Problem Statement and Requirement for Inband Flow Learning

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 and Alternate-Marking are such techniques, and provide the encapsulations for different applications. By applying these techniques per-flow SLA compliance monitoring becomes available and benefical for network operators, but there are still challenges as described in . Especially when deploying flow-oriented monitoring on a large scale, the traditional static configuration mode is no longer applicable.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 , which is out of scope of this document.

OAM: Operations, Administration, and Maintenance SLA: Service Level Agreement NFV: Network Function Virtualization UNI: User-Network-Interface CN: Core Network

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

This document has no request to IANA

TBD