Quality of Outcome (QoO)
draft-ietf-ippm-qoo-09
| Document | Type | Active Internet-Draft (ippm WG) | |
|---|---|---|---|
| Authors | Bjørn Ivar Teigen , Magnus Olden , Ike Kunze | ||
| Last updated | 2026-04-22 | ||
| Replaces | draft-olden-ippm-qoo | ||
| RFC stream | Internet Engineering Task Force (IETF) | ||
| Intended RFC status | Informational | ||
| Formats | |||
| Reviews |
ARTART IETF Last Call review
(of
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by Martin Thomson
Ready w/issues
GENART IETF Last Call review
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by Paul Kyzivat
Ready w/issues
ARTART Telechat Review due 2026-03-03
Incomplete
|
||
| Additional resources |
GitHub Repository
Related Implementations Mailing list discussion |
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| Stream | WG state | Submitted to IESG for Publication | |
| Document shepherd | Marcus Ihlar | ||
| Shepherd write-up | Show Last changed 2026-01-30 | ||
| IESG | IESG state | IESG Evaluation::AD Followup | |
| Action Holder | |||
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||
| Responsible AD | Mohamed Boucadair | ||
| Send notices to | marcus.ihlar@ericsson.com | ||
| IANA | IANA review state | Version Changed - Review Needed |
draft-ietf-ippm-qoo-09
IP Performance Measurement B. I. T. Monclair
Internet-Draft M. Olden
Intended status: Informational I. Kunze, Ed.
Expires: 24 October 2026 CUJO AI
22 April 2026
Quality of Outcome (QoO)
draft-ietf-ippm-qoo-09
Abstract
This document introduces the Quality of Outcome (QoO) network quality
score and the corresponding QoO framework as an approach to network
quality assessment designed to align with the needs of users,
application developers, and network operators.
Conceptually based on the Quality Attenuation metric, QoO provides a
method for defining and evaluating application-specific, quality-
focused network performance requirements to enable insights for
network troubleshooting and optimization, and simple Quality of
Service scores for end-users.
About This Document
This note is to be removed before publishing as an RFC.
Status information for this document may be found at
https://datatracker.ietf.org/doc/draft-ietf-ippm-qoo/.
Discussion of this document takes place on the IP Performance
Measurement Working Group mailing list (mailto:ippm@ietf.org), which
is archived at https://mailarchive.ietf.org/arch/browse/ippm/.
Subscribe at https://www.ietf.org/mailman/listinfo/ippm/.
Source for this draft and an issue tracker can be found at
https://github.com/getCUJO/QoOID.
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
and may be updated, replaced, or obsoleted by other documents at any
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material or to cite them other than as "work in progress."
This Internet-Draft will expire on 24 October 2026.
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/
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Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1. What's In? What's Out? . . . . . . . . . . . . . . . . . 4
1.2. Terminology . . . . . . . . . . . . . . . . . . . . . . . 5
2. Background and Design Considerations . . . . . . . . . . . . 7
2.1. Background on Quality Attenuation . . . . . . . . . . . . 7
2.2. QoO Design Considerations . . . . . . . . . . . . . . . . 8
3. The QoO Framework . . . . . . . . . . . . . . . . . . . . . . 9
3.1. Measuring Network Performance . . . . . . . . . . . . . . 10
3.1.1. Measurement Considerations . . . . . . . . . . . . . 11
3.1.2. Reporting Measurement Results . . . . . . . . . . . . 12
3.2. Describing Network Performance Requirements . . . . . . . 14
3.2.1. Specification Guidelines . . . . . . . . . . . . . . 15
3.2.2. Creating a Network Performance Requirement
Specification . . . . . . . . . . . . . . . . . . . . 16
3.3. Calculating QoO . . . . . . . . . . . . . . . . . . . . . 16
3.3.1. Overall QoO Calculation . . . . . . . . . . . . . . . 17
3.3.2. Latency Component . . . . . . . . . . . . . . . . . . 17
3.3.3. Packet Loss Component . . . . . . . . . . . . . . . . 18
3.3.4. Throughput Component . . . . . . . . . . . . . . . . 18
3.4. Example . . . . . . . . . . . . . . . . . . . . . . . . . 19
4. Operational Considerations . . . . . . . . . . . . . . . . . 20
4.1. QoO in the Quality Assessment Landscape . . . . . . . . . 20
4.2. Composability, Flexibility, and Use Cases . . . . . . . . 21
4.3. Aligning Measurements with Service Levels . . . . . . . . 21
4.4. Path Stability and Temporal Validity . . . . . . . . . . 22
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4.5. Multipath Protocols . . . . . . . . . . . . . . . . . . . 23
4.6. Adaptive Applications . . . . . . . . . . . . . . . . . . 23
4.7. Continuous Measurements . . . . . . . . . . . . . . . . . 24
4.8. Sensitivity to Sampling Accuracy . . . . . . . . . . . . 24
4.9. A Subjective Approach to Creating Network Performance
Requirement Specifications . . . . . . . . . . . . . . . 25
5. Known Weaknesses and Open Questions . . . . . . . . . . . . . 27
5.1. Volatile Networks . . . . . . . . . . . . . . . . . . . . 27
5.2. Missing Temporal Information in Distributions . . . . . . 27
5.3. Subsampling the Real Distribution . . . . . . . . . . . . 28
5.4. Assuming Linear Relationship Between Optimal Performance
and Unusable . . . . . . . . . . . . . . . . . . . . . . 28
5.5. Binary Throughput Threshold . . . . . . . . . . . . . . . 28
5.6. Arbitrary Selection of Percentiles . . . . . . . . . . . 28
6. Security Considerations . . . . . . . . . . . . . . . . . . . 29
6.1. Measurement Integrity and Authenticity . . . . . . . . . 29
6.2. Risk of Misuse and Gaming . . . . . . . . . . . . . . . . 29
6.3. Denial-of-Service (DoS) Risks . . . . . . . . . . . . . . 30
6.4. Trust in Application Requirements . . . . . . . . . . . . 30
7. Privacy Considerations . . . . . . . . . . . . . . . . . . . 30
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 31
9. Implementation status . . . . . . . . . . . . . . . . . . . . 31
9.1. qoo-c . . . . . . . . . . . . . . . . . . . . . . . . . . 31
9.2. goresponsiveness . . . . . . . . . . . . . . . . . . . . 32
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 33
10.1. Normative References . . . . . . . . . . . . . . . . . . 33
10.2. Informative References . . . . . . . . . . . . . . . . . 34
Appendix A. QoO Framework Design Considerations . . . . . . . . 40
A.1. General Requirements . . . . . . . . . . . . . . . . . . 42
A.2. Requirements from End-Users . . . . . . . . . . . . . . . 43
A.3. Requirements from Application and Platform Developers . . 44
A.4. Requirements from Network Operators and Network Solution
Vendors . . . . . . . . . . . . . . . . . . . . . . . . . 45
Appendix B. Comparison To Other Network Quality Metrics . . . . 46
B.1. Throughput . . . . . . . . . . . . . . . . . . . . . . . 48
B.2. Mean Latency . . . . . . . . . . . . . . . . . . . . . . 48
B.3. 99th Percentile of Latency . . . . . . . . . . . . . . . 49
B.4. Variance of Latency . . . . . . . . . . . . . . . . . . . 49
B.5. Inter-Packet Delay Variation (IPDV) . . . . . . . . . . . 49
B.6. Packet Delay Variation (PDV) . . . . . . . . . . . . . . 50
B.7. Trimmed Mean of Latency . . . . . . . . . . . . . . . . . 50
B.8. Round-trips Per Minute . . . . . . . . . . . . . . . . . 50
B.9. Quality Attenuation . . . . . . . . . . . . . . . . . . . 50
B.10. Quality of Outcome . . . . . . . . . . . . . . . . . . . 51
Appendix C. Preliminary Insights From a Small-Scale User Testing
Campaign . . . . . . . . . . . . . . . . . . . . . . . . 51
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 52
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 52
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1. Introduction
This document introduces the Quality of Outcome (QoO) network quality
score and the corresponding QoO framework.
QoO scores convey how well applications are expected to perform on
assessed networks, with higher scores indicating that applications
are more likely to perform well. To that end, QoO scores express
measured network conditions as a percentage on a linear scale bounded
by two application-specific thresholds: one for unacceptable
performance (0%) and one for optimal performance (100%). This allows
network quality to be communicated in easily understood terms such as
"This network provides 94% of optimal conditions for video
conferencing (relative to the threshold for unacceptable
performance)" while remaining objective and adaptable to different
network quality needs.
The QoO framework defines guidelines for conducting network
performance measurements, how stakeholders specify the quality-
focused network performance requirements (regarding latency, packet
loss, and throughput) at the two quality thresholds, and how the
user-facing QoO score is calculated based on such performance
requirements and network performance measurements.
This document and the QoO framework assume that it is sufficient to
assess network quality in terms of a minimum required throughput, a
set of latency percentiles, and packet loss ratios, with the
expectation that these dimensions will ultimately also capture the
effects of additional factors. Hence, similar to Quality Attenuation
[TR-452.1], the QoO framework assesses the network state based on
latency distributions and packet loss probabilities and additionally
considers throughput. This design ensures spatial composability
[RFC6049], enabling network operators to achieve fault isolation
(Section 5.4.4 of [I-D.ietf-opsawg-rfc5706bis]), advanced root-cause
analyses from within the network (Section 5.4.3 of
[I-D.ietf-opsawg-rfc5706bis]), and network planning while supporting
comprehensive end-to-end tests.
1.1. What's In? What's Out?
This document defines a minimum viable QoO framework consisting of:
* Guidelines for conducting network performance measurements
(Section 3.1)
* Guidelines for specifying quality-focused network performance
requirements (Section 3.2)
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* Calculation formulas for computing QoO scores (Section 3.3)
The document also discusses operational considerations (Section 4)
and known weaknesses and open questions (Section 5). The appendix
provides additional context on fundamental design considerations for
QoO (Appendix A), a comparison of QoO with existing quality metrics
(Appendix B), and preliminary insights from a small-scale user
testing campaign (Appendix C).
The document intentionally leaves certain aspects of the QoO
framework unspecified to allow for broad applicability across
different deployment contexts and to enable the gathering of
operational experience that can inform future, more prescriptive
documents. The following items are out of scope for this document
and may be addressed in future work:
* How applications define and share their network performance
requirements
* Which format is used to publish such requirement information
* How operators retrieve such data from applications or services
* How the precision of the resulting QoO scores is assessed
* What levels of precision are considered acceptable
1.2. Terminology
This document uses the following terminology:
Network: The communication infrastructure that facilitates data
transmission between endpoints, including all intermediate
devices, links, and protocols that affect the transmission of
data. This encompasses both the physical infrastructure and the
logical protocols that govern data transmission. The network may
support various communication patterns and may span multiple
administrative domains.
Network Segment: A portion of the complete end-to-end network path
between application endpoints. A network segment may represent a
specific administrative domain (e.g., access network, transit
network, or server-side infrastructure), a particular technology
domain (e.g., Wi-Fi or cellular), or any subset of the path for
which independent quality measurements and analysis are desired.
Quality Attenuation: A network quality metric defined in [TR-452.1]
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that combines latency and packet loss distributions in a unified
approach to jointly assess latency and loss characteristics of
network performance.
Quality of Experience (QoE): The degree of delight or annoyance of
the user of an application or service. See also [P.10].
Quality of Service (QoS): The totality of characteristics of a
telecommunications service that bear on its ability to satisfy
stated and implied needs of the user of the service. See also
[P.10].
Quality of Outcome (QoO): A network quality framework and metric
that evaluates network quality based on how closely measured
network conditions meet application-specific, quality-focused
network performance requirements. QoO is a QoS indicator that may
be related to, but cannot be considered the same as, the actual
QoE of end-users.
QoO Score: A numerical value that represents the distance-based
assessment of network quality relative to application-specific,
quality-focused network performance requirements for optimal and
unacceptable application performance, typically expressed as a
percentage.
Optimal performance: A level of performance beyond which further
improvements in network conditions do not result in perceptible
improvements in application performance or user experience.
Requirements for Optimal Performance (ROP): The network performance
characteristics at which an application achieves optimal
performance. When network performance exceeds ROP thresholds, any
sub-optimal user experience can be assumed not to be caused by the
part of the network path that has been measured for QoO
calculations.
Conditions at the Point of Unacceptable Performance (CPUP): The
network performance threshold below which an application fails to
provide acceptable user experience. Note that 'unacceptable' in
this context refers to degraded performance quality rather than
complete technical failure of the application. There is no
universally strict threshold defining when network conditions
become unacceptable for applications.
Composability: The mathematical property that allows network quality
measurements to be combined across different network segments or
decomposed to isolate specific network components for analysis and
troubleshooting.
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Accuracy and Precision: "Accuracy" refers to how close measurements
are to the value that reflects the real conditions. "Precision"
refers to the consistency and repeatability of measurements.
These terms are used with their standard statistical meanings and
are not interchangeable [ISO5725-1].
2. Background and Design Considerations
This section provides concise background information on Quality
Attenuation (Section 2.1) and a short summary of key design
considerations for QoO (Section 2.2) with further elaborations in
Appendix A.
2.1. Background on Quality Attenuation
Quality Attenuation is defined in the Broadband Forum standard
Quality of Experience Delivered (QED) [TR-452.1] and characterizes
network quality based on measurements of latency distributions and
packet loss probabilities.
Using latency distributions to measure network quality has been
proposed by various researchers and practitioners (e.g., [Kelly],
[RFC6049], and [RFC8239]). Quality Attenuation uses a latency
distribution as the basis for an Improper Random Variable (IRV). The
cumulative distribution function of the IRV captures the likelihood
that a packet "completes" within any given time, e.g., that it is
received at the destination when one-way latency is assessed. The
IRV incorporates packet loss by treating lost packets as infinite (or
too late to be of use, i.e., not arriving within an
application-specific time threshold) latency, similar to the One-Way
Loss Metric for IP Performance Metrics (IPPM) [RFC7680], which
defines packet loss as packets that fail to arrive within a specified
time threshold. The "intangible mass" of the IRV represents the
probability that a packet never completes within any useful time
(i.e., is lost or arrives too late).
Quality Attenuation enables spatial composition [RFC6049] of network
segments as two distributions can be composed using convolution.
Measurements from different network segments can be combined to
derive end-to-end quality assessments, or end-to-end measurements can
be decomposed to isolate the contribution of individual segments.
This composability enables network operators to perform fault
isolation and root cause analysis by identifying which portions of a
network path contribute most to performance degradation.
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2.2. QoO Design Considerations
Quality Attenuation provides a mathematically rigorous foundation for
network quality assessment and it can capture the ability of a
network to satisfy a variety of application needs. However,
interpreting its raw distributional outputs and component
decompositions can be difficult, especially for application
developers and end-users who might be primarily interested in
understanding whether specific applications will perform adequately.
The QoO framework is specifically designed to address this limitation
by translating the results of the underlying network performance
measurements, i.e., latency distributions and packet loss ratios,
into intuitive percentage scores that directly relate the measured
network conditions to application-specific network performance
requirements in an understandable and unambiguous way. To that end,
the design of the QoO framework is motivated by the needs of three
distinct stakeholder groups -- end-users, application developers, and
network operators -- and bridges the gap between the technical
aspects of network performance and the practical needs of those who
depend on it.
End-users need network quality metrics that are understandable and
that relate as directly as possible to application performance, such
as video smoothness, web page load times, or gaming responsiveness.
The QoO framework addresses this need by basing the QoO score on
objective QoS measurements while communicating network quality in
intuitive terms, thereby creating a middle ground between QoS and QoE
metrics and allowing end-users to understand if a network is a likely
source of impairment for the performance of applications.
Application developers need the ability to express quality-focused
network performance requirements for their applications across all
relevant dimensions of network quality (latency, packet loss,
throughput) in order to test or state their network requirements.
The QoO framework addresses this need by enabling both simple and
complex requirement specifications, accommodating developers with
varying levels of networking expertise.
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Network operators need tools for fault isolation, performance
comparison, and bottleneck identification. The QoO framework
addresses this need through its use of latency distributions and
packet loss probabilities, whose spatial composability enables
operators to measure network segments independently, combine results
to understand end-to-end quality, or decompose measurements to
isolate problem areas, enabling network analysis in general.
Additionally, operators can use the underlying raw measurement
results to derive Quality Attenuation measures if more fine-grained
insight is needed (see Section 4.1).
Overall, the QoO framework design acknowledges that all stakeholders
ultimately care about the performance of applications running over a
network by
1. capturing network performance metrics that correlate with
application performance as perceived by users,
2. supporting comparison against diverse application requirements,
and
3. providing composability for spatial decomposition and root cause
analysis.
Additional elaboration on these three core properties is provided in
Appendix A.
3. The QoO Framework
The QoO framework builds on the conceptual foundation of Quality
Attenuation [TR-452.1]. Similar to Quality Attenuation, QoO
evaluates network conditions using latency distributions and packet
loss probabilities under the assumption that other factors which
could in principle be measured but are not explicitly captured by QoO
will inevitably influence the observed latency and packet loss
behavior, so that QoO indirectly accounts for their effects. The QoO
framework additionally includes throughput, i.e., the available
network capacity, as applications usually have minimum throughput
requirements below which they do not work at all. The measured
conditions are compared against application-specific, quality-focused
network performance requirements. Latency requirements are specified
along multiple dimensions (such as 90th or 95th latency percentiles)
while packet loss requirements specify mean packet loss ratios. Both
latency and packet loss requirements are specified at two thresholds:
* Optimal performance (ROP): Network conditions under which
application performance becomes optimal
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* Unacceptable performance (CPUP): Network conditions under which
application performance becomes unacceptable
For throughput, requirements specify only a global minimum required
value.
When the measured network conditions fall between the defined
thresholds for any of the assessed performance dimensions for latency
and packet loss, the QoO framework calculates a score for each
dimension by expressing the current network quality as a relative
position (percentage) on the linear scale from 0 to 100 between the
corresponding CPUP (0) and ROP (100) thresholds. The minimum score
across all dimensions serves as the overall QoO score for the
assessed network based on the rationale that the most degraded
performance dimension is likely to determine the application's
perceived quality. If the throughput requirement is not met, the QoO
score is always 0.
The remainder of this section describes how network conditions can be
measured (Section 3.1), how QoO defines application-specific network
performance requirements (Section 3.2), and how QoO scores are
calculated (Section 3.3) with an example provided in Section 3.4.
3.1. Measuring Network Performance
The QoO framework relies on information on the latency and packet
loss conditions and the available capacity of the network to be
assessed. Latency distributions can be gathered via both passive
monitoring and active testing [RFC7799]. The active testing can use
any type of traffic, such as connection-oriented TCP and QUIC or
connectionless UDP. It can be applied across different layers of the
protocol stack and is network technology independent, meaning it can
be gathered in an end-user application, within some network
equipment, or anywhere in between. Passive methods rely on observing
and time-stamping packets traversing the network. Examples of this
include TCP SYN and SYN/ACK packets (Section 2.2 of [RFC8517]) and
the QUIC spin bit [RFC9000][RFC9312]. Similar considerations apply
to packet loss measurements while throughput measurements usually
involve active testing.
In addition to measurement approaches standardized in the QED
framework [TR-452.1], some relevant techniques are:
* Active probing with the Two-Way Active Measurement Protocol
(TWAMP) Light [RFC5357], the Simple Two-Way Active Measurement
Protocol (STAMP) [RFC8762], or the Isochronous Round-Trip Tester
(IRTT) [IRTT]
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* On-path telemetry methods (IOAM [RFC9197], AltMark [RFC9341])
* Latency tests under loaded network conditions
[I-D.ietf-ippm-responsiveness]
* Throughput tests with included latency measures [Throughputtest]
* DNS response latency measurements (Section 2.8 of [RFC8517],
[RFC8912])
* Passive TCP / QUIC handshake measurements [TCPHandshake][RFC9312]
* Continuous passive TCP / QUIC measurements
[TCPContinuous][RFC9312]
* Simulating or estimating real traffic [LatencyEstimation]
3.1.1. Measurement Considerations
The QoO framework does not mandate the use of specific measurement
techniques, measurement configurations, or measurement conditions.
For example, it is agnostic to traffic direction, does not prescribe
specific conditions for sampling, such as fixed time intervals or
defined network load levels, and it does not enforce a minimum sample
count, even though distributions formed from small numbers of samples
(e.g., 10) are clearly insufficient for statistical confidence
despite being technically valid.
Instead, the chosen measurement methodology must be able to capture
characteristics of applications to be studied with sufficient
resolution as different applications and application classes fail
under different network conditions. For example, downloads are
generally more tolerant of latency than real-time applications.
Video conferences are often sensitive to high 90th percentile latency
and to the difference between the 90th and 99th percentiles. Online
gaming typically has a low tolerance for high 99th percentile
latency. Similar considerations apply for a variety of other
factors. For example, applications usually require some minimum
level of throughput and tolerate some maximum packet loss ratio. The
intent of this underspecification is to balance rigor with
practicality, recognizing that constraints vary across devices,
applications, and deployment environments.
Another dimension to this is the modelling of full latency
distributions, which may be too complex to allow for easy adoption of
the framework. Instead, reporting latency at selected percentiles
offers a practical compromise between accuracy and deployment
considerations, trading off composability, which is only possible
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with distributions, for an easier deployment. A commonly accepted
set of percentiles spanning from the 0th to the 100th in a
logarithmic-like progression has been suggested by others [BITAG] and
is recommended here: [0th, 10th, 25th, 50th, 75th, 90th, 95th, 99th,
99.9th, 100th].
The choice of measurement methodology also needs to account for
network conditions and their variability. Idle-state measurements
capture baseline characteristics unaffected by competing traffic,
whereas measurements taken under load reflect conditions that are
more likely to challenge application performance, such as elevated
latencies and queuing. Both active and passive methods can capture
either state, although with different degrees of control. Passive
monitoring of production traffic usually reflects actual network load
but may not always allow capturing heavily loaded conditions. Active
measurements can target heavily loaded conditions by generating
synthetic traffic equivalent to the application load alongside the
probes but capturing the actual or idle network conditions may not be
possible depending on the footprint of the measurement method.
Furthermore, when performing active measurements or generating
artificial load, care must be taken not to degrade the network under
test or inadvertently enable denial-of-service abuse
[RFC2330][RFC4656]. See Section 6.3 for specific mitigations.
Internet forwarding paths can also shift on a variety of timescales
due to routing changes, load balancing, or traffic engineering,
meaning a measurement reflects the network's state only during the
sampling period. Such factors need to be considered when conducting
performance measurements. See Section 4.4 for a discussion of the
operational implications, and Section 5.1 for the more severe case of
volatile environments such as mobile cellular networks.
3.1.2. Reporting Measurement Results
This document defines a minimum viable framework, and often trades
precision for simplicity to facilitate adoption and usability in many
different contexts. To assess the corresponding loss of precision
and account for the underspecification of the measurement
methodology, each measurement must be accompanied by the following
metadata in order to support reproducibility and enable confidence
analysis, even across QoO deployments:
* Description of the measurement method, including:
- Standard name (if applicable) or reference
- Measurement class (Active, Passive, or Hybrid) [RFC7799]
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- Protocol layer used for measurements (ICMP, TCP, UDP, ...)
* Measurement configuration, including:
- Probe packet size (if applicable)
- Traffic class of probed traffic
- Sampling method, including but not limited to
o Cyclic: One sample every N milliseconds (specify N)
o Burst: X samples every N milliseconds (specify X and N)
o Passive: Opportunistic sampling of live traffic (non-uniform
intervals)
* Description of the measured path, including:
- Endpoints (source and destination)
- Network segments traversed
- Measurement points (if applicable)
- Direction (two-way, one-way source-to-destination, one-way
destination-to-source)
* Description of the measurement duration, including:
- Timestamp of first sample (e.g., in the format used in TWAMP
[RFC5357][RFC8877])
- Total duration of the sampling period (in milliseconds)
- Number of samples collected
These metadata elements are required for interpreting the precision
and reliability of the measurements. As demonstrated in
[QoOSimStudy] and discussed in Section 4.8, low sampling frequencies
and short measurement durations can lead to misleadingly optimistic
or imprecise QoO scores.
To assess the precision of network performance measurements,
implementers should consider:
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* The repeatability of measurements under similar network
conditions, which can also be affected by path variation across
multiple protocol layers (see Section 4.4)
* The impact of sampling frequency and duration on percentile
estimates, particularly for high percentiles (e.g., 99th, 99.9th)
* The measurement uncertainty introduced by hardware/software timing
jitter, clock synchronization errors, and other system-level noise
sources
* The statistical confidence intervals for percentile estimates
based on sample size
Acceptable levels of precision depend on the use case. Implementers
should document their precision assessment methodology and report
precision metrics alongside QoO scores when precision is critical for
the use case.
3.2. Describing Network Performance Requirements
The goal of the QoO framework is to establish a quantifiable distance
between unacceptable and optimal network conditions, thereby enabling
an objective assessment of relative quality, i.e., how close some
measured network conditions are to the optimal conditions specified
by corresponding requirements. Matching the scope of the network
performance measurements, corresponding network performance
requirement specifications cover three dimensions: latency, expressed
as a set of percentile-latency tuples; packet loss, expressed as a
single ratio; and throughput, expressed as a minimum required value.
For latency and packet loss, these specifications define both the
Requirements for Optimal Performance (ROP), and the Conditions at the
Point of Unacceptable Performance (CPUP). There is only one global
minimum throughput requirement as insufficient network capacity may
give unacceptable application outcomes without necessarily inducing a
lot of latency or packet loss.
Figure 1 illustrates one example requirement specification. For
optimal performance, the example application requires that 99% of
packets need to arrive within 100ms and 99.9% within 200ms while
tolerating at most 0.1% packet loss (ROP). The perceived service
becomes unacceptable if 99% of packets have not arrived within 200ms,
or 99.9% within 300ms, or if there is more than 2% packet loss
(CPUP). The underlying minimum throughput requirement is 4Mbps.
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CPUP ROP
v v
<-- Unacceptable --|<- Acceptable ->|-- Optimal -->
| |
Latency (99th pct) 200ms 100ms
| |
Latency (99.9th pct) 300ms 200ms
| |
Packet Loss 2% 0.1%
|
Throughput 4Mbps
Figure 1: Example Network Performance Requirement Specification
3.2.1. Specification Guidelines
The QoO framework provides the general structure for network
performance requirement specifications, enabling stakeholders to
specify the latency and loss requirements to be met while the end-to-
end network path is loaded in a way that is at least as demanding of
the network as the application itself.
The QoO framework does not mandate the use of specific latency
percentiles and it does not define standardized network performance
requirement specifications for specific applications or application
classes. Packet loss and throughput requirements can be arbitrary
non-negative values while latency requirements are specified as sets
of non-negative percentile-latency tuples. The set of included
percentiles can be minimal (e.g., 100% within 200ms) or extended as
needed and different percentiles may be used to characterize
different applications.
For ease of operation, this document does mandate that latency
percentiles specified in network performance requirement
specifications must match the information available in the network
performance measurements. This means that when the measurements
report full latency distributions, requirements can use arbitrary
percentiles. If the simplified latency reporting described in
Section 3.1.1 is used, the requirement specification must use
percentiles that are included in the reported measurements, i.e., one
or more of the [0th, 10th, 25th, 50th, 75th, 90th, 95th, 99th,
99.9th, 100th] percentiles if the [BITAG] recommendation is followed.
For simplicity, the document further mandates that latency
percentiles used in the ROP must also be used in the CPUP, and vice
versa. For example, if the CPUP uses the 99.9th percentile then the
ROP must also include the 99.9th percentile, and if the ROP uses the
99th percentile then the CPUP must also include the 99th percentile.
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Finally, the network performance requirement specification must
specify if the requirements are one-way or two-way. If the
requirement is one-way, the direction between the endpoints of the
assessed path, i.e., source-to-destination or destination-to-source,
must be specified (see Section 3.1.2). In case of a two-way
requirement, a decomposition into source-to-destination and
destination-to-source components may be specified.
3.2.2. Creating a Network Performance Requirement Specification
This document does not define a standardized approach for creating
quality-focused network performance requirement specifications.
Instead, this section provides general considerations for deriving
requirement specifications.
Deriving consistent and reproducible thresholds for ROP and CPUP
specifications requires standardized testing conditions. Application
developers should therefore publish their testing methodologies,
including the network conditions, hardware configurations, and
measurement procedures used to establish these thresholds, so that
results can be independently validated and compared across
implementations. Developers are encouraged to follow relevant
standards for testing methodologies, such as ITU-T P-series
recommendations for subjective quality assessment ([P.800], [P.910],
[P.1401]) and IETF IPPM standards for network performance measurement
([RFC7679], [RFC7680], [RFC6673]). These standards provide guidance
on test design, measurement procedures, and statistical analysis that
can help ensure consistent and reproducible threshold definitions.
To illustrate the large design-space for such testing, Section 4.9
sketches an arguably subjective approach to identifying thresholds
for ROP and CPUP specifications, which should not be used in
deployments due to its subjectiveness. Future documents may define
new methods for deriving appropriate network performance requirements
for QoO and could also recommend a fixed set of latency percentiles
to be used, either for all applications or on a per-application /
per-application-class basis to make QoO measurements between
different networks or providers more comparable.
3.3. Calculating QoO
The QoO score compares network performance measurements to
application-specific, quality-focused network performance
requirements by assessing how close the measured network performance
is to the network conditions needed for optimal application
performance. The QoO score reflects the directionality (one-way or
two-way) used in the measurements and the requirements; all need to
use the same directionality and, for one-way assessments, the same
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direction (source-to-destination or destination-to-source). There
are three key scenarios:
* The network meets all requirements for optimal performance (ROP)
and the throughput requirement. QoO Score: 100%.
* The network fails one or more criteria for conditions at the point
of unacceptable performance (CPUP) or the throughput requirement.
QoO Score: 0%.
* The network performance falls between optimal and unacceptable
while meeting the throughput requirement. In this case, a
continuous QoO score between 0% and 100% is computed by taking the
worst score derived from latency and packet loss.
3.3.1. Overall QoO Calculation
The overall QoO score is the minimum of a latency (QoO_latency), a
packet loss (QoO_loss), and a throughput (QoO_throughput) score:
QoO = min(QoO_latency, QoO_loss, QoO_throughput)
with QoO_latency, QoO_loss, and QoO_throughput as defined in the
following and illustrated in Figure 2.
CPUP ROP
v v
Latency <-- Unacceptable -->|<-- Acceptable -->|<-- Optimal -->
(per QoO = 0% | QoO = 0%..100% | QoO = 100%
percentile) | |
| |
Packet Loss <-- Unacceptable -->|<-- Acceptable -->|<-- Optimal -->
QoO = 0% | QoO = 0%..100% | QoO = 100%
| |
Throughput <-- Insufficient --|-------- Sufficient ------------->
QoO forced | QoO not capped
to 0% |
|
min. throughput^
Figure 2: The three dimensions of QoO.
3.3.2. Latency Component
The QoO latency score is based on linear interpolations of the
latency values at all latency percentiles defined in ROP / CPUP and
represents the minimum value for all percentiles:
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for i in latency_percentiles:
m = 1 - ((ML[i] - ROP[i]) / (CPUP[i] - ROP[i]))
metrics[i] = clamp(0, m, 1)
QoO_latency = find_min(metrics) * 100
Where:
* latency_percentiles are the latency percentiles contained in the
ROP and CPUP definitions.
* ML[i] is the measured latency at percentile
latency_percentiles[i].
* ROP[i] is the latency indicated in ROP at percentile
latency_percentiles[i].
* CPUP[i] is the latency indicated in CPUP at percentile
latency_percentiles[i].
3.3.3. Packet Loss Component
Packet loss is considered as a separate, single measurement that
applies across the entire traffic sample, not at each percentile.
The packet loss score is calculated using a similar interpolation
formula, but based on the measured mean packet loss ratio (MLoss) and
the packet loss thresholds defined in the ROP and CPUP:
m = 1 - ((M_Loss - ROP_Loss) / (CPUP_Loss - ROP_Loss))
QoO_loss = clamp(0, m, 1) * 100
Where:
* M_Loss is the measured mean packet loss ratio.
* ROP_Loss is the packet loss ratio indicated in ROP.
* CPUP_Loss is the packet loss ratio indicated in CPUP.
3.3.4. Throughput Component
In contrast to the latency and packet loss components, throughput
uses a binary threshold:
QoO_throughput = (M_Throughput >= R_Throughput) ? 100 : 0
Where:
* M_Throughput is the measured throughput.
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* R_Throughput is the minimum required throughput.
3.4. Example
The following example illustrates the QoO calculations.
Example requirements:
* Minimum throughput: 4Mbps
* ROP: {99%, 200ms}, {99.9%, 300ms}, 1% packet loss
* CPUP: {99%, 500ms}, {99.9%, 600ms}, 5% packet loss
Example measured conditions:
* Measured latency: 99% = 350ms, 99.9% = 375ms
* Measured mean packet loss ratio: 2%
* Measured throughput: 28Mbps
Latency component:
m1 = 1 - ((350ms - 200ms) / (500ms - 200ms)) = 1 - 0.5 = 0.5
m2 = 1 - ((375ms - 300ms) / (600ms - 300ms)) = 1 - 0.25 = 0.75
metrics = [clamp(0, m1, 1), clamp(0, m2, 1)] = [0.5, 0.75]
QoO_latency = find_min(metrics) * 100 = 50
Packet loss component:
m = 1 - ((2% - 1%) / (5% - 1%)) = 1 - 0.25 = 0.75
QoO_loss = clamp(0, m, 1) * 100 = 75
Throughput component:
28Mbps > 4Mbps
-> QoO_throughput = 100
Overall QoO score:
QoO = min(QoO_latency, QoO_loss, QoO_throughput) = min(50, 75, 100) = 50
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In this example, the network scores 50% on the QoO assessment range
between unacceptable and optimal for the given application when using
the measured network conditions and considering latency, packet loss,
and throughput. The score implies that the latency impact dominates
the packet loss and throughput impacts and that the network overall
provides conditions at the midway point of the performance range.
4. Operational Considerations
Aiming to ensure broad and easy applicability of the QoO framework
across diverse use cases, the document imposes only a few strict
mandates. Instead, this section provides general guidance concerning
the operation of the QoO framework based on intuitions and
assumptions that guided the development of the framework. Future
documents are expected to capture and refine best practices once more
operational experience has been gathered.
Some preliminary insights from a small-scale user testing campaign
are provided in Appendix C. More comprehensive and large-scale
testing are needed to assess the QoO framework.
4.1. QoO in the Quality Assessment Landscape
QoS, QoO, and QoE, as well as Quality Attenuation occupy different
positions on a spectrum from raw network characterization to
subjective user experience. QoS characterizes raw network behavior
(latency, packet loss, throughput), usually without direct reference
to any particular application or user. QoE, on the other hand,
focuses on the subjective user perception directly.
QoO, by design, measures network service quality, not subjective user
experience. However, as QoO scores are anchored to application-
defined thresholds, they are expected to correlate with QoE metrics,
such as Mean Opinion Score (MOS) [P.800.1], positioning QoO between
QoS and QoE. The QoO framework itself does not define where QoO
scores fall on this spectrum. Instead, the exact position primarily
depends on how the ROP and CPUP thresholds are chosen. With
appropriate threshold selection based on user-acceptance testing and
application performance analysis, QoO scores can likely be tuned to
closely approximate QoE metrics, while still maintaining the
objectivity and composability benefits of QoS metrics.
Quality Attenuation is complementary to QoO in that it also aims to
provide QoE-oriented QoS assessments. Both draw on the same latency
distributions and packet loss probabilities, but differ in how those
measurements are transformed: Quality Attenuation preserves the full
distributional detail needed for convolution and per-hop
decomposition, while QoO trades that detail for an application-
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anchored score that is immediately actionable. Since both rely on
the same underlying data, switching between Quality Attenuation and
QoO requires no additional measurements, so operators can use QoO to
produce a score that is immediately meaningful to all stakeholders
and Quality Attenuation if they need more detailed root-cause
analysis, capacity planning, or segment comparisons.
4.2. Composability, Flexibility, and Use Cases
One of the key strengths of the QoO framework is the mathematical
composability of the underlying latency distributions and packet loss
probabilities (see Appendix B.10), which allows measurements from
different network segments to be combined or decomposed to isolate
per-segment contributions. The composability also enables flexible
deployment scopes as QoO scores may be computed for the complete end-
to-end path (e.g., from application clients to servers), or focused
on specific network segments, such as the customer-facing access
network, intermediate transit networks, or server-side
infrastructure. The network performance requirement specifications
provide another dimension of flexibility as specifications can have
different scopes, such as per-application, per application-type, or
per-Service Level Agreement (SLA).
A holistic use of QoO with a fine-grained attribution of per-segment
contributions requires sharing the measured distributions and
probabilities for the involved segments among all relevant
stakeholders, which can be challenging across different operators or
networks. However, even without sharing raw data across all networks
of an end-to-end path, QoO remains valuable for analyzing and
troubleshooting individual network segments. Operators can use QoO
to assess specific segments within their own networks, and end-users
can gain insights into their own connectivity as long as their
network providers support QoO. Hence, QoO is well-suited for
incremental deployment (Section 2.1.2 of [RFC5218]).
4.3. Aligning Measurements with Service Levels
The QoO framework assumes that measurements reflect the actual
connectivity service that will be provided to application flows.
However, networks may offer multiple connectivity service levels
(e.g., VPN services [RFC2764], corporate customer tiers, and network
slicing configurations [RFC9543]). In such deployments, it is
important to ensure that:
* Measurements are taken using the same connectivity service level
that will be used by the application
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* The measurement methodology accounts for any traffic
prioritization, differentiated services, or QoS mechanisms that
may affect application performance
* Network configurations and policies that will apply to application
traffic are reflected in the measurement conditions
Failing to align measurements with the actual service delivery may
result in QoO scores that do not accurately reflect the application's
expected performance.
4.4. Path Stability and Temporal Validity
Even when measurements are correctly aligned with the intended
service delivery level, network behavior can vary within that service
level over time.
Network conditions along a given path can fluctuate with varying
traffic load: congestion, for example, can cause latency to increase
and packet loss to rise transiently. The multi-percentile
representation of latency in the QoO requirements specifications
naturally captures such fluctuations within a measurement window,
although the shape of the distribution depends on the load conditions
present during that window.
Beyond load-driven fluctuation, forwarding paths themselves can also
shift on a variety of timescales: routing changes, load balancing
decisions, and traffic engineering policies may cause packets or
flows to traverse different physical paths, each with potentially
different latency, loss, and capacity characteristics. Load
balancing, such as Equal-Cost Multi-Path (ECMP), can even mean that
measurements represent a mixture of the characteristics across all
active routes rather than those of a single coherent path.
Together, congestion-driven variation and path diversity mean that a
QoO assessment captures a snapshot of network behavior during the
sampling period, and conditions may differ significantly at other
times. Operators should therefore repeat measurements periodically,
and interpret individual QoO scores in light of when and under what
load conditions they were obtained. Implementers also need to decide
whether measurement traffic is steered consistently (e.g., by tuning
flow tuples to follow specific ECMP paths) or deliberately varied to
sample full path diversity, and document which approach was used in
the measurement metadata.
These challenges are even more pronounced for mobile cellular
networks, where path and capacity can change by an order of magnitude
within seconds (see also Section 5.1).
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4.5. Multipath Protocols
A related challenge arises when the application itself uses a
transport protocol that exploits multiple paths concurrently, such as
Multipath TCP (MPTCP) [RFC8684] or Multipath QUIC
[I-D.ietf-quic-multipath]. In such cases, application traffic can be
spread across several paths simultaneously, and a single-flow
measurement necessarily follows only one of them. Such single-path
QoO measurements may therefore underestimate aggregate capacity and
fail to represent the full latency and loss distribution that the
application actually experiences across its concurrent paths.
Implementers deploying QoO alongside multipath-capable applications
should account for this by measuring across multiple representative
flow tuples or by using passive monitoring of the actual application
traffic. As with path diversity and load-driven variation, this
means that a QoO score reflects only the conditions observable on the
measured path subset during the sampling period.
4.6. Adaptive Applications
Many modern applications are adaptive, meaning they can adjust their
behavior based on network conditions. For example, video streaming
applications may reduce bit rate when available network capacity is
limited, or increase buffer size when latency is high.
For adaptive applications, there are typically different levels of
optimal performance rather than a single absolute threshold. For
example, a video streaming application might provide different
available video resolutions, ranging from 4K to 480p resolution.
Combined with different transmission latencies, each of these
resolutions can induce varying levels of perceived quality.
The QoO framework can accommodate such applications by defining
multiple ROP/CPUP thresholds corresponding to different quality
levels. The framework can then assess how well the application will
achieve each quality level, providing a more nuanced view of
application performance than a simple binary pass/fail metric.
Another, less complex approach at the cost of reduced fidelity in the
QoO score, is to set the threshold for optimal performance at the
highest rendition available for the video stream, and the threshold
for unacceptability where the lowest rendition cannot be delivered
without resulting in stalling events.
Application developers implementing adaptive applications should
consider publishing quality profiles that define network performance
requirements for different adaptation levels, enabling more accurate
QoO assessment.
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4.7. Continuous Measurements
The QoO framework does not define measurement periods: it can be
applied to measurements taken over a specific, bounded interval
(e.g., when conducting a scheduled test run) as well as to continuous
measurements collected from live traffic over an extended period.
Deployments may use either mode depending on the use case and
available infrastructure [RFC6703].
When measurements are collected continuously, implementations must
decide how to window or aggregate samples into the latency
distribution and packet loss estimate used to compute a QoO score.
Several approaches are possible, and each involves trade-offs:
Fixed time windows (e.g., last hour, last day, or last week) are
simple to implement and compare across operators. Longer windows
smooth out short-term anomalies but may obscure recent degradation;
shorter windows are more responsive but less stable.
Rolling or sliding windows of the most recent N samples or the most
recent T seconds provide a continuously updated view of network
quality, balancing responsiveness with stability.
Measurements can also be grouped around specific events, such as user
sessions or application usage periods. This approach can improve
relevance for end-user-facing scores but may introduce selection
bias.
The choice of windowing strategy affects which percentiles are
observed and therefore the resulting QoO score. Implementations
should document the windowing strategy used alongside the reported
QoO scores and the measurement approach to ensure results are
interpretable and comparable. Standardization of specific windowing
approaches is considered out of scope for this document and left for
future work.
4.8. Sensitivity to Sampling Accuracy
While the QoO framework itself places no strict requirement on
sampling patterns or measurement technology, a simulation study
[QoOSimStudy] conducted to inform the creation of this document
examined the metric's real-world applicability under varying
conditions and made the following conclusions:
1. Sampling Frequency: Slow sampling rates (e.g., <1Hz) risk missing
rare, short-lived latency spikes, resulting in overly optimistic
QoO scores.
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2. Measurement Noise: Measurement errors on the same scale as the
thresholds (ROP, CPUP) can distort high-percentile latencies and
cause overly pessimistic QoO scores.
3. Requirement Specification: Slightly adjusting the latency
thresholds or target percentiles can cause significant changes in
QoO, especially when the measurement distribution is near a
threshold.
4. Measurement Duration: Shorter tests with sparse sampling tend to
underestimate worst-case behavior for heavy-tailed latency
distributions, biasing QoO in a positive direction.
In summary, overly noisy or inaccurate latency samples can
artificially inflate worst-case percentiles, thereby driving QoO
scores lower than actual network conditions would warrant.
Conversely, coarse measurement intervals can miss short-lived spikes
entirely, resulting in an inflated QoO.
From these findings, the following guidelines for practical
application are deduced:
* Calibrate the combination of sampling rate and total measurement
period to capture fat-tailed distributions of latency with
sufficient accuracy.
* Avoid or account for significant measurement noise where possible
(e.g., by calibrating time sources, accounting for clock drift,
considering hardware/software measurement jitter).
* Thoroughly test ROP and CPUP thresholds so that the resulting QoO
scores accurately reflect application performance.
These guidelines are non-normative but reflect empirical evidence on
how QoO performs.
4.9. A Subjective Approach to Creating Network Performance Requirement
Specifications
This document does not define a standardized approach for creating a
quality-focused network performance requirement specification.
Instead, this section provides general guidance on and a rough
outline for deriving an admittingly subjective requirement
specification, aiming to create a basis for future standardization
efforts focusing on developing a standardized, objective requirement
creation framework. Additional information is provided in
[QoOAppQualityReqs]. Direct use of the approach described below in
production scenarios is discouraged due to its inherently subjective
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nature.
When determining quality-focused network performance requirements for
an application, the goal is to identify the network conditions where
application performance is optimal and where it becomes unacceptable.
There is no universally strict threshold at which network conditions
render an application unacceptable. For optimal performance, some
applications may have clear definitions, but for others, such as web
browsing and gaming, lower latency and loss is always preferable.
One approach for deriving possible thresholds is to run the
application over a controlled network segment with adjustable quality
and then vary the network conditions while continuously observing the
resulting application-level performance. The latter can be assessed
manually by the entity performing the testing or using automated
methods, such as recording video stall duration within a video
player. Additionally, application developers could set thresholds
for acceptable fps, animation fluidity, i/o latency (voice, video,
actions), or other metrics that directly affect the user experience
and measure these user-facing metrics during tests to correlate the
metrics with the network conditions.
Using this scenario, one can first establish a baseline under
excellent network conditions. Network conditions can then be
gradually worsened by adding delay or packet loss or decreasing
network capacity until the application no longer performs optimally.
The corresponding network conditions identify the minimal
requirements for optimal performance (ROP). Continuing to worsen the
network conditions until the application fails completely eventually
yields the network conditions at the point of unacceptable
performance (CPUP).
Note that different users may have different tolerance levels for
application degradation. Hence, tests conducted by a single entity
likely result in highly subjective thresholds. The thresholds
established should represent acceptable performance for the target
user base, which may require user studies or market research to
determine appropriate values.
As stated at the beginning of this section, this document does not
define a standardized approach for creating a quality-focused network
performance requirement specification and directly using the approach
described above is discouraged.
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5. Known Weaknesses and Open Questions
Network performance measurements can be interpreted in different ways
to derive quality assessments. QoO does so by comparing measured
conditions against application-specific, quality-focused network
performance requirements to produce a percentage-based score, which
introduces several artifacts whose significance may vary depending on
the context. The following section discusses some known limitations.
A general assumption underlying the framework is that factors not
explicitly captured by QoO (such as temporal packet ordering, fine-
grained throughput variations, or the full shape of the latency
distribution) will inevitably influence the observed latency and
packet loss behavior, so that QoO indirectly accounts for their
effects.
5.1. Volatile Networks
Volatile networks - in particular, mobile cellular networks - pose a
challenge for network quality prediction [CellularPredictability],
with the level of assurance of the prediction likely to decrease as
session duration increases [QoSPrediction]. Historic network
conditions for a given cell may help indicate times of network load
or reduced transmission power, and their effect on
throughput/latency/loss. However, as terminals are mobile, the
available capacity for a given terminal can change by an order of
magnitude within seconds due to physical radio factors. These
include whether the terminal is at the edge of a cell for a radio
network, or undergoing cell handover, the radio interference and
fading from the local environment, and any switch between radio
bearers with differing capacity and transmission-time intervals
(e.g., 3GPP 4G and 5G).
The above suggests a requirement for measuring network quality to and
from an individual terminal, as that can account for the factors
described above. How that facility is provisioned onto individual
terminals and how terminal-hosted applications can trigger a network
quality query, is an open question.
5.2. Missing Temporal Information in Distributions
The two latency series (1,200,1,200,1,200,1,200,1,200) and
(1,1,1,1,1,200,200,200,200,200) have identical distributions, but may
have different application performance. Ignoring this information is
a tradeoff between simplicity and precision. To capture all
information necessary to adequately capture outcomes quickly gets
into extreme levels of overhead and high computational complexity.
An application's performance depends on reactions to varying network
conditions, meaning nearly all different series of latencies may have
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different application outcomes.
5.3. Subsampling the Real Distribution
Additionally, it is not feasible to capture latency for every packet
transmitted. Probing and sampling can be performed, but some aspects
will always remain unknown. This introduces an element of
uncertainty and perfect predictions cannot be achieved; rather than
disregarding this reality, it is more practical to acknowledge it.
Therefore, discussing the assessment of outcomes provides a more
accurate and meaningful approach.
5.4. Assuming Linear Relationship Between Optimal Performance and
Unusable
It has been shown that, for example, interactivity cannot be modeled
by a linear scale [G.1051]. Thus, the linear modeling proposed in
the QoO framework adds an error in estimating the perceived
performance of interactive applications.
One can conjure up scenarios where 50ms latency is actually worse
than 51ms latency as developers may have chosen 50ms as the threshold
for changing quality, and the threshold may be imperfect. Taking
these scenarios into account would add another magnitude of
complexity to determining network performance requirements and
finding a distance measure (between requirement and actual measured
capability).
5.5. Binary Throughput Threshold
Choosing a binary throughput threshold is to reduce complexity, but
it must be acknowledged that many applications are not that simple.
Network requirements can be set up per quality level (resolution,
frames per-second, etc.) for the application if necessary.
5.6. Arbitrary Selection of Percentiles
A selection of percentiles is necessary for simplicity, because more
complex methods may slow adoption of the framework. The 0th
(minimum) and 50th (median) percentiles are commonly used for their
inherent significance. According to [BITAG], the 90th, 98th, and
99th percentiles are particularly important for certain applications.
Generally, higher percentiles provide more insight for interactive
applications, but only up to a certain threshold beyond which
applications may treat excessive delays as packet loss and adapt
accordingly. The choice between percentiles such as the 95th, 96th,
96.5th, or 97th is not universally prescribed and may vary between
application types. Therefore, percentiles must be selected
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arbitrarily, based on the best available knowledge and the intended
use case.
6. Security Considerations
The QoO framework introduces a method for assessing network quality
based on probabilistic outcomes derived from latency, packet loss,
and throughput measurements. While the framework itself is primarily
analytical and does not define a new protocol, some security
considerations arise from its deployment and use. In addition to the
considerations discussed below, implementers are also encouraged to
consider the security considerations outlined in [RFC7594].
6.1. Measurement Integrity and Authenticity
QoO relies upon accurate and trustworthy measurements of network
performance. If an attacker can manipulate these measurements,
either by injecting falsified data or tampering with the measurement
process, they could distort the resulting QoO scores. This could
mislead users, operators, or regulators into making incorrect
assessments of network quality.
To mitigate this risk:
* Measurement agents have to authenticate with the systems
collecting or analyzing QoO data.
* Measurement data has to be transmitted over secure channels (e.g.,
(D)TLS) to ensure confidentiality and integrity.
* Digital signatures may be used to verify the authenticity of
measurement reports.
6.2. Risk of Misuse and Gaming
As QoO scores may influence regulatory decisions, SLAs, or user
trust, there is a risk that network operators or application
developers might attempt to "game" the system. For example, they
might optimize performance only for known test conditions or falsify
requirement thresholds to inflate QoO scores.
Mitigations include:
* Independent verification of application requirements and
measurement methodologies.
* Use of randomized testing procedures.
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* Transparency in how QoO scores are derived and what assumptions
are made.
6.3. Denial-of-Service (DoS) Risks
Active measurement techniques used to gather QoO data (e.g., TWAMP,
STAMP, and synthetic traffic generation) can place additional load on
a network. If not properly rate-limited, this may inadvertently
degrade services offered by a network or be exploited by malicious
actors to launch DoS attacks [RFC2330].
To mitigate these risks, the following is recommended:
* Implement rate-limiting and access control for active measurement
tools, e.g., similar to recommendations for the One-way Active
Measurement Protocol [RFC4656].
* Ensure that measurement traffic does not interfere with critical
services.
* Monitor for abnormal measurement patterns that may indicate abuse.
6.4. Trust in Application Requirements
QoO depends on application developers to define ROP and CPUP. If
these are defined inaccurately-either unintentionally or maliciously-
the resulting QoO scores may be misleading.
To address such risks, the following recommendations are made:
* Encourage peer review and publication of application requirement
profiles.
* Where QoO is used for regulatory or SLA enforcement, require
independent validation of requirement definitions.
7. Privacy Considerations
QoO measurements may involve collecting detailed performance data
from end-user devices or applications, especially in the context of
passive measurements [RFC2330]. Depending on the deployment model,
this includes metadata such as IP addresses, timestamps, or
application usage patterns.
To protect user privacy:
* Data collection should be subject to user consent prior to
collecting data.
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* Data collection should follow the principle of data minimization,
only collecting what is strictly necessary.
* Privacy-sensitive information (e.g., Personally Identifiable
Information (PII)) should be anonymized or pseudonymized where
possible.
* Users should be informed about what data is collected and how it
is used, in accordance with applicable privacy regulations (e.g.,
General Data Protection Regulation (GDPR)).
8. IANA Considerations
This document has no IANA actions.
9. Implementation status
Note to RFC Editor: This section must be removed before publication
of the document.
This section records the status of known implementations of the
protocol defined by this specification at the time of posting of this
Internet-Draft, and is based on a proposal described in [RFC7942].
The description of implementations in this section is intended to
assist the IETF in its decision processes in progressing drafts to
RFCs. Please note that the listing of any individual implementation
here does not imply endorsement by the IETF. Furthermore, no effort
has been spent to verify the information presented here that was
supplied by IETF contributors. This is not intended as, and must not
be construed to be, a catalog of available implementations or their
features. Readers are advised to note that other implementations may
exist.
According to [RFC7942], "this will allow reviewers and working groups
to assign due consideration to documents that have the benefit of
running code, which may serve as evidence of valuable experimentation
and feedback that have made the implemented protocols more mature.
It is up to the individual working groups to use this information as
they see fit".
9.1. qoo-c
* Link to the open-source repository:
https://github.com/getCUJO/qoo-c
* The organization responsible for the implementation:
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CUJO AI
* A brief general description:
A C library for calculating Quality of Outcome
* The implementation's level of maturity:
A complete implementation of the specification described in this
document
* Coverage:
The library is tested with unit tests
* Licensing:
MIT
* Implementation experience:
Tested by the author. Needs additional testing by third parties.
* Contact information:
Bjørn Ivar Teigen Monclair: bjorn.monclair@cujo.com
* The date when information about this particular implementation was
last updated:
27th of May 2025
9.2. goresponsiveness
* Link to the open-source repository:
https://github.com/network-quality/goresponsiveness
The specific pull-request: https://github.com/network-
quality/goresponsiveness/pull/56
* The organization responsible for the implementation:
University of Cincinatti for goresponsiveness as a whole, Domos
for the QoO part.
* A brief general description:
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A network quality test written in Go. Capable of measuring RPM
and QoO.
* The implementation's level of maturity:
Under active development; partial QoO support integrated.
* Coverage:
The QoO part is tested with unit tests
* Licensing:
GPL 2.0
* Implementation experience:
Needs testing by third parties
* Contact information:
Bjørn Ivar Teigen Monclair: bjorn.monclair@cujo.com
William Hawkins III: hawkinwh@ucmail.uc.edu
* The date when information about this particular implementation was
last updated:
10th of January 2024
10. References
10.1. Normative References
[RFC6049] Morton, A. and E. Stephan, "Spatial Composition of
Metrics", RFC 6049, DOI 10.17487/RFC6049, January 2011,
<https://www.rfc-editor.org/rfc/rfc6049>.
[RFC6390] Clark, A. and B. Claise, "Guidelines for Considering New
Performance Metric Development", BCP 170, RFC 6390,
DOI 10.17487/RFC6390, October 2011,
<https://www.rfc-editor.org/rfc/rfc6390>.
[TR-452.1] Broadband Forum, "TR-452.1: Quality Attenuation
Measurement Architecture and Requirements", September
2020,
<https://www.broadband-forum.org/download/TR-452.1.pdf>.
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10.2. Informative References
[BITAG] BITAG, "Latency Explained", October 2022,
<https://www.bitag.org/documents/
BITAG_latency_explained.pdf>.
[Bufferbloat]
"Bufferbloat: Dark buffers in the Internet", n.d.,
<https://queue.acm.org/detail.cfm?id=2071893>.
[CellularPredictability]
Basit, O., Dinh, P., Khan, I., Kong, Z., Hu, Y.,
Koutsonikolas, D., Lee, M., and C. Liu, "On the
Predictability of Fine-Grained Cellular Network Throughput
Using Machine Learning Models", IEEE, 2024 IEEE 21st
International Conference on Mobile Ad-Hoc and Smart
Systems (MASS) pp. 47-56,
DOI 10.1109/mass62177.2024.00018, September 2024,
<https://doi.org/10.1109/mass62177.2024.00018>.
[CSGO] Xu, X., Liu, S., and M. Claypool, "The Effects of Network
Latency on Counter-strike: Global Offensive Players",
IEEE, 2022 14th International Conference on Quality of
Multimedia Experience (QoMEX) pp. 1-6,
DOI 10.1109/qomex55416.2022.9900915, September 2022,
<https://doi.org/10.1109/qomex55416.2022.9900915>.
[G.1051] ITU-T, "Latency measurement and interactivity scoring
under real application data traffic patterns",
ITU-T G.1051, March 2023,
<https://www.itu.int/rec/T-REC-G.1051>.
[Haeri22] "Mind Your Outcomes: The ΔQSD Paradigm for Quality-Centric
Systems Development and Its Application to a Blockchain
Case Study", n.d.,
<https://www.mdpi.com/2073-431X/11/3/45>.
[I-D.ietf-ippm-responsiveness]
Paasch, C., Meyer, R., Cheshire, S., and W. Hawkins,
"Responsiveness under Working Conditions", Work in
Progress, Internet-Draft, draft-ietf-ippm-responsiveness-
08, 20 October 2025,
<https://datatracker.ietf.org/doc/html/draft-ietf-ippm-
responsiveness-08>.
[I-D.ietf-opsawg-rfc5706bis]
Claise, B., Clarke, J., Farrel, A., Barguil, S.,
Pignataro, C., and R. Chen, "Guidelines for Considering
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Operations and Management in IETF Specifications", Work in
Progress, Internet-Draft, draft-ietf-opsawg-rfc5706bis-04,
15 March 2026, <https://datatracker.ietf.org/doc/html/
draft-ietf-opsawg-rfc5706bis-04>.
[I-D.ietf-quic-multipath]
Liu, Y., Ma, Y., De Coninck, Q., Bonaventure, O., Huitema,
C., and M. Kühlewind, "Managing multiple paths for a QUIC
connection", Work in Progress, Internet-Draft, draft-ietf-
quic-multipath-21, 17 March 2026,
<https://datatracker.ietf.org/doc/html/draft-ietf-quic-
multipath-21>.
[IRTT] "Isochronous Round-Trip Tester", n.d.,
<https://github.com/heistp/irtt>.
[ISO5725-1]
ISO, "Accuracy (trueness and precision) of measurement
methods and results Part 1: General principles and
definitions", ISO 5725-1:2023, July 2022,
<https://www.iso.org/standard/69418.html>.
[JamKazam] Wilson, D., "What is Latency and Why does it matter?",
n.d., <https://jamkazam.freshdesk.com/support/solutions/
articles/66000122532-what-is-latency-why-does-it-
matter-?>.
[Kelly] Kelly, F. P., "Networks of Queues", n.d.,
<https://www.cambridge.org/core/journals/advances-in-
applied-probability/article/abs/networks-of-
queues/38A1EA868A62B09C77A073BECA1A1B56>.
[LatencyEstimation]
Li, C., Nasr-Esfahany, A., Zhao, K., Noorbakhsh, K.,
Goyal, P., Alizadeh, M., and T. Anderson, "m3: Accurate
Flow-Level Performance Estimation using Machine Learning",
ACM, Proceedings of the ACM SIGCOMM 2024 Conference pp.
813-827, DOI 10.1145/3651890.3672243, August 2024,
<https://doi.org/10.1145/3651890.3672243>.
[P.10] ITU-T, "Vocabulary for performance, quality of service and
quality of experience", ITU-T P.10/G.100, November 2017,
<https://www.itu.int/rec/T-REC-P.10>.
[P.1401] ITU-T, "Methods, metrics and procedures for statistical
evaluation, qualification and comparison of objective
quality prediction models", ITU-T P.1401, January 2020,
<https://www.itu.int/rec/T-REC-P.1401>.
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[P.800] ITU-T, "Methods for subjective determination of
transmission quality", ITU-T P.800, August 1996,
<https://www.itu.int/rec/T-REC-P.800>.
[P.800.1] ITU-T, "Mean opinion score (MOS) terminology",
ITU-T P.800.1, July 2016,
<https://www.itu.int/rec/T-REC-P.800.1>.
[P.910] ITU-T, "Subjective video quality assessment methods for
multimedia applications", ITU-T P.910, October 2023,
<https://www.itu.int/rec/T-REC-P.910>.
[QoOAppQualityReqs]
Østensen, T., "Performance Measurement of Web
Applications", n.d., <https://domos.ai/storage/
U6TlxIlbcl1dQfcNhnCleziJWF23P5w0xWzOARh8-published.pdf>.
[QoOSimStudy]
Monclair, B. I. T., "Quality of Outcome Simulation Study",
n.d., <https://github.com/getCUJO/qoosim>.
[QoOUserStudy]
Monclair, B. I. T., "Application Outcome Aware Root Cause
Analysis", n.d., <https://domos.ai/storage/
LaiW4tJQ2kj4OOTiZbnf48MbS22rQHcZQmCriih9-published.pdf>.
[QoSPrediction]
Moreno, N., Dsouza, F., Kassler, A., Pullem, F., Xu, B.,
Amend, M., and C. Choi, "QoS and Capacity Prediction for
5G Network Slicing", IEEE, 2025 21st International
Conference on Network and Service Management (CNSM) pp.
1-5, DOI 10.23919/cnsm67658.2025.11297458, October 2025,
<https://doi.org/10.23919/cnsm67658.2025.11297458>.
[RFC2330] Paxson, V., Almes, G., Mahdavi, J., and M. Mathis,
"Framework for IP Performance Metrics", RFC 2330,
DOI 10.17487/RFC2330, May 1998,
<https://www.rfc-editor.org/rfc/rfc2330>.
[RFC2681] Almes, G., Kalidindi, S., and M. Zekauskas, "A Round-trip
Delay Metric for IPPM", RFC 2681, DOI 10.17487/RFC2681,
September 1999, <https://www.rfc-editor.org/rfc/rfc2681>.
[RFC2764] Gleeson, B., Lin, A., Heinanen, J., Armitage, G., and A.
Malis, "A Framework for IP Based Virtual Private
Networks", RFC 2764, DOI 10.17487/RFC2764, February 2000,
<https://www.rfc-editor.org/rfc/rfc2764>.
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[RFC3393] Demichelis, C. and P. Chimento, "IP Packet Delay Variation
Metric for IP Performance Metrics (IPPM)", RFC 3393,
DOI 10.17487/RFC3393, November 2002,
<https://www.rfc-editor.org/rfc/rfc3393>.
[RFC4656] Shalunov, S., Teitelbaum, B., Karp, A., Boote, J., and M.
Zekauskas, "A One-way Active Measurement Protocol
(OWAMP)", RFC 4656, DOI 10.17487/RFC4656, September 2006,
<https://www.rfc-editor.org/rfc/rfc4656>.
[RFC5218] Thaler, D. and B. Aboba, "What Makes for a Successful
Protocol?", RFC 5218, DOI 10.17487/RFC5218, July 2008,
<https://www.rfc-editor.org/rfc/rfc5218>.
[RFC5357] Hedayat, K., Krzanowski, R., Morton, A., Yum, K., and J.
Babiarz, "A Two-Way Active Measurement Protocol (TWAMP)",
RFC 5357, DOI 10.17487/RFC5357, October 2008,
<https://www.rfc-editor.org/rfc/rfc5357>.
[RFC5481] Morton, A. and B. Claise, "Packet Delay Variation
Applicability Statement", RFC 5481, DOI 10.17487/RFC5481,
March 2009, <https://www.rfc-editor.org/rfc/rfc5481>.
[RFC6349] Constantine, B., Forget, G., Geib, R., and R. Schrage,
"Framework for TCP Throughput Testing", RFC 6349,
DOI 10.17487/RFC6349, August 2011,
<https://www.rfc-editor.org/rfc/rfc6349>.
[RFC6673] Morton, A., "Round-Trip Packet Loss Metrics", RFC 6673,
DOI 10.17487/RFC6673, August 2012,
<https://www.rfc-editor.org/rfc/rfc6673>.
[RFC6703] Morton, A., Ramachandran, G., and G. Maguluri, "Reporting
IP Network Performance Metrics: Different Points of View",
RFC 6703, DOI 10.17487/RFC6703, August 2012,
<https://www.rfc-editor.org/rfc/rfc6703>.
[RFC7594] Eardley, P., Morton, A., Bagnulo, M., Burbridge, T.,
Aitken, P., and A. Akhter, "A Framework for Large-Scale
Measurement of Broadband Performance (LMAP)", RFC 7594,
DOI 10.17487/RFC7594, September 2015,
<https://www.rfc-editor.org/rfc/rfc7594>.
[RFC7679] Almes, G., Kalidindi, S., Zekauskas, M., and A. Morton,
Ed., "A One-Way Delay Metric for IP Performance Metrics
(IPPM)", STD 81, RFC 7679, DOI 10.17487/RFC7679, January
2016, <https://www.rfc-editor.org/rfc/rfc7679>.
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[RFC7680] Almes, G., Kalidindi, S., Zekauskas, M., and A. Morton,
Ed., "A One-Way Loss Metric for IP Performance Metrics
(IPPM)", STD 82, RFC 7680, DOI 10.17487/RFC7680, January
2016, <https://www.rfc-editor.org/rfc/rfc7680>.
[RFC7799] Morton, A., "Active and Passive Metrics and Methods (with
Hybrid Types In-Between)", RFC 7799, DOI 10.17487/RFC7799,
May 2016, <https://www.rfc-editor.org/rfc/rfc7799>.
[RFC7942] Sheffer, Y. and A. Farrel, "Improving Awareness of Running
Code: The Implementation Status Section", BCP 205,
RFC 7942, DOI 10.17487/RFC7942, July 2016,
<https://www.rfc-editor.org/rfc/rfc7942>.
[RFC8033] Pan, R., Natarajan, P., Baker, F., and G. White,
"Proportional Integral Controller Enhanced (PIE): A
Lightweight Control Scheme to Address the Bufferbloat
Problem", RFC 8033, DOI 10.17487/RFC8033, February 2017,
<https://www.rfc-editor.org/rfc/rfc8033>.
[RFC8239] Avramov, L. and J. Rapp, "Data Center Benchmarking
Methodology", RFC 8239, DOI 10.17487/RFC8239, August 2017,
<https://www.rfc-editor.org/rfc/rfc8239>.
[RFC8290] Hoeiland-Joergensen, T., McKenney, P., Taht, D., Gettys,
J., and E. Dumazet, "The Flow Queue CoDel Packet Scheduler
and Active Queue Management Algorithm", RFC 8290,
DOI 10.17487/RFC8290, January 2018,
<https://www.rfc-editor.org/rfc/rfc8290>.
[RFC8517] Dolson, D., Ed., Snellman, J., Boucadair, M., Ed., and C.
Jacquenet, "An Inventory of Transport-Centric Functions
Provided by Middleboxes: An Operator Perspective",
RFC 8517, DOI 10.17487/RFC8517, February 2019,
<https://www.rfc-editor.org/rfc/rfc8517>.
[RFC8684] Ford, A., Raiciu, C., Handley, M., Bonaventure, O., and C.
Paasch, "TCP Extensions for Multipath Operation with
Multiple Addresses", RFC 8684, DOI 10.17487/RFC8684, March
2020, <https://www.rfc-editor.org/rfc/rfc8684>.
[RFC8762] Mirsky, G., Jun, G., Nydell, H., and R. Foote, "Simple
Two-Way Active Measurement Protocol", RFC 8762,
DOI 10.17487/RFC8762, March 2020,
<https://www.rfc-editor.org/rfc/rfc8762>.
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[RFC8877] Mizrahi, T., Fabini, J., and A. Morton, "Guidelines for
Defining Packet Timestamps", RFC 8877,
DOI 10.17487/RFC8877, September 2020,
<https://www.rfc-editor.org/rfc/rfc8877>.
[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>.
[RFC9000] Iyengar, J., Ed. and M. Thomson, Ed., "QUIC: A UDP-Based
Multiplexed and Secure Transport", RFC 9000,
DOI 10.17487/RFC9000, May 2021,
<https://www.rfc-editor.org/rfc/rfc9000>.
[RFC9197] Brockners, F., Ed., Bhandari, S., Ed., and T. Mizrahi,
Ed., "Data Fields for In Situ Operations, Administration,
and Maintenance (IOAM)", RFC 9197, DOI 10.17487/RFC9197,
May 2022, <https://www.rfc-editor.org/rfc/rfc9197>.
[RFC9312] Kühlewind, M. and B. Trammell, "Manageability of the QUIC
Transport Protocol", RFC 9312, DOI 10.17487/RFC9312,
September 2022, <https://www.rfc-editor.org/rfc/rfc9312>.
[RFC9318] Hardaker, W. and O. Shapira, "IAB Workshop Report:
Measuring Network Quality for End-Users", RFC 9318,
DOI 10.17487/RFC9318, October 2022,
<https://www.rfc-editor.org/rfc/rfc9318>.
[RFC9341] Fioccola, G., Ed., Cociglio, M., Mirsky, G., Mizrahi, T.,
and T. Zhou, "Alternate-Marking Method", RFC 9341,
DOI 10.17487/RFC9341, December 2022,
<https://www.rfc-editor.org/rfc/rfc9341>.
[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/rfc/rfc9543>.
[RFC9817] Kunze, I., Wehrle, K., Trossen, D., Montpetit, M., de Foy,
X., Griffin, D., and M. Rio, "Use Cases for In-Network
Computing", RFC 9817, DOI 10.17487/RFC9817, August 2025,
<https://www.rfc-editor.org/rfc/rfc9817>.
[RPM] "Responsiveness under Working Conditions", July 2022,
<https://datatracker.ietf.org/doc/html/draft-ietf-ippm-
responsiveness>.
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[RRUL] "Real-time response under load test specification", n.d.,
<https://www.bufferbloat.net/projects/bloat/wiki/
RRUL_Spec/>.
[TCPContinuous]
Sengupta, S., Kim, H., and J. Rexford, "Continuous in-
network round-trip time monitoring", ACM, Proceedings of
the ACM SIGCOMM 2022 Conference pp. 473-485,
DOI 10.1145/3544216.3544222, August 2022,
<https://doi.org/10.1145/3544216.3544222>.
[TCPHandshake]
Apostolaki, M., Singla, A., and L. Vanbever, "Performance-
Driven Internet Path Selection", ACM, Proceedings of the
ACM SIGCOMM Symposium on SDN Research (SOSR) pp. 41-53,
DOI 10.1145/3482898.3483366, October 2021,
<https://doi.org/10.1145/3482898.3483366>.
[Throughputtest]
MacMillan, K., Mangla, T., Saxon, J., Marwell, N., and N.
Feamster, "A Comparative Analysis of Ookla Speedtest and
Measurement Labs Network Diagnostic Test (NDT7)",
Association for Computing Machinery (ACM), Proceedings of
the ACM on Measurement and Analysis of Computing
Systems vol. 7, no. 1, pp. 1-26, DOI 10.1145/3579448,
February 2023, <https://doi.org/10.1145/3579448>.
[XboxNetReqs]
Microsoft, "Understanding your remote play setup test
results", n.d., <https://support.xbox.com/en-US/help/
hardware-network/connect-network/console-streaming-test-
results>.
Appendix A. QoO Framework Design Considerations
This section describes the features and attributes that a network
quality framework must have to be useful for different stakeholders:
application developers, end-users, and network operators.
At a high level, end-users need an understandable network quality
metric. Application developers require a network quality metric that
allows them to evaluate how well their application is likely to
perform given the measured network performance. Network operators
need a metric that facilitates troubleshooting and optimization of
their networks. Existing network quality metrics and frameworks
address the needs of one or two of these stakeholders, but there is
none that bridges the needs of all three. Examples include
throughput metrics that operators use to prove regulatory compliance
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but with little relevance to application performance, or subjective
QoE metrics that are understandable to users but difficult for
operators to collect at meaningful scale.
A key motivation for the QoO framework is to bridge the gap between
the technical aspects of network performance and the practical needs
of those who depend on it. For example, while solutions exist for
many of the problems causing high and unstable latency in the
Internet, such as bufferbloat mitigation techniques [RFC8290] and
improved congestion control algorithms [RFC8033], the incentives to
deploy them have remained relatively weak. A unifying framework for
assessing network quality can serve to strengthen these incentives
significantly.
Network capacity alone is necessary but not sufficient for high-
quality modern network experiences. High idle and working latencies,
large delay variations, and unmitigated packet loss are major causes
of poor application outcomes. The impact of latency is widely
recognized in network engineering circles [BITAG], but benchmarking
the quality of network transport remains complex. Most end-users are
unable to relate to metrics other than Mbps, which they have long
been conditioned to think of as the only dimension of network
quality.
Real Time Response under load tests [RRUL] and Responsiveness tests
[RPM] make significant strides toward creating a network quality
metric that is intended to be closer to application outcomes than
available network capacity alone. [RPM], in particular, is
successful at being relatively relatable and understandable to end-
users. However, as noted in [RPM], "Our networks remain
unresponsive, not from a lack of technical solutions, but rather a
lack of awareness of the problem". This lack of awareness means that
some operators might have little incentive to improve network quality
beyond increasing the available network capacity. For example,
despite the availability of open-source solutions such as FQ_CoDel
[RFC8290], which has been available for over a decade, vendors rarely
implement them in widely deployed equipment (e.g., Wi-Fi routers
still commonly exhibit bufferbloat). A universally accepted network
quality framework that successfully captures the degree to which
networks provide the quality required by applications may help to
increase the willingness of vendors to implement such solutions.
The IAB workshop on measuring Internet quality for end-users
identified a key insight: users care primarily about application
performance rather than network performance. Among the conclusions
was the statement, "A really meaningful metric for users is whether
their application will work properly or fail because of a lack of a
network with sufficient characteristics" [RFC9318]. Therefore, one
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critical requirement for a meaningful framework is its ability to
answer the following question: "Will networking conditions prevent an
application from working as intended?".
Answering this question requires several considerations. First, the
Internet is inherently stochastic from the perspective of any given
client, so absolute certainty is unattainable. Second, different
applications have different needs and adapt differently to network
conditions. A framework aiming to answer the stated question must
accommodate such diverse application requirements. Third, end-users
have individual tolerances for degradation in network conditions and
the resulting effects on application experience. These variations
must be factored into the design of a suitable network quality
framework.
A.1. General Requirements
This section describes the requirements for an objective network
quality framework and metric that is useful for end-users,
application developers, and network operators/vendors alike.
Specifically, this section outlines the three main general
requirements for such a framework while the sections therafter
describe requirements from the perspective of each of the target
groups: end-users (Appendix A.2), application developers
(Appendix A.3), and network operators (Appendix A.4).
In general, all stakeholders ultimately care about the performance of
applications running over a network. Application performance does
not only depend on the available network capacity but also on the
delay and delay variation of network links and computational steps
involved in making the application function. These delays depend on
how the application places load on the network, how the network is
affected by environmental conditions, and the behavior of other users
and applications sharing the network resources. Likewise, packet
loss (e.g., caused by congestion) can also negatively impact
application performance in different ways depending on the class of
application.
Different applications may have different needs from a network and
may impose different patterns of load. To determine whether an
application will likely work well or fail, a network quality
framework must compare measurements of network performance to a wide
variety of application requirements. It is important that these
measurements reflect the actual network service configuration that
will handle the application flows, including any traffic
prioritization, network slicing, VPN services, or other
differentiated service mechanisms (see Section 4.3). Flexibility in
describing application requirements and the ability to capture the
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delay and loss characteristics of a network with sufficient accuracy
and precision are necessary to compute a meaningful QoO network
quality score that can be used to better estimate application
performance.
The framework must also support spatial composition
[RFC6049][RFC6390] to enable operators to take actions when
measurements show that applications fail too often. In particular,
spatial composition allows results to be divided into sub-results,
each measuring the performance of a required sub-milestone that must
be reached in time for the application to perform adequately.
To summarize, the QoO framework and the corresponding QoO score
should have the following properties in order to be meaningful:
1. Capture a set of network performance metrics which provably
correlate to the application performance of a set of different
applications as perceived by users.
2. Compare meaningfully to different application requirements.
3. Be composable so operators can isolate and quantify the
contributions of different sub-outcomes and sub-paths of the
network.
Next, the document presents requirements from the perspective of each
of the three target groups: end-users (Appendix A.2), application
developers (Appendix A.3), and network operators (Appendix A.4).
A.2. Requirements from End-Users
The QoO framework should facilitate a metric that is based on
objective QoS measurements (such as throughput [RFC6349], packet loss
[RFC6673][RFC7680], delays [RFC2681][RFC7679], and (one-way) delay
variations [RFC3393]), correlated to application performance, and
relatively understandable for end-users, similar to QoE metrics, such
as MOS [P.800.1].
If these requirements are met, QoO is a middle ground between QoS and
QoE metrics and allows end-users to understand if a network is a
likely source of impairment for what they care about: the outcomes of
applications. Examples are how quickly a web page loads, the
smoothness of a video conference, or whether or not a video game has
any perceptible lag.
End-users may have individual tolerances of session quality (i.e.,
the quality experienced during a single application usage period,
such as a video call or gaming session), below which their quality of
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experience becomes personally unacceptable. However, it may not be
feasible to capture and represent these tolerances per user as the
user group scales. A compromise is for the QoO framework to place
the responsibility for sourcing and representing end-user
requirements onto the application developer. Application developers
are expected to perform user-acceptance testing (UAT) of their
application across a range of users, terminals, and network
conditions to determine the terminal and network requirements that
will meet the acceptability thresholds for a representative subset of
their end-users. Performing UAT helps developers estimate what QoE
new end-users are likely to experience based on the application's
network performance requirements. These requirements can evolve and
improve based on feedback from end-users, and in turn better inform
the application's requirements towards the network. Some real world
examples where 'acceptable levels' have been derived by application
developers include:
* Remote music collaboration: <20ms one-way latency [JamKazam]
* Cloud gaming: >15Mbps downlink throughput and 80ms round-trip time
(RTT) [XboxNetReqs] (specific requirements vary by game and
platform; see [CSGO] for an example study on the impact of latency
on Counter Strike: Global Offensive)
* Virtual reality (VR): <20ms RTT from head motion to rendered
update in VR ([RFC9817]; see [G.1051] for latency measurement and
interactivity scoring)
These numbers only serve as examples and their exact value depends on
the specific application and the test methodology used to derive
them, such that they are not to be interpreted as universally
applicable (see also Section 3.2.2 and Section 4.9). Instead,
additional standardization efforts are needed to derive more
universally applicable thresholds for different classes of
applications.
A.3. Requirements from Application and Platform Developers
The QoO framework needs to provide developers the ability to describe
the quality-focused network performance requirements of their
applications. The network performance requirements must include all
relevant dimensions of network quality so that applications sensitive
to different network quality dimensions can all evaluate the network
accurately. Not all developers have network expertise, so to make it
easy for developers to use the framework, developers must be able to
specify network performance requirements approximately. Therefore,
it must be possible to describe both simple and complex network
performance requirements. The framework also needs to be flexible so
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that it can be used with different kinds of traffic and that extreme
network performance requirements which far exceed the needs of
today's applications can also be articulated.
If these requirements are met, developers of applications and
platforms can state or test their network requirements and evaluate
whether the network is sufficient for an optimal application outcome.
Both the application developers with networking expertise and those
without can use the framework.
A.4. Requirements from Network Operators and Network Solution Vendors
From an operator perspective, the key is to have a framework that
allows finding the network quality bottlenecks and objectively
comparing different networks, network configurations, and
technologies. To achieve this goal, the framework must support
mathematically sound compositionality ('addition' and 'subtraction')
as network operators rarely manage network traffic end-to-end. If a
test is purely end-to-end, the ability to find bottlenecks may be
gone. If, however, measurements can be taken in both end-to-end
(e.g., a-b-c-d-e) and partial (e.g., a-b-c) fashion, the results can
be decomposed to isolate the areas outside the influence of a network
operator. In other words, the network quality of a-b-c and d-e can
be separated. Compositionality is essential for fault detection and
accountability.
By having mathematically correct composition, a network operator can
measure two segments separately, perhaps even with different
approaches, and combine or correlate the results to understand the
end-to-end network quality.
Another example where composition is useful is troubleshooting a
typical web page load sequence over TCP. If web page load times are
too slow, DNS resolution time, TCP RTT, and the time it takes to
establish TLS connections can be measured separately to get a better
idea of where the problem is. A network quality framework should
support this kind of analysis to be maximally useful for operators.
The framework must be applicable in both lab testing and monitoring
of production networks. It must be useful on different time scales,
and it cannot have a dependency on network technology or OSI layers.
If these requirements are met, network operators can monitor and test
their network and understand where the true bottlenecks are,
regardless of network technology.
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Appendix B. Comparison To Other Network Quality Metrics
Numerous network quality metrics and associated frameworks have been
proposed, adopted, and, at times, misapplied over the years. The
following is a brief overview of several key network quality metrics
in comparison to QoO.
Each metric is evaluated against the three criteria established in
Appendix A.1. Table 1 summarizes the properties of each of the
surveyed metrics.
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+=============+=====================+==============+==============+
| Metric | Can Assess How Well | Easy to | Composable |
| | Applications Are | Articulate | |
| | Expected to Work | Application | |
| | | Requirements | |
+=============+=====================+==============+==============+
| Throughput | Yes for some | Yes | No |
| | applications | | |
+-------------+---------------------+--------------+--------------+
| Mean | Yes for some | Yes | Yes |
| latency | applications | | |
+-------------+---------------------+--------------+--------------+
| 99th | No | No | No |
| Percentile | | | |
| of Latency | | | |
+-------------+---------------------+--------------+--------------+
| Variance of | No | No | Yes |
| latency | | | |
+-------------+---------------------+--------------+--------------+
| IPDV | Yes for some | No | No |
| | applications | | |
+-------------+---------------------+--------------+--------------+
| PDV | Yes for some | No | No |
| | applications | | |
+-------------+---------------------+--------------+--------------+
| Trimmed | Yes for some | Yes | No |
| mean of | applications | | |
| latency | | | |
+-------------+---------------------+--------------+--------------+
| Round Trips | Yes for some | Yes | No |
| Per Minute | applications | | |
+-------------+---------------------+--------------+--------------+
| Quality | Yes | No | Yes |
| Attenuation | | | |
+-------------+---------------------+--------------+--------------+
| Quality of | Yes | Yes | Yes (Through |
| Outcome | | | Quality |
| | | | Attenuation) |
+-------------+---------------------+--------------+--------------+
Table 1: Summary of Performance Metrics Properties
The column "Can Assess How Well Applications Are Expected to Work"
indicates whether a metric can, in principle, capture relevant
information to assess application performance, assuming that
measurements cover the properties of the end-to-end network path that
the application uses. "Easy to Articulate Application Requirements"
refers to the ease with which application-specific requirements can
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be expressed using the respective metric. "Composable" indicates
whether the metric supports spatial composition as described in
Appendix A.4 and [RFC6049]: the ability to combine measurements from
individual path segments to derive end-to-end properties, or to
decompose end-to-end measurements to isolate per-segment
contributions.
B.1. Throughput
Throughput relates to user-observable application outcomes as
acceptable performance is impossible when throughput falls below an
application's minimum requirement. Above that minimum threshold, the
relationship weakens and additional capacity above a certain
threshold will, at best, yield diminishing returns (and any returns
are often due to reduced latency). While throughput can be compared
to a variety of application requirements, it is not generally
possible to conclude from sufficient throughput alone that an
application will work well.
Throughput is not composable in the spatial sense. While the
throughput of a composed path a-b-c equals the minimum of the two
individual segment throughputs, the bottleneck segment cannot be
identified from the composed value: if b-c is the bottleneck, then
the throughput of a-b-c equals the throughput of b-c, and the higher
capacity of segment a-b is not recoverable from the combined
measurement.
B.2. Mean Latency
Mean latency relates to user-observable application outcomes in the
sense that the mean latency must be low enough to support a good
experience. However, it is not possible to conclude that a general
application will work well if the mean latency is good enough
[BITAG].
Mean latency can be composed. For example, if the mean latency
values of links a-b and b-c are known, then the mean latency of the
composition a-b-c is the sum of a-b and b-c. Since this composition
is additive, it is also invertible: knowing the end-to-end mean
latency of a-b-c and the mean latency of one segment is sufficient to
recover the mean latency of the other segment.
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B.3. 99th Percentile of Latency
The 99th percentile of latency relates to user-observable application
outcomes because it captures some information about how bad the tail
latency is. If an application can handle 1% of packets being too
late, for instance by maintaining a playback buffer, then the 99th
percentile can be a good metric for measuring application
performance. It does not work as well for applications that are very
sensitive to overly delayed packets because the 99th percentile
disregards all information about the delays of the worst 1% of
packets.
It is not possible to compose 99th-percentile values as the Nth
percentile of a composed distribution cannot in general be derived
from the Nth percentile of its constituent distributions without
access to the full distributions.
B.4. Variance of Latency
The variance of latency can be calculated from any collection of
samples, but network latency is not necessarily normally distributed.
As such, it can be difficult to extrapolate from a measure of the
variance of latency to how well specific applications will work.
The variance of latency can be composed. For example, if the
variance values of links a-b and b-c are known, then the variance of
the composition a-b-c is the sum of the variances a-b and b-c. This
composition is also invertible for independent segments, enabling
decomposition: knowing the end-to-end variance and the variance of
one segment is sufficient to recover the variance of the other
segment.
B.5. Inter-Packet Delay Variation (IPDV)
The most common definition of IPDV [RFC5481] measures the difference
in one-way delay between subsequent packets. Some applications are
very sensitive to this performance characteristic because of time-
outs that cause later-than-usual packets to be discarded. For some
applications, IPDV can be useful in assessing application
performance, especially when it is combined with other latency
metrics. IPDV does not contain enough information to assess how well
a wide range of applications will work.
IPDV cannot be composed.
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B.6. Packet Delay Variation (PDV)
The most common definition of PDV [RFC5481] measures the difference
in one-way delay between the smallest recorded latency and each value
in a sample.
PDV cannot be composed.
B.7. Trimmed Mean of Latency
The trimmed mean of latency is the mean computed after the worst x
percent of samples have been removed. Trimmed means are typically
used in cases where there is a known rate of measurement errors that
should be filtered out before computing results.
In the case where the trimmed mean simply removes measurement errors,
the result can be composed in the same way as the mean latency. In
cases where the trimmed mean removes real measurements, the trimming
operation introduces errors that may compound when composed.
B.8. Round-trips Per Minute
Round-trips per minute [RPM] is a metric and test procedure
specifically designed to measure delays as experienced by
application-layer protocol procedures, such as HTTP GET, establishing
a TLS connection, and DNS lookups. Hence, it measures something very
close to the user-perceived application performance of HTTP-based
applications. RPM loads the network before conducting latency
measurements and is, therefore, a measure of loaded latency (also
known as working latency), and well-suited to detecting bufferbloat
[Bufferbloat].
RPM is not composable.
B.9. Quality Attenuation
Quality Attenuation is a network quality metric that combines
dedicated latency distributions and packet loss probabilities into a
single variable [TR-452.1]. It relates to user-observable outcomes
in the sense that they can be measured using the Quality Attenuation
metric directly, or the Quality Attenuation value describing the
time-to-completion of a user-observable outcome can be computed if
the Quality Attenuation of each sub-goal required to reach the
desired outcome is known [Haeri22].
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Quality Attenuation is composable via convolution of the underlying
distributions, which allows computing the time it takes to reach
specific outcomes given the Quality Attenuation of each sub-goal and
the causal dependency conditions between them [Haeri22].
B.10. Quality of Outcome
Quality of Outcome (QoO) builds upon Quality Attenuation by adding
application-specific, dual-threshold network performance requirements
(ROP and CPUP) and translating the comparison between measured
network conditions and these requirements into a percentage-based
score. By incorporating latency distributions, packet loss ratios,
and throughput measurements, QoO can assess how well a wide range of
applications are expected to perform under given network conditions.
The underlying Quality Attenuation measurements used in QoO are
mathematically composable via convolution [TR-452.1]. This
composability extends to QoO in the sense that operators can measure
individual network segments, compose the underlying Quality
Attenuation distributions, and then compute QoO scores from the
composed result. This composability requires the full distributional
representation. When QoO score calculation is based only on scalar
percentile summaries (see Section 3.1.1), this composability is not
available.
Appendix C. Preliminary Insights From a Small-Scale User Testing
Campaign
While subjective QoE testing as specified in the ITU-T P-series
recommendations ([P.800], [P.910], and [P.1401]) is out of scope of
this document, a study involving 25 participants tested the QoO
framework in real-world settings [QoOUserStudy]. Participants used
specially equipped routers in their homes for ten days, providing
both network performance data and feedback through pre- and post-
trial surveys.
Participants found QoO scores more intuitive and actionable than
traditional metrics (e.g., speed tests). QoO directly aligned with
their self-reported experiences, increasing trust and engagement.
These results indicate that users find it easier to correlate QoO
scores with real-world application performance than, for example, a
speed test. As such, QoO is expected to help bridge technical
metrics with application performance. However, the specific impact
of QoO should be studied further, for example, via comparative
studies with blinded methodologies that compare QoO to other QoS-type
approaches or application-provided QoE ratings as the mentioned
study's design might have introduced different forms of bias.
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Acknowledgments
The authors would like to thank Karl Magnus Kalvik, Olav Nedrelid,
and Knut Joar Strømmen for their conceptual and technical
contributions to the QoO framework.
The authors would like to thank Gavin Young, Brendan Black, Kevin
Smith, Gino Dion, Mayur Sarode, Greg Mirsky, Wim De Ketelaere, Peter
Thompson, and Neil Davies for their contributions to the initial
version of this document.
The authors would like to thank Gorry Fairhurst, Jörg Ott, Paul
Aitken, Mohamed Boucadair, Stuart Cheshire, Neil Davies, Guiseppe
Fioccola, Ruediger Geib, Will Hawkins, Marcus Ihlar, Mehmet Şükrü
Kuran, Paul Kyzivat, Jason Livingood, Greg Mirsky, Tal Mizrahi, Luis
Miguel Contreras Murillo, Tommy Pauly, Alexander Raake, Werner
Robitza, Kevin Smith, Martin Thomson, and Michael Welzl for their
feedback and input to this document throughout the IETF process.
Authors' Addresses
Bjørn Ivar Teigen Monclair
CUJO AI
Gaustadalléen 21
0349
Norway
Email: bjorn.monclair@cujo.com
Magnus Olden
CUJO AI
Gaustadalléen 21
0349
Norway
Email: magnus.olden@cujo.com
Ike Kunze (editor)
CUJO AI
Gaustadalléen 21
0349
Norway
Email: ike.kunze@cujo.com
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