ETSI GR ENI 001 V1.1.1 (2018-04)

Experiential Networked Intelligence (ENI); ENI use cases

DGR/ENI-001_Use_cases

General Information

• Status: Published
• Publication Date: 19-Apr-2018
• Technical Committee: ENI - Experiential Networked Intelligence
• Current Stage: 12 - Completion
• Due Date: 08-May-2018
• Completion Date: 20-Apr-2018

Standards Content (Sample)

GROUP REPORT
Experiential Networked Intelligence (ENI);
ENI use cases
Disclaimer
The present document has been produced and approved by the Experiential Networked Intelligence (ENI) ETSI Industry
Specification Group (ISG) and represents the views of those members who participated in this ISG.
It does not necessarily represent the views of the entire ETSI membership.

2 ETSI GR ENI 001 V1.1.1 (2018-04)

Reference
DGR/ENI-001
Keywords
artificial intelligence, management, network,
use case
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3 ETSI GR ENI 001 V1.1.1 (2018-04)
Contents
Intellectual Property Rights . 6
Foreword . 6
Modal verbs terminology . 6
1 Scope . 7
2 References . 7
2.1 Normative references . 7
2.2 Informative references . 7
3 Definitions and abbreviations . 7
3.1 Definitions . 7
3.2 Abbreviations . 8
4 Overview . 9
4.1 Background . 9
4.2 Overview of the ENI System . 9
4.2.1 Brief Description . 9
4.2.2 Expected Benefits . 10
5 General use cases . 10
5.1 Introduction . 10
5.2 Infrastructure Management . 11
5.2.1 Use Case #1-1: Policy-driven IDC Traffic Steering . 11
5.2.1.1 Use case context . 11
5.2.1.2 Description of the use case . 11
5.2.1.2.1 Overview . 11
5.2.1.2.2 Motivation . 12
5.2.1.2.3 Actors and Roles. 13
5.2.1.2.4 Initial context configuration . 13
5.2.1.2.5 Trigger conditions . 13
5.2.1.2.6 Operational Flow of the actions . 13
5.2.1.2.7 Post-conditions . 14
5.2.2 Use Case #1-2: Handling of Peak Planned Occurrences. 14
5.2.2.1 Use case context . 14
5.2.2.2 Description of the use case . 14
5.2.2.2.1 Overview . 14
5.2.2.2.2 Motivation . 15
5.2.2.2.3 Actors and Roles. 15
5.2.2.2.4 Initial context configuration . 15
5.2.2.2.5 Triggering conditions . 15
5.2.2.2.6 Operational flow of actions . 15
5.2.2.2.7 Post-conditions . 16
5.2.3 Use Case #1-3: Energy optimization using AI . 16
5.2.3.1 Use case context . 16
5.2.3.2 Description of the use case . 16
5.2.3.2.1 Overview . 16
5.2.3.2.2 Motivation . 17
5.2.3.2.3 Actors and Roles. 17
5.2.3.2.4 Initial context configuration . 17
5.2.3.2.5 Triggering conditions . 18
5.2.3.2.6 Operational flow of actions . 18
5.2.3.2.7 Post-conditions . 18
5.3 Network Operations . 19
5.3.1 Use Case #2-1: Policy-driven IP managed networks . 19
5.3.1.1 Use case context . 19
5.3.1.2 Description of the use case . 19
5.3.1.2.1 Overview . 19
5.3.1.2.2 Motivation . 19
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5.3.1.2.3 Actors and Roles. 20
5.3.1.2.4 Initial context configuration . 20
5.3.1.2.5 Triggering conditions . 20
5.3.1.2.6 Operational flow of actions . 20
5.3.1.2.7 Post-conditions . 21
5.3.2 Use Case #2-2: Radio Coverage and capacity optimization . 21
5.3.2.1 Use case context . 21
5.3.2.2 Description of the use case . 21
5.3.2.2.1 Overview . 21
5.3.2.2.2 Motivation . 22
5.3.2.2.3 Actors and Roles. 22
5.3.2.2.4 Initial context configuration . 22
5.3.2.2.5 Triggering conditions . 22
5.3.2.2.6 Operational flow of actions . 22
5.3.2.2.7 Post-conditions . 23
5.3.3 Use Case #2-3: Intelligent Software Rollouts . 23
5.3.3.1 Use Case context . 23
5.3.3.2 Description of the Use Case . 23
5.3.3.2.1 Overview . 23
5.3.3.2.2 Motivation . 23
5.3.3.2.3 Actors and Roles. 24
5.3.3.2.4 Initial context configuration . 24
5.3.3.2.5 Triggering conditions . 24
5.3.3.2.6 Operational flow of actions . 24
5.3.3.2.7 Post-conditions . 25
5.3.4 Use Case #2-4: Policy-based network slicing for IoT security . 25
5.3.4.1 Use Case context . 25
5.3.4.2 Description of the Use Case . 26
5.3.4.2.1 Motivation . 26
5.3.4.2.2 Actors and Roles. 26
5.3.4.2.3 Initial context configuration . 26
5.3.4.2.4 Triggering conditions . 27
5.3.4.2.5 Operational flow of actions . 27
5.3.4.2.6 Post-conditions . 27
5.3.5 Use Case #2-5: Intelligent Fronthaul Management and Orchestration . 27
5.3.5.1 Use Case context . 27
5.3.5.2 Description of the use case . 28
5.3.5.2.1 Overview . 28
5.3.5.2.2 Motivation . 28
5.3.5.2.3 Actors and Roles. 29
5.3.5.2.4 Initial context configuration . 29
5.3.5.2.5 Triggering conditions . 29
5.3.5.2.6 Operational flow of actions . 29
5.3.5.2.7 Post-conditions . 29
5.4 Service Orchestration and Management . 29
5.4.1 Use Case #3-1: Context-aware VoLTE Service Experience Optimization . 29
5.4.1.1 Use case context . 29
5.4.1.2 Description of the use case . 30
5.4.1.2.1 Overview . 30
5.4.1.2.2 Motivation . 30
5.4.1.2.3 Actors and Roles. 30
5.4.1.2.4 Initial context configuration . 31
5.4.1.2.5 Triggering conditions . 31
5.4.1.2.6 Operational flow of actions . 31
5.4.1.2.7 Post-conditions . 31
5.4.2 Use Case #3-2: Intelligent network slicing management . 31
5.4.2.1 Use case context . 31
5.4.2.2 Description of the use case . 31
5.4.2.2.1 Overview . 31
5.4.2.2.2 Motivation . 32
5.4.2.2.3 Actors and Roles. 32
5.4.2.2.4 Initial context configuration . 33
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5.4.2.2.5 Triggering conditions . 33
5.4.2.2.6 Operational flow of actions . 33
5.4.2.2.7 Post-conditions . 33
5.4.3 Use Case #3-3: Intelligent carrier-managed SD-WAN . 33
5.4.3.1 Use case context . 33
5.4.3.2 Description of the use case . 34
5.4.3.2.1 Overview . 34
5.4.3.2.2 Motivation . 34
5.4.3.2.3 Actors and Roles. 35
5.4.3.2.4 Initial context configuration . 35
5.4.3.2.5 Triggering conditions . 35
5.4.3.2.6 Operational flow of actions . 35
5.4.3.2.7 Post-conditions . 36
5.5 Assurance . 36
5.5.1 Use Case #4-1: Network fault identification and prediction . 36
5.5.1.1 Use case context . 36
5.5.1.2 Description of the use case . 36
5.5.1.2.1 Overview . 36
5.5.1.2.2 Motivation . 37
5.5.1.2.3 Actors and Roles. 37
5.5.1.2.4 Initial context configuration . 37
5.5.1.2.5 Triggering conditions . 37
5.5.1.2.6 Operational flow of actions . 37
5.5.1.2.7 Post-conditions . 37
5.5.2 Use Case #4-2: Assurance of Service Requirements . 38
5.5.2.1 Use Case context . 38
5.5.2.2 Description of the Use Case . 38
5.5.2.2.1 Overview . 38
5.5.2.2.2 Motivation . 39
5.5.2.2.3 Actors and Roles. 39
5.5.2.2.4 Initial context configuration . 40
5.5.2.2.5 Triggering conditions . 40
5.5.2.2.6 Operational flow of actions . 40
5.5.2.2.7 Post-conditions . 40
6 Recommendations to ENI . 40
Annex A: Bibliography . 41
Annex B: Authors & contributors . 42
History . 43

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Intellectual Property Rights
Essential patents
IPRs essential or potentially essential to normative deliverables may have been declared to ETSI. The information
pertaining to these essential IPRs, if any, is publicly available for ETSI members and non-members, and can be found
in ETSI SR 000 314: "Intellectual Property Rights (IPRs); Essential, or potentially Essential, IPRs notified to ETSI in
respect of ETSI standards", which is available from the ETSI Secretariat. Latest updates are available on the ETSI Web
server (https://ipr.etsi.org/).
Pursuant to the ETSI IPR Policy, no investigation, including IPR searches, has been carried out by ETSI. No guarantee
can be given as to the existence of other IPRs not referenced in ETSI SR 000 314 (or the updates on the ETSI Web
server) which are, or may be, or may become, essential to the present document.
Trademarks
The present document may include trademarks and/or tradenames which are asserted and/or registered by their owners.
ETSI claims no ownership of these except for any which are indicated as being the property of ETSI, and conveys no
right to use or reproduce any trademark and/or tradename. Mention of those trademarks in the present document does
not constitute an endorsement by ETSI of products, services or organizations associated with those trademarks.
Foreword
This Group Report (GR) has been produced by ETSI Industry Specification Group (ISG) Experiential Networked
Intelligence (ENI).
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.

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1 Scope
The present document includes a collection of use cases from a variety of stakeholders, where the use of an Experiential
Networked Intelligence (ENI) system can be applied to the fixed network, the mobile network, or both, to enhance the
operator experience through the use of network intelligence.
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long term validity.
The following referenced documents are not necessary for the application of the present document but they assist the
user with regard to a particular subject area.
[i.1] NGMN Alliance, Description of Network Slicing Concept, Version 1.0, January 13, 2016.
NOTE: Available at https://www.ngmn.org/fileadmin/user_upload/160113_Network_Slicing_v1_0.pdf.
[i.2] 3GPP TR 23.799: "3rd Generation Partnership Project; Technical Specification Group Services and
System Aspects; Study on Architecture for Next Generation System, 3GPP TR 23.799, V14.0.0,
Release 14", December 2016.
[i.3] 5G Service-Guaranteed Network Slicing White Paper, Issue 1, March 2017.
NOTE: Available at http://www-file.huawei.com/~/media/CORPORATE/PDF/white%20paper/5g-service-
guaranteed-network-slicing-whitepaper.pdf.
[i.4] A. Morton, AT&T Labs: "Considerations for Benchmarking Virtual Network Functions and Their
Infrastructure", July 2017.
[i.5] ETSI TS 132 101 (V11.4.0): "Digital cellular telecommunications system (Phase 2+); Universal
Mobile Telecommunications System (UMTS); LTE; Telecommunication management; Principles
and high level requirements (3GPP TS 32.101 version 11.4.0 Release 11)".
[i.6] ETSI GS ENI 002 (V1.1.1): "Experiential Networked Intelligence (ENI); Requirements".
[i.7] ETSI GS ENI 005: "Experiential Networked Intelligence (ENI); Architecture".
[i.8] ETSI GR ENI 004: "Experiential Networked Intelligence (ENI); Terminology".
3 Definitions and abbreviations
3.1 Definitions
For the purposes of the present document, the terms and definitions given in ETSI GR ENI 004 [i.8] apply.
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3.2 Abbreviations
For the purposes of the present document, the following abbreviations apply:
AI Artificial Intelligence
AP Access Point
API Application Programming Interface
BBU Baseband Unit
BRAS Broadband Remote Access Server
BSS Business Support System
CCO Capacity and Coverage Optimization
CGN Carrier Grade Network address translation
CPRI Common Public Radio Interface
CPU Computing Processing Unit
C-RAN Centralized RAN
DC Data Centre
DDOS Distributed Denial Of Service
DHCP Dynamic Host Configuration Protocol
D-RAN Distributed RAN
E2E End-to-End
ENI Experiential Networked Intelligence
FTP File Transfer Protocol
IDC Internet Data Centre
INFP Intelligent Network Failure Prevention
IP Internet Protocol
KPI Key Performance Indicator
MANO Management and Orchestration
MEC Multi-access Edge Computing
MIMO Multiple Input Mutliple Output
MPLS Multi-Protocol Label Switching
NFV Network Function Virtualisation
NFVI NFV Infrastructure
NGFI Next Generation Fronthaul interface
NGMN Next Generation Mobile Networks
NSI Network Slice Instances
OPEX OPerational EXpenditure
OS Operating Systems
OSS Operations Support System
PHY PHYsical layer
QoE Quality-of-Experience
QoS Quality-of-Service
RAM Random Access Memory
RAN Radio Access Network
RAU Remote Aggregation Unit
RCC Radio Cloud Centre
RF Radio Frequency
RRU Remote Radio Units
RSRP Reference Signal Received Power
SDN Software Defined Networking
SD-WAN Software-Defined Wide Area Network
SLA Service-Level Agreement
TCP Transmission Control Protocol
UE User Equipment
VM Virtual Machines
VNF Virtualised Network Functions
WAN Wireless Access Network
WLAN Wireless Local Area Network
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4 Overview
4.1 Background
Operators see human-machine interaction as slow, error-prone, expensive, and cumbersome. For example, operators are
worried about the increasing complexity of integration of different standardization platforms in their network and
operational environment; this is due to the vast differences inherent in programming different devices as well as the
difficulty in building agile, personalized services that can be easily created and torn down. These human-machine
interaction challenges are considered by operators as barriers to reducing the time to market of innovative and advanced
services. Moreover, there is no efficient and extensible standards-based mechanism to provide contextually-aware
services (e.g. services that adapt to changes in user needs, business goals, or environmental conditions).
These and other factors contribute to a very high OPerational EXpenditure (OPEX) for network management. Operators
need the ability to automate their network configuration and monitoring processes to reduce OPEX. More importantly,
operators need to improve the use and maintenance of their networks. In particular, this requires the ability to visualize
services and their underlying operations so that the proper changes can be applied to protect offered services and
resources (e.g. ensure that their Quality-of-Service (QoS) and Quality-of-Experience (QoE) requirements are not
violated). If such visualization could be provided, then operators would be better able to maintain their networks.
The associated challenges may be stated as:
a) automating complex human-dependent decision-making processes;
b) determining which services should be offered, and which services are in danger of not meeting their
Service-Level Agreement (SLA)s, as a function of changing context;
c) defining how best to visualize how network services are provided and managed to improve network
maintenance and operation; and
d) providing an experiential architecture (i.e. an architecture that uses various mechanisms to observe and learn
from the experience an operator has in managing the network) to improve its understanding of the operator
experience, over time.
The aforementioned challenges will require advances in network telemetry, big data mechanisms to gather appropriate
data at speed and scale, machine learning for intelligent analysis and decision making, and applying innovative,
policy-based, model-driven functionality to simplify and scale complex device configuration and monitoring.
4.2 Overview of the ENI System
4.2.1 Brief Description
The ENI system is an innovative, policy-based, model-driven functional entity that understands the configuration and
takes actions in accordance with changes in context, such as the environment, the dynamic demand of the resources, and
the varying service requirements. By exploiting emerging technologies, such as big data analysis and artificial
intelligence mechanisms, and also by automating (where possible) complex human-dependent decision-making
processes, the ENI system enables intelligent service operation and management, and provides the ability to ensure that
automated decisions taken by the system are correct and are made to increase the stability and maintainability of the
network and the applications that it supports.
Examples of the possible functionalities of an ENI system are given in figure 4-1.
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Figure 4-1: Example of functionalities of ENI system
4.2.2 Expected Benefits
ENI system delivers enhanced customer experience by allowing operators to understand the operating status of their
network and networked applications in near-real-time, and reconfigure their network. The ENI system automatically
collects network status and associated metrics, faults, and errors, and then uses artificial intelligence to ensure network
performance and quality of service are met at the highest possible efficiency (e.g. with the minimum required
resources). An ENI system can also be used to find bottlenecks of service and/or failure of network. Both of these
benefits are done on-demand, in response to changing contextual information.
The ENI system helps to increase the value of services provided by an operator to its customers by rapidly on-boarding
new services, enabling the creation of a new ecosystem of cloud consumer and enterprise services, reducing Capital and
Operational Expenditures, and providing efficient operations.
5 General use cases
5.1 Introduction
This clause describes the use cases and scenarios identified by the ENI ISG. Each use case includes a description of
how an ENI system can be applied, and the benefits it provides.
A list of the use cases included in the present document are categorized into the following four categories (table 5-1):
1) Infrastructure Management: This category of use cases covers the processes related to the management of the
network infrastructure (e.g. adjustment of allocated and provided services, maintenance, capability
specification, and planning). In particular, it is about using policies for managing the network infrastructure,
enabled by placing analytics in the control loop and using the results of the analytics as part of the input to
policy-based management of the infrastructure.
2) Network Operations: Use cases described in this category are concerned with running the network, where the
runtime contexts of the network are extracted and analysed, and the management operations are performed and
optimized dynamically at runtime.
3) Service Orchestration and Management: This category of use cases relates to the service and order
management, covering processes such as activation using the operator's business channels or customer portals.
It is about providing differentiated SLAs for different applications, including vertical applications, through the
application of machine learning in an intelligent entity, i.e. ENI. For example, services can be differentiated
based on level (e.g. gold vs. silver vs. bronze classes of service) as well as based on the type of application
within a level (e.g. a video streaming service has a different service than FTP, even though both are
applications that a particular customer has).
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4) Assurance: Use cases described in this category are concerned with the functionality of network monitoring,
trending, and prediction, as well as taking policy-based actions using knowledge learned from the network to
facilitate network maintenance. This includes service runtime operations dedicated to guarantee continuous
service delivery.
Table 5-1: Summary of ENI Use Cases
Category
1 - Infrastructure
Use Case #1-1: Use Case #1-2: Use Case #1-3:
Management Policy-driven IDC Handling of DC Energy
Traffic Steering Peak Planned Saving using AI
Occurrences
2 - Network Operations
Use Case #2-1: Use Case #2-2: Use Case #2-3: Use Case Use Case#2-5:
Policy-driven IP Radio Intelligent #2-4: Intelligent
Managed Networks Coverage and Software Rollouts Policy- Fronthaul
Capacity based Management
Optimization Network and
Slicing for Orchestration
IoT Security
3 - Service Orchestration
Use Case #3-1: Use Case #3-2: Use Case #3-3:
and Management Context-Aware VoLTE Intelligent Intelligent
Service Experience Network Slicing Carrier-Managed
Optimization Management SD-WAN
4 - Assurance Use Case #4-1: Use Case #4-2:
Network Fault Assurance of
Identification and Service
Prediction Requirements
5.2 Infrastructure Management
5.2.1 Use Case #1-1: Policy-driven IDC Traffic Steering
5.2.1.1 Use case context
This use case relates to intelligent link load balancing and bandwidth allocation between Internet Data Centres (IDCs).
The tenants of IDCs include enterprises that have requirements that dynamically adjust service and/or resource
behaviour (e.g. reliable network connectivity and changes to an offered service based on network load).
There are a number of problems with how current traffic steering is performed between IDCs. These include the use of
multiple possible links between IDCs (e.g. which link is the best to use at a given time). Currently, the link for a tenant
is normally determined as the shortest path between the IDC that the tenant resides in and the IDC that the tenant is
connecting to. in addition, the link load is not considered when calculating the traffic path. Furthermore, the bandwidth
allocated to a tenant is not always fully used.
5.2.1.2 Description of the use case
5.2.1.2.1 Overview
Operators are deploying IDCs in Metropolitan Area Networks (MANs) to provide network access with load-balancing
and resiliency. Current network configuration practices include:
• In order to provide service assurance for important tenants, network administrators typically schedule the
traffic in specific periods. Traditional network management is usually complex, with a long cycle caused by
manual actions, so it is difficult to meet the requirement of real-time traffic optimization.
• Large service provider's traffic usually is sensitive to the events of a day. For example, online big sales and
usage of social media with video steaming cause a significant increase in traffic. This means that the network
administrator cannot provide bandwidth assurance for some important tenants.
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• The bandwidth requirements of tenants tend to change dynamically. Traditional static bandwidth allocation
leads to low bandwidth utilization and redundancy.
• The imbalance across multiple links leads to inefficient resource utilization. For example, it is possible that the
utilization of a link reaches a certain threshold, while other links' loads remain low.
5.2.1.2.2 Motivation
The ENI system can be used to achieve intelligent link load balancing and intelligent bandwidth allocation. In ENI,
policies can be modified by using machine learning to fill in important parameters, such as available links, link
bandwidth, real-time link utilization, and other predefined constraints. Three examples of the predefined constraints to
be considered before modifying the policies are:
1) each link is predefined with a threshold of the maximum bandwidth and cannot be exceeded;
2) flow of a client at a specific service level (e.g. gold) cannot be switched;
3) the maximum times of switching specific service from one link to another link is predefined and cannot be
exceeded.
Such policies can be used to better manage the network and achieve autonomous service traffic monitoring and network
resource optimization. It can also be used to adjust the service along different links of an IDC, thus improving the
operator's experience through enhanced network resilience and service QoS and QoE.
The ENI system also:
• predicts changes by using AI in the tenant's service requirements based on historical data (e.g. the type of QoS
to be provided for a given service based on the type of application and metadata);
• collects and analyses real-time data, given the service adjustment recommendations (e.g. which metadata and
metrics to monitor based on the type of serv
...