ETSI GR NFV-REL 013 V5.1.1 (2023-02)

Network Functions Virtualisation (NFV) Release 5; Reliability; Report on cognitive use of operations data for reliability

DGR/NFV-REL013

General Information

• Status: Not Published
• Technical Committee: NFV REL - Reliability & Availability
• Current Stage: 12 - Completion
• Due Date: 07-Feb-2023
• Completion Date: 06-Feb-2023

Standards Content (Sample)

GROUP REPORT
Network Functions Virtualisation (NFV) Release 5;
Reliability;
Report on cognitive use of operations data for reliability
Disclaimer
The present document has been produced and approved by the Network Functions Virtualisation (NFV) 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 NFV-REL 013 V5.1.1 (2023-02)

Reference
DGR/NFV-REL013
Keywords
availability, NFV
ETSI
650 Route des Lucioles
F-06921 Sophia Antipolis Cedex - FRANCE

Tel.: [phone]  Fax: [phone]

Siret N° 348 623 562 00017 - APE 7112B
Association à but non lucratif enregistrée à la
Sous-Préfecture de Grasse (06) N° w061004871

Important notice
The present document can be downloaded from:
http://www.etsi.org/standards-search
The present document may be made available in electronic versions and/or in print. The content of any electronic and/or
print versions of the present document shall not be modified without the prior written authorization of ETSI. In case of any
existing or perceived difference in contents between such versions and/or in print, the prevailing version of an ETSI
deliverable is the one made publicly available in PDF format at www.etsi.org/deliver.
Users of the present document should be aware that the document may be subject to revision or change of status.
Information on the current status of this and other ETSI documents is available at
https://portal.etsi.org/TB/ETSIDeliverableStatus.aspx
If you find errors in the present document, please send your comment to one of the following services:
https://portal.etsi.org/People/CommiteeSupportStaff.aspx
If you find a security vulnerability in the present document, please report it through our
Coordinated Vulnerability Disclosure Program:
https://www.etsi.org/standards/coordinated-vulnerability-disclosure
Notice of disclaimer & limitation of liability
The information provided in the present deliverable is directed solely to professionals who have the appropriate degree of
experience to understand and interpret its content in accordance with generally accepted engineering or
other professional standard and applicable regulations.
No recommendation as to products and services or vendors is made or should be implied.
No representation or warranty is made that this deliverable is technically accurate or sufficient or conforms to any law
rule and/or regulation and further, no representation or warranty is made of merchantability or fitness
and/or governmental
for any particular purpose or against infringement of intellectual property rights.
In no event shall ETSI be held liable for loss of profits or any other incidental or consequential damages.

Any software contained in this deliverable is provided "AS IS" with no warranties, express or implied, including but not
limited to, the warranties of merchantability, fitness for a particular purpose and non-infringement of intellectual property
rights and ETSI shall not be held liable in any event for any damages whatsoever (including, without limitation, damages
for loss of profits, business interruption, loss of information, or any other pecuniary loss) arising out of or related to the use
of or inability to use the software.
Copyright Notification
No part may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying and
microfilm except as authorized by written permission of ETSI.
The content of the PDF version shall not be modified without the written authorization of ETSI.
The copyright and the foregoing restriction extend to reproduction in all media.

© ETSI 2023.
All rights reserved.
ETSI
3 ETSI GR NFV-REL 013 V5.1.1 (2023-02)
Contents
Intellectual Property Rights . 7
Foreword . 7
Modal verbs terminology . 7
1 Scope . 8
2 References . 8
2.1 Normative references . 8
2.2 Informative references . 8
3 Definition of terms, symbols and abbreviations . 10
3.1 Terms . 10
3.2 Symbols . 10
3.3 Abbreviations . 10
4 Overview . 11
5 Operations data . 12
5.1 Nature of the data . 12
5.2 Data contents . 14
6 Cognitive analysis of operations data . 17
6.1 Data driven techniques . 17
6.1.1 Cognitive computing. 17
6.1.2 Supervised learning. 18
6.1.2.1 Introduction . 18
6.1.2.2 Techniques used for supervised learning . 18
6.1.2.3 Challenges . 18
6.1.3 Unsupervised learning . 19
6.1.3.1 Introduction . 19
6.1.3.2 Clustering . 19
6.1.3.3 Association . 19
6.1.3.4 Dimensionality reduction . 19
6.1.3.5 Challenges . 20
6.1.4 Reinforcement learning . 20
6.1.4.1 Introduction . 20
6.1.4.2 Temporal difference learning . 21
6.1.4.3 Multi-agent reinforcement learning . 21
6.1.4.4 Challenges . 21
6.2 Application to the NFV environment for reliability . 22
7 Use cases . 23
7.1 Service Availability Assurance . 23
7.1.1 Introduction. 23
7.1.2 Design-time model creation . 25
7.1.2.1 Design-time process of creating ANN models . 25
7.1.2.2 Example of creating ANN models for runtime adjustments . 28
7.1.2.2.1 Input and output information of the NS design . 28
7.1.2.2.2 Creating the ANN model for the VNFs . 31
7.1.2.2.3 Model for the VL redundancy . 34
7.1.3 Runtime use of ANN models . 35
7.1.3.1 Overview of the model-based runtime adjustment . 35
7.1.3.2 Examples of model-based configuration adjustments at runtime . 36
7.1.3.2.1 Introduction and goal . 36
7.1.3.2.2 Actors and roles . 36
7.1.3.2.3 Pre-conditions . 37
7.1.3.2.4 Post-conditions . 37
7.1.3.2.5 Flow description of NS scaling with no other configuration adjustment. 37
7.1.3.2.6 Flow description of configuration adjustments due to monitored parameter change . 38
ETSI
4 ETSI GR NFV-REL 013 V5.1.1 (2023-02)
7.1.3.2.7 Flow description of NS scaling with additional configuration adjustments . 39
7.1.3.2.8 Flow description for configuration adjustments due to change in VL characteristics . 40
7.1.3.2.9 Flow description of handling changes in the characteristics of virtualised resources used by a
VNF . 40
7.2 Root cause analysis . 41
7.2.1 Introduction. 41
7.2.2 Using self-organizing maps for root cause analysis . 41
7.2.2.1 Challenge of root cause analysis . 41
7.2.2.2 Traditional SOM . 42
7.2.2.3 Anomaly detection using 2-layered SOMs . 43
7.2.2.4 Fault localization using 2-layered SOMS . 43
7.2.2.5 Evaluation of 2-layered SOM for RCA . 44
7.2.2.6 Runtime use of the 2-layered SOM model for NFVI resource fault localization . 45
7.2.2.6.1 Introduction . 45
7.2.2.6.2 Actors and roles . 45
7.2.2.6.3 Pre-conditions . 46
7.2.2.6.4 Post-conditions . 46
7.2.2.6.5 Flow description . 46
7.3 Anomaly prediction . 47
7.3.1 Introduction. 47
7.3.2 Use of log messages for anomaly prediction . 47
7.3.2.1 Introduction . 47
7.3.2.2 Training based on the use of "normal" log messages . 48
7.3.2.2.1 Rationale . 48
7.3.2.2.2 Data preparation and model training . 49
7.3.2.2.3 Training refinement and validation . 50
7.3.2.2.4 Continuous learning . 50
7.3.2.3 Runtime use of anomaly prediction models using log messages . 50
7.3.2.3.1 Introduction . 50
7.3.2.3.2 Actors and roles . 50
7.3.2.3.3 Pre-conditions . 51
7.3.2.3.4 Post-conditions . 51
7.3.2.3.5 Flow description . 51
7.3.3 Use of KPIs for anomaly prediction . 52
7.3.3.1 Rationale . 52
7.3.3.2 Use of discrete data . 53
7.3.3.2.1 Linear regression . 53
7.3.3.2.2 Distributed-lag regression. 53
7.3.3.3 Use of functional data . 54
7.3.3.3.1 Logistic regression . 54
7.3.3.3.2 Random forest . 54
7.3.3.4 Runtime use of anomaly prediction models based on KPIs . 55
7.3.3.4.1 Introduction . 55
7.3.3.4.2 Actors and roles . 55
7.3.3.4.3 Pre-conditions . 56
7.3.3.4.4 Post-conditions . 56
7.3.3.4.5 Flow description . 56
8 Recommendations . 57
8.1 Introduction . 57
8.2 Recommendations related to service availability assurance . 57
8.3 Recommendations related to RCA . 59
8.4 Recommendations related to anomaly prediction . 59
9 Conclusion . 60
Annex A: NFVI interfaces and operations . 62
A.1 Introduction . 62
A.2 NFV common domain . 62
A.2.1 Fault management . 62
A.2.2 Performance management . 64
ETSI
5 ETSI GR NFV-REL 013 V5.1.1 (2023-02)
A.2.3 Policy management . 66
A.3 NFV-MANO OAM domain . 67
A.3.1 NFV-MANO configuration and information management . 67
A.3.2 NFV-MANO log management . 70
A.4 NS domain . 70
A.4.1 NSD management. 70
A.4.2 NS lifecycle management . 72
A.4.3 NS instance usage notification . 79
A.4.4 NS lifecycle operation granting . 79
A.4.5 LCM coordination . 79
A.5 Resource domain . 80
A.5.1 Software image management . 80
A.5.2 Virtualised compute . 80
A.5.2.1 Virtualised compute resources management . 80
A.5.2.2 Virtualised compute resources change notification . 82
A.5.2.3 Virtualised compute resources information management . 83
A.5.2.4 Virtualised compute resources capacity management . 84
A.5.2.5 Virtualised compute flavour management . 85
A.5.3 Virtualised network . 86
A.5.3.1 Virtualised network resources management . 86
A.5.3.2 Virtualised network resources change notification . 87
A.5.3.3 Virtualised network resources information management. 87
A.5.3.4 Virtualised network resources capacity management . 88
A.5.3.5 Network forwarding path management . 89
A.5.4 Virtualised storage . 89
A.5.4.1 Virtualised storage resources management . 89
A.5.4.2 Virtualised storage resources change notification . 91
A.5.4.3 Virtualised storage resources information management . 91
A.5.4.4 Virtualised storage resources capacity management . 92
A.5.5 Virtualised resource reservation . 92
A.5.5.1 Virtualised compute resources reservation management . 92
A.5.5.2 Virtualised network resources reservation management . 94
A.5.5.3 Virtualised storage resources reservation management . 96
A.5.5.4 Virtualised resources reservation change notification . 97
A.5.6 Virtualised resource quota . 98
A.5.6.1 Virtualised compute resources quota management . 98
A.5.6.2 Virtualised network resources quota management . 99
A.5.6.3 Virtualised storage resources quota management . 100
A.5.6.4 Virtualised resources quota change notification . 100
A.5.7 NFVI capacity information. 101
A.5.8 Multi-site Connectivity Services (MSCS) . 102
A.5.8.1 MSCS management . 102
A.5.8.2 MSCS capacity management . 104
A.5.9 Compute host reservation management . 105
A.5.10 Compute host capacity management . 106
A.6 VNF domain . 107
A.6.1 Virtualised resources quota available notification . 107
A.6.2 VNF configuration interface. 107
A.6.3 VNF lifecycle operation granting . 108
A.6.4 VNF indicator . 109
A.6.5 VNF lifecycle management . 110
A.6.6 VNF package management. 114
A.6.7 VNF snapshot package management . 116
A.6.8 LCM coordination . 118
A.7 Information elements of the NFV core model used in the interfaces and operations . 118
A.7.1 NFV common domain . 118
A.7.2 NS domain . 121
A.7.3 Resource domain . 123
A.7.4 VNF domain . 125
ETSI
6 ETSI GR NFV-REL 013 V5.1.1 (2023-02)
A.8 Definition of enumerations . 130
Annex B: NFV interfaces and operations . 136
Annex C: ANN model creation experiment for the service availability assurance use case. 139
Annex D: Illustrations for the fault localization use case . 141
D.1 Data collection, pre-processing and model building . 141
D.2 Fault localization performance . 142
Annex E: Illustrations for the anomaly prediction use case . 143
E.1 Anomaly prediction using syslogs . 143
E.2 Comparison of prediction models using KPIs . 143
History . 146

ETSI
7 ETSI GR NFV-REL 013 V5.1.1 (2023-02)
Intellectual Property Rights
Essential patents
IPRs essential or potentially essential to normative deliverables may have been declared to ETSI. The declarations
pertaining to these essential IPRs, if any, are 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 Directives including the ETSI IPR Policy, no investigation regarding the essentiality of IPRs,
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.
DECT™, PLUGTESTS™, UMTS™ and the ETSI logo are trademarks of ETSI registered for the benefit of its

Members. 3GPP™ and LTE™ are trademarks of ETSI registered for the benefit of its Members and of the 3GPP
Organizational Partners. oneM2M™ logo is a trademark of ETSI registered for the benefit of its Members and of the ®
oneM2M Partners. GSM and the GSM logo are trademarks registered and owned by the GSM Association.
Foreword
This Group Report (GR) has been produced by ETSI Industry Specification Group (ISG) Network Functions
Virtualisation (NFV).
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.

ETSI
8 ETSI GR NFV-REL 013 V5.1.1 (2023-02)
1 Scope
The present document aims at studying how operations data (KPIs, metrics, alarm notifications, event logs, debug
information) can be exploited to ensure the availability and reliability of NFV-MANO and of the network services it
manages using data analysis/data driven techniques. This includes, among others, the use of machine learning to find
patterns for cases where detailed semantics information is unavailable (e.g. due to confidentiality) or the amount of data
is overwhelming. Use cases are created describing how the information can be used offline (for example for root cause
analysis and predictive maintenance resulting in, e.g. creation and/or changes of deployment flavours) and online (to
identify appropriate LCM operations and policy changes in order to achieve the intended service availability
objectives).
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] ETSI GS NFV-IFA 005 (V3.4.1): "Network Functions Virtualisation (NFV) Release 3;
Management and Orchestration; Or-Vi reference point - Interface and Information Model
Specification".
[i.2] ETSI GS NFV-IFA 006 (V3.4.1): "Network Functions Virtualisation (NFV) Release 3;
Management and Orchestration; Vi-Vnfm reference point - Interface and Information Model
Specification".
[i.3] ETSI GS NFV-IFA 007 (V3.4.1): "Network Functions Virtualisation (NFV) Release 3;
Management and Orchestration; Or-Vnfm reference point - Interface and Information Model
Specification".
[i.4] ETSI GS NFV-IFA 008 (V3.4.1): "Network Functions Virtualisation (NFV) Release 3;
Management and Orchestration; Ve-Vnfm reference point - Interface and Information Model
Specification".
[i.5] ETSI GS NFV-IFA 013 (V3.4.1): "Network Functions Virtualisation (NFV) Release 3;
Management and Orchestration; Os-Ma-Nfvo reference point - Interface and Information Model
Specification".
[i.6] ETSI GS NFV-IFA 030 (V3.4.1): "Network Functions Virtualisation (NFV) Release 3;
Management and Orchestration; Multiple Administrative Domain Aspect Interfaces
Specification".
[i.7] ETSI GS NFV-IFA 031 (V3.4.1): "Network Functions Virtualisation (NFV) Release 3;
Management and Orchestration; Requirements and interfaces specification for management of
NFV-MANO".
ETSI
9 ETSI GR NFV-REL 013 V5.1.1 (2023-02)
[i.8] ETSI GS NFV-IFA 032 (V3.4.1): "Network Functions Virtualisation (NFV) Release 3;
Management and Orchestration; Interface and Information Model Specification for Multi-Site
Connectivity Services".
[i.9] ETSI GR NFV-IFA 015 (V3.4.1): "Network Functions Virtualisation (NFV) Release 3;
Management and Orchestration; Report on NFV Information Model".
[i.10] ETSI GR NFV-REL 010 (V3.1.1): "Network Functions Virtualisation (NFV) Release 3;
Reliability; Report on NFV Resiliency for the Support of Network Slicing".
[i.11] Ivar Thokle Hovden: "Optimizing Artificial Neural Network Hyperparameters and Architecture",
University of Oslo, 2019.
NOTE: Available at www.mn.uio.no/fysikk/english/people/aca/ivarth/works/in9400_nn_hpo_nas_hovden_r2.pdf.
[i.12] ETSI GS NFV-REL 005 (V1.1.1): "Network Functions Virtualisation (NFV); Accountability;
Report on Quality Accountability Framework".
[i.13] ETSI GR NFV 003 (V1.6.1): "Network Functions Virtualisation (NFV); Terminology for Main
Concepts in NFV".
[i.14] S. Azadiabad et al.: "Availability and Service Disruption of Network Services: from High-level
Requirements to Low-level Configuration Constraints", Elsevier Computer Standards and
Interfaces, Vol 80, March 2022.
NOTE: Available at doi.org/10.1016/j.csi.2021.103565.
[i.15] ETSI GS NFV-SOL 009 (V3.5.1): "Network Functions Virtualisation (NFV) Release 3; Protocols
and Data Models: RESTful protocols specification for the management of NFV-MANO".
[i.16] Z. Li et al.: "Predictive Analysis in Network Function Virtualization", IMC'18, October 31-
November 2, Boston, MA, USA.
NOTE: Available at dl.acm.org/doi/10.1145/3278532.3278547.
[i.17] I. Hadj-Kacem et al.: "Anomaly prediction in mobile networks: a data driven approach for
machine learning algorithm selection", NOMS 2020, April 20-24, Budapest, Hungary.
NOTE: Available at ieeexplore.ieee.org/document/9110429.
[i.18] A.L. Dennis: "Cognitive Computing Demystified: The What, Why, and How", DATAVERSITY,
February 2017.
NOTE: Available at www.dataversity.net/cognitive-computing-the-what-why-and-how/.
[i.19] B. Marr: "What Everyone Should Know About Cognitive Computing", Forbes, March 2016.
NOTE: Available at www.forbes.com/sites/bernardmarr/2016/03/23/what-everyone-should-know-about-
cognitive-computing/.
[i.20] "Supervised Learning", IBM Cloud Education, August 2020.
NOTE: Available at www.ibm.com/cloud/learn/supervised-learning.
[i.21] "Unsupervised Learning", IBM Cloud Education, September 2020.
NOTE: Available at www.ibm.com/cloud/learn/unsupervised-learning.
[i.22] M. Tim Jones: "Train a software agent to behave rationally with reinforcement learning",
October 2017.
NOTE: Available at developer.ibm.com/articles/cc-reinforcement-learning-train-software-agent/.
[i.23] "Machine learning", Wikipedia.
NOTE: Available at en.wikipedia.org/wiki/Machine_learning.
ETSI
10 ETSI GR NFV-REL 013 V5.1.1 (2023-02)
[i.24] R.S. Sutton and A.G. Barto: "Reinforcement Learning: An Introduction", Second Edition MIT
Press, 2018.
NOTE: Available at www.andrew.cmu.edu/course/10-703/textbook/BartoSutton.pdf.
[i.25] T. Hastie et al.: "The elements of Statistical Learning - Data Mining, Inference, and Prediction",
Second Edition, Springer, January 2017.
NOTE: Available at https://hastie.su.domains/Papers/ESLII.pdf.
[i.26] J. Ahmed et al.: "Automated diagnostic of virtualized service performance degradation", NOMS
2018, April 23-27, Taipei, Taiwan.
NOTE: Available at ieeexplore.ieee.org/document/8406234.
[i.27] G. Jung et al.: "Detecting bottleneck in n-tier IT applications through analysis", International
Workshop on Distributed Systems: Operations and Management (DSOM 2006), October 23-25,
Dublin, Ireland.
[i.28] D.J. Dean et al.: "UBL: Unsupervised behavior learning for predicting performance anomalies in
th
virtualized cloud systems", 9 International Conference on Autonomic Computing (ICAC'12),
September 18-20, San Jose, CA, USA.
3 Definition of terms, symbols and abbreviations
3.1 Terms
For the purposes of the present document, the terms given in ETSI GR NFV 003 [i.13] apply.
3.2 Symbols
Void.
3.3 Abbreviations
For the purposes of the present document, the abbreviations given in ETSI GR NFV 003 [i.13] and the following apply:
AAF Availability Assurance Function
ACK Acknowledgement
ADAM Adaptive Moment Estimation
AFR Average Failure Rate
ANN Artificial Neural Network
APF Anomaly Prediction Function
ASDD Acceptable Service Data Disruption
ASDT Acceptable Service Disruption Time
BA Balanced Accuracy
BMU Best Matching Unit
BW Bandwidth
c/n/s compute/network/storage
CA Classification Accuracy
CoM Composite Model
CpI Checkpointing Interval
CSCF Call Session Control Function
DLR Distributed-Log Regression
DS dissimilarity vector
FDLF Fault Detection and Localization Function
FE Functional Entity
FLM Fault Localization Model
ETSI
11 ETSI GR NFV-REL 013 V5.1.1 (2023-02)
FN False Negative
FP False Positive
FPCA Functional Principal Component Analysis
GRE Generic Routing Encapsulation
HI Health-Check Interval
ICMP Internet Control Message Protocol
IPV4 Internet Protocol Version 4
IPV6 Internet Protocol Version 6
IPVLAN Internet Protocol Virtual Local Area Network
LB Load Balancing
LiReg Linear Regression
LoReg Logistic Regression
LSTM Long Short-Term Memory
LTE Long Term Evolution
MP MultiPoint
MSCS Multi-Site Connectivity Services
MTTR Mean Time to Repair/Restore
NL Latency
NN Neural Network
NsDf NS Deployment Flavour
NsQoS Network service Quality of Service
ODU2 Optical Data Unit 2
PCA Principal Components Analysis
PTP Precision Time Protocol
RA Required Availability
RAID Redundant Array of Independent Disks
RCA Root Cause Analysis
ReLU Rectified Linear Unit
RF Random Forest
SARSA State-Action-Reward-State-Action
SB Standby Capacity
SL2 Scale Level 2
SL3 Scale Level 3
SLAAC Stateless Address Autoconfiguration
SLO Service Level Objectives
SOM Self-Organizing Map
SVM Support Vector Machine
Tanh Hyperbolic Tangent
TN True Negative
TP True Positive
UDP User Datagram Protocol
vPE Virtualised Provider Edge
4 Overview
Operations of communication networks produce huge amounts of data related to aspects such as characteristics,
lifecycle, behavior or performance/fault monitoring.
In the context of the multi-vendor, multi-layer NFV architecture, the exploitation of such massive data with the use of
cognitive approaches would ease the networks management, and could provide, in particular, the assurance of resilient
runtime operations of these networks.
The present document studies how machine learning could be applied to NFV operations data for reliability and
availability purposes. Clause 5 details the NFV architecture interfaces through which the field data may be collected,
together with the operations which create these data.
Three families of data-driven techniques are described in clause 6: supervised, unsupervised and reinforcement
learning. Used for operations control and management, they may help to build zero-touch control loops and pave the
way to autonomous networking.
ETSI
12 ETSI GR NFV-REL 013 V5.1.1 (2023-02)
Three selected use cases for the use of operations data for reliability and availability are described in clause 7:
• Service availability assurance for meeting and maintaining a given network service availability.
• Root cause analysis, i.e. real-time fault localization for mitigation of incidents which underly detected
anomalies.
• Anomaly prediction for anticipation of failures which could lead to outages.
A list of recommendations, some of which call for requirements specification, is finally provided in clause 8 for each
use case.
5 Operations data
5.1 Nature of the data
The main input needed for cognitive approaches such as machine learning is data. Operations data of the NFV
ecosystem arise from operations launched at the numerous NFV-MANO interfaces. Figure 5.1-1 shows all the
interfaces through which operations data can be collected. In this figure, the interfaces are grouped, when it is possible,
according to the reference points of the NFV architecture. The number following each interface refers to the clause
number in Annex A of the present document elaborating on the interface. Two interfaces are common to all reference
points: performance management and fault management. In addition, the policy management interface is also common
with two exceptions currently. A number of interfaces exists in similar form for different resources. To improve
readability, similar interfaces related to compute, network and storage are grouped through the symbol "c/n/s". Their
references are also combined (e.g. A.5.2/3/4.1 meaning respectively A.5.2.1, A.5.3.1 and A.5.4.1).
The operations executed at these interfaces are of different nature:
• Lifecycle manageme
...