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Versions: 00                                                            
IPPM Working Group                                                L. Han
Internet-Draft                                                   M. Wang
Intended status: Informational                              China Mobile
Expires: January 9, 2022                                         F. Yang
                                                                J. Huang
                                                     Huawei Technologies
                                                           July 08, 2021


       Problem Statement and Requirement for Inband Flow Learning
               draft-hwyh-ippm-ps-inband-flow-learning-00

Abstract

   Alternate-Marking (coloring) provides a method to perform packet
   loss, delay, and jitter measurements on live traffic.  At the same
   time, on-path telemetry techniques are used to enable the collection
   and correlation of performance information to further support
   autonomous network operations.  However, network operators still face
   the challenge of inband flow identification in large scale
   deployment.  This document addresses the problems by introducing the
   real network scenarios, and proposes the requirements of supporting
   inband flow learning of flow information telemetry.

Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
   "OPTIONAL" in this document are to be interpreted as described in BCP
   14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at https://datatracker.ietf.org/drafts/current/.

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on January 9, 2022.



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Copyright Notice

   Copyright (c) 2021 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (https://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
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   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Problem Statement . . . . . . . . . . . . . . . . . . . . . .   3
     3.1.  Frequent and Dynamic Change of Flows  . . . . . . . . . .   3
       3.1.1.  Tidal Effect  . . . . . . . . . . . . . . . . . . . .   4
       3.1.2.  UPF Expansion . . . . . . . . . . . . . . . . . . . .   4
     3.2.  Enterprise Service Demand . . . . . . . . . . . . . . . .   4
     3.3.  Large Scale Network Monitor Deployment and Maintenance  .   4
     3.4.  Service Flow Path Change  . . . . . . . . . . . . . . . .   5
   4.  Requirement . . . . . . . . . . . . . . . . . . . . . . . . .   5
     4.1.  Ingress Flow Learning . . . . . . . . . . . . . . . . . .   5
     4.2.  Egress Flow Learning  . . . . . . . . . . . . . . . . . .   5
     4.3.  Hop-by-Hop Path Learning  . . . . . . . . . . . . . . . .   6
     4.4.  Auto Flow Aging . . . . . . . . . . . . . . . . . . . . .   6
   5.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   6
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .   6
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   6
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .   6
     7.2.  Informative References  . . . . . . . . . . . . . . . . .   6
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   7

1.  Introduction

   Alternate-Marking (coloring) [RFC8321] provides a method to perform
   packet loss, delay, and jitter measurements on live traffic.
   [I-D.ietf-mpls-inband-pm-encapsulation] and
   [I-D.ietf-6man-ipv6-alt-mark] introduce the MPLS and IPv6 performance
   measurement applications of alternate marking method respectively,
   and specifies the encapsulations for MPLS and IPv6.  On-path
   telemetry techniques are used to enable the collection and
   correlation of performance information from the network.  By coloring



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   the real service flow and telemetry flow statistics, per-flow SLA
   compliance monitoring becomes available and scalable for network
   operators.  When deployed in network, per-flow monitoring can be
   applied based on CLI configuration or via Netconf YANG.

   However, even though Netconf YANG can provide feasibility to network
   administration, the characteristic of a flow (e.g.  IP 5-tupe) can
   vary dynamically and mislead the service flow identification.  Inband
   flow learning becomes a challenge in large scale deployment to
   network operators.  This document addresses the problems by
   introducing the real network scenarios, and proposes the requirements
   of supporting inband flow learning of flow information telemetry.

2.  Terminology

   OAM: Operations, Administration, and Maintenance

   SLA: Service Level Agreement

   NFV: Network Function Virtualization

   UNI: User-Network-Interface

   CN: Core Network

3.  Problem Statement

   In an alternate marking application, it is usually to utilize the
   characteristic fields of packet to identify a service flow.  For
   example, IP 5-tuple is usually to be used as the identifier of a
   service flow at source node.  A concept of flow identifier, such as
   Flow-ID Label Indicator [I-D.ietf-mpls-inband-pm-encapsulation] or
   FlowMonID [I-D.ietf-6man-ipv6-alt-mark] is used to identify service
   flow at transit or egress nodes.  The change of packet data fields
   would mislead the flow identification for flow monitoring and
   statistics telemetry in large scale.

3.1.  Frequent and Dynamic Change of Flows

   In 4G/5G mobile backhaul networks, IP address of one service can be
   changed based on location, time or even with business growth.  The
   following scenarios describes the challenges which 4G/5G mobile
   service encounters.








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3.1.1.  Tidal Effect

   A Tidal Effect phenomenon has been recognized as traffics between
   base station and Core Network (CN) show repetitive patterns with
   spatio-temporal variations.  A typical example of Tidal phenomenon is
   the traffic difference happened in day and night time of a commercial
   and business area.  In day time, eNodeB allocates more core network
   resources when a large number of user equipment accesses eNodeB, and
   less resources at night accordingly.  The change of the number of UEs
   and the core network resources may affect the change on source and
   destination IP address of service flows.

   Moreover, NFV used in core network makes the traffic change even
   worse as the IP address at CN cannot be manually configured or even
   predicted.  In this case, it is impossible for operators to
   statically deploy flow monitoring and statistics telemetry.

3.1.2.  UPF Expansion

   In 5G deployment, the increase of number of subscribers triggers the
   expansion of UPF resources on data plane of 5G core network.  After
   new UPF resource is added, eNodeB sets up a connection to the new
   UPF.  Correspondingly, a new IP flow is created in mobile bearer
   network.  In this scenario, if flow monitoring and statistics
   telemetry is deployed in a static mode, operators would need to
   manually add related configurations to mobile bearer network after
   the core network capacity is expanded, which is very difficult to
   deploy in practice.

3.2.  Enterprise Service Demand

   The enterprise services usually connect different private networks
   between Headquarter and Branches, Branches and Branches.  Network
   operator has very limited or even no information about end users.
   Besides, information from one site could be changed from time to
   time.  Unpredictable information on enterprise customer side makes
   impossible for network operators to set up real time flow monitoring,
   and to avoid the omission of flow monitoring.

3.3.  Large Scale Network Monitor Deployment and Maintenance

   In a large-scale mobile bearer network, a large number of base
   stations and corresponding access points may lead to a large number
   of IP addresses in core network.  From network maintenance
   perspective, when flow monitoring and statistics telemetry is
   deployed in a static mode, network operator had to manually set up
   each monitoring instance between base station and core network, then
   separately delegate configurations to a large number of network



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   entities.  It is difficult for network operators to find an effective
   way of monitoring creation and maintenance.

   Note that traffic monitoring is comprised of uplink and downlink
   directions, which makes twice of workload on configurations.

3.4.  Service Flow Path Change

   When a hop-by-hop flow monitoring is required by critical traffic for
   deep SLA investigation, the actual forwarding path of service flow
   and the every forwarding nodes along the path are obtained.  Network
   operator delegates different configurations to each node including
   ingress, transit, and egress nodes on the path.

   Once the traffic forwarding path is changed because of service flow
   switching or route convergence, the monitoring instance on each node
   needs to be re-deployed on the new path.  In this situation, a
   flexible and efficient deployment approach is required by network
   operators.

4.  Requirement

   To face the flow deployment challenges mentioned in preceding
   section, an approach of inband flow learning is required.  It should
   simplify the deployment of flow monitoring and achieve an automatic
   mode of telemetry in large scale networks.

4.1.  Ingress Flow Learning

   On the UNI side of network node, ingress flow learning can help to
   capture the characteristic data fields of packet and create the
   monitoring instance when the flow is created from base station.
   Flexible policy based on access control list (ACL) can facilitate the
   identification of flow characteristic.  For example, IP 2-tuple
   (DIP+SIP), DSCP value, etc.

4.2.  Egress Flow Learning

   Similar to the requirement on ingress node, traffic egress node
   should support the same capability of inband flow learning to create
   traffic monitoring instance for completing a monitor.  When the
   egress node or egress port of a service flow is changed, the egress
   node or egress port of service flow can be triggered to re-learn and
   re-monitor the service flow.







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4.3.  Hop-by-Hop Path Learning

   When hop-by-hop flow monitoring and telemetry is required, the flow
   learning and monitor deployment should be created on all the ingress,
   transit, and egress nodes that service flows pass through.  When the
   path of a service flow changes due to the service switching or
   network convergence, the service flow re-triggers the flow learning
   on the new path and starts the new monitoring of service flow.

4.4.  Auto Flow Aging

   In all the inband flow learning scenarios described above, when the
   path of a service flow changes, the flow learning on new path is
   triggered and new monitoring instances are created on devices.
   Regarding the monitoring instances that have been created before the
   path change, if there is no traffic detected within a certain period
   of time, automatic aging and resource recycle should be supported.

5.  IANA Considerations

   This document has no request to IANA

6.  Security Considerations

   TBD

7.  References

7.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/info/rfc8174>.

7.2.  Informative References

   [I-D.ietf-6man-ipv6-alt-mark]
              Fioccola, G., Zhou, T., Cociglio, M., Qin, F., and R.
              Pang, "IPv6 Application of the Alternate Marking Method",
              draft-ietf-6man-ipv6-alt-mark-04 (work in progress), March
              2021.





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   [I-D.ietf-mpls-inband-pm-encapsulation]
              Cheng, W., Min, X., Zhou, T., Dong, X., and Y. Peleg,
              "Encapsulation For MPLS Performance Measurement with
              Alternate Marking Method", draft-ietf-mpls-inband-pm-
              encapsulation-01 (work in progress), April 2021.

   [RFC8321]  Fioccola, G., Ed., Capello, A., Cociglio, M., Castaldelli,
              L., Chen, M., Zheng, L., Mirsky, G., and T. Mizrahi,
              "Alternate-Marking Method for Passive and Hybrid
              Performance Monitoring", RFC 8321, DOI 10.17487/RFC8321,
              January 2018, <https://www.rfc-editor.org/info/rfc8321>.

Authors' Addresses

   Liuyan Han
   China Mobile
   Beijing
   China

   Email: hanliuyan@chinamobile.com


   Minxue Wang
   China Mobile
   Beijing
   China

   Email: wangminxue@chinamobile.com


   Fan Yang
   Huawei Technologies
   Beijing
   China

   Email: shirley.yangfan@huawei.com


   Jinming Huang
   Huawei Technologies
   Dongguan
   China

   Email: zhangshengli4@huawei.com







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