IPPM Working Group L. Han Internet-Draft M. Wang Intended status: Informational China Mobile Expires: 28 January 2024 X. Wang J. Huang Huawei Technologies 27 July 2023 Problem Statement and Requirement for Inband Flow Learning draft-hwyh-ippm-ps-inband-flow-learning-03 Abstract On-path telemetry techniques can provide high-precision inband flow insight and real-time network performance monitoring. Although they are benefical, network operators still face challenges applying such techniques, especially flow identification when deploying flow- oriented monitoring on a large scale. This document introduces the real network scenarios, and intends to address the problems by proposing the requirements of inband flow learning mechenism that can be used to implement inband flow information telemetry for deployability and flexibility. 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 28 January 2024. Han, et al. Expires 28 January 2024 [Page 1] Internet-Draft Inband Flow Learning July 2023 Copyright Notice Copyright (c) 2023 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 carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Revised BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Revised 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 Flow Learning . . . . . . . . . . . . . . . . 6 4.4. Auto Flow Aging . . . . . . . . . . . . . . . . . . . . . 6 4.5. Flow Learning Policy . . . . . . . . . . . . . . . . . . 6 5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 6 6. Security Considerations . . . . . . . . . . . . . . . . . . . 6 7. References . . . . . . . . . . . . . . . . . . . . . . . . . 6 7.1. Normative References . . . . . . . . . . . . . . . . . . 6 7.2. Informative References . . . . . . . . . . . . . . . . . 7 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 8 1. Introduction On-path telemetry techniques can provide high-precision inband flow insight and real-time network performance monitoring (e.g., jitter, latency, packet loss) by embedding instructions or metadata into user packets. IOAM [RFC9197] and Alternate-Marking [RFC9341] are such techniques, and [RFC9197] [RFC9326] [RFC9343] [I-D.ietf-mpls-inband-pm-encapsulation] provide the encapsulations for different applications. By applying these techniques per-flow SLA compliance monitoring becomes available and benefical for network Han, et al. Expires 28 January 2024 [Page 2] Internet-Draft Inband Flow Learning July 2023 operators, but there are still challenges as described in [I-D.song-opsawg-ifit-framework]. Especially when deploying flow- oriented monitoring on a large scale, the traditional static configuration mode is no longer applicable. Per-flow monitoring can be applied using network management tools, such as Netconf YANG, to deliver the characteristics of specified flows. Then network nodes can identify, match and monitor the flows based on the characteristics. However, even though Netconf YANG can provide feasibility to network operators, some problems or inconveniences may occur during the deployment. For example, the characteristic of a flow (e.g. IP 5-tupe) can vary dynamically and mislead the service flow identification, or the monitored flow needs to be reconfigured for the changes of the path. So inband flow identification becomes a challenge in large scale deployment to network operators. This document introduces the real network scenarios, and intends to address the problems by proposing the requirements of inband flow learning mechanism that can be used to implement inband flow information telemetry for deployability and flexibility. A proposed framework for inband flow learning mechanism is described in [I-D.hwy-opsawg-ifl-framework], which is out of scope of this document. 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 The following sections describe scenarios that may occur in real network that make it difficult to deploy flow-oriented monitoring quickly and effectively at a 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. Han, et al. Expires 28 January 2024 [Page 3] Internet-Draft Inband Flow Learning July 2023 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 Han, et al. Expires 28 January 2024 [Page 4] Internet-Draft Inband Flow Learning July 2023 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. Han, et al. Expires 28 January 2024 [Page 5] Internet-Draft Inband Flow Learning July 2023 4.3. Hop-by-Hop Flow 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. 4.5. Flow Learning Policy It is valuable to specify the flow learning policy on equipment when thousands or millions of flows are transmitted. Flow learning policy specifies the metrics and explicit rules executed on equipment, for example the flow is filtered based on a particular range of protocol number. Centralized controller specifies the flow learning policy via management and control plane to equipment, then data plane executes the policies to generate monitoring instance. 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, . Han, et al. Expires 28 January 2024 [Page 6] Internet-Draft Inband Flow Learning July 2023 [RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC 2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174, May 2017, . 7.2. Informative References [I-D.hwy-opsawg-ifl-framework] Han, L., Wang, M., Wang, X., and T. Zhou, "Inband Flow Learning Framework", Work in Progress, Internet-Draft, draft-hwy-opsawg-ifl-framework-03, 3 July 2023, . [I-D.ietf-mpls-inband-pm-encapsulation] Cheng, W., Min, X., Zhou, T., Dai, J., and Y. Peleg, "Encapsulation For MPLS Performance Measurement with Alternate Marking Method", Work in Progress, Internet- Draft, draft-ietf-mpls-inband-pm-encapsulation-06, 14 June 2023, . [I-D.song-opsawg-ifit-framework] Song, H., Qin, F., Chen, H., Jin, J., and J. Shin, "Framework for In-situ Flow Information Telemetry", Work in Progress, Internet-Draft, draft-song-opsawg-ifit- framework-20, 24 April 2023, . [RFC9197] Brockners, F., Ed., Bhandari, S., Ed., and T. Mizrahi, Ed., "Data Fields for In Situ Operations, Administration, and Maintenance (IOAM)", RFC 9197, DOI 10.17487/RFC9197, May 2022, . [RFC9326] Song, H., Gafni, B., Brockners, F., Bhandari, S., and T. Mizrahi, "In Situ Operations, Administration, and Maintenance (IOAM) Direct Exporting", RFC 9326, DOI 10.17487/RFC9326, November 2022, . [RFC9341] Fioccola, G., Ed., Cociglio, M., Mirsky, G., Mizrahi, T., and T. Zhou, "Alternate-Marking Method", RFC 9341, DOI 10.17487/RFC9341, December 2022, . Han, et al. Expires 28 January 2024 [Page 7] Internet-Draft Inband Flow Learning July 2023 [RFC9343] Fioccola, G., Zhou, T., Cociglio, M., Qin, F., and R. Pang, "IPv6 Application of the Alternate-Marking Method", RFC 9343, DOI 10.17487/RFC9343, December 2022, . Authors' Addresses Liuyan Han China Mobile Beijing China Email: hanliuyan@chinamobile.com Minxue Wang China Mobile Beijing China Email: wangminxue@chinamobile.com Xuanxuan Wang Huawei Technologies Beijing China Email: wxxuan@huawei.com Jinming Huang Huawei Technologies Dongguan China Email: zhangshengli4@huawei.com Han, et al. Expires 28 January 2024 [Page 8]