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. Han, et al. Expires January 9, 2022 [Page 1] Internet-Draft Inband Flow Learning July 2021 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 carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must 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 Han, et al. Expires January 9, 2022 [Page 2] Internet-Draft Inband Flow Learning July 2021 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. Han, et al. Expires January 9, 2022 [Page 3] Internet-Draft Inband Flow Learning July 2021 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 January 9, 2022 [Page 4] Internet-Draft Inband Flow Learning July 2021 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 January 9, 2022 [Page 5] Internet-Draft Inband Flow Learning July 2021 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, . [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.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. Han, et al. Expires January 9, 2022 [Page 6] Internet-Draft Inband Flow Learning July 2021 [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, . 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 Han, et al. Expires January 9, 2022 [Page 7]