Path Computation Based on Precision Availability Metrics
draft-contreras-pce-pam-07
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| Document | Type | Active Internet-Draft (individual) | |
|---|---|---|---|
| Authors | Luis M. Contreras , Fernando Agraz , Salvatore Spadaro , Quan Xiong | ||
| Last updated | 2026-07-06 | ||
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draft-contreras-pce-pam-07
PCE L. M. Contreras
Internet-Draft Telefonica
Intended status: Informational F. Agraz
Expires: 7 January 2027 S. Spadaro
Universitat Politecnica de Catalunya
Q. Xiong
ZTE Corporation
6 July 2026
Path Computation Based on Precision Availability Metrics
draft-contreras-pce-pam-07
Abstract
This document extends PCEP to support Precision Availability Metrics
(PAM) [RFC9544] for path computation. The optimization objectives
for PAM computations are encoded using the Objective Function (OF)
object defined in [RFC5541], allowing PCCs to specify precise
optimization criteria for services with SLO requirements. And a PCE
can report the statistical characterization associated with a
computed path.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on 7 January 2027.
Copyright Notice
Copyright (c) 2026 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (https://trustee.ietf.org/
license-info) in effect on the date of publication of this document.
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Please review these documents carefully, as they describe your rights
and restrictions with respect to this document. Code Components
extracted from this document must include Revised BSD License text as
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provided without warranty as described in the Revised BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. Rationale of the usage of PAM for path calculation . . . . . 3
3.1. Dynamic behavior of performance parameters . . . . . . . 3
3.2. Applicability . . . . . . . . . . . . . . . . . . . . . . 4
3.3. Usage of collected metrics . . . . . . . . . . . . . . . 4
3.4. Calculation or selection of the path . . . . . . . . . . 6
4. PAM Objective Functions . . . . . . . . . . . . . . . . . . . 7
4.1. PAM Compliance Objective Function . . . . . . . . . . . . 8
4.2. Minimum Violated Intervals Objective Function . . . . . . 9
4.3. Minimum Severe Violated Intervals Objective Function . . 10
4.4. Interaction with Path Constraints . . . . . . . . . . . . 11
5. PAM report TLV . . . . . . . . . . . . . . . . . . . . . . . 11
6. Security and operational considerations . . . . . . . . . . . 12
6.1. Security considerations . . . . . . . . . . . . . . . . . 12
6.2. Operational considerations . . . . . . . . . . . . . . . 12
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 12
7.1. New PCE Objective Function Codes . . . . . . . . . . . . 12
7.2. PCEP TLV . . . . . . . . . . . . . . . . . . . . . . . . 13
8. Informative References . . . . . . . . . . . . . . . . . . . 13
Appendix A. Path Histogram Composition . . . . . . . . . . . . . 15
A.1. Additive Metrics . . . . . . . . . . . . . . . . . . . . 15
A.2. Multiplicative Metrics . . . . . . . . . . . . . . . . . 15
A.3. Maximization / Minimization Metrics (Bottleneck
Metrics) . . . . . . . . . . . . . . . . . . . . . . . . 15
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 16
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 16
1. Introduction
Some network services, such as Network Slices [RFC9543] and
Deterministic Networking [RFC8578] [RFC8655], express their
performance requirements using Service Level Objectives (SLOs).
At the time of calculating a path by the PCE, the METRIC object
[RFC5440] serves for the purposes of indicating either the metric
that MUST be optimized by the path computation algorithm, or a bound
on the path cost that MUST NOT be exceeded for the path to be
considered as acceptable. The value of the metric refers to the
instantaneous observed behavior of that parameter, without a notion
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of behavior along the preceding time. This cannot be sufficient for
certain networking services which require to experience stable
behavior along the time according to their SLOs.
Precision Availability Metrics (PAM) [RFC9544] introduce statistical
performance attributes, including Violated Intervals (VI), Severe
Violated Intervals (SVI), Violated Interval Ratio (VIR), and Severely
Violated Interval Ratio (SVIR). These metrics describe the
historical or probabilistic behavior of a path across observation
intervals, rather than its instantaneous state.
This document extends PCEP to support PAM-based path computation. It
defines:
* PAM-specific Objective Functions (OFs), encoded using the OF
object defined in [RFC5541], to instruct the PCE on how to rank
and select paths based on statistical SLO compliance.
* PAM Report TLVs, used by the PCE to convey the statistical
characterization of the computed path to the Path Computation
Client (PCC).
2. Terminology
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
NOT","SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in
this document are to be interpreted as described in [RFC2119].
In addition, the terms defined in [RFC9544] are also used in this
document.
3. Rationale of the usage of PAM for path calculation
3.1. Dynamic behavior of performance parameters
[RFC9544] introduced the concept of intervals for measuring the
behavior of measurable performance parameters against some predefined
thresholds. Those intervals consider a given time window. Thus, it
is possible to define a Violated Interval (VI) as the time interval
during which at least one of the performance parameters presents
degradation respect to a predefined optimal level threshold.
Similarly, when the threshold is defined as critical, the degradation
of the performance parameter in a time window generates a Severe
Violated Interval (SVI).
Taking into account the VIs and SVIs it is feasible to generate
availability metrics showing some degree of historic behavior in the
form of the following ratios:
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* Violated Interval Ratio (VIR), defined as the ratio of the summed
numbers of VIs and SVIs to the total number of time unit intervals
along a predefined availability period.
* Severely Violated Interval Ratio (SVIR), defined as the ratio of
SVIs to the total number of time unit intervals along a predefined
availability period.
At the time of provisioning a networking service which requires
stable SLOs along the time, it is important to ensure that the
selected path has shown such stable behavior in the past. Despite
the fact that the past behavior is not a guarantee of future
behavior, it can be presumed that those paths with lower VIR and SVIR
will better satisfy the SLOs of the intended networking service.
Alternatively, PAM can be used by the path computation entity for
fine-grained path computation. Then PAM are useful criteria for
calculating and selecting paths.
3.2. Applicability
Three situations of applicability of precision metrics can be
identified:
* The provision of a path according to the desired behavior along
the time. In this scenario different segments of a potential path
could be monitored before the path is created. The path
calculation can take into consideration the measured
characteristics of the segments forming that path for decision.
* The selection of a path according to its long-run characteristics.
In this scenario, an existing path being monitored along the time
can be selected if its behavior is compliant with the long-run
behavior expected by the customer.
* The triggering of corrective actions for a selected path. It
could be the case that a selected path suffers degradation. The
precision metrics can assist on the identification of such
potential problems, e.g, raising incidents or anomalies to
operational groups, as described in
[I-D.ietf-nmop-network-incident-yang].
3.3. Usage of collected metrics
The Traffic Engineering Database (TED) defined in [RFC4655] could be
considered as the component providing the precision metrics of
interest.
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The TED stores information related to the network topology, including
nodes, links, link attributes (e.g., bandwidth, delay), and any
constraints relevant for traffic engineering. It is dynamically
updated with information received via routing protocols (e.g., OSPF-
TE, IS-IS-TE), ensuring the PCE has up-to-date knowledge of the
network status and behavior. It is also possible to define policies
like administrative group (coloring), to be used in constraint-based
path computation.
In order to support precision metrics, the TED could be extended to
support e.g. time-series storage and processing capabilities (e.g.,
to derive histograms from them, as described for instance in
Appendix A). The metrics could be gathered from in-band telemetry,
active probing mechanisms, or streaming telemetry via standardized
interfaces, as complementary information sources to the information
received from routing protocols.
Assuming that capability, the PCE queries the TED for compliance with
precision constraints.
- Topology
Path computation request - Single value metrics
based on PAM metrics - Precision metrics
+-------------+ +-------------+ +-------------+
| Path | | Path | | Traffic |
| Computation |<------->| Computation |<------->| Engineering |
| Client | | Element | | Database |
+-------------+ +-------------+ +-------------+
^
|
v
+-------------+
| Data |
| Sources |
+-------------+
- Link state info
- Active Probes
- Streaming telemetry
- In-band OAM
- etc
Figure 1: Usage of precision metrics stored in TED
The implementation of the TED and its support to the collection,
processing and generation of the precision metrics is out of scope of
this document. Similarly, the mechanisms used to collect telemetry
and build the statistical characterisation are outside the scope of
this document.
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3.4. Calculation or selection of the path
For a given metric, i.e. metric X, it is defined a frequency of
values per bin for such a metric (e.g., if the metric refers to
latency, a way of expressing it could be to consider the latency
below 20 ms the 90% of the time, and below 25 ms the 99% of the
time). Thus, the calculation or selection of a path for such a
metric X will consist on the comparison of the frequency of the
metric values per bin, so that the intended path behaves equal or
better than such described behavior. For that purpose, the
statistical behavior of the path is characterized e.g. as described
in Appendix A.
When selecting a path, the PCE evaluates candidate paths according to
the following procedure:
1. Retrieve PAM information associated with links and path segments.
2. Build the end-to-end statistical characterization of each
candidate path.
3. Apply mandatory PAM constraints to eliminate infeasible paths and
rank feasible ones.
4. Apply PAM objective functions across the remaining feasible
candidate paths.
5. Select the path maximizing the objective function value and
report back the statistical characterization associated with the
selected path by means of a PAM Report TLV..
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+---------------------+
| Candidate Paths |
+----------+----------+
|
v
+---------------------+
| PAM Constraints |
| Evaluation |
+----------+----------+
|
Feasible Paths
|
v
+---------------------+
| PAM Objective |
| Function |
+----------+----------+
|
v
+---------------------+
| Selected Path |
+---------------------+
Figure 2: Process
The construction of end-to-end statistical distributions derived from
the per-link observations is implementation dependent and outside the
scope of this document. The resulting end-to-end distribution SHALL
be represented using the PAM TLVs defined by this document.
4. PAM Objective Functions
The Path Computation Element (PCE) can use Precision Availability
Metrics (PAM) in two different ways during path computation. First,
PAM information can be used as a path constraint. In this mode,
candidate paths that do not satisfy the requested PAM characteristics
are discarded during the path computation process.
Second, PAM information can be used as an optimization criterion. In
this case, multiple candidate paths can satisfy the requested PAM
constraints and the PCE uses one or more PAM-related objective
functions to identify the preferred path among the feasible
alternatives. The specific algorithms used by a PCE implementation
are outside the scope of this document.
This section defines PAM objective functions that extend the OF
object defined in [RFC5541]. These objective functions enable PAM-
aware path computation and selection.
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4.1. PAM Compliance Objective Function
The PAM Compliance Objective Function aims at selecting the candidate
path whose statistical behavior exhibits the highest level of
compliance with the requested Service Level Objective (SLO).
When multiple feasible paths are available, the PCE evaluates the
statistical characterization associated with each path and selects
the one providing the highest degree of compliance with the requested
PAM profile.
The method used to determine the level of compliance is
implementation-specific.
This objective function is particularly useful when the PCC requests
a specific statistical distribution describing the expected service
behavior over a given observation interval.
The description of the new objective function is as follows.
* Objective Function Code: TBD1
* Name: PAM Compliance (PAM-COMP)
* Description: Selects the candidate path with highest compliance to
requested PAM characteristics. The PCE evaluates statistical
distributions and selects the path providing optimal alignment
with requested SLO profiles.
The objective function is formulated using the following terminology:
* A network comprises a set of N links {Li, (i=1...N)}.
* A path P is a list of K links {Lpi,(i=1...K)}.
* Metric of link L is denoted M(L). This can be any metric such as
path delay, path delay variation, or path loss as per [RFC8233].
* For a given metric X, the requested PAM profile is denoted R_X.
R_X is composed of a set of bins {Bj, (j=1...B)} and the
corresponding requested frequency values {rj, (j=1...B)}. Each
pair (Bj, rj) describes the expected statistical behavior of the
metric over the observation interval. For example, for a delay
metric, a bin may indicate that the path delay is expected to be
below a given threshold for at least a specified fraction of the
observation interval.
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* The statistical characterization of metric X for a candidate path
P is denoted H_X(P). H_X(P) is composed of the same set of bins
{Bj, (j=1...B)} and the corresponding observed, estimated, or
derived frequency values {pj(P), (j=1...B)} for the candidate
path.
* For each bin Bj, the compliance of path P with respect to the
requested PAM profile R_X is denoted c_j(P,R_X). The definition
of c_j(P,R_X) depends on the semantics of the metric and on
whether lower or higher values are preferable. For metrics where
lower values are preferable, such as delay, a path is considered
more compliant when the frequency of values not exceeding the
requested bin threshold is greater than or equal to the requested
frequency. For metrics where higher values are preferable, the
comparison is applied in the opposite direction.
* The overall PAM compliance of a path P with respect to the
requested PAM profile R_X is denoted C(P,R_X), where C(P,R_X) =
min { c_j(P,R_X), j=1...B }. This formulation captures the weakest
compliance level across all the requested bins and therefore
favors paths whose statistical behavior is aligned with the
complete requested PAM profile, rather than with only a subset of
the requested bins.
* The PAM-COMP OF is to find a path P such that C(P,R_X) is
maximized among the feasible candidate paths through Maximize
C(P,R_X), subject to the path constraints applicable to the
computation.
* A path P is fully compliant with the requested PAM profile R_X
when all the requested bin-level conditions are satisfied. When
more than one fully compliant path exists, the PCE SHOULD prefer
the path with the highest value of C(P,R_X). When no candidate
path can fully satisfy the requested PAM profile and the PAM
profile is used as a desirable optimization criterion rather than
as a mandatory constraint, the PCE MAY select the path with the
highest value of C(P,R_X) and SHOULD report the resulting
statistical characterization using the PAM Report TLV.
4.2. Minimum Violated Intervals Objective Function
The Minimum Violated Intervals (Min-VI) Objective Function aims at
selecting the path exhibiting the lowest occurrence of Violated
Intervals (VI). When several candidate paths satisfy the requested
constraints, the PCE SHOULD prefer the path associated with the
lowest VI occurrence. This objective function favors paths
presenting fewer periods of performance degradation with respect to
the requested service objectives.
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The description of the new objective function is as follows:
* Objective Function Code: TBD2
* Name: Minimum Violated Intervals (MIN-VI)
* Description: Selects the path with lowest Violated Interval (VI)
occurrence, minimizing periods of performance degradation relative
to SLO thresholds.
The objective function is formulated using the following terminology:
* A network comprises a set of N links {Li, (i=1...N)}.
* A path P is a list of K links {Lpi,(i=1...K)}.
* Violated Interval on link L is denoted VI(L)
* The Violated Intervals of a path P is denoted VI(P), where VI(P) =
Max {VI(Lpi), (i=1...K)}.
* The Min-VI OF is to find a path P such that ( Max { VI(Lpi),
i=1...K } ) is minimized.
4.3. Minimum Severe Violated Intervals Objective Function
The Minimum Severe Violated Intervals (Min-SVI) Objective Function
aims at selecting the path exhibiting the lowest occurrence of Severe
Violated Intervals (SVI). When several candidate paths satisfy the
requested constraints, the PCE SHOULD prefer the path associated with
the lowest SVI occurrence. This objective function is particularly
relevant for services where severe service degradation events must be
minimized.
The description of the new objective function is as follows:
* Objective Function Code: TBD3
* Name: Minimum Severe Violated Intervals (MIN-SVI)
* Description: Selects the path with lowest Severe Violated Interval
(SVI) occurrence, minimizing severe degradation events.
The objective function is formulated using the following terminology:
* A network comprises a set of N links {Li, (i=1...N)}.
* A path P is a list of K links {Lpi,(i=1...K)}.
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* Severe Violated Interval on link L is denoted SVI(L)
* The Severe Violated Intervals of a path P is denoted SVI(P), where
SVI(P) = Max {SVI(Lpi), (i=1...K)}.
* The Min-SVI OF is to find a path P such that ( Max { SVI(Lpi),
i=1...K } ) is minimized.
4.4. Interaction with Path Constraints
A PCC MAY request PAM information to be treated as a mandatory
constraint or as a desirable optimization criterion. Thus, when PAM
information is expressed as a mandatory constraint, candidate paths
not satisfying the requested PAM requirements SHALL be excluded from
the solution set. On the other hand, when PAM information is
expressed as a desirable criterion, candidate paths not satisfying
the requested PAM requirements MAY still be considered by the PCE.
In such cases, PAM objective functions can be used to identify the
most suitable path among the available alternatives.
Potential mechanisms used to compare statistical distributions and
determine the preferred candidate path are outside the scope of this
document.
5. PAM report TLV
The PAM Report TLV is used by a PCE to report the statistical
characterization associated with a computed path. This TLV allows a
PCC to understand the Precision Availability Metrics (PAM)
information that has been used during path computation and path
selection. Such information can be particularly useful when the
requested PAM profile has been expressed as a desirable objective
rather than as a mandatory constraint.The PAM Report TLV MAY be
included in PCRep messages and other PCEP messages carrying path
computation results.
The PAM Report TLV contains the statistical characterization
associated with the selected path and MAY include:
* The performance metric being described (e.g., one-way delay,
round-trip delay, jitter, packet loss).
* The observation interval used for deriving the statistical
characterization.
* The kind of statistical distribution associated with the selected
path.
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* The distribution representation, including the set of bins or
intervals used to characterize the metric values. The encoding of
specific statistical distributions and the representation of bins
are outside the scope of this document.
* The corresponding PAM indicators, such as Violated Intervals (VI)
and Severe Violated Intervals (SVI), as defined in [RFC9544].
When the requested PAM profile cannot be satisfied exactly, the PAM
Report TLV provides visibility about the statistical characteristics
of the path actually selected by the PCE. Moreover, when PAM
information is requested as a desirable optimization criterion, the
PCE MAY select a path whose statistical characterization differs from
the one requested by the PCC. In such case, the PCE SHOULD include a
PAM Report TLV allowing the PCC to know (and evaluate) the
characteristics of the selected path.
6. Security and operational considerations
6.1. Security considerations
Same security and operational considerations as described in
[RFC5440] apply also in this document.
Other security considerations will be addressed in future versions of
the document.
6.2. Operational considerations
The work with precision metrics can impose stringent requirements in
terms of collection, processing and assessment of metrics of
interest. Such capabilities are expected to be supported by external
systems, such as the TED, with the role of the PCE being limited to
the work with processed information (e.g., histograms) so to assess
that the precision metric used as constraint is compliant with the
expectation of the PCC. Such external supportive systems are out of
scope of this document.
7. IANA Considerations
7.1. New PCE Objective Function Codes
IANA is requested to assign the following values in the "Objective
Function" subregistry within the "Path Computation Element Protocol
(PCEP) Numbers" registry:
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+-----------+--------------------------------------------+--------------+
|Code Point | Name | Reference |
+-----------+--------------------------------------------+--------------+
| TBD1 | PAM Compliance (PAM-COMP) | This document|
| TBD2 | Minimum Violated Intervals (MIN-VI) | This document|
| TBD3 | Minimum Severe Violated Intervals (MIN-SVI)| This document|
+--------------+-----------------------------------------+--------------+
7.2. PCEP TLV
IANA is requested to assign the following values in the "PCEP TLV
Type Indicators" subregistry within the "Path Computation Element
Protocol (PCEP) Numbers" registry:
+----------+--------------------------------+--------------+
|TLV Type | Name | Reference |
+----------+--------------------------------+--------------+
| TBD4 | PAM Report | This document|
+----------+--------------------------------+--------------+
8. Informative References
[I-D.ietf-nmop-network-incident-yang]
Hu, T., Contreras, L. M., Wu, Q., Davis, N., and C. Feng,
"A YANG Data Model for Network Incident Management", Work
in Progress, Internet-Draft, draft-ietf-nmop-network-
incident-yang-10, 6 July 2026,
<https://datatracker.ietf.org/doc/html/draft-ietf-nmop-
network-incident-yang-10>.
[IANA_METRIC_Object]
"METRIC Object T Field", n.d.,
<https://www.iana.org/assignments/pcep/pcep.xhtml#metric-
object-ni-field>.
[IEEE.754.2019]
"754-2019 - IEEE Standard for Floating-Point Arithmetic",
22 July 2019,
<https://ieeexplore.ieee.org/document/8766229>.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
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[RFC4655] Farrel, A., Vasseur, J.-P., and J. Ash, "A Path
Computation Element (PCE)-Based Architecture", RFC 4655,
DOI 10.17487/RFC4655, August 2006,
<https://www.rfc-editor.org/info/rfc4655>.
[RFC5440] Vasseur, JP., Ed. and JL. Le Roux, Ed., "Path Computation
Element (PCE) Communication Protocol (PCEP)", RFC 5440,
DOI 10.17487/RFC5440, March 2009,
<https://www.rfc-editor.org/info/rfc5440>.
[RFC5541] Le Roux, JL., Vasseur, JP., and Y. Lee, "Encoding of
Objective Functions in the Path Computation Element
Communication Protocol (PCEP)", RFC 5541,
DOI 10.17487/RFC5541, June 2009,
<https://www.rfc-editor.org/info/rfc5541>.
[RFC8233] Dhody, D., Wu, Q., Manral, V., Ali, Z., and K. Kumaki,
"Extensions to the Path Computation Element Communication
Protocol (PCEP) to Compute Service-Aware Label Switched
Paths (LSPs)", RFC 8233, DOI 10.17487/RFC8233, September
2017, <https://www.rfc-editor.org/info/rfc8233>.
[RFC8578] Grossman, E., Ed., "Deterministic Networking Use Cases",
RFC 8578, DOI 10.17487/RFC8578, May 2019,
<https://www.rfc-editor.org/info/rfc8578>.
[RFC8655] Finn, N., Thubert, P., Varga, B., and J. Farkas,
"Deterministic Networking Architecture", RFC 8655,
DOI 10.17487/RFC8655, October 2019,
<https://www.rfc-editor.org/info/rfc8655>.
[RFC9543] Farrel, A., Ed., Drake, J., Ed., Rokui, R., Homma, S.,
Makhijani, K., Contreras, L., and J. Tantsura, "A
Framework for Network Slices in Networks Built from IETF
Technologies", RFC 9543, DOI 10.17487/RFC9543, March 2024,
<https://www.rfc-editor.org/info/rfc9543>.
[RFC9544] Mirsky, G., Halpern, J., Min, X., Clemm, A., Strassner,
J., and J. François, "Precision Availability Metrics
(PAMs) for Services Governed by Service Level Objectives
(SLOs)", RFC 9544, DOI 10.17487/RFC9544, March 2024,
<https://www.rfc-editor.org/info/rfc9544>.
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Appendix A. Path Histogram Composition
In order to obtain the statistical distribution of a metric over a
complete path from the corresponding distributions of its constituent
segments (e.g., hops) it is necessary to consider the class of the
metric under evaluation, i.e., if the metric is additive,
multiplicative, or maximal/minimal.
A.1. Additive Metrics
Additive metrics are those that sum along the path, such as delay or
IGP cost [RFC4655], [RFC8233]. To generate a path histogram from
segment histograms, the total path value can be obtained by summing
the individual segment values along a period, and then forming the
histogram.
Alternatively, considering that a histogram is divided into discrete
bins representing value ranges, it is possible to perform a bin-by-
bin summation. The histogram for the path is then obtained by
summing the bin values across the segments.
A.2. Multiplicative Metrics
Multiplicative metrics, for example link availability or success
probability [RFC8233], combine along a path by multiplying segment
(e.g., per hop) values. The path histogram can be obtained by
combining the segment values and computing the product for each
combination.
Alternatively, logarithmic transformation can be applied to convert
multiplicative aggregation into additive form, enabling reuse of
additive histogram composition techniques. In this method, the
values of each histogram bin are transformed by taking the logarithm,
effectively converting multiplication into addition. The histograms
can then be combined by summing the log-transformed bin values across
segments, using the values of each bin per segment to calculate the
resulting distribution. After aggregating the histograms in the log
domain, the path histogram can be transformed back to the original
metric domain by applying the exponential function, yielding the
final probabilities for the multiplicative path values.
A.3. Maximization / Minimization Metrics (Bottleneck Metrics)
Bottleneck metrics are defined by taking the maximum or minimum value
along the path, such as bandwidth, MTU, etc [RFC4655]. To construct
a path histogram, the values of each segment are considered to build
the cumulative distribution function (CDF) of the path.
Contreras, et al. Expires 7 January 2027 [Page 15]
Internet-Draft PAM-based PCE July 2026
Acknowledgements
The authors thank Dhruv Dhody, Rakesh Gandhi, Ruediger Geib, Amal
Karboubi and Greg Mirsky for the comments received that helped to
improve the document.
This work has been partially funded by the European Commission
Horizon Europe SNS JU PREDICT-6G project (GA 101095890), and the
Spanish Ministry of Economic Affairs and Digital Transformation and
the European Union NextGenerationEU UNICO 5G I+D "Towards a smart and
efficient telecom infrastructure meeting current and future industry
needs" (TIMING) project (TSI-063000-2021-145, -148, -149).
Authors' Addresses
Luis M. Contreras
Telefonica
Ronda de la Comunicacion, s/n
28050 Madrid
Spain
Email: luismiguel.contrerasmurillo@telefonica.com
URI: http://lmcontreras.com
Fernando Agraz
Universitat Politecnica de Catalunya
08034 Barcelona
Spain
Email: fernando.agraz@upc.edu
Salvatore Spadaro
Universitat Politecnica de Catalunya
08034 Barcelona
Spain
Email: salvatore.spadaro@upc.edu
Quan Xiong
ZTE Corporation
China
Email: xiong.quan@zte.com.cn
Contreras, et al. Expires 7 January 2027 [Page 16]