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
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Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
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material or to cite them other than as "work in progress."
This Internet-Draft will expire on January 9, 2022.
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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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