



Computing-Aware Traffic Steering                                Y. Kehan
Internet-Draft                                              China Mobile
Intended status: Standards Track                                   C. Li
Expires: 6 August 2026                               Huawei Technologies
                                                         L. M. Contreras
                                                              Telefonica
                                                           J. Ros-Giralt
                                                   Qualcomm Europe, Inc.
                                                                 G. Zeng
                                                     Huawei Technologies
                                                         2 February 2026


                        CATS Metrics Definition
                  draft-ietf-cats-metric-definition-05

Abstract

   Computing-Aware Traffic Steering (CATS) is a traffic engineering
   approach that optimizes the steering of traffic to a given service
   instance by considering the dynamic nature of computing and network
   resources.  In order to consider the computing and network resources,
   a system needs to share information (metrics) that describes the
   state of the resources.  Metrics from network domain have been in use
   in network systems for a long time.  This document defines a set of
   metrics from the computing domain used for CATS.

Discussion Venues

   This note is to be removed before publishing as an RFC.

   Discussion of this document takes place on the Computing-Aware
   Traffic Steering Working Group mailing list (cats@ietf.org), which is
   archived at https://mailarchive.ietf.org/arch/browse/cats/.

   Source for this draft and an issue tracker can be found at
   https://github.com/VMatrix1900/draft-cats-metric-definition.

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/.




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   Internet-Drafts are draft documents valid for a maximum of six months
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Conventions and Definitions . . . . . . . . . . . . . . . . .   4
   3.  Design Principles . . . . . . . . . . . . . . . . . . . . . .   4
     3.1.  Three-Level Metrics . . . . . . . . . . . . . . . . . . .   4
     3.2.  Level 0: Raw Metrics  . . . . . . . . . . . . . . . . . .   5
     3.3.  Level 1: Normalized Metrics in Categories . . . . . . . .   6
     3.4.  Level 2: Single Normalized Metric.  . . . . . . . . . . .   7
   4.  CATS Metrics Framework and Specification  . . . . . . . . . .   8
     4.1.  CATS Metric Fields  . . . . . . . . . . . . . . . . . . .   8
     4.2.  Aggregation and Normalization Functions . . . . . . . . .  11
       4.2.1.  Aggregation . . . . . . . . . . . . . . . . . . . . .  11
       4.2.2.  Normalization . . . . . . . . . . . . . . . . . . . .  12
     4.3.  On the Meaning of Scores in Heterogeneous Metrics
           Systems . . . . . . . . . . . . . . . . . . . . . . . . .  13
     4.4.  Level Metric Representations  . . . . . . . . . . . . . .  13
       4.4.1.  Level 0 Metrics . . . . . . . . . . . . . . . . . . .  14
       4.4.2.  Level 1 Metrics . . . . . . . . . . . . . . . . . . .  14
       4.4.3.  Level 2 Metrics . . . . . . . . . . . . . . . . . . .  15
   5.  Comparison among Metric Levels  . . . . . . . . . . . . . . .  16
   6.  CATS L2 Metric Registry Entry . . . . . . . . . . . . . . . .  18
     6.1.  Summary . . . . . . . . . . . . . . . . . . . . . . . . .  18
       6.1.1.  ID (Identifier) . . . . . . . . . . . . . . . . . . .  18
       6.1.2.  Name  . . . . . . . . . . . . . . . . . . . . . . . .  18
       6.1.3.  URI . . . . . . . . . . . . . . . . . . . . . . . . .  18
       6.1.4.  Description . . . . . . . . . . . . . . . . . . . . .  19



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       6.1.5.  Change Controller . . . . . . . . . . . . . . . . . .  19
       6.1.6.  Version . . . . . . . . . . . . . . . . . . . . . . .  19
     6.2.  Metric Definition . . . . . . . . . . . . . . . . . . . .  19
       6.2.1.  Reference Definition  . . . . . . . . . . . . . . . .  19
       6.2.2.  Fixed Parameters  . . . . . . . . . . . . . . . . . .  19
     6.3.  Method of Measurement . . . . . . . . . . . . . . . . . .  19
       6.3.1.  Reference Methods . . . . . . . . . . . . . . . . . .  19
       6.3.2.  Packet Stream Generation  . . . . . . . . . . . . . .  20
       6.3.3.  Traffic Filtering (Observation) Details . . . . . . .  20
       6.3.4.  Sampling Distribution . . . . . . . . . . . . . . . .  20
       6.3.5.  Runtime Parameters and Data Format  . . . . . . . . .  20
       6.3.6.  Roles . . . . . . . . . . . . . . . . . . . . . . . .  20
     6.4.  Output  . . . . . . . . . . . . . . . . . . . . . . . . .  20
       6.4.1.  Type  . . . . . . . . . . . . . . . . . . . . . . . .  21
       6.4.2.  Reference Definition  . . . . . . . . . . . . . . . .  21
       6.4.3.  Metric Units  . . . . . . . . . . . . . . . . . . . .  21
       6.4.4.  Calibration . . . . . . . . . . . . . . . . . . . . .  21
     6.5.  Administrative Items  . . . . . . . . . . . . . . . . . .  21
       6.5.1.  Status  . . . . . . . . . . . . . . . . . . . . . . .  21
       6.5.2.  Requester . . . . . . . . . . . . . . . . . . . . . .  21
       6.5.3.  Revision  . . . . . . . . . . . . . . . . . . . . . .  21
       6.5.4.  Revision Date . . . . . . . . . . . . . . . . . . . .  21
       6.5.5.  Comments and Remarks  . . . . . . . . . . . . . . . .  21
   7.  Implementation Guidance on Using CATS Metrics . . . . . . . .  22
   8.  Security Considerations . . . . . . . . . . . . . . . . . . .  22
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  22
   10. References  . . . . . . . . . . . . . . . . . . . . . . . . .  22
     10.1.  Normative References . . . . . . . . . . . . . . . . . .  22
     10.2.  Informative References . . . . . . . . . . . . . . . . .  23
   Appendix A.  Appendix A . . . . . . . . . . . . . . . . . . . . .  24
     A.1.  Level 0 Metric Representation Examples  . . . . . . . . .  24
       A.1.1.  Compute Raw Metrics . . . . . . . . . . . . . . . . .  24
       A.1.2.  Communication Raw Metrics . . . . . . . . . . . . . .  24
       A.1.3.  Delay Raw Metrics . . . . . . . . . . . . . . . . . .  25
   Contributors  . . . . . . . . . . . . . . . . . . . . . . . . . .  25
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  26

1.  Introduction

   Service providers are deploying computing capabilities across the
   network for hosting applications such as distributed AI workloads,
   AR/VR and driverless vehicles, among others.  In these deployments,
   multiple service instances are replicated across various sites to
   ensure sufficient capacity for maintaining the required Quality of
   Experience (QoE) expected by the application.  To support the
   selection of these instances, a framework called Computing-Aware
   Traffic Steering (CATS) is introduced in [I-D.ietf-cats-framework].




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   CATS is a traffic engineering approach that optimizes the steering of
   traffic to a given service instance by considering the dynamic nature
   of computing and network resources.  To achieve this, CATS components
   require performance metrics for both communication and compute
   resources.  Since these resources are deployed by multiple providers,
   standardized metrics are essential to ensure interoperability and
   enable precise traffic steering decisions, thereby optimizing
   resource utilization and enhancing overall system performance.

   Metrics from network domain have already been defined in previous
   documents, e.g., [RFC9439], [RFC8912], and [RFC8911], and been in use
   in network systems for a long time.  This document focuses on
   categorizing the relevant metrics at the computing domain for CATS
   into three levels based on their complexity and granularity.

2.  Conventions and Definitions

   This document uses the following terms defined in
   [I-D.ietf-cats-framework]:

   *  Computing-Aware Traffic Steering (CATS)

   *  Service

   *  Service site

   *  Service contact instance

   *  CATS Service Contact Instance ID (CSCI-ID)

   *  CATS Service Metric Agent (C-SMA)

   *  CATS Network Metric Agent (C-NMA)

3.  Design Principles

3.1.  Three-Level Metrics

   As outlined in [I-D.ietf-cats-usecases-requirements], the resource
   model that defines CATS metrics MUST be scalable, ensuring that its
   implementation remains within a reasonable and sustainable cost.
   Additionally, it MUST be useful in practice.  To that end, a CATS
   system should select the most appropriate metric(s) for instance
   selection, recognizing that different metrics may influence outcomes
   in distinct ways depending on the specific use case.






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   Introducing a definition of metrics requires balancing the following
   trade-off: if the metrics are too fine-grained, they become
   unscalable due to the excessive number of metrics that must be
   communicated through the metrics distribution protocol.  (See
   [I-D.rcr-opsawg-operational-compute-metrics] for a discussion of
   metrics distribution protocols.)  Conversely, if the metrics are too
   coarse-grained, they may not have sufficient information to enable
   proper operational decisions.

   Conceptually, it is necessary to define at least two fundamental
   levels of metrics: one comprising all raw metrics, and the other
   representing a simplified form---consisting of a single value that
   encapsulates the overall capability of a service instance.

   However, such a definition may, to some extent, constrain
   implementation flexibility across diverse CATS use cases.
   Implementers often seek balanced approaches that consider trade-offs
   among encoding complexity, accuracy, scalability, and extensibility.

   To ensure scalability while providing sufficient detail for effective
   decision-making, this document provides a definition of metrics that
   incorporates three levels of abstraction:

   *  *Level 0 (L0): Raw metrics.* These metrics are presented without
      abstraction, with each metric using its own unit and format as
      defined by the underlying resource.

   *  *Level 1 (L1): Metrics normalized within categories.* These
      metrics are derived by aggregating L0 metrics into multiple
      categories, such as network and computing.  Each category is
      summarized with a single L1 metric by normalizing it into a value
      within a defined range of scores.

   *  *Level 2 (L2): Single normalized metric.* These metrics are
      derived by aggregating lower level metrics (L0 or L1) into a
      single L2 metric, which is then normalized into a value within a
      defined range of scores.

3.2.  Level 0: Raw Metrics

   Level 0 metrics encompass detailed, raw metrics, including but not
   limited to:

   *  CPU: Base Frequency, boosted frequency, number of cores, core
      utilization, memory bandwidth, memory size, memory utilization,
      power consumption.





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   *  GPU: Frequency, number of render units, memory bandwidth, memory
      size, memory utilization, core utilization, power consumption.

   *  NPU: Computing power, utilization, power consumption.

   *  Network: Bandwidth, capacity, throughput, bytes transmitted, bytes
      received, host bus utilization.

   *  Storage: Available space, read speed, write speed.

   *  Delay: Time taken to process a request.

   L0 metrics serve as foundational data and do not require
   classification.  They provide basic information to support higher-
   level metrics, as detailed in the following sections.

   L0 metrics can be encoded and exposed using an Application
   Programming Interface (API), such as a RESTful API, and can be
   solution-specific.  Different resources can have their own metrics,
   each conveying unique information about their status.  These metrics
   can generally have units, such as bits per second (bps) or floating
   point instructions per second (flops).

   Regarding network-related information, [RFC8911] and [RFC8912] define
   various performance metrics and their registries.  Additionally, in
   [RFC9439], the ALTO WG introduced an extended set of metrics related
   to network performance, such as throughput and delay.  For compute
   metrics, [I-D.rcr-opsawg-operational-compute-metrics] lists a set of
   cloud resource metrics.

3.3.  Level 1: Normalized Metrics in Categories

   L1 metrics are organized into distinct categories, such as computing,
   communication, service, and composed metrics.  Each L0 metric is
   classified into one of these categories.  Within each category, a
   single L1 metric is computed using an _aggregation function_ and
   normalized to a unitless score that represents the performance of the
   underlying resources according to that category.  Potential
   categories include:

   *  *Computing:* A normalized value derived from computing-related L0
      metrics, such as CPU, GPU, and NPU utilization.

   *  *Communication:* A normalized value derived from communication-
      related L0 metrics, such as communication throughput.

   *  *Service:* A normalized value derived from service-related L0
      metrics, such as tokens per second and service availability



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   *  *Composed:* A normalized value derived from an aggregation
      function that takes as input a combination of computing,
      communication and service metrics.  For example, end-to-end delay
      computed as the sum of all delays along a path.

   Editor note: detailed categories can be updated according to the CATS
   WG discussion.

   L0 metrics, such as those defined in [RFC8911], [RFC8912], [RFC9439],
   and [I-D.rcr-opsawg-operational-compute-metrics], can be categorized
   into the aforementioned categories.  Each category will employ its
   own aggregation function (e.g., weighted summary) to generate the
   normalized value.  This approach allows the protocol to focus solely
   on the metric categories and their normalized values, thereby
   avoiding the need to process solution-specific detailed metrics.

3.4.  Level 2: Single Normalized Metric.

   The L2 metric is a single score value derived from the lower level
   metrics (L0 or L1) using an aggregation function.  Different
   implementations may employ different aggregation functions to
   characterize the overall performance of the underlying compute and
   communication resources.  The definition of the L2 metric simplifies
   the complexity of collecting and distributing numerous lower-level
   metrics by consolidating them into a single, unified score.

   TODO: Some implementations may support the configuration of Ingress
   CATS-Forwarders with the metric normalizing method so that it can
   decode the information from the L1 or L0 metrics.

   Figure 1 provides a summary of the logical relationships between
   metrics across the three levels of abstraction.

                                     +--------+
                          L2 Metric: |   M2   |
                                     +---^----+
                                         |
                     +-------------+-----+-----+------------+
                     |             |           |            |
                 +---+----+        |       +---+----+   +---+----+
     L1 Metrics: |  M1-1  |        |       |  M1-2  |   |  M1-3  | (...)
                 +---^----+        |       +---^----+   +----^---+
                     |             |           |             |
                +----+---+         |       +---+----+        |
                |        |         |       |        |        |
             +--+---+ +--+---+ +---+--+ +--+---+ +--+---+ +--+---+
  L0 Metrics:| M0-1 | | M0-2 | | M0-3 | | M0-4 | | M0-5 | | M0-6 | (...)
             +------+ +------+ +------+ +------+ +------+ +------+



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               Figure 1: Logic of CATS Metrics in levels

4.  CATS Metrics Framework and Specification

   The CATS metrics framework is a key component of the CATS
   architecture.  It defines how metrics are encoded and transmitted
   over the network.  The representation should be flexible enough to
   accommodate various types of metrics along with their respective
   units and precision levels, yet simple enough to enable easy
   implementation and deployment across heterogeneous edge environments.

4.1.  CATS Metric Fields

   This section defines the detailed structure used to represent CATS
   metrics.  The design follows principles established in related IETF
   specifications, such as the network performance metrics outlined in
   [RFC9439].

   Each CATS metric is expressed as a structured set of fields, with
   each field describing a specific property of the metric.  The
   following definition introduces the fields used in the CATS metric
   representations.





























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   - Cats_metric:
         - Metric_type:
               The type of the CATS metric.
               Examples: compute_cpu, storage_disk_size, network_bw,
               compute_delay, network_delay, compute_norm,
               storage_norm, network_norm, delay_norm.
         - Format:
               The encoding format of the metric.
               Examples: int, float.
         - Format_std (optional):
               The standard used to encode and decode the value
               field according to the format field.
               Example: ieee_754, ascii.
         - Length:
               The size of the value field measured in octets.
               Examples: 2, 4, 8, 16, 32, 64.
         - Unit:
               The unit of this metric.
               Examples: mhz, ghz, byte, kbyte, mbyte,
               gbyte, bps, kbps, mbps, gbps, tbps, tflops, none.
         - Source (optional):
               The source of information used to obtain the value field.
               Examples: nominal, estimation, normalization,
               aggregation.
         - Statistics(optional):
               The statistical function used to obtain the value field.
               Examples: max, min, mean, cur.
         - Level:
               The level this metric belongs to.
               Examples: L0, L1, L2.
         - Value:
               The value of this metric.
               Examples: 12, 3.2.

                        Figure 2: CATS Metric Fields

   Next, we describe each field in more detail:

   *  *Metric_Type (type)*: This field specifies the category or kind of
      CATS metric being measured, such as computational resources,
      storage capacity, or network bandwidth.  It acts as a label that
      enables network devices to identify the purpose of the metric.

   *  *Format (format)*: This field indicates the data encoding format
      of the metric, such as whether the value is represented as an
      integer, a floating-point number, or has no specific format.





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   *  *Format standard (format_std, optional)*: This optional field
      indicates the standard used to encode and decode the value field
      according to the format field.  It is only required if the value
      field is encoded using a specific standard, and knowing this
      standard is necessary to decode the value field.  Examples of
      format standards include ieee_754 and ascii.  This field ensures
      that the value can be accurately interpreted by specifying the
      encoding method used.

   *  *Length (length)*: This field indicates the size of the value
      field measured in octets (bytes).  It specifies how many bytes are
      used to store the value of the metric.  Examples include 4, 8, 16,
      32, and 64.  The length field is important for memory allocation
      and data handling, ensuring that the value is stored and retrieved
      correctly.

   *  *Unit (unit)*: This field defines the measurement units for the
      metric, such as frequency, data size, or data transfer rate.  It
      is usually associated with the metric to provide context for the
      value.

   *  *Source (source, optional)*: This field describes the origin of
      the information used to obtain the metric.  It may include one or
      more of the following non-mutually exclusive values:

      -  'nominal'.  Similar to [RFC9439], "a 'nominal' metric indicates
         that the metric value is statically configured by the
         underlying devices.  For example, bandwidth can indicate the
         maximum transmission rate of the involved device.

      -  'estimation'.  The 'estimation' source indicates that the
         metric value is computed through an estimation process.

      -  'directly measured'.  This source indicates that the metric can
         be obtained directly from the underlying device and it does not
         need to be estimated.

      -  'normalization'.  The 'normalization' source indicates that the
         metric value was normalized.  For instance, a metric could be
         normalized to take a value from 0 to 1, from 0 to 10, or to
         take a percentage value.  This type of metrics do not have
         units.

      -  'aggregation'.  This source indicates that the metric value was
         obtained by using an aggregation function.






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      Nominal metrics have inherent physical meanings and specific units
      without any additional processing.  Aggregated metrics may or may
      not have physical meanings, but they retain their significance
      relative to the directly measured metrics.  Normalized metrics, on
      the other hand, might have physical meanings but lack units.

   *  *Statistics (statistics, optional)*: This field provides
      additional details about the metrics, particularly if there is any
      pre-computation performed on the metrics before they are
      collected.  It is useful for services that require specific
      statistics for service instance selection.

      -  'max'.  The maximum value of the data collected over intervals.

      -  'min'.  The minimum value of the data collected over intervals.

      -  'mean'.  The average value of the data collected over
         intervals.

      -  'cur'.  The current value of the data collected.

   *  *Level (level)*: This field specifies the level at which the
      metric is measured.  It is used to categorize the metric based on
      its granularity and scope.  Examples include L0, L1, and L2.  The
      level field helps in understanding the level of detail and
      specificity of the metric being measured.

   *  *Value (value)*: This field represents the actual numerical value
      of the metric being measured.  It provides the specific data point
      for the metric in question.

4.2.  Aggregation and Normalization Functions

   In the context of CATS metric processing, aggregation and
   normalization are two fundamental operations that transform raw and
   derived metrics into forms suitable for decision-making and
   comparison across heterogeneous systems.

4.2.1.  Aggregation

   Aggregation functions combine multiple metric values into a single
   representative value.  This is particularly useful when metrics are
   collected from multiple sources or over time intervals.  For example,
   CPU usage metrics from multiple service instances may be aggregated
   to produce a single load indicator for a service.  Common aggregation
   functions include:

   *  Mean average: Computes the arithmetic average of a set of values.



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   *  Minimum/maximum: Selects the lowest or highest value from a set.

   *  Weighted average: Applies weights to values based on relevance or
      priority.

   The output of an aggregation function is typically a Level 2 metric,
   derived from multiple Level 0 metrics, or a level 2 metric, derived
   from multiple Level 0 or 1 metrics.

         +------------+     +-------------------+
         | Metric 1.1 |---->|                   |
         +------------+     |    Aggregation    |     +----------+
              ...           |     Function      |---->| Metric 2 |
         +------------+     |                   |     +----------+
         | Metric 1.n |---->|                   |
         +------------+     +-------------------+

         Input: Multiple values                   Output: Single value

                       Figure 3: Aggregation function

4.2.2.  Normalization

   Normalization functions convert metric values with or without units
   into unitless scores, enabling comparison across different types of
   metrics and systems.  This is essential when combining metrics from a
   heterogeneous set of resources (e.g, latency measured in milliseconds
   with CPU usage measured in percentage) into a unified decision model.

   Normalization functions often map values into a bounded range, such
   as integers fron 0, to 5, or real numbers from 0 to 1, using
   techniques like:

   *  Sigmoid function: Smoothly maps input values to a bounded range.

   *  Min-max scaling: Rescales values based on known minimum and
      maximum bounds.

   *  Z-score normalization: Standardizes values based on statistical
      distribution.

   Normalized metrics facilitate composite scoring and ranking, and can
   be used to produce Level 1 and Level 2 metrics.








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      +----------+     +------------------------+     +----------+
      | Metric 1 |---->| Normalization Function |---->| Metric 2 |
      +----------+     +------------------------+     +----------+

      Input:  Value with or without units         Output: Unitless value

                   Figure 4: Normalization function

4.3.  On the Meaning of Scores in Heterogeneous Metrics Systems

   In a system like CATS, where metrics originate from heterogeneous
   resources---such as compute, communication, and storage---the
   interpretation of scores requires careful consideration.  While
   normalization functions can convert raw metrics into unitless scores
   to enable comparison, these scores may not be directly comparable
   across different implementations.  For example, a score of 4 on a
   scale from 1 to 5 may represent a high-quality resource in one
   implementation, but only an average one in another.

   This ambiguity arises because different implementations may apply
   distinct normalization strategies, scaling methods, or semantic
   interpretations.  As a result, relying solely on unitless scores for
   decision-making can lead to inconsistent or suboptimal outcomes,
   especially when metrics are aggregated from multiple sources.

   To mitigate this, implementors of CATS metrics SHOULD provide clear
   and precise definitions of their metrics---particularly for unitless
   scores---and explain how these scores should be interpreted.  This
   documentation should be designed to support operators in making
   informed decisions, even when comparing metrics from different
   implementations.

   Similarly, operators SHOULD exercise caution when making potentially
   impactful decisions based on unitless metrics whose definitions are
   unclear or underspecified.  In such cases, especially when decisions
   are critical or sensitive, operators MAY choose to rely on Level 0
   (L0) metrics with units, which typically offer a more direct and
   unambiguous understanding of resource conditions.

4.4.  Level Metric Representations











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4.4.1.  Level 0 Metrics

   Several definitions have been developed within the compute and
   communication industries, as well as through various standardization
   efforts---such as those by the [DMTF]---that can serve as L0 metrics.
   L0 metrics contain all raw metrics which are not considered to be
   standardized in this document, considering about their diversity and
   many other existing work.

   See Appendix A for examples of L0 metrics.

4.4.2.  Level 1 Metrics

   L1 metrics are normalized from L0 metrics.  Although they don't have
   units, they can still be classified into types such as compute,
   communication, service and composed metrics.  This classification is
   useful because it makes L1 metrics semantically meaningful.

   The sources of L1 metrics is normalization.  Based on L0 metrics,
   service providers design their own algorithms to normalize metrics.
   For example, assigning different cost values to each raw metric and
   do weighted summation.  L1 metrics do not need further statistical
   values.

4.4.2.1.  Normalized Compute Metrics

   The metric type of normalized compute metrics is "compute_norm", and
   its format is unsigned integer.  It has no unit.  It will occupy an
   octet.  Example:

   Basic fields:
         Metric type: compute_norm
         Level: L1
         Format: unsigned integer
         Length: one octet
         Value: 5
   Source:
         normalization


   |Metric Type|Level|Format|Length|Value|Source|
       8bits    2bits  1bit   3bits 8bits  3bits

            Figure 5: Example of a normalized L1 compute metric







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4.4.2.2.  Normalized Communication Metrics

   The metric type of normalized communication metrics is
   "communication_norm", and its format is unsigned integer.  It has no
   unit.  It will occupy an octet.  Example:

   Basic fields:
         Metric type: communication_norm
         Level: L1
         Format: unsigned integer
         Length: one octet
         Value: 1
   Source:
         normalization

   |Metric Type|Level|Format|Length|Value|Source|
       8bits    2bits  1bit   3bits 8bits  3bits

         Figure 6: Example of a normalized L1 communication metric

4.4.2.3.  Normalized Composed Metrics

   The metric type of normalized composed metrics is "delay_norm", and
   its format is unsigned integer.  It has no unit.  It will occupy an
   octet.  Example:

   Basic fields:
         Metric type: composed_norm
         Level: L1
         Format: unsigned integer
         Length: an octet
         Value: 8
   Source:
         normalization

   |Metric Type|Level|Format|Length|Value|Source|
       8bits    2bits  1bit   3bits 8bits  3bits

            Figure 7: Example of a normalized L1 composed metric

4.4.3.  Level 2 Metrics

   A Level 2 metric is a single-value, normalized metric that does not
   carry any inherent physical unit or meaning.  While each provider may
   employ its own internal methods to compute this value, all providers
   must adhere to the representation guidelines defined in this section
   to ensure consistency and interoperability of the normalized output.




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   Metric type is "norm_fi".  The format of the value is unsigned
   integer.  It has no unit.  It will occupy an octet.  Example:

   Basic fields:
         Metric type: norm_fi
         Level: L2
         Format: unsigned integer
         Length: an octet
         Value: 1
   Source:
         normalization

   |Metric Type|Level|Format|Length|Value|Source|
       8bits    2bits  1bit   3bits 8bits  3bits

                Figure 8: Example of a normalized L2 metric

   The single normalized value also facilitates aggregation across
   multiple service instances.  When each instance provides its own
   normalized value, no additional statistical processing is required at
   the instance level.  Instead, aggregation can be performed externally
   using standardized methods, enabling scalable and consistent
   interpretation of metrics across distributed environments.

5.  Comparison among Metric Levels

   Metrics are progressively consolidated from L0 to L1 to L2, with each
   level offering a different degree of abstraction to address the
   diverse requirements of various services.  Table 1 provides a
   comparative overview of these metric levels.

       +=======+============+===============+===========+==========+
       | Level | Encoding   | Extensibility | Stability | Accuracy |
       |       | Complexity |               |           |          |
       +=======+============+===============+===========+==========+
       | Level | High       | Low           | Low       | High     |
       |   0   |            |               |           |          |
       +-------+------------+---------------+-----------+----------+
       | Level | Medium     | Medium        | Medium    | Medium   |
       |   1   |            |               |           |          |
       +-------+------------+---------------+-----------+----------+
       | Level | Low        | High          | High      | Medium   |
       |   2   |            |               |           |          |
       +-------+------------+---------------+-----------+----------+

                  Table 1: Comparison among Metrics Levels





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   Since Level 0 metrics are raw and service-specific, different
   services may define their own sets---potentially resulting in
   hundreds or even thousands of unique metrics.  This diversity
   introduces significant complexity in protocol encoding and
   standardization.  Consequently, L0 metrics are generally confined to
   bespoke implementations tailored to specific service needs, rather
   than being standardized for broad protocol use.  In contrast, Level 1
   metrics organize raw data into standardized categories, each
   normalized into a single value.  This structure makes them more
   suitable for protocol encoding and standardization.  Level 2 metrics
   take simplification a step further by consolidating all relevant
   information into a single normalized value, making them the easiest
   to encode, transmit, and standardize.

   Therefore, from the perspective of encoding complexity, Level 1 and
   Level 2 metrics are recommended.

   When considering extensibility, Level 0 metrics allow new services to
   define their own custom metrics.  However, this flexibility requires
   corresponding protocol extensions, and the proliferation of metric
   types can introduce significant overhead, ultimately reducing the
   protocol's extensibility.  In contrast, Level 1 metrics introduce
   only a limited set of standardized categories, making protocol
   extensions more manageable.  Level 2 metrics go even further by
   consolidating all information into a single normalized value, placing
   the least burden on the protocol.

   Therefore, from an extensibility standpoint, Level 1 and Level 2
   metrics are recommended.

   Regarding stability, Level 0 raw metrics may require frequent
   protocol extensions as new metrics are introduced, leading to an
   unstable and evolving protocol format.  For this reason,
   standardizing L0 metrics within the protocol is not recommended.  In
   contrast, Level 1 metrics involve only a limited set of predefined
   categories, and Level 2 metrics rely on a single consolidated value,
   both of which contribute to a more stable and maintainable protocol
   design.

   Therefore, from a stability standpoint, Level 1 and Level 2 metrics
   are preferred.










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   In conclusion, for CATS, Level 2 metrics are recommended due to their
   simplicity and minimal protocol overhead.  If more advanced
   scheduling capabilities are required, Level 1 metrics offer a
   balanced approach with manageable complexity.  While Level 0 metrics
   are the most detailed and dynamic, their high overhead makes them
   unsuitable for direct transmission to network devices and thus not
   recommended for standard protocol integration.

6.  CATS L2 Metric Registry Entry

   This section gives an initial Registry Entry for the CATS L2 metric.

6.1.  Summary

   This category includes multiple indexes to the Registry Entry: the
   element ID, Metric Name, URI, Metric Description, Metric Controller,
   and Metric Version.

6.1.1.  ID (Identifier)

   IANA has allocated the Identifier 1 for the Named Metric Entry in
   Section 5.  See Section 5.1.2 for mapping to Names.

6.1.2.  Name

   Norm_Passive_CATS-L2_RFCXXXXsecY_Unitless_Singleton

   Naming Rule Explanation

   *  Norm: Metric type (Normalized Score)

   *  Passive: Measurement method

   *  CATS-L2: Metric level (CATS Metric Framework Level 2)

   *  RFCXXXXsecY: Specification reference (To-be-assigned RFC number
      and section number)

   *  Unitless: Metric has not units

   *  Singleton: Metric is a single value

6.1.3.  URI

   To-be-assigned.






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6.1.4.  Description

   This metric represents a single normalized score used within CATS.
   It is derived by aggregating one or more CATS L0 and/or L1 metrics,
   followed by a normalization process that produces a unitless value.
   The resulting score provides a concise assessment of the overall
   capability of a service instance, enabling rapid comparison across
   instances and supporting efficient traffic steering decisions.

6.1.5.  Change Controller

   IETF

6.1.6.  Version

   1.0

6.2.  Metric Definition

6.2.1.  Reference Definition

   [I-D.ietf-cats-metric-definition] Core referenced sections:
   Section 3.4 (L2 Level Metric Definition), Section 4.2 (Aggregation
   and Normalization Functions)

6.2.2.  Fixed Parameters

   *  Normalization score range: 0-10 (0 indicates the poorest
      capability, 10 indicates the optimal capability)

   *  Data precision: decimal number (unsigned integer)

6.3.  Method of Measurement

   This category includes columns for references to relevant sections of
   the RFC(s) and any supplemental information needed to ensure an
   unambiguous method for implementations.

6.3.1.  Reference Methods

   Raw Metrics collection: Collect L0 service and compute raw metrics
   using platform-specific management protocols or tools (e.g.,
   Prometheus [Prometheus] in Kubernetes).  Collect L0 network
   performance raw metrics using existing standardized protocols (e.g.,
   NETCONF [RFC6241], IPFIX [RFC7011]).

   Aggregation logic: Refer to [I-D.ietf-cats-metric-definition]
   Section 4.2.1 (e.g., Weighted Average Aggregation).



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   Normalization logic: Refer to [I-D.ietf-cats-metric-definition]
   Section 4.2.2 (e.g., Sigmoid Normalization).

   The reference method aggregates and normalizes L0 metrics to generate
   L1 metrics in different categories, and further calculates a L2
   singleton score for full normalization.

6.3.2.  Packet Stream Generation

   N/A

6.3.3.  Traffic Filtering (Observation) Details

   N/A

6.3.4.  Sampling Distribution

   Sampling method: Continuous sampling (e.g., collect L0 metrics every
   10 seconds)

6.3.5.  Runtime Parameters and Data Format

   CATS Service Contact Instance ID (CSCI-ID): an identifier of CATS
   service contact instance.  According to [I-D.ietf-cats-framework], a
   unicast IP address can be an example of identifier. (format: ipv4-
   address-no-zone or ipv6-address-no-zone, complying with [RFC6991])

   Service_Instance_IP: Service instance IP address (format: ipv4-
   address-no-zone or ipv6-address-no-zone, complying with [RFC6991])

   Measurement_Window: Metric measurement time window (Units: seconds,
   milliseconds; Format: uint64; Default: 10 seconds)

6.3.6.  Roles

   C-SMA: Collects L0 service and compute raw metrics, and optionally
   calculates L1 and L2 metrics according to service-specific
   strategies.

   C-NMA: Collects L0 network performance raw metrics, and optionally
   calculates L1 and L2 metrics according to service-specific
   strategies.

6.4.  Output

   This category specifies all details of the output of measurements
   using the metric.




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6.4.1.  Type

   Singleton value

6.4.2.  Reference Definition

   Output format: Refer to [I-D.ietf-cats-metric-definition]
   Section 4.4.3

   Score semantics: 0-3 (Low capability, not recommended for steering),
   4-7 (Medium capability, optional for steering), 8-10 (High
   capability, priority for steering)

6.4.3.  Metric Units

   Unitless

6.4.4.  Calibration

   Calibration method: Conduct benchmark calibration based on standard
   test sets (fixed workload) to ensure the output score deviation of
   C-SMA and C-NMA is lower than 0.1 (one abnormal score in every ten
   test rounds).

6.5.  Administrative Items

6.5.1.  Status

   Current

6.5.2.  Requester

   To-be-assgined

6.5.3.  Revision

   1.0

6.5.4.  Revision Date

   2026-01-20

6.5.5.  Comments and Remarks

   None






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7.  Implementation Guidance on Using CATS Metrics

   <Authors’ Note: This section has been moved to
   [I-D.ietf-cats-framework] at the suggestion of the chairs, since this
   document focuses primarily on metric definitions rather than
   implementation details.>

8.  Security Considerations

   TBD

9.  IANA Considerations

   TBD

10.  References

10.1.  Normative References

   [I-D.ietf-cats-framework]
              Li, C., Du, Z., Boucadair, M., Contreras, L. M., and J.
              Drake, "A Framework for Computing-Aware Traffic Steering
              (CATS)", Work in Progress, Internet-Draft, draft-ietf-
              cats-framework-19, 20 November 2025,
              <https://datatracker.ietf.org/doc/html/draft-ietf-cats-
              framework-19>.

   [I-D.ietf-cats-metric-definition]
              Yao, K., Li, C., Contreras, L. M., Ros-Giralt, J., and H.
              Shi, "CATS Metrics Definition", Work in Progress,
              Internet-Draft, draft-ietf-cats-metric-definition-04, 20
              October 2025, <https://datatracker.ietf.org/doc/html/
              draft-ietf-cats-metric-definition-04>.

   [RFC6241]  Enns, R., Ed., Bjorklund, M., Ed., Schoenwaelder, J., Ed.,
              and A. Bierman, Ed., "Network Configuration Protocol
              (NETCONF)", RFC 6241, DOI 10.17487/RFC6241, June 2011,
              <https://www.rfc-editor.org/rfc/rfc6241>.

   [RFC6991]  Schoenwaelder, J., Ed., "Common YANG Data Types",
              RFC 6991, DOI 10.17487/RFC6991, July 2013,
              <https://www.rfc-editor.org/rfc/rfc6991>.

   [RFC7011]  Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
              "Specification of the IP Flow Information Export (IPFIX)
              Protocol for the Exchange of Flow Information", STD 77,
              RFC 7011, DOI 10.17487/RFC7011, September 2013,
              <https://www.rfc-editor.org/rfc/rfc7011>.



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   [RFC8911]  Bagnulo, M., Claise, B., Eardley, P., Morton, A., and A.
              Akhter, "Registry for Performance Metrics", RFC 8911,
              DOI 10.17487/RFC8911, November 2021,
              <https://www.rfc-editor.org/rfc/rfc8911>.

   [RFC8912]  Morton, A., Bagnulo, M., Eardley, P., and K. D'Souza,
              "Initial Performance Metrics Registry Entries", RFC 8912,
              DOI 10.17487/RFC8912, November 2021,
              <https://www.rfc-editor.org/rfc/rfc8912>.

   [RFC9439]  Wu, Q., Yang, Y., Lee, Y., Dhody, D., Randriamasy, S., and
              L. Contreras, "Application-Layer Traffic Optimization
              (ALTO) Performance Cost Metrics", RFC 9439,
              DOI 10.17487/RFC9439, August 2023,
              <https://www.rfc-editor.org/rfc/rfc9439>.

10.2.  Informative References

   [DMTF]     "DMTF", n.d., <https://www.dmtf.org/>.

   [I-D.ietf-cats-usecases-requirements]
              Yao, K., Contreras, L. M., Shi, H., Zhang, S., and Q. An,
              "Computing-Aware Traffic Steering (CATS) Problem
              Statement, Use Cases, and Requirements", Work in Progress,
              Internet-Draft, draft-ietf-cats-usecases-requirements-13,
              28 January 2026, <https://datatracker.ietf.org/doc/html/
              draft-ietf-cats-usecases-requirements-13>.

   [I-D.rcr-opsawg-operational-compute-metrics]
              Randriamasy, S., Contreras, L. M., Ros-Giralt, J., and R.
              Schott, "Joint Exposure of Network and Compute Information
              for Infrastructure-Aware Service Deployment", Work in
              Progress, Internet-Draft, draft-rcr-opsawg-operational-
              compute-metrics-08, 21 October 2024,
              <https://datatracker.ietf.org/doc/html/draft-rcr-opsawg-
              operational-compute-metrics-08>.

   [performance-metrics]
              "performance-metrics", n.d.,
              <https://www.iana.org/assignments/performance-metrics/
              performance-metrics.xhtml>.

   [Prometheus]
              "Prometheus", n.d., <https://prometheus.io/>.







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Appendix A.  Appendix A

A.1.  Level 0 Metric Representation Examples

   Several definitions have been developed within the compute and
   communication industries, as well as through various standardization
   efforts---such as those by the [DMTF]---that can serve as L0 metrics.
   This section provides illustrative examples.

A.1.1.  Compute Raw Metrics

   This section uses CPU frequency as an example to illustrate the
   representation of raw compute metrics.  The metric type is labeled as
   compute_CPU_frequency, with the unit specified in GHz.  The format
   should support both unsigned integers and floating-point values.  The
   corresponding metric fields are defined as follows:

   Basic fields:
         Metric Type: compute_CPU_frequency
         Level: L0
         Format: unsigned integer, floating point
         Unit: GHz
         Length: four octets
         Value: 2.2
   Source:
         nominal

   |Metric Type|Level|Format| Unit|Length| Value|Source|
       8bits    2bits  1bit  4bits  3bits 32bits  3bits

                Figure 9: An Example for Compute Raw Metrics

A.1.2.  Communication Raw Metrics

   This section takes the total transmitted bytes (TxBytes) as an
   example to show the representation of communication raw metrics.
   TxBytes are named as "communication type_TxBytes".  The unit is Mega
   Bytes (MB).  Format is unsigned integer or floating point.  It will
   occupy 4 octets.  The source of the metric is "Directly measured" and
   the statistics is "mean".  Example:











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   Basic fields:
         Metric type: "communication type_TXBytes"
         Level: L0
         Format: unsigned integer, floating point
         Unit: MB
         Length: four octets
         Value: 100
   Source:
         Directly measured
   Statistics:
         mean

   |Metric Type|Level|Format| Unit|Length| Value|Source|Statistics|
       8bits    2bits  1bit  4bits  3bits 32bits  3bits   2bits

            Figure 10: An Example for Communication Raw Metrics

A.1.3.  Delay Raw Metrics

   Delay is a kind of synthesized metric which is influenced by
   computing, storage access, and network transmission.  Usually delay
   refers to the overal processing duration between the arrival time of
   a specific service request and the departure time of the
   corresponding service response.  It is named as "delay_raw".  The
   format should support both unsigned integer or floating point.  Its
   unit is microseconds, and it occupies 4 octets.  For example:

   Basic fields:
         Metric type: "delay_raw"
         Level: L0
         Format: unsigned integer, floating point
         Unit: Microsecond(us)
         Length: four octets
         Value: 231.5
   Source:
         aggregation
   Statistics:
         max

   |Metric Type|Level|Format| Unit|Length| Value|Source|Statistics|
       8bits    2bits  1bit  4bits  3bits 32bits  3bits   2bits

                Figure 11: An Example for Delay Raw Metrics

Contributors

   Mohamed Boucadair
   Orange



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   Email: mohamed.boucadair@orange.com


   Zongpeng Du
   China Mobile
   Email: duzongpeng@chinamobile.com


   Hang Shi
   Huawei
   Email: shihang9@huawei.com


Authors' Addresses

   Kehan Yao
   China Mobile
   China
   Email: yaokehan@chinamobile.com


   Cheng Li
   Huawei Technologies
   China
   Email: c.l@huawei.com


   L. M. Contreras
   Telefonica
   Email: luismiguel.contrerasmurillo@telefonica.com


   Jordi Ros-Giralt
   Qualcomm Europe, Inc.
   Email: jros@qti.qualcomm.com


   Guanming Zeng
   Huawei Technologies
   China
   Email: zengguanming@huawei.com










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