collection
mechanisms based on gaps in collected data, suggest new analytical methods, or suggest new targets for
application of analysis. |
64 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 57 7.29.2 Use case specific figure s
7.30 Demand forecasting and live service migration methods in edge computing systems
Use case id FG-AN-usecase -030
Use case name Demand forecasting and live service migration methods in edge computing
systems
Base contribution FGAN -I-109
Creation date 01 July 2021
Use case context Discussions in the weekly meeting during presentation of FGAN -I-109
Use case description Virtualization and cloudification of services have enabled automation, flexible
placement and programmability to network topology. Efficiency of service delivery
can be significantly improved using these techniques. However, there are significant
challenge s to host Ultra -reliable low -latency communication (URLLC) and massive
machine type communications (mMTC) services in 5G, in centralized topologies.
Monitoring of networks by telco operators have revealed that network topology is
not static and load is not uniform over a long service time.
This use case describes a dynamic network topology and service placement using
the Genetic Algorithm to analyze and predict services. In addition, an efficient
forecasting and live migration methods of service as an appl ication to edge
computing systems are introduced. This approach can enable intelligent allocation
of operator equipment resources, for providing flexible and efficient topologies.
Simulation based analysis of results proved that the network equipment effic iency
can significantly be increased by these techniques.
The optimization of mobile edge computing network performance for a service by
addressing the service placement problem is described below:
“Match -making” and analytics service is hosted by REx p latform, exposed as APIs
to 3rd party service providers. Services (e.g. gaming) can now utilize the resources
at the edge efficiently. Edge network reports the resource status (and other metadata)
to the REx platform via the “REx client”@the edge. 3rd party service provider
Figure 15: Figure 16: Actor interaction for intelligent maintenance |
65 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 58 interfaces with the REx platform via “REx client”@service provider for service
deployment at the edge.
In addition, the following extensions are proposed:
- REx platform will host “composition” service for controllers (closed loops).
- Edge networks will expose APIs to deploy and manage controllers
- REx platform will analyse the requirements from applications and manage
the composition service towards the edge networks.
- The sub/pub mechanism described in [FGAN -I-109_att] is extended to
include controller metadata.
1. Registration of MEC on the platform will include controller
capabilities.
2. Pub MEC status information to the platform will include controller
status.
3. Service provider subscribed to this platform receives information
about available MECs along with the controllers.
1. Connection, Discovery and capability exchange (info exchange) between the
hosted (prediction) service and clients (e.g. REx server and client) on the network
operator side (edge) and the (application) service provider s on the ISP side.
2. Data (including traffic characteristics and controller metadata) is measured and
analysed to predict the resource utilization and automation at the edge.
3. Placement of services, migration, and composition of corresponding control lers
are managed by REx platform.
Open issues (as seen
by the proponent) #where is the prediction done? is it done at edge Rex client or Rex server?
#is the prediction online?real -time?
#Is the use of the intelligent aspects (genetic algorithm) trained/evolved/updated
online as the system runs, or is there an a priori training step.
#in slide 8: can we show where is the prediction done?
#where is the migration decision taken?
#slide -6: can we map the arrow numbers to the step numbers in the pseudo code in
slide 7?
#Investigate service composition.
#investigate the trust aspects – how to relate/fit.
#investigate autonomy metrics – to describe the achievement of the use case. in
terms of sec, availability – workflow execution vs. adaptability.
Notes on use case
category Cat 2: describes a scenario related to application of autonomous behaviour in the
network.
Notes on priority of
the use case High
has the potential to impact future service development and evolution.
Reference [b-AI4Good -4]
7.30.1 Use case requirements
Critical requirements
● AN-UC030-REQ -001: it is critical that AN integrate with Edge networks that will expose APIs
to deploy and manage controllers
● AN-UC030-REQ -002: it is critical that AN enable analysis of the requirements from
applications and manage the composition service towards the edge networks.
● AN-UC030-REQ -003: it is critical that AN enable sub/pub mechanism including controller
metadata. |
66 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 59 NOTE – Thus, Registration of MEC on the platform will include controller capabilities. Pub MEC status
information to the platform will include controller status. Service provider subscribed to this platform receives
information about available MECs along with the contr ollers.
● AN-UC030-REQ -004: it is critical that AN enable Connection, Discovery and capability
exchange (info exchange) between the hosted (prediction) service and clients on the network operator
side (edge) and the (application) service providers on the ISP side.
● AN-UC030-REQ -005: it is critical that Placement of services, migration, and composition of
corresponding controllers are managed by domain specific orchestrators.
7.30.2 Use case specific figures
Figure 17: actor interaction for Demand forecasting and live service migration
7.31 OpenCN: An open repository of intents for controllers and modules
Use case id FG-AN-usecase -031
Use case name OpenCN: An open repository of intents for controllers and modules
Base contribution None
Creation date 21 July 2021
Use case context Discussions during 3rd virtual meeting of FG AN, ITU -T Y.3176, Webinars
supporting ITU AI/ML in 5G Challenge
Use case description
As controllers/closed loops evolve to solve practical problems in the networks, this
use case aims to provide a baseline repository (called OpenCN ) of intents for
different forms of controllers. As in the case of various opensource repositories and
AI/ML marketplaces, an open repository will form a baseline for reusable definitions
for controllers, provide components for composing and chaining toge ther
controllers. In addition, open repo will increase trust in controllers.
Metadata related to controllers which describes the controllers and related modules
would be enables discovery and other related services like subscription/publication |
67 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 60 of new con trollers etc. Metadata also allows to specify guidelines for integrating
controllers with service -x and underlays.
Not only does the baseline intents allow stakeholders to reuse, extend and
interoperate controller implementations, but it also allows the de velopment of an
ecosystem of services around it – providing customizations (adaptations),
integrations (post experimentations) and finally evolutions.
NOTE - collaboration/integration of OpenCN with closed loop frameworks e.g.
ETSI ZSM may be explored.
Steps in the use case are as follows:
- Initial version of controllers are formed from intent or composition from modules
(by evolution controllers)
- These may be stored in the repo labelled as “untested” or candidate controllers
- Experimentation (Ex) ma nager pulls the candidates from the repo and (uses AN
sandbox to) evaluate and test and compare the controllers
- Evolution (Ev) manager uses the open repo to pull and apply ev strategies
- Operational (Op) controllers are stored in the open repo and pulle d and deployed
in underlays by various closed loop automation frameworks.
NOTE - Standardized intent formats may be used for storing controllers.
Open issues (as seen
by the proponent) - there is no unified standard format for intents for closed loops
- there are no opensource solutions to convert intents to closed loops.
Notes on use case
category Cat 1: describes a scenario related to core autonomous behaviour itself.
Notes on priority of
the use case High
has the potential to impact future service development and evolution.
Reference
7.31.1 Use case requirements
Critical requirements
● AN-UC031-REQ -001: it is critical that AN enable storage of controllers in an open repository.
NOTE – Experimentation (Ex) manager pulls the candidates from the repo and (uses AN sandbox to) evaluate
and test and compare the controllers. Evolution (Ev) manager uses the op en repo to pull and apply ev strategies.
Operational (Op) controllers are stored in the open repo and pulled and deployed in underlays by various closed
loop automation frameworks. |
68 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 61 7.31.2 Use case specific figures
Figure 18: actor interaction for open repository of intents
7.32 AI enabled game theory -based mechanism for resource allocation
Use case id FG-AN-usecase -032
Use case name AI enabled Game theory based mechanism for resource allocation
Base contribution FGAN -I-134
Creation date 06 July 2021
Use case context Discussions during 3rd virtual meeting of ITU FG AN
Use case description With the advent of Internet of Things, large number of devices may be trying to
connect to the network. Macro base station may be capacity limited to serve these
devices. An alternative is device to device communication, but complexity of
algorithms and privacy concerns are limiting factors here. Another option is
deploying small base stations known as small cells and each small cell try to serve
set of users.
Microcell, metro cell, Pico cell and femtocells are collectively known as small cells
and network containing all these base stations are termed as Heterogeneous
networks. To optimize the use of bandwidth, we assume co -channel deployment
mechanisms, macro cell and small cells will share the same set of sub -channels.
Transmission among small cell s may cause interference not only among them but
also to the macro cell users. Hence we need to consider efficient resource allocation
algorithms to mitigate interference among them so as to satisfy QoS constraints of
all the devices/users.
Distrib uted algorithms may be needed to mitigate the interference since there
may not be coordination (e.g. X2 interface) among the small cells.
In this scenario, we would like to propose distributed resource allocation
(subchannel allocation and power al location to each subchannel) so as to satisfy QoS
requirements of all the devices. NOTE - Game theory based model is proposed and
to reach equilibrium points we will be proposing both traditional based algorithms
like ML algorithms. |
69 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 62 Story -1 for the use ca se: In an enterprise deployment of small cell, say in a factory
floor, low latency is required for certain UEs, e.g. robotic arms or self -guided
vehicles such as in Industry 4.0 use cases. Macro cell coverage is limited in such
indoor factory floors and interference is a problem. Resource allocation for low -
latency UEs in the presence o f interference and low coverage by macro is the
problem addressed in this “story”.
● Co-existence of UEs that require low latency service and that does not
require low latency service
o Each small cell can allocate certain channels (power has to be
allocated via game theory) to UEs that require low latency (high
priority basis) and for other UEs it can allocate channel and power
by playing game model
o Power allocation is based on UEs rate requirement
● Serving UEs that all require low latency
o The small cell that serves these users can act as a leader and other
cells can act as a follower.
o Here priority will go to the leader and after allocation of resource
by this cell, all others will allocate resources accordingly by playing
stackelberg game model.
● Given th e measurements such as channel gains observed by user on a
particular subchannel along with Interference plus noise on that channel →
we need to allocate channel and power per subchannel so as to satisfy user
requirements (e.g., rate, low latency)
o Here chan nel allocation matrix and power per subchannel (Known
as Nash Equilibrium -NE point) has to be computed iteratively via
game theory approach. We can use three different kind of
algorithms such as traditional optimised algorithm, reinforcement
learning algor ithm and genetic algorithm.
o We can compare the above algorithms for better usage.
o Once we create data set that consists of various measurements -
channel gains, interference plus noise, set of sub channels, user per
cell, user requirements along with NE poi nt → we can train the
supervised learning model and this model sits at the Small BSs to
obtain subchannel allocation and power per subchannel depends on
the local measurements along with other inputs at each small BSs.
o #compare the output from SL with trad itional methods.
● Suppose there are multiple users try to access the channel at a particular
time and resources are scarce, then we need to schedule the users into
different frame durations (msec). For this we consider auctioning
mechanism to schedule the user in a particul ar time slot. Utility for each
auction is a function of user requirements such as rate, latency etc.,
● Once we perform user scheduling, then we can allocate channel and
power per channel according to algorithm described above.
● In the above procedure we are performing both time domain and
frequency domain scheduling separately.
story -2: surveillance videos – high UL rate is required for cameras – macro cell
coverage and interference is problem. Allocation for rate -required UEs in the
presence of interferenc e and low coverage by macro. Priority allocation for UL
intensive UEs to achieve QoS.
story -3: power constrained – wearables etc – privacy – user specific data cannot be
exposed to 3rd party – to do analytics, no data should be taken out of the trust zone
(enterprise or private network). All AI/ML, analytics etc needs to be done within the
private network. |
70 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 63 The steps in the use case (related to autonomy) are:
1. Controllers are formed based on, e.g. Intents and/or Evolution (Ev) to optimize
resource allocation with various considerations including latency, throughput or
privacy preserving analytics.
2. Modelling of inter -controller interaction using game theory: “Players” in the
game would be the equivalence classes of controllers. “players” may be selected
from the evolvable population.
3. (Initial) Strategies/gains/payoffs are defined and initialized for each equivalence
class and game is modeled.
4. Players [controllers] can be cooperating or non -cooperating. They may
participate in the ga me based on trust.
5. Modelling of strategies/gains/payoff for each player (controller) may change
based on Evolution.
6. Use experimentation (Ex) to study the model (strategies/gains ) which is evolved
and the elements of trust in this game can be studied. And also to investigate the
strategies for maximizing the gains, either by cooperation or non -cooperation.
7. Adaptations to be applied to the Controllers are arrived at – in the form of
changes to strategies, gains/payoffs and trust. Formation o f new “players” may be
part of adaptation.
8. Outer loop: Collect the data from the set of solutions, train the AI/ML model.
infer the equilibrium from the new input data using trained model.
Open issues (as seen
by the proponent) 1. what are the control lers trying to achieve as players? – use case story.
2. How to quantify computational cost?
3. how to evaluate whether other controllers (players) are trustable (among
themselves, whereas so far FG have thought of whether controllers are
trusted/deployable)? – take this aspect of trust an input from another layer.
a. #using AI/ML to overcome some of the disadv . of game theory.
What are those? – “the different Nash equilibria and the approaches
to reach them could be learnt over some experiments using AI/ML.
And then the learnings could be used to optimize these
experiments”.
4. in the context of trust: players can be selfish, transparent. This
understanding may change as Ev progress. Trust index can be studied. –
“this is a new problem”
5. how to use RL (need quick feedback) here? use real network or simulated
data? – Use different RL mechanisms like 0 regret. Use simu lated data.
#convergence may take long time in RL. Use DTwin to introduce RL in AN.
6. Small cell coverage is small, num of users is less, so the distribution of
service requirement is sparse (?). – as the coverage density increases, to
serve large num of users… capacity also increases…specific indoor
scenario like factory…but for the resour ce allocation may be more
complicated for macro cell.. interference with macro may be considered..
game with macro (coordination with macro) may be considered later.
Notes on use case
category Cat 1: describes a scenario related to core autonomous behaviour itself.
Notes on priority of
the use case High
Justification: the architecture components derived and introduced here will help in
defining meta -evolution controllers [I -98].
Reference [b-Sankar -1, [b-Sankar -2], [b-Ahmad ], [b-Al-Turjman] , [b-Ciavaglia -2] |
71 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 64 7.32.1 Use case requirements
Critical requirements
● AN-UC032-REQ -001: It is critical that autonomous networks (AN) enable characterisation of
controllers using metadata, which may be updated dynamically based on monitoring of the
controllers.
NOTE – The metadata associated with controllers would be used for modelling controllers as players.
Example – the closed loops aiming to optimize transmit power and those intending to optimize coverage may
be modelled as players in a game.
Expected requirements
● AN-UC032-REQ -002: It is expected that autonomous networks (AN) enable experimentation
with various gaming strategies, payoffs and equilibria with controllers are players.
NOTE – Experimentation may be conducted in the Sandbox and coordinated e.g. by exper imentation
manager, may result in analysis of strategies, payoffs and equilibria.
● AN-UC032-REQ -003: It is expected that autonomous networks (AN) enable classification of
controllers with respect to trustability of controllers.
NOTE – Parameters related to measurement of trust as applied to controllers and the methods of
classification may be out of scope of this particular use case.
● AN-UC032-REQ -004: It is expected that autonomous networks (AN) enable analysis of
experimentation resu lts with AI/ML based techniques.
NOTE – Learnings from AI/ML may be used in optimizing the gaming strategies, payoffs and equilibria.
● AN-UC032-REQ -005: It is expected that autonomous networks (AN) enable adaptation of
controllers with new strategies.
NOTE – Learnings from AI/ML may be used in optimizing the gaming strategies, payoffs and equilibria.
● AN-UC032-REQ -006: It is expected that autonomous networks (AN) enable derivation and
application of different combinations of game theory mec hanisms such as auction theory based on
the use case specification.
NOTE – Use case specification may be captured and formalized in the form of Intent. Derivation of game
theory mechanisms such as auctioneer may use human or automated mechanisms. Applicati on or integration
of such mechanisms in underlays may use specific architecture and interface considerations such as in Open
RAN. |
72 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 65 7.32.2 Use case specific figure
Figure 20: component cloud for AI enabled Game theory based resource allocation
Figure 19: AI enabled Game theory based resource allocation |
73 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 66
Figure 21: AI enabled Game theory based resource allocation
7.33 Service automation using workflows
Use case id FG-AN-usecase -033
Use case name Service automation using workflows
Base contribution FGAN -I-135
Creation date 27 July 2021
Use case context Follow up discussions after TIP webinar on automation
Use case description Network automation scenarios in fut ure networks include large scale automation of
management of network devices, services and retrieval of operational state data
from a network. User specific workflows, along with modularized tasks are one of
the mechanisms to achieve this automation.
Comb ined with an interworking of OpenConfig NETCONF & YANG models,
vendor native models, and the CLI, use specific, customized solutions can be
created and dockerized containers can be designed and tested.
The workflows are defined using a JSON based domain sp ecific language (DSL)
by wiring a set of tasks together. The tasks are either control tasks (fork,
conditional, etc.) or application tasks (i.e. encoding a file) that are executed on a
remote device. Atomic tasks are chained together into more complex work flows.
NOTE - The FRINX Machine distribution comes pre -loaded with a number of
standardized workflows.
Steps in this use case are as follows:
Step-1: create or compose workflow:
Operations or functions on workflows include: In addition to create, the workflow
designer can also
- edit a workflow
- delete a workflow
These operations may be achieved using a workflow manager.
This may include an API based interface or a GUI based i nterface.
A graphical user interface may be used to create, edit or run workflows and monitor
any open tasks. The GUI may also help in explainability.
NOTE - This would help to on -board new services (e.g. in network underlays) and
view their status. |
74 | /content/sample_data/Use-case-AN.pdf | Error! Reference source not found. (2021 -10) 67
NOT E- the composing step (from the GUI) may produce output in a generic form
(e.g. TOSCA or YANG) and translating this representation of workflows into
deployable instances can be using a generic and can take different types of inputs
and produce different ty pes outputs.
Step-2: store in Resource database : workflow specification and execution data
are stored in a resource database.
Step-3: link Tasks : A task corresponds to a worker utilized in the workflow. Tasks
in our workflow may receive input parameters and execution logic of our task may
be implemented in python functions called worker. Worker tasks may be registered
in the main python file “main.py” in the same directory where you just created your
worker.
NOTE - All workers which one want to use in Fri nx Machine must be included in
this file.
Step-5: deploy workflows: Workflows may be deployed on simulated underlays and
their performance and benchmarking may be tested and monitored. Workflows may
also be deployed on real network underlays once their pe rformance in the
experimentation is satisfactory.
Step-4: Monitor: The following service states may be mapped to workflows:
- experimental state: workflows which are deployed in the sandbox are in
experimental state. In combination with simulators, these are tested and
experimented upon.
- evolutionary state: workflows which are in ev state are selected for
evolution, and based on ev strategies, various experiments may be designed
for them.
- deployed state: workflows which are in deployed state are in combination
with service -x, acting upon real underlays. They may be monitored for
performance and other parameters.
Description of the relation (if any) of the use case with autonomous behavi our or
the key technical enablers.
- In the context of the use case, controllers (closed loops) are represented as
workflows. Modules are modelled as tasks.
- controller specification and module specifications are created using designer,
sanity checked an d stored in the resource db.
- workflow manager is used to visualize the controllers, monitor and analyse
- deploy will link service -x with controllers
Open issues (as seen
by the proponent) • FFS
Notes on use case
category
• Cat 1: describes a scenario related to core autonomous behaviour itself.
Notes on priority of
the use case High
Justification: gives potential collaboration opportunities and reuse possibilities.
Reference [b-FRINX -1], [b-FRINX ], [b-TIP 5G ]
7.33.1 Use case requirements
● AN-UC033-REQ -001: It is critical that autonomous networks (AN) enable creation of
controllers in a generic format agnostic to the type and characteristics of the underlay network. |
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