]> ETRI

byyun@etri.re.kr

woori-net

young@woori-net.com

IPPM Working Group performance management collection measurement This document specifies a YANG data model for Collection Measurement based on the Performance Management (PM) Collection function requirements defined in ITU-T G.7710. The model processes raw performance data sampled at a network node and produces structured data that can be retrieved by clients via pull-based mechanisms or delivered via push-based mechanisms such as YANG-Push. This document does not define new performance metrics; the base metrics are those of the IPPM framework, and the model specifies the collection and exposure of the operationally important subset identified by ITU-T G.7710. The latest revision of this draft can be found at . Status information for this document may be found at . Source for this draft and an issue tracker can be found at .

Introduction Performance Management (PM) data generated by a Network Element (NE) undergoes a systematic processing pipeline that transforms raw observations into actionable operational insights. Traditionally, PM data has been managed through pull-based mechanisms such as SNMP polling, but the increasing scale and dynamic nature of modern networks require a more efficient, streaming-oriented approach. This document distinguishes three functional stages of the PM processing pipeline -- Sampling, Collection, and Reporting -- and defines a YANG data model for the Collection stage, referred to in this document as the Collection Measurement model. The Collection-stage data and notifications defined in this model can be retrieved by clients via pull-based mechanisms (e.g., NETCONF get-data operations) or delivered via push-based subscription mechanisms. When push-based delivery is used, the IETF subscribed- notifications framework , its YANG Push extension for datastore updates , and the NETCONF binding for dynamic subscriptions are applicable. These three specifications together are referred to in the remainder of this document as the push-based subscription mechanisms.

PM Processing Pipeline in a Network Element The PM processing pipeline considered in this document comprises the following three stages, as illustrated in . Stage 2 (Collection) is the scope of this document.

| Collection |--->| Reporting | | | | (this document) | | | | - Physical layer | | | | - YANG-Push | | - OWAMP/TWAMP/ | | - Counts | | RFC 8639/8640/ | | STAMP/IOAM | | - Snapshot | | 8641 | | - Sensors | | - Tidemarks | | - OS/NDT/AI APP | | | | - Thresholding | | | +------------------+ +====================+ +------------------+ raw samples processed PM data delivery ]]>

Stage 1 -- Sampling. This stage observes signals or protocol behaviours to produce raw samples. It encompasses direct observation of physical-layer signals as well as various OAM-based techniques. As examples of active measurement protocols that may be used in this stage, OWAMP , TWAMP , and STAMP inject synthetic probe packets to measure one-way or round-trip delay, packet loss, and delay variation, while In-situ OAM (IOAM) records performance data directly within user packets as they traverse the network. Stage 2 -- Collection. This stage processes raw samples into summarised statistics over defined intervals and manages their retention. Historically, PM collection mechanisms based on SNMP MIBs used the performance-history textual conventions defined in , with the high-capacity variants in . The Collection stage modelled in this document follows the common equipment management function requirements of ITU-T G.7710 and supports three collection types -- Counts, Snapshot, and Tidemarks -- together with threshold evaluation and the associated periodic and non-periodic events. Stage 3 -- Reporting. This stage delivers Collection-stage data and notifications to external management systems or controllers. While traditional pull-based retrieval remains available, this document assumes the push-based subscription mechanisms introduced above (, , ). The role of the Collection-stage models defined here is to provide the data structures that those mechanisms carry. Typical clients of this Reporting stage include operations systems (OS), network digital twins (NDT), and AI-driven applications (AI APP), as illustrated in .

The YANG data model specified in this document is implemented within the Equipment Management Function (EMF) of an NE, as defined in ITU-T G.7710 Section 6.2. It is exposed at the NE's management interface and is consumed by remote clients such as operations systems (OS), Physical Network Controllers (PNCs), network digital twins (NDT), and AI-driven applications. Throughout this document the term "client" refers to any such consumer of the Collection Measurement interface, regardless of whether it acts as a controller, an analytics engine, or an operator-facing management system.

Relationship to the IETF Performance Metric Framework The three-stage pipeline described above aligns with the framework for performance metric development given in . The Collection stage modelled in this document corresponds to the Computed Performance Metrics described in Section 5.3 of : the Counts and Tidemarks collection types are temporal aggregations (Section 5.3.1 of ) of base metrics over a collection interval, while the Snapshot collection type is a sampled value organised as a singleton in the sense of Section 5.6 of and the IPPM framework . summarises how the three collection types map onto the result organization of the IPPM framework.

Snapshot sample = a set of singletons (one interval) statistic = value computed from a sample -> Counts, Tidemarks ]]>

The base metrics aggregated by this model are produced in Stage 1 (Sampling) and are expected to be specified following the Performance Metric Specification of Section 5.4 of . This document does not define or redefine those base metrics, nor does it introduce new performance metrics or parameters; following ITU-T G.7710, it identifies the operationally important subset (counts, snapshot, and tidemarks) and defines the YANG structures that compute, retain, and expose their aggregated values over the configured collection intervals. The acceptable ranges of sampling and collection intervals, and the required timing accuracy called for in Section 5.4.2 of , are determined by the referenced ITU-T G.7710 recommendation and by the parameter profiles defined in this document, rather than being fixed by the YANG model itself. Except where is explicitly referenced, this document uses the term "Sampling" for the Stage 1 activity described above and "Collection" for the Stage 2 activity. A collection interval (YANG node collection-interval) is the window over which sampled values are aggregated within the Collection stage; it is distinct from the sampling interval at which raw values are produced, and both retain the meaning derived from ITU-T G.7710.

Motivation and Scope The legacy SNMP-based collection conventions and the associated pull-based polling architecture limit real-time visibility and flexible interval design at the scale of modern networks. The push-based subscription mechanisms provide an efficient and scalable alternative for streaming management data, but those specifications define only the subscription and notification mechanisms themselves and do not prescribe the structure of the PM data being carried. A standardised YANG data model is therefore needed at the Collection stage to bridge low-level measurement data and high-level streaming subscriptions, since existing YANG models tend to focus on static configuration or simple state data and do not offer the structures required to express counts, snapshot, and tidemarks collection together with profile-based parameter grouping and threshold semantics aligned with ITU-T G.7710. A further motivation concerns which performance information to collect and in what form. The IETF performance metric framework provides methods for defining and computing metrics, but the space of possible computed metrics is effectively unbounded (for example, averages, variances, or percentiles could all be derived). ITU-T G.7710 instead reflects several decades of operational experience by fixing a deliberately small set of collection forms and parameters: per clause 10.1.6.2 of , gauge measurements are limited to snapshots (an instantaneous value) and tidemarks (the maximum and minimum), and counts to a simple accumulation, rather than general statistical aggregations; and per clause 10.1.6.4 of , the collected parameters are confined to an operational set such as ES, SES, BBE, BBC, and UAS for maintenance and quality-of-service purposes. The collection types are themselves defined as the form in which results are stored, independent of the collection interval. This document adopts that operationally-validated minimal set, rather than attempting to enumerate all statistically possible metrics, so that the resulting YANG model expresses what network operators actually need to retain and expose for fault management, maintenance, and SLA evaluation. This is also why the model is parameter-agnostic in structure: it standardises the collection forms (counts, snapshot, tidemarks) and leaves the specific parameters to be referenced from and related Recommendations. The scope of this document is therefore limited to the Collection stage. Within that scope, this document specifies YANG data models that enable: (a) flexible processing of performance metrics beyond simple counts, including Snapshot and Tidemarks, as defined in ITU-T G.7710; (b) configurable sampling and collection intervals that are not restricted to the legacy 15-minute or 24-hour windows; and (c) data and notification structure that supports flexible delivery of processed PM data to management systems, compatible with both pull-based retrieval and push-based subscription mechanisms. The data model defined in this document realizes the Collection-stage functions described in , in particular performance-monitoring data, collection types, and threshold reporting. The Collection-stage data and notifications defined here are compatible with the existing YANG-Push framework (, , ) for push-based delivery as well as with traditional pull-based retrieval via NETCONF or RESTCONF; this document does not define any new subscription or notification.

PM parameters

Types Performance monitoring (PM) in networks encompasses a wide variety of parameters that reflect operational health, service quality, reliability, and environmental conditions. These parameters are used across many technologies, network layers, and functional domains to enable fault management, SLA compliance, trend analysis, predictive maintenance, and operational optimization. PM parameter types include but are not limited to: Classical transport and packet layer metrics: such as errored seconds (ES), severely errored seconds (SES), unavailable seconds (UAS), background block errors (BBE), background block counts (BBC), delay, jitter, and packet loss, as defined in standards like ITU-T G.7710, and others. Layer-specific metrics: Physical layer: optical power levels, laser bias current, loss of signal Data link layer: Ethernet frame errors, FCS errors Network layer: dropped packets, route flaps Transport/Service layers: MPLS LSP statistics, OTN TCM/BIP counters Network environment parameters: including temperature, humidity, fan speed, voltage, and airflow. These are essential for equipment safety, energy management, and predictive failure analysis. Energy and sustainability metrics: such as power consumption, energy efficiency indicators, and cooling utilization, aligned with emerging sustainability standards and operational efficiency goals. Security and integrity parameters: such as pointer justification events (PJE), synchronization loss, or intrusion anomaly flags. Application-aware or SLA metrics: such as service availability, throughput consistency, and application-layer latency. Mobile network-specific metrics: including radio link failures, handover success/failure rates, RRC connection setup time, PDCP discard rate, and throughput per bearer. These metrics are critical for monitoring the performance of RAN, core, and edge network components in 4G/5G mobile environments. These parameters may be grouped flexibly within the YANG model using parameter profiles that reflect shared characteristics, purpose, or applicable network domains. The architecture supports extension through identity-based typing to accommodate future parameter definitions introduced by standard bodies like ITU-T, IEEE, IETF, MEF, and TM Forum. The data model defined in this document is parameter-agnostic: it specifies how performance parameters are collected, aggregated, and exposed, rather than defining the parameters themselves. The network and transport performance parameters listed above (for example, ES, SES, UAS, BBE, BBC) are the primary parameters in scope, and each is expected to be specified following the Performance Metric Specification template of Section 5.4.4 of (metric name, description, method of measurement or calculation, units, measurement point, and measurement timing). The remaining categories (network environment, energy and sustainability, security, mobile network, and application-aware parameters) are listed as illustrative examples of the model's extensibility and are not themselves defined by this document; the specification of any such parameter is left to the relevant standards body.

Profiles

The YANG model defines the concept of a parameter profile to logically group performance parameters that are commonly measured together for a specific operational purpose. Each parameter profile is represented as a list entry keyed by a name of type profile-names (a string conforming to the format defined in the model). These profiles serve as named collections of performance parameters and are intended to facilitate streamlined configuration, management, and reporting of measurement data across network elements and management systems. The use of parameter profiles improves operational efficiency by allowing operators, applications, and controllers to activate or reference a coherent set of parameters using a single profile identifier. For example, the itu-transport-maintenance-15min profile may include parameters such as errored seconds (ES), severely errored seconds (SES), and unavailable seconds (UAS), which are typically monitored together for network maintenance and fault detection purposes. Similarly, the example-ip-qos-24hr profile may include delay, jitter, and loss parameters used in service quality reporting. Parameter profiles support role-based access control, operational alignment, and measurement policy abstraction, enabling network operators and analytics systems to tailor data collection and reporting according to the needs of different users and services. The profile abstraction also aligns with ITU-T G.7710, which identifies multiple classes of performance monitoring (e.g., maintenance, service-level, and compliance monitoring), each requiring specific sets of performance parameters. By modeling profiles as list entries keyed by a structured name (profile-names type), the YANG design ensures extensibility and vendor interoperability, allowing future profiles to be defined without changes to the core data structures. This approach promotes consistent configuration and integration across multi-vendor environments and supports dynamic service assurance use cases where parameter sets may vary by service type, SLA, or operational context.

Naming Parameter profiles are named to reflect their operational purpose, origin, applicable network domain, and, optionally, the primary collection interval. This naming structure supports clarity, modularity, and automation across diverse network and service layers. The naming follows this format:

--[-] ]]>

Where: <source>: Standards body or organization Examples: itu, ieee, ietf, example, vendorX <network>: Network domain or layer Examples: transport, access, core, ip, mpls, ethernet, otn, wdm, flexo <purpose>: Intended use or function Examples: maintenance, qos, availability, sla, compliance, analytics <characteristic> (optional): Optional qualifying information Examples: 15min, 24hr, high-priority Examples: itu-transport-maintenance-15min itu-transport-qos-24hr ieee-access-availability example-ip-qos-24hr vendorx-otn-sla Note: profile names that begin with ietf- are reserved for profiles that IETF actually defines; placeholder examples in this document use example- to avoid implying IETF endorsement of profiles not yet specified. The term 'transport' means that the profile applies to multiple technologies (e.g., OTN, MPLS-TP, Transport Ethernet, etc.). The 15-minute interval provides granular, real-time monitoring, allowing network operators to quickly detect and address short-term issues such as spikes in latency or packet loss. It is particularly useful for ensuring compliance with Service-Level Agreements (SLAs) and for managing highly dynamic networks where rapid changes can occur. In contrast, the 24-hour interval is used for long-term performance monitoring and trend analysis, helping operators understand overall network health, detect slow-developing issues, and plan for future capacity needs. This longer interval offers a broader view of the network's performance over a full day, making it ideal for strategic planning and infrastructure maintenance. Together, these intervals enable both immediate responses to network conditions and long-term network optimization.

Periodic Collection

Interval Timing

Interval timing parameters are key components of network performance management, offering standardized definitions for the time-related aspects of sampling, collection, and reporting performance data. These parameters apply to the three main collection types for network equipment: counts, snapshot, and tidemarks. They include the sampling interval, collection interval, and uniform time, all of which support consistent, accurate, and systematic performance monitoring and management. Sampling interval defines the period at which network performance data is collected at consistent, predetermined time points. It ensures the continuous and timely capture of performance metrics, enabling accurate assessments of network conditions. Collection interval specifies the duration over which sampled performance data is aggregated or statistically processed. It helps manage large volumes of data by summarizing it into meaningful indicators for analysis, anomaly detection, and resource management. Uniform time is a fixed, predefined point within each collection interval at which a snapshot measurement is taken. It enables a consistent and instantaneous view of network performance across intervals, without requiring data aggregation. This approach facilitates quick diagnostics and synchronization across monitoring systems. The acceptable ranges of sampling and collection intervals, and the timing accuracy required for a given parameter, are not fixed by this YANG model. As noted in Section 5.4.2 of , these constraints are part of the specification of each performance metric; in this document they are determined by ITU-T G.7710 and by the parameter profile in use, and a server advertises the intervals it supports as part of its capabilities.

Use Cases The hierarchical design of the ietf-pm-collection YANG module, where a performance parameter can be associated with one or more sampling intervals and each sampling interval can be associated with multiple collection intervals and collection types, supports a wide range of operational objectives. A key benefit of this structure is that, even when the sampling interval is fixed, different collection intervals can be used to derive distinct operational views of the same parameter. The collection type is selected to match the semantics of the monitored parameter: counts for cumulative event parameters, tidemarks for varying parameters where extremes are operationally significant, and snapshot for point-in-time operating state. In a Network Operations Center (NOC), errored seconds (ES) can be sampled every second and processed with multiple collection intervals using the counts collection type. A 1-minute collection interval supports rapid fault indication, enabling fast recognition of service degradation. A 15-minute collection interval supports routine maintenance monitoring and aligns with established operational practices described in ITU-T G.7710. A 24-hour collection interval supports daily QoS reporting and provides a broader view of service quality over time. For latency monitoring in a NOC, a parameter may be sampled every 500 milliseconds and processed using the tidemarks collection type. A 1-minute collection interval helps detect short-lived delay spikes. A 30-minute collection interval helps identify recurring burst patterns that affect path stability. A 24-hour collection interval provides a daily worst-case view of path quality, supporting operational assessment of persistent delay behavior. For digital twin applications, packet delay variation (PDV) may be sampled every 100 milliseconds and processed using the tidemarks collection type. A 1-minute collection interval provides a synchronization stability envelope that helps the digital twin remain closely aligned with the physical network. A 5-minute collection interval supports feedback-loop tuning by capturing short-term variation patterns that influence control adjustments. A 1-hour collection interval supports model calibration by providing longer-span information about the range of delay variation observed in operation. Environmental monitoring also benefits from the same hierarchical timing structure. For example, temperature may be sampled every 10 seconds and processed using the snapshot collection type. A 1-minute collection interval supports a current operating state check. A 15-minute collection interval supports periodic baseline comparison. A 24-hour collection interval supports daily fleet-wide correlation at a common observation point, helping operators compare equipment behavior across systems and sites. Similar timing structures can also support AI/ML pipelines, where short, medium, and long collection intervals applied to the same sampled parameter provide feature sets for anomaly detection, trend analysis, and model training within a single analytics application. These examples show that the same sampling interval can support multiple operational purposes when paired with different collection intervals, and that the collection type should match the semantics of the monitored parameter. The hierarchical list structure, where parameters contain multiple sampling intervals and each sampling interval defines one or more collection intervals and collection types, supports operational flexibility, avoids configuration duplication, and enables fine-grained control of measurement strategies. The use cases summarized in are consistent with the collection types defined in , where counts represent cumulative event occurrences, snapshot represents point-in-time values, and tidemarks represent interval extremes. Client (param) Samp. Meas. Coll. Purpose NOC (ES) 1s 1min counts Rapid fault alert   1s 15min counts Maintenance   1s 24hr counts Daily QoS report NOC (latency) 500ms 1min tidemarks Delay spike detect   500ms 30min tidemarks Burst pattern obs.   500ms 24hr tidemarks Daily worst-case Digital Twin 100ms 1min tidemarks Sync stability (PDV) 100ms 5min tidemarks Feedback tuning   100ms 1hr tidemarks Model calibration NOC (temp) 10s 1min snapshot State check   10s 15min snapshot Periodic baseline   10s 24hr snapshot Fleet-wide daily

Collection Types

The collection types defined based on ITU-T G.7710 establish a focused and efficient framework for network performance monitoring by specifying three core collection types: counts, snapshot, and tidemarks. This intentional limitation supports key objectives such as implementation simplicity, operational efficiency, and cross-vendor interoperability. It emphasizes real-time network monitoring, favoring instantaneous or interval-based metrics over complex statistical calculations. The counts and snapshot collection types provide immediate operational data without incurring the processing overhead associated with metrics like averages and variances. These statistical measures require significant aggregation logic, which can vary across implementations and devices. By keeping computation within network elements minimal, the approach reduces both processing and memory overhead, maintaining lightweight implementations. It establishes a clear separation between raw data collection (handled by network elements) and deeper analysis (delegated to external management systems). This separation not only simplifies device requirements but also enables more consistent and flexible analytics in centralized systems, which are better equipped to apply standardized analytical frameworks. Limiting collection types also contributes to energy efficiency by reducing the operational burden on Network Elements (NEs), while offloading data analysis to external management applications. Despite the simplicity, the selected collection types offer sufficient expressiveness to support comprehensive performance monitoring without excessive resource use. They are specifically optimized for the NE-to-client interface -- referred to as the Southbound Interface (SBI) from a controller perspective (e.g., between a Physical Network Controller (PNC) and an NE), or equivalently as the northbound management interface from the NE's perspective -- to ensure as follows: Lightweight to implement Consistently supported across vendors Efficient for transport and storage in network management systems The collection types are applicable to a wide range of monitored objects, including both network topology elements (e.g., links, tunnels) and physical equipment parameters (e.g., temperature, voltage). In terms of the performance metric framework of , the three collection types correspond to the following operations, as summarised in . Collection type operation Note counts Temporal aggregation (Section 5.3.1): cumulative sum over the collection interval Resets at the end of each interval snapshot Sampled value at a uniform time within the interval Organised as a singleton (Section 5.6 of ; ) tidemarks Temporal aggregation (Section 5.3.1): minimum and maximum over the interval High and low values retained per interval The counts and tidemarks types are computed (aggregated) metrics in the sense of Section 5.3 of , whereas the snapshot type is a directly sampled value rather than an aggregation.

Counts Counts measurement in network performance monitoring tracks the cumulative occurrences of specific events over a defined collection interval, such as 15 minutes or 24 hours. This method captures how frequently certain network activities, like errors or transmission issues, occur, providing a historical view of recurring problems. Counts reset at the end of each interval, ensuring that every period starts with a fresh count for accurate monitoring. The primary purpose of counts is to identify trends and patterns in network behavior over time, helping operators detect anomalies or areas where issues frequently arise. This type of measurement is particularly useful for long-term analysis, enabling preventive maintenance and optimizing network performance. Unlike instantaneous measurements, counts focus on aggregation over time, making it easier to understand the persistence or recurrence of faults. The data gathered through counts helps in fault management and planning by highlighting repeated errors, congestion, or performance degradation that may affect service delivery. As a result, counts provide network operators with actionable insights for troubleshooting and capacity planning, ensuring smooth operation and reliability across the network.

Snapshot Snapshot is an instantaneous measurement taken at a specific point in time. It captures the instantaneous value of specific performance parameters at a regular, predefined point (uniform time) within each time interval. Snapshot provides a "momentary view" of network conditions, allowing operators to observe the network's status at specific intervals. The data from these uniform-time snapshots is then aggregated and analyzed to understand the immediate state across the entire network. By taking snapshots simultaneously across all network elements, operators can correlate data between different parts of the transport network. Snapshots are collected at pre-determined uniform times within fixed collection intervals. The uniform time and fixed intervals can be configured based on the needs of the network.

Tidemarks Tidemarks measurements record the maximum (high tidemarks) and minimum (low tidemarks) values that a performance parameter reaches during a specified collection interval. This approach captures the extreme values and performance fluctuations, highlighting the best and worst conditions that occur within the monitoring period. Tidemarks measurements provide deeper insights by capturing performance spikes or drops that may go unnoticed in average or cumulative data, enabling precise troubleshooting of intermittent or extreme conditions. For instance, while the average error rate over a period may appear acceptable, a high tidemark could reveal intermittent spikes in errors that require attention. Conversely, a low tidemark may expose periods of severely degraded signal quality or throughput.

Thresholding

Periodic Thresholding Periodic threshold events are triggered when the counts or gauge value reaches a pre-defined threshold during periodic measurements including counts, snapshot, and tidemarks for performance parameters. The counts measurement has two types of threshold reporting methods: transient and standing condition methods. The transient condition method treats each measurement period separately. As soon as a threshold is reached or crossed in a collection interval for a given performance measurement, a threshold report (TR) is generated. The standing condition method is optional. The standing condition is raised, and a TR (Threshold Report) is generated, when the set threshold is reached or crossed. The standing condition is cleared, and a reset threshold report (RTR) is generated at the end of the period when the current value is below or equal to the reset threshold, provided that there was no unavailable time during that period. For gauge measurements ("snapshot" and "tidemarks"), an overflow condition is determined and an out-of-range report is generated as soon as the gauge value reaches or crosses the high threshold. An underflow condition is determined and an out-of-range report is generated as soon as the gauge value is at or below the low threshold.

Non-Periodic Thresholding Non-periodic threshold events are triggered regardless of the collection types (counts, snapshot, or tidemarks). The following parameters are used for non-periodic events. BUT (Begin Unavailable Time): The event marking the start of a period when a network element or connection is unavailable. EUT (End Unavailable Time): The event marking the end of a period when a network element or connection was unavailable. CSES (Consecutive Severely Errored Seconds): A sequence of severely errored seconds (SES) detected consecutively within a specified time interval. The reporting metrics include BUT, EUT, and the count of errors during that period.

Data Access Clients can access PM parameter values produced by the counts, snapshot, and tidemarks collection types using either pull-based retrieval or push-based subscription mechanisms. This section provides practical examples for both access patterns. This document does not define any new retrieval operation or subscription mechanism; the examples below use only standard NETCONF operations and YANG-Push RPCs and notifications. The data retrieved or delivered is used for maintenance and Quality of Service (QoS) monitoring in networks.

Pull-Based Polling The operational state data defined in this document can be retrieved by clients using standard NETCONF or RESTCONF operations, providing a pull-based access pattern analogous to traditional SNMP polling. In traditional SNMP-based PM collection, a management system periodically issues GET or GET-BULK requests to retrieve counter values from the agent's MIB, such as the performance-history MIBs defined in and . The YANG data model defined here provides equivalent data structures accessible via NETCONF <get-data> operations against the operational datastore , or via RESTCONF HTTP GET requests, at any polling interval chosen by the client. The collection-value leaf (config false) in the counts collection type, and the corresponding measurement leaves in snapshot and tidemarks, are read-only operational state nodes that reflect the current accumulated or instantaneous PM values within the active collection interval. These leaves are the YANG equivalents of SNMP counter and gauge objects in classical PM MIBs. shows a NETCONF <get-data> request that retrieves the current errored-seconds (ES) count for the itu-transport-maintenance-15min profile, sampled every second over a 15-minute collection interval. A client may issue this request at any time to read the in-progress counter value, or at the end of each collection interval to collect the final result, replicating the behavior of a traditional SNMP polling cycle.

ds:operational itu-transport-maintenance-15min es 1s 15min ]]>

shows the corresponding reply, in which the server returns the current ES count of 7 accumulated during the collection interval in progress. This is equivalent to the value that a classical SNMP GET would retrieve from a performance-history MIB object.

itu-transport-maintenance-15min es 1s 15min 7 ]]>

Periodic Events The YANG-Push subscription model, as defined in , enables clients to subscribe to periodic performance measurement data from network elements. This model supports dynamic subscription establishment, modification, and termination for real-time streaming of PM data. Clients can specify subscription parameters including the target datastore (operational), encoding format (XML/JSON), and filtering criteria to receive only relevant performance metrics. The subscription mechanism allows for configurable update periods, enabling both high-frequency monitoring and long-term trend analysis (e.g., 24-hour intervals). Network elements generate periodic event notifications containing the requested PM data, which clients can process for real-time monitoring, historical analysis, or triggering automated responses based on performance thresholds. shows a subscription request for the ES parameter in the itu-transport-maintenance-15min profile. It requests counts measurement data sampled every second and aggregated over a 15-minute interval. The reporting period is set to 900 seconds, so a notification is sent at the end of each collection interval.

YANG-PUSH encode-xml operational /pm-coll:pm-periodic-collection/ parameter-profile[name='itu-transport-maintenance-15min']/ pm-parameter[name='es']/ sampling-interval[id='1s']/ collection-interval[id='15min']/ collection-types/counts/collection-value 900 2024-07-01T00:00:00Z ]]>

shows a notification for the ES parameter in the itu-transport-maintenance-15min profile. It reports the counts measurement value sampled every second and aggregated over a 15-minute interval. The measured value (10) represents the total errored seconds in that period.

2024-07-01T00:15:00Z itu-transport-maintenance-15min es 1s 1 second 15min 15 minute 10 ]]>

Threshold Events Threshold event subscriptions enable clients to receive immediate notifications when performance metrics cross predefined thresholds, providing proactive monitoring capabilities. This subscription type uses standard YANG-Push datastore change notifications to deliver the threshold events defined in this document.

Periodic Threshold Events

When a performance parameter exceeds or falls below configured thresholds for the periodic collection types of counts, snapshot, and tidemarks, the network element generates event-driven notifications containing the threshold crossing event type and occurrence time (parameter values at the time of the event can be read from the operational datastore if needed). This mechanism supports four types of threshold events: count-transient-event for immediate threshold crossings, count-standing-event for persistent threshold violations, snapshot-event for instantaneous value threshold crossings, and tidemark-event for extreme value threshold crossings. These events enable rapid response to network performance degradation and automated fault management. The threshold event subscription complements periodic subscriptions by providing real-time alerts for critical performance issues that require immediate attention. shows an example of the NETCONF request to subscribe to all pm-threshold-events notifications in the ietf-pm-collection model.

YANG-PUSH encode-xml operational /pm-coll:pm-threshold-events 1 2024-07-01T00:00:00Z ]]>

reports a high-OOR-event threshold crossing for the snapshot measurement of the ES parameter in the itu-transport-maintenance-15min profile, with 1-second sampling and 15-minute collection interval. It shows the event type, occurrence, and timestamp as defined in the YANG model.

2024-07-01T00:05:23Z itu-transport-maintenance-15min es 1s 1 second 15min 15 minute High-OOR-event true 2024-07-01T00:05:23Z ]]>

Non-Periodic Threshold Events

Non-periodic threshold event subscriptions provide immediate notifications for critical network availability and error conditions that occur independently of regular collection intervals. These subscriptions monitor for specific events such as BUT, EUT, and CSES that indicate significant network performance degradation or service interruptions. When these events occur, the network element generates immediate notifications containing event details, timing information, and duration data. This subscription type enables proactive network management by providing real-time awareness of critical network conditions that require immediate operator attention or automated intervention. Non-periodic threshold events complement periodic monitoring by capturing exceptional conditions that may not be detected through regular interval-based measurements.

YANG Data Model The YANG module for PM measurements is defined below:

Editor: Bin Yeong Yoon "; description "This YANG module defines a data model for Collection Measurement of performance management (PM) data in network equipment, based on ITU-T G.7710. It specifies three core collection types: counts (cumulative events), snapshot (instantaneous values), and tidemarks (extreme values), as defined in ITU-T G.7710. The module enables proactive network monitoring through configurable sampling and collection intervals, supporting both high-frequency real-time monitoring and long-term trend analysis. It provides threshold event notifications for both periodic measurements and non-periodic events (BUT, EUT, CSES). The collected data can be retrieved by clients via pull-based mechanisms (e.g., NETCONF get-data) or delivered via push-based subscription mechanisms (e.g., YANG-Push). The design supports AI-driven applications, network digital twins, and dynamic network environments by enabling multiple simultaneous views of the same performance parameter with different temporal resolutions. This hierarchical structure allows operators, analytics systems, and digital twin platforms to access performance data at appropriate granularities while maintaining operational efficiency and cross-vendor interoperability. Copyright (c) 2026 IETF Trust and the persons identified as authors of the code. All rights reserved. Redistribution and use in source and binary forms, with or without modification, is permitted pursuant to, and subject to the license terms contained in, the Revised BSD License set forth in Section 4.c of the IETF Trust's Legal Provisions Relating to IETF Documents (https://trustee.ietf.org/license-info). This version of this YANG module is part of RFC XXXX (https://www.rfc-editor.org/info/rfcXXXX); see the RFC itself for full legal notices."; revision 2026-05-02 { description "Renamed the module from ietf-pm-measurements to ietf-pm-collection (including namespace and prefix). Terminology and model alignment: prior wording and the measurement-methods container for counts, snapshot, and tidemarks were replaced by collection types and the collection-types container, per ITU-T G.7710 collection- type concepts."; reference "RFC XXXX: A YANG Data Model for Collection Measurement"; } /* * TYPEDEFs */ typedef profile-names { type string { pattern '[a-zA-Z][a-zA-Z0-9_-]*-[a-zA-Z][a-zA-Z0-9_-]*-' + '[a-zA-Z][a-zA-Z0-9_-]*(-[a-zA-Z][a-zA-Z0-9_-]*)?'; } description "Parameter profile name following the format: --[-] Where: - : Standards body or organization (e.g., itu, ieee, ietf) - : Network domain or layer (e.g., transport, access, core) - : Intended use or function (e.g., maintenance, qos, availability) - : Optional qualifying information (e.g., 15min, 24hr, high-priority) Examples: - itu-transport-maintenance-15min - itu-transport-maintenance-24hr - itu-transport-qos-24hr - ieee-access-availability"; } typedef time-interval-unit { type enumeration { enum millisecond { description "Time interval in milliseconds."; } enum second { description "Time interval in seconds."; } enum minute { description "Time interval in minutes."; } enum hour { description "Time interval in hours."; } } description "Units for expressing time intervals."; } /* * IDENTITIES */ identity periodic-events { description "Base identity for periodic event notifications."; } identity counts-transient { base periodic-events; description "Notification for transient threshold events in counts measurements."; } identity counts-standing { base periodic-events; description "Notification for standing threshold events in counts measurements."; } identity snapshot { base periodic-events; description "Notification for snapshot measurement threshold events."; } identity tidemarks { base periodic-events; description "Notification for tidemarks measurement threshold events."; } identity non-periodic-events { description "Base identity for non-periodic event notifications."; } identity but { base non-periodic-events; description "Notification for Begin Unavailable Time (BUT) events."; } identity eut { base non-periodic-events; description "Notification for End Unavailable Time (EUT) events."; } identity cses { base non-periodic-events; description "Notification for Consecutive Severely Errored Seconds (CSES) events."; } /* * COMMON GROUPINGS */ grouping threshold-config { description "Common threshold configuration for snapshot and tidemarks measurement types (high and low thresholds)."; leaf high-threshold { type uint32; description "High threshold that triggers alerts when exceeded."; } leaf low-threshold { type uint32; description "Low threshold that triggers alerts when performance falls below acceptable levels."; } } grouping transient-threshold-config { description "Threshold configuration for transient conditions. Transient thresholds only support high threshold crossings and report immediately when the count value reaches or crosses the configured transient threshold value. Transient thresholds do not support low threshold (underflow) conditions, unlike snapshot and tidemarks measurements which support both high and low thresholds."; leaf transient-threshold { type uint32; description "Transient threshold that triggers alerts when exceeded. Transient thresholds report immediately when the count value reaches or crosses this threshold value."; } } grouping event-state-info { description "Common event state information for all event types."; leaf event-occurred { type boolean; description "Indicates whether a threshold crossing or performance event has occurred."; } leaf event-time { type yang:date-and-time; description "Precise timestamp of when the event occurred."; } } grouping oor-event-type { description "Common out-of-range event type definition."; leaf event-type { type enumeration { enum High-OOR-event { description "High OOR threshold exceeded."; } enum Low-OOR-event { description "Low OOR threshold crossed."; } } description "Specifies whether the high or low OOR threshold was crossed."; } } grouping triggered-oor-event-info { description "Combined threshold event type and event information."; uses oor-event-type; uses event-state-info; } grouping count-transient-event-type { description "Transient threshold event type definition for counts measurements. Transient thresholds report immediately when the count value reaches or crosses a configured threshold value."; leaf event-type { type enumeration { enum Threshold-Crossed-Event { description "Threshold crossing event generated when count value reaches or crosses the configured threshold value."; } } description "Specifies that a threshold crossing event occurred."; } } grouping triggered-count-transient-event-info { description "Combined transient threshold event type and event information for counts measurements. Transient thresholds are independent threshold mechanisms that report immediately when count values cross configured threshold values."; uses count-transient-event-type; uses event-state-info; } grouping time-interval-config { description "Common time interval configuration."; leaf interval-value { type uint32; description "Numeric value for the interval."; } leaf unit { type time-interval-unit; description "Time unit for the interval value."; } } /* * COLLECTION TYPE GROUPINGS */ grouping count-collection-gr { description "Counts measurement for cumulative event tracking over a collection interval. Supports transient and standing threshold reporting, as defined in G.7710."; container counts { description "Contains counts measurement values and configuration."; container transient-condition-config { description "Configuration for transient threshold conditions. Transient thresholds report immediately when the count value reaches or crosses the configured transient threshold value. Transient thresholds do not support low threshold (underflow) conditions, unlike snapshot and tidemarks measurements which support both high and low thresholds."; uses transient-threshold-config; } container standing-condition-config { must "not(standing-threshold and reset-threshold) or " + "standing-threshold >= reset-threshold" { error-message "Standing threshold must be >= reset threshold."; } description "Configuration for standing condition monitoring. When both thresholds are set, standing-threshold must be greater than or equal to reset-threshold (hysteresis)."; leaf standing-threshold { type uint32; description "Threshold value that triggers standing condition alerts."; } leaf reset-threshold { type uint32; description "Reset threshold value that clears standing conditions."; } } leaf collection-value { type uint32; config false; description "Current cumulative count value for the collection interval."; } } } grouping snapshot-collection-gr { description "Snapshot measurements for instantaneous values at uniform time within each collection interval. Supports high/low OOR threshold reporting, as defined in G.7710."; container snapshot { description "Contains snapshot measurement configuration and values."; container uniform-time-config { description "Configuration for uniform time intervals between snapshots."; leaf interval-value { type uint32; default 1; description "Numeric value for the sampling interval between snapshots."; } leaf unit { type time-interval-unit; description "Time unit for the snapshot sampling interval."; } } container threshold-config { description "Configuration for snapshot threshold monitoring."; uses threshold-config; } leaf collection-value { type uint32; config false; description "Current instantaneous snapshot value."; } } } grouping tidemarks-collection-gr { description "Tidemarks measurements for maximum and minimum values over the collection interval. Supports high/low OOR threshold reporting, as defined in G.7710."; container tidemarks { description "Contains tidemarks measurement values and threshold configuration."; container threshold-config { description "Configuration for tidemarks threshold monitoring."; uses threshold-config; } leaf high-collection-value { type uint32; config false; description "Current maximum value recorded during the collection interval."; } leaf low-collection-value { type uint32; config false; description "Current minimum value recorded during the collection interval."; } } } grouping collection-types-gr { description "Grouping for the three core collection types (counts, snapshot, tidemarks) per ITU-T G.7710."; container collection-types { description "Container for the counts, snapshot, and tidemarks collection types."; uses count-collection-gr; uses snapshot-collection-gr; uses tidemarks-collection-gr; } } /* * EVENT GROUPINGS */ grouping counts-transient-event-gr { description "Transient threshold events for counts measurements. Transient thresholds are independent threshold mechanisms that report immediately when count values cross configured threshold values."; container counts-transient { description "Contains information about transient threshold events for counts."; uses triggered-count-transient-event-info; } } grouping counts-standing-event-gr { description "Standing condition events for counts measurements."; container counts-standing { description "Contains information about standing threshold events for counts."; leaf event-type { type enumeration { enum Threshold-Report { description "Threshold Report (TR) generated when the count value reaches or exceeds the standing-threshold configured in standing-condition-config."; } enum Reset-Threshold-Report { description "Reset Threshold Report (RTR) generated at the end of the period when the count value is at or below the reset-threshold (G.7710 standing condition clear)."; } } description "Specifies the type of standing threshold event that occurred, as defined in G.7710. A Threshold-Report (TR) is generated when the collection-value reaches or exceeds the standing-threshold. A Reset-Threshold-Report (RTR) is generated at the end of the period when the collection-value is at or below the reset-threshold."; } uses event-state-info; } } grouping snapshot-events-gr { description "Threshold events for snapshot measurements."; container snapshot { description "Contains snapshot threshold event information."; uses triggered-oor-event-info; } } grouping tidemarks-events-gr { description "Threshold events for tidemarks measurements."; container tidemarks { description "Contains tidemarks threshold event information."; uses triggered-oor-event-info; } } /* * COLLECTION INTERVAL STRUCTURES */ grouping periodic-collection-intervals { description "Hierarchical structure for periodic measurement timing and collection types."; list sampling-interval { key "id"; description "List of sampling intervals defining data collection frequency."; leaf id { type string; description "Unique identifier for this sampling interval configuration."; } leaf interval-value { type uint32; default 1; description "Numeric value for the sampling interval."; } leaf unit { type time-interval-unit; default second; description "Time unit for the sampling interval value."; } list collection-interval { key "id"; description "List of collection intervals defining aggregation periods."; leaf id { type string; description "Unique identifier for this collection interval configuration."; } leaf interval-value { type uint32; default 15; description "Numeric value for the collection interval."; } leaf unit { type time-interval-unit; default minute; description "Time unit for the collection interval value."; } uses collection-types-gr; } } } grouping non-periodic-events-gr { description "Grouping for non-periodic performance event parameters (BUT, EUT, CSES)."; container BUT-event { description "Begin Unavailable Time (BUT) event."; uses event-state-info; } container EUT-event { description "End Unavailable Time (EUT) event."; uses event-state-info; leaf duration { type uint32; units "seconds"; description "Total duration of unavailability in seconds."; } } container CSES-event { description "Consecutive Severely Errored Seconds (CSES) event."; leaf event-occurred { type boolean; description "Indicates whether a CSES event was generated."; } leaf start { type yang:date-and-time; description "Timestamp indicating when the CSES period began."; } leaf end { type yang:date-and-time; description "Timestamp indicating when the CSES period ended."; } leaf duration { type uint32; units "seconds"; description "Duration of the CSES period in seconds."; } leaf error-count { type uint32; description "Number of errors during the CSES period."; } } } grouping pm-periodic-collection-gr { description "Hierarchical structure for periodic performance measurements."; list parameter-profile { key "name"; description "List of performance parameter profiles."; leaf name { type profile-names; description "Name of the parameter profile."; } list pm-parameter { key "name"; description "List of PM parameters within the parameter profile."; leaf name { type string; description "Name of the performance parameter being measured."; } uses periodic-collection-intervals; } } } /* * MAIN CONTAINER */ container pm-periodic-collection { description "Main container for periodic performance measurements."; uses pm-periodic-collection-gr; } /* * NOTIFICATIONS */ notification pm-threshold-events { description "Notification for periodic threshold crossing events and non-periodic performance events (BUT, EUT, CSES)."; container periodic-events { description "Container for periodic threshold events."; list parameter-profile { key "name"; description "List of performance parameter profiles for event monitoring."; leaf name { type profile-names; description "Name of the parameter profile."; } list pm-parameter { key "name"; description "List of PM parameters within the parameter profile."; leaf name { type string; description "Name of the performance parameter being monitored."; } list sampling-interval { key "id"; description "List of sampling intervals for event monitoring."; leaf id { type string; description "Unique identifier for this sampling interval configuration."; } uses time-interval-config; list collection-interval { key "id"; description "List of collection intervals for event aggregation."; leaf id { type string; description "Unique identifier for this collection interval configuration."; } uses time-interval-config; container event-types { description "Container for different threshold event types."; uses counts-transient-event-gr; uses counts-standing-event-gr; uses snapshot-events-gr; uses tidemarks-events-gr; } } } } } } container non-periodic-events { description "Container for non-periodic performance events (BUT, EUT, CSES)."; uses non-periodic-events-gr; } } } ]]>

YANG Data Trees

Manageability Considerations This section will be completed in a future revision of this document. Considerations to be addressed include the operational impact of large numbers of concurrent YANG-Push subscriptions for PM data, alignment of collection intervals with NE clock sources, and interaction with existing fault and configuration management workflows.

Security Considerations This section will be completed in a future revision of this document. The YANG module defined in this document defines data nodes that are designed to be accessed via network management protocols such as NETCONF or RESTCONF. The lowest NETCONF layer is the secure transport layer, and the mandatory- to-implement secure transport is Secure Shell (SSH). The Network Configuration Access Control Model (NACM) provides the means to restrict access for particular NETCONF or RESTCONF users to a preconfigured subset of all available NETCONF or RESTCONF protocol operations and content. Detailed analysis of sensitive readable nodes, writable nodes, and RPC operations will be added in a future revision.

IANA Considerations This document requests IANA to register the following URI in the "ns" subregistry within the "IETF XML Registry" : URI: urn:ietf:params:xml:ns:yang:ietf-pm-collection Registrant Contact: The IESG. XML: N/A; the requested URI is an XML namespace. This document also requests IANA to register the following YANG module in the "YANG Module Names" registry : Name: ietf-pm-collection Namespace: urn:ietf:params:xml:ns:yang:ietf-pm-collection Prefix: pm-coll Reference: RFC XXXX

ITU-T This document defines a YANG data model and associated mechanisms enabling subscriber-specific subscriptions to a publisher's event streams. Applying these elements allows a subscriber to request and receive a continuous, customized feed of publisher-generated information. This document describes a mechanism that allows subscriber applications to request a continuous and customized stream of updates from a YANG datastore. Providing such visibility into updates enables new capabilities based on the remote mirroring and monitoring of configuration and operational state. This document provides a Network Configuration Protocol (NETCONF) binding to the dynamic subscription capability of both subscribed notifications and YANG-Push. Datastores are a fundamental concept binding the data models written in the YANG data modeling language to network management protocols such as the Network Configuration Protocol (NETCONF) and RESTCONF. This document defines an architectural framework for datastores based on the experience gained with the initial simpler model, addressing requirements that were not well supported in the initial model. This document updates RFC 7950. The Network Configuration Protocol (NETCONF) defined in this document provides mechanisms to install, manipulate, and delete the configuration of network devices. It uses an Extensible Markup Language (XML)-based data encoding for the configuration data as well as the protocol messages. The NETCONF protocol operations are realized as remote procedure calls (RPCs). This document obsoletes RFC 4741. [STANDARDS-TRACK] YANG is a data modeling language used to model configuration and state data manipulated by the Network Configuration Protocol (NETCONF), NETCONF remote procedure calls, and NETCONF notifications. [STANDARDS-TRACK] The One-Way Active Measurement Protocol (OWAMP) measures unidirectional characteristics such as one-way delay and one-way loss. High-precision measurement of these one-way IP performance metrics became possible with wider availability of good time sources (such as GPS and CDMA). OWAMP enables the interoperability of these measurements. [STANDARDS-TRACK] The One-way Active Measurement Protocol (OWAMP), specified in RFC 4656, provides a common protocol for measuring one-way metrics between network devices. OWAMP can be used bi-directionally to measure one-way metrics in both directions between two network elements. However, it does not accommodate round-trip or two-way measurements. This memo specifies a Two-Way Active Measurement Protocol (TWAMP), based on the OWAMP, that adds two-way or round-trip measurement capabilities. The TWAMP measurement architecture is usually comprised of two hosts with specific roles, and this allows for some protocol simplifications, making it an attractive alternative in some circumstances. [STANDARDS-TRACK] This document describes the Simple Two-way Active Measurement Protocol (STAMP), which enables the measurement of both one-way and round-trip performance metrics, like delay, delay variation, and packet loss. In situ Operations, Administration, and Maintenance (IOAM) collects operational and telemetry information in the packet while the packet traverses a path between two points in the network. This document discusses the data fields and associated data types for IOAM. IOAM-Data-Fields can be encapsulated into a variety of protocols, such as Network Service Header (NSH), Segment Routing, Generic Network Virtualization Encapsulation (Geneve), or IPv6. IOAM can be used to complement OAM mechanisms based on, e.g., ICMP or other types of probe packets. This document defines a set of Textual Conventions for MIB modules that make use of performance history data based on 15 minute intervals. This memo replaces RFC 2493. Changes relative to RFC 2493 are summarized in the MIB module's REVISION clause. [STANDARDS-TRACK] This document presents a set of High Capacity Textual Conventions for use in MIB modules which require performance history based upon 15 minute intervals. The Textual Conventions defined in this document extend the conventions presented in RFC 3593 to 64 bit resolution using the conventions presented in RFC 2856. [STANDARDS-TRACK] This document describes a framework and a process for developing Performance Metrics of protocols and applications transported over IETF-specified protocols. These metrics can be used to characterize traffic on live networks and services. This memo documents an Internet Best Current Practice. The purpose of this memo is to define a general framework for particular metrics to be developed by the IETF's IP Performance Metrics effort. This memo provides information for the Internet community. It does not specify an Internet standard of any kind. The standardization of network configuration interfaces for use with the Network Configuration Protocol (NETCONF) or the RESTCONF protocol requires a structured and secure operating environment that promotes human usability and multi-vendor interoperability. There is a need for standard mechanisms to restrict NETCONF or RESTCONF protocol access for particular users to a preconfigured subset of all available NETCONF or RESTCONF protocol operations and content. This document defines such an access control model. This document obsoletes RFC 6536. This document describes an IANA maintained registry for IETF standards which use Extensible Markup Language (XML) related items such as Namespaces, Document Type Declarations (DTDs), Schemas, and Resource Description Framework (RDF) Schemas.

Contributors KT

kwangkoog.lee@kt.com

SK Telecom

jongyoon.shin@sk.com

LGU+

sy.nam@lguplus.co.kr