



Computing-Aware Traffic Steering                                  M. Zhu
Internet-Draft                                              China mobile
Intended status: Informational                             24 April 2026
Expires: 26 October 2026


           Operational Semantics for CATS Metric Consumption
                   draft-zhu-cats-metric-semantics-00

Abstract

   The CATS framework introduces computing-related information into
   traffic steering decisions.  Existing work defines how such metrics
   are represented, distributed, and used within the CATS architecture.
   However, it does not fully address whether a metric remains suitable
   for use at the point of consumption.

   This document introduces a set of operational semantics for CATS
   metrics, including Freshness, Operational acceptability, and
   Assurance exposure.  These semantics describe whether a metric
   remains temporally aligned with the underlying condition, whether it
   remains suitable for operational use in steering, and whether
   degraded consumption is externally visible to management or OAM
   functions.

   The document further explains how these semantics apply across
   centralized, distributed, and hybrid deployments, including cases
   where different metric sources contribute under different conditions.
   The goal is to provide a consistent basis for interpreting metric
   usability in CATS without introducing a new metric level or
   prescribing a single derivation method.

Status of This Memo

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   This Internet-Draft will expire on 26 October 2026.



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Copyright Notice

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Scope and positioning . . . . . . . . . . . . . . . . . . . .   4
   3.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   4
   4.  Operational gap . . . . . . . . . . . . . . . . . . . . . . .   5
   5.  Operational semantics in different deployment modes . . . . .   5
     5.1.  Freshness . . . . . . . . . . . . . . . . . . . . . . . .   6
     5.2.  Operational acceptability . . . . . . . . . . . . . . . .   6
     5.3.  Assurance exposure  . . . . . . . . . . . . . . . . . . .   6
     5.4.  Deployment-specific considerations  . . . . . . . . . . .   7
       5.4.1.  Centralized deployments . . . . . . . . . . . . . . .   7
       5.4.2.  Distributed deployments . . . . . . . . . . . . . . .   8
       5.4.3.  Hybrid deployments  . . . . . . . . . . . . . . . . .   8
   6.  Operational implications  . . . . . . . . . . . . . . . . . .   9
     6.1.  Relationship to service continuity  . . . . . . . . . . .   9
     6.2.  Control, management, and OAM relevance  . . . . . . . . .   9
     6.3.  Lightweight signaling considerations  . . . . . . . . . .  10
   7.  Illustrative example  . . . . . . . . . . . . . . . . . . . .  10
   8.  Security Considerations . . . . . . . . . . . . . . . . . . .  11
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  12
   10. Informative References  . . . . . . . . . . . . . . . . . . .  12
   Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . .  12
   Illustrative Multi-Factor Derivation Model  . . . . . . . . . . .  12
   Author's Address  . . . . . . . . . . . . . . . . . . . . . . . .  15

1.  Introduction

   Computing-Aware Traffic Steering (CATS) extends traffic steering
   beyond traditional network reachability and path selection by
   incorporating computing-related inputs into forwarding and service-
   selection decisions.  This change is not merely an incremental
   extension of traditional routing inputs.  Many computing-related
   metrics vary more quickly, are aggregated and distributed through



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   more diverse paths, and lose operational meaning more rapidly.  As a
   result, the difficulty in CATS is not only how to define more
   metrics, but also how to determine whether a received metric remains
   suitable for operational consumption as a steering input.

   Existing CATS work explains how metrics are represented, distributed,
   and used [CATS-FRAMEWORK] [CATS-METRIC-DEFINITION].  Related
   requirements and OAM work identify update, stability, service-
   continuity, consistency, and black-holing concerns
   [CATS-REQUIREMENTS] [CATS-OAM].  However, such metrics cannot always
   be directly consumed by existing steering or routing protocols.  Many
   computing-related metrics evolve at timescales that are shorter than
   those assumed by traditional control-plane mechanisms.  Excessively
   frequent metric updates may introduce instability or oscillation into
   the steering process.  Infrequent updates, by contrast, may cause
   decisions to rely on stale conditions that no longer reflect the
   current operational state.

   This document addresses a related issue at the point of consumption:
   whether a metric remains operationally suitable when it is consumed
   for steering.  A metric may remain visible and well-formed while no
   longer remaining suitable for normal steering use.  This problem is
   more likely to arise when computing-related information changes
   quickly, is collected and redistributed before use, or is consumed
   under different deployment conditions.

   In conventional routing, slightly outdated cost information often
   leads only to a suboptimal path.  In CATS, a decision may rely on
   utilization, admission headroom, or service-state information that no
   longer reflects the current operational condition.  In centralized
   deployments, this may result from control-loop delay.  In distributed
   deployments, it may result from divergence across local observations.
   In hybrid deployments, it may result from the joint use of inputs
   that do not share the same temporal behavior or operational
   conditions.  The result may be admission rejection, degraded service
   continuity, or steering behavior resembling black-holing.

   This document defines an orthogonal set of operational semantics that
   can be associated with any CATS metric, regardless of abstraction
   level.  These semantics are intended to express whether a metric
   remains sufficiently fresh, whether it remains operationally
   acceptable for steering use, and whether degraded consumption becomes
   externally visible to OAM or management functions.








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2.  Scope and positioning

   The semantics are intended to complement the metric abstraction
   model.  Metric abstraction explains how raw measurements are
   normalized or combined into higher-level indicators.  This document
   addresses a different dimension: the operational condition of a
   metric at the point of consumption.  More specifically, it defines
   three orthogonal semantics, i.e., Freshness, Operational
   acceptability, and Assurance exposure, to describe whether a metric
   remains temporally suitable for use, whether it remains acceptable
   for operational consumption in steering, and whether degraded
   consumption or fallback become externally visible.

   These semantics are not tied to any single deployment model.  They
   can be applied across existing abstraction levels and across
   centralized, distributed, and hybrid operation.  This document also
   does not define a new transport, encoding, or control-plane protocol.
   Instead, it defines semantic information that may later be carried,
   derived, or exposed by future protocol elements, data models,
   management objects, or OAM procedures.  A steering consumer may use
   these semantics to determine whether a metric can still participate
   in normal steering logic.  A control, management, or OAM function may
   use them to distinguish normal consumption from degraded consumption,
   fallback behavior, or source-specific semantic degradation.

3.  Terminology

   Metric-consuming decision point: A functional point at which CATS
   metrics are consumed to derive or support steering, path-selection,
   or service-selection decisions.  Depending on the deployment model,
   this function may be realized by a centralized CATS Path Selector
   (C-PS), by an Ingress CATS-Forwarder with embedded decision logic, or
   by a combination of both in hybrid deployments.

   Freshness: The extent to which a metric remains temporally suitable
   for its intended operational use.

   Operational acceptability: The extent to which a metric remains
   suitable for operational consumption at the current time.

   Assurance exposure: The extent to which degraded metric consumption,
   inconsistency, or fallback behavior is visible to OAM or management
   systems.








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4.  Operational gap

   The gap addressed in this document is the lack of an explicit
   description of metric usability at the point of consumption.  A
   metric may remain visible and well-formed while no longer remaining
   suitable for normal steering use.

   This missing layer appears in three ways.  First, a metric may lose
   temporal alignment with the condition it is intended to describe
   while still remaining available to the consumer.  For example, a
   controller-based deployment may continue to distribute a site-level
   utilization metric whose indicated admission headroom no longer
   reflects the current service state.

   Second, a metric may remain present and syntactically valid while no
   longer remaining suitable for normal operational consumption in
   steering.  For example, repeated delay, poor update continuity, or
   inconsistency with other observations may make a metric unsuitable
   for fine-grained steering even though it is still retained for
   reduced-trust or fallback use.

   Third, degraded metric consumption may remain invisible to management
   or OAM even after steering shifted into fallback or reduced-trust
   behavior.  In such a case, the problem is not only metric degradation
   itself, but also the lack of external visibility into the semantic
   condition under which steering is proceeding.

   These gaps are amplified by deployment conditions.  In centralized
   operation, semantic degradation may be introduced within the control
   loop before the metric is used.  In distributed operation, different
   decision points may rely on different local versions of what is
   nominally the same condition.  In hybrid operation, the problem is
   further complicated by the joint use of metric inputs that do not
   share the same temporal behavior or consumption assumptions.

5.  Operational semantics in different deployment modes

   This document introduces three operational semantics for CATS
   metrics: Freshness, Operational acceptability, and Assurance
   Exposure.  They describe the operational condition of a metric at the
   point of consumption.  These semantics can support consistent
   steering, path-selection, and service-selection decisions across
   centralized, distributed, and hybrid deployments.








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   A derivation method for these semantics may depend on observable
   factors such as metric age, update continuity, source consistency,
   and deployment-specific trust conditions.  These factors may be
   combined differently depending on the dynamics of the metric and the
   operational objectives of the deployment.  Appendix A provides one
   illustrative realization of such logic.

5.1.  Freshness

   Freshness captures whether a metric remains temporally aligned with
   the condition it represents, particularly when update frequency does
   not match the dynamics of the underlying system.  In many
   deployments, Freshness depends at least in part on the elapsed age of
   the metric relative to the time sensitivity of the condition it
   represents.  A metric that is only a few seconds old may remain
   operationally usable for relatively stable capability information,
   while the same age may be excessive for rapidly varying utilization
   or admission-related state.  Freshness therefore concerns whether the
   temporal separation between metric generation and metric consumption
   remains consistent with the operational purpose for which the metric
   is used.

5.2.  Operational acceptability

   Operational acceptability captures whether the metric remains
   suitable for operational consumption in steering at the current time.
   A metric may remain visible, syntactically valid, and even partially
   informative while no longer remaining appropriate for normal fine-
   grained steering use.  For clarity, this document uses a lightweight
   three-state interpretation: acceptable, degraded, and unacceptable.
   More detailed state distinctions are possible, but they are outside
   the scope of this document.  An acceptable metric remains suitable
   for normal steering input under the assumptions of the deployment.  A
   degraded metric no longer supports normal steering use, but may still
   be retained for fallback or reduced-trust behavior.  An unacceptable
   metric is not suitable for steering input.  A deployment may derive
   these states from one or more factors, including metric age, update
   continuity, source consistency, or other deployment-specific
   conditions.

5.3.  Assurance exposure

   Assurance exposure captures whether degraded usage, inconsistency, or
   fallback behavior is externally visible to management or OAM.  A
   system may continue to forward traffic and may continue to retain
   metric values internally while no longer operating under the semantic
   conditions that would justify normal steering.  Assurance exposure
   therefore concerns whether degraded consumption, semantic divergence,



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   or fallback-driven behavior can be distinguished from normal
   operation by external functions for diagnosis, monitoring, or
   operational control.

5.4.  Deployment-specific considerations

   The effect of these semantics depends on where metrics are consumed
   for decisions and how metric-related information is exchanged among
   CATS functional entities.  In centralized deployments, decisions are
   made primarily in a centralized CATS Path Selector (C-PS) or
   equivalent control-side function.  In distributed deployments,
   decisions are made at, or near, an Ingress CATS-Forwarder.  In hybrid
   deployments, decision logic is split across centralized and ingress-
   side functions.

   In this document, communication among network elements refers mainly
   to the exchange of metric information or decision-related
   information, such as metric reporting from computing or service nodes
   to a decision function, or decision distribution from a C-PS to an
   Ingress CATS-Forwarder.  These exchanges are distinct from data-plane
   traffic, where user traffic is forwarded toward a selected service
   instance.  Degraded semantic conditions may also need to be exposed
   through management or OAM functions.

5.4.1.  Centralized deployments

   In centralized deployments, metrics typically reach the decision
   point only after collection, transport, processing, and possible
   aggregation.  Metric information may be reported from computing or
   service nodes, possibly through metric agents, to a centralized C-PS
   or equivalent control-side function.  The resulting decision-related
   information may then be provided to Ingress CATS-Forwarders for
   steering execution.  As a result, a metric may no longer accurately
   reflect the condition on which the centralized decision is intended
   to rely by the time it reaches the decision function.

   In this setting, freshness helps determine whether the metric remains
   temporally aligned with the underlying operational condition.
   Operational acceptability helps determine whether the metric can
   still be used as normal input to centralized steering logic, or
   whether it should instead be treated as degraded or reduced-trust
   input.  Assurance exposure helps determine whether such degradation
   in metric consumption is externally visible, even when the
   centralized system continues to steer traffic and continues to
   receive metrics from the underlying sources.






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5.4.2.  Distributed deployments

   In distributed deployments, metrics are consumed at, or close to, the
   ingress-side decision point.  Metric information may be distributed
   directly to an Ingress CATS-Forwarder or to a co-located decision
   function, and the resulting steering decision may be applied locally.
   The main issue is that different local decision points may consume
   different observations, update histories, or local versions of what
   is operationally treated as the same condition.

   A locally available metric may remain fresh from the perspective of
   one ingress decision point, while another ingress decision point has
   shifted to a different view of the same service or resource
   condition.  In this setting, freshness helps determine whether the
   locally available metric remains temporally suitable.  Operational
   acceptability helps determine whether that local metric can still
   support normal steering at that decision point, or whether it should
   instead be treated as degraded or reduced-trust input.  Assurance
   exposure helps determine whether divergence across local decision
   points is externally visible, rather than remaining only an internal
   difference among distributed observations.

5.4.3.  Hybrid deployments

   In hybrid deployments, metric-consuming decisions are split across
   centralized and ingress-side functions, and different metric sources
   may be consumed at different layers of the same steering process.
   Some metric information may be collected and interpreted by a
   centralized C-PS, while other metric information may be consumed
   directly by an Ingress CATS-Forwarder or local decision function.
   The main issue is that jointly consumed inputs may not share the same
   temporal behavior, trust conditions, or operational scope.  A
   relatively stable local metric may remain suitable for normal
   steering use, while a centrally distributed dynamic metric may be
   suitable only for degraded or reduced-trust use.

   In this setting, freshness helps distinguish inputs whose temporal
   validity differs across sources.  Operational acceptability helps
   distinguish source-specific degradation, so that one input may remain
   acceptable while another is retained only for degraded use.
   Assurance exposure helps determine whether such partial semantic
   degradation is externally visible.









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   For this reason, a hybrid deployment should be able to distinguish
   metrics that arrive from different sources and that do not share the
   same consumption conditions.  It should also support source-specific
   degradation, so that one degraded input does not force all other
   inputs into the same state, and one acceptable input does not hide
   degradation in another.

6.  Operational implications

6.1.  Relationship to service continuity

   In CATS, service continuity depends not only on whether traffic can
   still be forwarded, but also on whether the selected service instance
   or computing target remains suitable after the steering decision is
   made.  A steering outcome may therefore remain valid from a
   forwarding perspective while no longer remaining valid from a service
   perspective.  At the same time, the steering decision itself may
   depend on traffic- and service-related conditions whose validity is
   highly sensitive to metric freshness.  Excessively frequent metric
   updates may introduce instability or oscillation into the steering
   process.  Infrequent updates, by contrast, may cause steering
   decisions to rely on stale conditions that no longer reflect the
   current operational state.  For this reason, freshness is relevant
   not only to the suitability of the selected service target, but also
   to the continued validity of the steering decision that directs
   traffic toward it.

   Freshness helps determine whether a metric reflects the service
   condition on which continuity-related steering depends.  Operational
   acceptability helps determine whether that metric can support normal
   continuity-sensitive steering or should instead be treated as
   degraded or fallback input.  Assurance exposure helps make
   continuity-relevant degradation externally visible once the system
   shifted away from normal semantic conditions.

   These semantics do not themselves provide continuity procedures,
   migration behavior, or affinity handling.  They indicate when a
   metric should no longer be treated as a normal input to continuity-
   sensitive steering.

6.2.  Control, management, and OAM relevance

   These semantics are relevant not only at the metric-consuming
   decision point, but also to control, management, and OAM functions
   around it.






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   A control function may use these semantics to distinguish normal
   metric use from degraded or fallback use in the steering process.  A
   management function may use them to determine whether steering is
   operating under normal semantic conditions or shifted into reduced-
   confidence behavior.  An OAM function may use them to observe whether
   degraded consumption, semantic divergence, or fallback handling is
   operationally visible even though forwarding succeeds.

6.3.  Lightweight signaling considerations

   This document does not define protocol fields, but the semantics
   above are intended to be protocol-ready and lightweight.

   Freshness could be reflected by a timestamp, an age value, or a
   validity window carried with the metric or its enclosing object,
   allowing the consumer to interpret the metric under different update
   frequencies.  Operational acceptability could be represented as a
   compact three-state indication associated with the metric or with the
   result of consuming that metric.  Assurance exposure could be
   realized by exposing degraded-consumption state to management or OAM
   systems, for example by attaching state to an OAM record, a
   management object, or a troubleshooting signal.  Such signaling may
   occur on different information paths depending on deployment, such as
   metric reporting toward a decision function, decision distribution
   toward an ingress forwarder, or exposure toward management and OAM
   systems.

7.  Illustrative example

   Consider a hybrid deployment in which a consumer uses relatively
   stable site capability information learned through one path and fast-
   changing utilization information received through a centralized
   controller path.  At time T1, both inputs are current enough that the
   consumer selects Site B for dynamic steering.  At time T2, the
   capability information remains unchanged, but the utilization
   information distributed by the controller is several seconds old.  If
   the consumer continues to treat both inputs as equally current, it
   may still steer new requests toward Site B even though Site B has
   lost the headroom assumed by the old utilization value.

   The semantic decision flow can be illustrated as follows:










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             +------------------+
             |  Metric arrives  |
             +---------+--------+
                       |
                       v
             +------------------+
             | Check freshness  |
             +---------+--------+
                       |
                       v
             +-----------------------------+
             | Derive operational state    |
             | acceptable / degraded /     |
             | unacceptable                |
             +---------+-------------------+
                       |
           +-----------+-----------+
           |                       |
           v                       v
   +---------------+     +----------------------+
   | steering uses |     | fallback / reduced   |
   | normal input  |     | trust behavior       |
   +---------------+     +----------+-----------+
                                    |
                                    v
                        +-------------------------+
                        | expose condition to     |
                        | management / OAM        |
                        +-------------------------+

   Under the semantics defined here, the capability information may
   remain acceptable, while the utilization information is only
   degradedly acceptable or even unacceptable.  The consumer may
   therefore fall back to a coarser policy, and that fallback can be
   exposed to management or OAM.

8.  Security Considerations

   If an attacker can manipulate freshness-related metadata,
   acceptability state, or assurance visibility, traffic may be steered
   on the basis of information that appears valid but is not.  This can
   amplify the impact of stale or falsified compute-related inputs and
   may lead to traffic mis-steering, localized resource exhaustion, or
   service disruption.







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9.  IANA Considerations

   This document has no IANA actions.

10.  Informative References

   [CATS-FRAMEWORK]
              IETF CATS Working Group, "A Framework for Computing-Aware
              Traffic Steering (CATS)", n.d.,
              <https://datatracker.ietf.org/doc/draft-ietf-cats-
              framework/>.

   [CATS-METRIC-DEFINITION]
              IETF CATS Working Group, "Computing-Aware Traffic Steering
              (CATS) Metrics Definition", n.d.,
              <https://datatracker.ietf.org/doc/draft-ietf-cats-metric-
              definition/>.

   [CATS-OAM] IETF, "CATS OAM Framework", n.d.,
              <https://datatracker.ietf.org/doc/draft-fu-cats-oam-fw/>.

   [CATS-REQUIREMENTS]
              IETF CATS Working Group, "Use Cases and Requirements for
              Computing-Aware Traffic Steering (CATS)", n.d.,
              <https://datatracker.ietf.org/doc/draft-ietf-cats-
              usecases-requirements/>.

Acknowledgments

Illustrative Multi-Factor Derivation Model

   This appendix provides one illustrative realization of the
   acceptable, degraded, and unacceptable states described in the main
   body of this document.  It is included for illustration only.

   For illustration, let T_age denote the elapsed time since metric
   generation.  A deployment may compute T_age as the difference between
   the current time and the timestamp associated with the metric.  Let
   T_validity denote a duration within which the metric is considered
   suitable for normal steering use.  Let T_grace denote an additional
   duration during which the metric may still be retained for degraded
   or fallback use.

   In addition, let U_gap denote the elapsed time since the last
   successful metric update, or more generally a measure of update
   continuity.  This allows the example to capture not only whether a
   metric is old, but also whether the metric source is updating in a
   sufficiently continuous manner for operational use.



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   In this example, metric age provides the baseline timing model:

   *  acceptable: T_age <= T_validity

   *  degraded: T_validity < T_age <= T_validity + T_grace

   *  unacceptable: T_age > T_validity + T_grace

   Update continuity then acts as an additional modifying condition.
   One simple interpretation is that poor update continuity may trigger
   an earlier transition to degraded or unacceptable states, even when
   the nominal age-based condition alone would not yet do so.  For
   example:

   *  if U_gap > U_threshold, the metric may be treated as at least
      degraded, even if T_age <= T_validity

   *  if both T_age > T_validity + T_grace and U_gap > U_threshold, the
      metric may be treated as unacceptable

   Under this example, T_validity represents a normal-use interval,
   while T_grace represents a degraded-use interval rather than an
   extension of full validity.  The point of the example is not to
   define a universal formula, but to illustrate that a simple state
   space may still depend on more than one observable condition.

   A simplified state transition model can be represented as:
























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                    metric received
                         |
                         v
                  +-------------+
                  | acceptable  |
                  +------+------+
                         |
             age exceeds validity
             or continuity degrades
                         |
                         v
                  +-------------+
                  |  degraded   |
                  +------+------+
                         |
         age exceeds degraded-use limit
         or multiple adverse conditions hold
                         |
                         v
                  +-------------+
                  |unacceptable |
                  +-------------+

   A newer valid update may move the metric back to acceptable.

   The same example may be summarized as:

     +============================+==============+===================+
     | Condition                  | Derived      | Interpretation    |
     |                            | State        |                   |
     +============================+==============+===================+
     | T_age <= T_validity and    | acceptable   | usable for normal |
     | U_gap <= U_threshold       |              | steering          |
     +----------------------------+--------------+-------------------+
     | T_validity < T_age <=      | degraded     | usable only for   |
     | T_validity + T_grace, or   |              | reduced-trust or  |
     | U_gap > U_threshold        |              | fallback behavior |
     +----------------------------+--------------+-------------------+
     | T_age > T_validity +       | unacceptable | not suitable for  |
     | T_grace, or multiple       |              | steering input    |
     | adverse conditions persist |              |                   |
     +----------------------------+--------------+-------------------+

                                  Table 1







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   In a centralized deployment, T_age may dominate because the control
   loop of collection, processing, and redistribution can introduce
   significant delay before the metric reaches the decision point.  In
   such a case, age-based degradation may become the primary reason that
   a metric transitions from acceptable to degraded.

   In a distributed deployment, update continuity may become more
   significant because local decision points may rely on rapidly
   refreshed but independently observed inputs.  In such a case, poor
   continuity or irregular local update behavior may cause a metric to
   lose normal steering utility even if its nominal age remains small.

   In a hybrid deployment, different sources may be interpreted under
   different semantic conditions within the same decision process.  A
   relatively stable local capability-related metric may remain
   acceptable, while a centrally distributed utilization-related metric
   may only remain suitable for degraded or reduced-trust use.  This
   illustrates that state derivation may be both multi-factor and
   source-specific, rather than globally uniform across all inputs.

Author's Address

   Mengfei Zhu
   China mobile
   Email: zhumengfei@cmdi.chinamobile.com


























Zhu                      Expires 26 October 2026               [Page 15]
