< draft-lee-teas-actn-pm-telemetry-autonomics-16.txt   draft-lee-teas-actn-pm-telemetry-autonomics-17.txt >
TEAS Working Group Y. Lee (Editor) TEAS Working Group Y. Lee (Editor)
Internet Draft Dhruv Dhody Internet Draft Dhruv Dhody
Intended Status: Standard Track Satish Karunanithi Intended Status: Standard Track Satish Karunanithi
Expires: October 18, 2019 Huawei Expires: November 8, 2019 Huawei
Ricard Vilalta Ricard Vilalta
CTTC CTTC
Daniel King Daniel King
Lancaster University Lancaster University
Daniele Ceccarelli Daniele Ceccarelli
Ericsson Ericsson
April 18, 2019 May 8, 2019
YANG models for VN & TE Performance Monitoring Telemetry and Scaling YANG models for VN & TE Performance Monitoring Telemetry and Scaling
Intent Autonomics Intent Autonomics
draft-lee-teas-actn-pm-telemetry-autonomics-16 draft-lee-teas-actn-pm-telemetry-autonomics-17
Status of this Memo Status of this Memo
This Internet-Draft is submitted to IETF in full conformance with This Internet-Draft is submitted to IETF in full conformance with
the provisions of BCP 78 and BCP 79. the provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF), its areas, and its working groups. Note that Task Force (IETF), its areas, and its working groups. Note that
other groups may also distribute working documents as Internet- other groups may also distribute working documents as Internet-
Drafts. Drafts.
skipping to change at page 1, line 44 skipping to change at page 1, line 44
months and may be updated, replaced, or obsoleted by other documents months and may be updated, replaced, or obsoleted by other documents
at any time. It is inappropriate to use Internet-Drafts as at any time. It is inappropriate to use Internet-Drafts as
reference material or to cite them other than as "work in progress." reference material or to cite them other than as "work in progress."
The list of current Internet-Drafts can be accessed at The list of current Internet-Drafts can be accessed at
http://www.ietf.org/ietf/1id-abstracts.txt http://www.ietf.org/ietf/1id-abstracts.txt
The list of Internet-Draft Shadow Directories can be accessed at The list of Internet-Draft Shadow Directories can be accessed at
http://www.ietf.org/shadow.html. http://www.ietf.org/shadow.html.
This Internet-Draft will expire on October 18, 2019. This Internet-Draft will expire on November 8, 2019.
Copyright Notice Copyright Notice
Copyright (c) 2019 IETF Trust and the persons identified as the Copyright (c) 2019 IETF Trust and the persons identified as the
document authors. All rights reserved. document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents Provisions Relating to IETF Documents
(http://trustee.ietf.org/license-info) in effect on the date of (http://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with carefully, as they describe your rights and restrictions with
skipping to change at page 2, line 33 skipping to change at page 2, line 33
The models presented in this draft allow customers to subscribe to The models presented in this draft allow customers to subscribe to
and monitor their key performance data of their interest on the and monitor their key performance data of their interest on the
level of TE-tunnel or VN. The models also provide customers with the level of TE-tunnel or VN. The models also provide customers with the
ability to program autonomic scaling intent mechanism on the level ability to program autonomic scaling intent mechanism on the level
of TE-tunnel as well as VN. of TE-tunnel as well as VN.
Table of Contents Table of Contents
1. Introduction...................................................3 1. Introduction...................................................3
1.1. Terminology...............................................4 1.1. Terminology...............................................4
1.2. Tree diagram..............................................4 1.2. Tree diagram..............................................5
1.3. Prefixes in Data Node Names...............................4 1.3. Prefixes in Data Node Names...............................5
2. Use-Cases......................................................4 2. Use-Cases......................................................5
3. Design of the Data Models......................................6 3. Design of the Data Models......................................7
3.1. TE KPI Telemetry Model....................................6 3.1. TE KPI Telemetry Model....................................7
3.2. VN KPI Telemetry Model....................................7 3.2. VN KPI Telemetry Model....................................8
4. Autonomic Scaling Intent Mechanism.............................8 4. Autonomic Scaling Intent Mechanism.............................9
5. Notification..................................................10 5. Notification..................................................11
5.1. YANG Push Subscription Examples..........................10 5.1. YANG Push Subscription Examples..........................11
6. YANG Data Tree................................................12 6. YANG Data Tree................................................13
7. Yang Data Model...............................................14 7. Yang Data Model...............................................15
7.1. ietf-te-kpi-telemetry model..............................14 7.1. ietf-te-kpi-telemetry model..............................15
7.2. ietf-vn-kpi-telemetry model..............................20 7.2. ietf-vn-kpi-telemetry model..............................21
8. Security Considerations.......................................24 8. Security Considerations.......................................25
9. IANA Considerations...........................................25 9. IANA Considerations...........................................26
10. Acknowledgements.............................................26 10. Acknowledgements.............................................27
11. References...................................................26 11. References...................................................27
11.1. Normative References....................................26 11.1. Normative References....................................27
11.2. Informative References..................................27 11.2. Informative References..................................28
12. Contributors.................................................28 12. Contributors.................................................29
Authors' Addresses...............................................28 Authors' Addresses...............................................29
1. Introduction 1. Introduction
The YANG model discussed in [VN] is used to operate customer-driven The YANG model discussed in [VN] is used to operate customer-driven
Virtual Networks (VNs) during the VN instantiation, VN computation, Virtual Networks (VNs) during the VN instantiation, VN computation,
and its life-cycle service management and operations. YANG model and its life-cycle service management and operations. YANG model
discussed in [TE-Tunnel] is used to operate TE-tunnels during the discussed in [TE-Tunnel] is used to operate TE-tunnels during the
tunnel instantiation, and its life-cycle management and operations. tunnel instantiation, and its life-cycle management and operations.
The models presented in this draft allow the applications hosted by The models presented in this draft allow the applications hosted by
skipping to change at page 4, line 5 skipping to change at page 4, line 5
This document provides YANG data models generically applicable to This document provides YANG data models generically applicable to
any VN/TE-Tunnel service clients to provide an ability to program any VN/TE-Tunnel service clients to provide an ability to program
their customized performance monitoring subscription and publication their customized performance monitoring subscription and publication
data models and automatic scaling in/out intent data models. These data models and automatic scaling in/out intent data models. These
models can be utilized by a client network controller to initiate models can be utilized by a client network controller to initiate
these capability to a transport network controller communicating these capability to a transport network controller communicating
with the client controller via a NETCONF [RFC8341] or a RESTCONF with the client controller via a NETCONF [RFC8341] or a RESTCONF
[RFC8040] interface. [RFC8040] interface.
The term performance monitoring being used in this document is
different from the term that has been used in transport networks for
many years. Performance monitoring in this document refers to
subscription and publication of streaming telemetry data.
Subscription is initiated by the client (e.g., CNC) while
publication is provided by the network (e.g., MDSC/PNC) based on the
client's subscription. As the scope of performance monitoring in
this document is telemetry data on the level of client's VN or TE-
tunnel, the entity interfacing the client (e.g., MDSC) has to
provide VN or TE-tunnel level information. This would require
controller capability to derive VN or TE-tunnel level performance
data based on lower-level data collected via PM counters in the
Network Elements (NE). How the controller entity derives such
customized level data (i.e., VN or TE-tunnel level) is out of the
scope of this document.
The data model includes configuration and state data according to The data model includes configuration and state data according to
the new Network Management Datastore Architecture [RFC8342]. the new Network Management Datastore Architecture [RFC8342].
1.1. Terminology 1.1. Terminology
Refer to [RFC8453], [RFC7926], and [RFC8309] for the key terms used Refer to [RFC8453], [RFC7926], and [RFC8309] for the key terms used
in this document. in this document.
Key Performance Data: This refers to a set of data the customer is
interested in monitoring for their instantiated VNs or TE-tunnels.
Key performance data and key performance indicators are inter-
exchangeable in this draft.
Scaling: This refers to the network ability to re-shape its own
resources. Scale out refers to improve network performance by
increasing the allocated resources, while scale in refers to
decrease the allocated resources, typically because the existing
resources are unnecessary.
Scaling Intent: To declare scaling conditions, scaling intent is
used. Specifically, scaling intent refers to the intent expressed by
the client that allows the client to program/configure conditions of
their key performance data either for scaling out or scaling in.
Various conditions can be set for scaling intent on either VN or TE-
tunnel level.
Network Autonomics: This refers to the network automation capability
that allows client to initiate scaling intent mechanisms and
provides the client with the status of the adjusted network
resources based on the client's scaling intent in an automated
fashion.
1.2. Tree diagram 1.2. Tree diagram
A simplified graphical representation of the data model is used in A simplified graphical representation of the data model is used in
Section 5 of this this document. The meaning of the symbols in Section 5 of this this document. The meaning of the symbols in
these diagrams is defined in [RFC8340]. these diagrams is defined in [RFC8340].
1.3. Prefixes in Data Node Names 1.3. Prefixes in Data Node Names
In this document, names of data nodes and other data model objects In this document, names of data nodes and other data model objects
are prefixed using the standard prefix associated with the are prefixed using the standard prefix associated with the
 End of changes. 7 change blocks. 
25 lines changed or deleted 65 lines changed or added

This html diff was produced by rfcdiff 1.47. The latest version is available from http://tools.ietf.org/tools/rfcdiff/