



IP Performance Measurement                             B. I. T. Monclair
Internet-Draft                                                  M. Olden
Intended status: Informational                             I. Kunze, Ed.
Expires: 23 August 2026                                          CUJO AI
                                                        19 February 2026


                        Quality of Outcome (QoO)
                         draft-ietf-ippm-qoo-07

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 application
   developers, users, and operators.

   By leveraging the Quality Attenuation metric, QoO provides a method
   for defining and evaluating application-specific, quality-focused
   network performance requirements to enable insights for network
   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
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   5
   3.  Overview  . . . . . . . . . . . . . . . . . . . . . . . . . .   7
   4.  Requirements  . . . . . . . . . . . . . . . . . . . . . . . .   8
     4.1.  General Requirements  . . . . . . . . . . . . . . . . . .  10
     4.2.  Requirements from End-Users . . . . . . . . . . . . . . .  11
     4.3.  Requirements from Application and Platform Developers . .  12
     4.4.  Requirements from Network Operators and Network Solution
           Vendors . . . . . . . . . . . . . . . . . . . . . . . . .  13
   5.  The QoO Framework . . . . . . . . . . . . . . . . . . . . . .  14
     5.1.  Measuring Network Conditions  . . . . . . . . . . . . . .  14
     5.2.  Describing Network Performance Requirements . . . . . . .  17
     5.3.  Calculating QoO . . . . . . . . . . . . . . . . . . . . .  19
       5.3.1.  Overall QoO Calculation . . . . . . . . . . . . . . .  20
       5.3.2.  Latency Component . . . . . . . . . . . . . . . . . .  20
       5.3.3.  Packet Loss Component . . . . . . . . . . . . . . . .  21
     5.4.  Example . . . . . . . . . . . . . . . . . . . . . . . . .  21
   6.  Operational Considerations  . . . . . . . . . . . . . . . . .  22
     6.1.  Creating Network Performance Requirement
           Specifications  . . . . . . . . . . . . . . . . . . . . .  22
     6.2.  Composability and Use Cases . . . . . . . . . . . . . . .  23
     6.3.  Deployment Considerations . . . . . . . . . . . . . . . .  24
     6.4.  Adaptive Applications . . . . . . . . . . . . . . . . . .  24
     6.5.  Sensitity to Sampling Accuracy  . . . . . . . . . . . . .  25
     6.6.  Insights From User Testing  . . . . . . . . . . . . . . .  26



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   7.  Known Weaknesses and Open Questions . . . . . . . . . . . . .  26
     7.1.  Volatile Networks . . . . . . . . . . . . . . . . . . . .  27
     7.2.  Missing Temporal Information in Distributions.  . . . . .  27
     7.3.  Subsampling the Real Distribution . . . . . . . . . . . .  27
     7.4.  Assuming Linear Relationship Between Optimal Performance
           and Unusable  . . . . . . . . . . . . . . . . . . . . . .  28
     7.5.  Binary Bandwidth Threshold  . . . . . . . . . . . . . . .  28
     7.6.  Arbitrary Selection of Percentiles  . . . . . . . . . . .  28
   8.  Security Considerations . . . . . . . . . . . . . . . . . . .  28
     8.1.  Measurement Integrity and Authenticity  . . . . . . . . .  29
     8.2.  Risk of Misuse and Gaming . . . . . . . . . . . . . . . .  29
     8.3.  Denial-of-Service (DoS) Risks . . . . . . . . . . . . . .  29
     8.4.  Trust in Application Requirements . . . . . . . . . . . .  30
   9.  Privacy Considerations  . . . . . . . . . . . . . . . . . . .  30
   10. Comparison To Other Network Quality Metrics . . . . . . . . .  31
     10.1.  Throughput . . . . . . . . . . . . . . . . . . . . . . .  33
     10.2.  Mean Latency . . . . . . . . . . . . . . . . . . . . . .  33
     10.3.  99th Percentile of Latency . . . . . . . . . . . . . . .  33
     10.4.  Variance of Latency  . . . . . . . . . . . . . . . . . .  34
     10.5.  Inter-Packet Delay Variation (IPDV)  . . . . . . . . . .  34
     10.6.  Packet Delay Variation (PDV) . . . . . . . . . . . . . .  34
     10.7.  Trimmed Mean of Latency  . . . . . . . . . . . . . . . .  34
     10.8.  Round-trips Per Minute . . . . . . . . . . . . . . . . .  35
     10.9.  Quality Attenuation  . . . . . . . . . . . . . . . . . .  35
     10.10. Quality of Outcome . . . . . . . . . . . . . . . . . . .  35
   11. IANA Considerations . . . . . . . . . . . . . . . . . . . . .  36
   12. Implementation status . . . . . . . . . . . . . . . . . . . .  36
     12.1.  qoo-c  . . . . . . . . . . . . . . . . . . . . . . . . .  36
     12.2.  goresponsiveness . . . . . . . . . . . . . . . . . . . .  37
   13. References  . . . . . . . . . . . . . . . . . . . . . . . . .  38
     13.1.  Normative References . . . . . . . . . . . . . . . . . .  38
     13.2.  Informative References . . . . . . . . . . . . . . . . .  38
   Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . .  43
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  43

1.  Introduction

   This document introduces the Quality of Outcome (QoO) network quality
   score.  It is designed to be easy to understand, while at the same
   time being objective, adaptable to different network quality needs,
   and allowing advanced analyses to identify the root cause of network
   problems (Section 5.4.3 of [I-D.ietf-opsawg-rfc5706bis]).  Centered
   around the QoO score, this document defines the QoO framework which
   allows application developers to specify quality-focused network
   performance requirements (for example, regarding latency, throughput,
   and packet loss) and provides a way to derive the user-facing QoO
   score by comparing these application-specific performance
   requirements to network performance measurements.



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   The QoO framework builds on Quality Attenuation [TR-452.1], a network
   quality metric that enables network operators to achieve fault
   isolation (Section 5.4.4 of [I-D.ietf-opsawg-rfc5706bis]) and
   effective network planning through composability [RFC6049].  Quality
   Attenuation meets most of the requirements for a meaningful network
   quality score set out in Section 4.1: it is composable, captures the
   ability of a network to satisfy application requirements, and can be
   compared to a variety of application needs.  However, interpreting
   raw Quality Attenuation values is difficult for end-users and
   application developers.  The challenge is simplifying the entailed
   information to present results in an understandable and unambiguous
   way without losing too much precision and accuracy.

   The QoO framework takes a probabilistic approach to this challenge
   because network stacks and applications adapt dynamically to changing
   network conditions.  Also, applications and underlying networking
   protocols make separate optimizations based on the perceived network
   quality over time, making absolute certainty about outcomes
   practically impossible.  However, educated assessments of expected
   outcomes remain achievable for which the QoO framework uses a per-
   application, per application-type, or per-Service Level Agreement
   (SLA) granularity.

   This document assumes that network quality can be represented by a
   minimum required throughput, a set of latency percentiles, and packet
   loss rates as these measurables will ultimately also capture changes
   to additional factors.  Application developers, regulatory bodies,
   and other interested parties can use this representation to describe
   quality-focused network performance requirements.  The QoO framework
   gives structure to this approach by defining two network quality
   thresholds: one for optimal application performance and one for
   unacceptable application performance.  The QoO score serves as a
   linear distance measure between the two distinct thresholds and
   allows network conditions to be expressed in easily understood terms
   such as "This network provides 94% of optimal conditions for video
   conferencing (relative to the threshold for unacceptable
   performance)" while supporting both comprehensive end-to-end tests
   and analyses from within the network.

   The QoO framework is designed to be flexible in its application
   scope.  QoO scores may be calculated for the complete end-to-end path
   (from application client to server), or focused on specific network
   segments, such as the customer-facing access network, intermediate
   transit networks, or server-side infrastructure.  Through the
   composability properties of the underlying Quality Attenuation
   metric, measurements from different segments can be combined or
   decomposed to isolate performance issues regardless of where they
   occur in the network path.



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   This document defines a minimum viable framework, and often trades
   precision for simplicity to facilitate adoption and usability in many
   different contexts, such as active testing from applications and
   monitoring from network equipment.  Assessing the corresponding loss
   of precision is important and requires combining measurement results
   with a description of the measurement approach; Section 5.1 provides
   corresponding guidelines.

   Note that this document focuses specifically on the general framework
   of using quality-focused network performance requirements and
   corresponding network performance measurements to calculate QoO
   network quality scores.  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, and what levels of precision are considered
   acceptable is out of the scope of this document.

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]
      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





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      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
      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 network performance
      requirements for optimal and unacceptable application performance
      on a given network for a specific application, typically expressed
      as a percentage.

   Requirements for Optimal Performance (ROP):  The network performance
      characteristics at which an application achieves optimal
      performance and quality, beyond which further improvements in
      network conditions do not result in perceptible improvements in
      application performance or user experience.  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.

   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].







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3.  Overview

   The QoO framework produces simple percentage scores that express
   network quality in relation to pre-defined, application-specific
   network performance requirements.  For example: "This network
   provides 94% of optimal conditions for video conferencing".  This
   way, QoO conveys an intuition for how well an application is expected
   to perform in the assessed network with higher scores intended to
   convey that applications are more likely to have optimal performance.

   The QoO framework builds upon Quality Attenuation and evaluates
   network conditions using latency distributions, packet loss rates,
   and throughput rates.  These measured conditions are compared against
   application-specific, quality-focused network performance
   requirements along multiple dimensions (such as 90th or 95th latency
   percentiles or mean packet loss rate) at two thresholds:

   *  Optimal performance (ROP): Network conditions under which
      application performance becomes optimal

   *  Unacceptable performance (CPUP): Network conditions under which
      application performance becomes unacceptable

   When the measured network conditions fall between the defined
   thresholds for any of the assessed performance dimensions, the QoO
   framework calculates a score for each dimension by expressing the
   current network quality as a relative position (percentage) on the
   linear scale between the corresponding ROP and CPUP 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.

   The dual-threshold approach of the QoO framework enables meaningful
   scores even when networks are not perfect while accounting for
   different application sensitivities.  The underlying Quality
   Attenuation measurements are mathematically composable, enabling
   network operators to isolate performance bottlenecks across different
   network segments for precise fault diagnosis and network planning.

   QoO scores are expected to correlate with QoE metrics, such as Mean
   Opinion Score (MOS) [P.800.1], but they are not designed to deliver
   MOS or QoE scores directly.  QoO measures network service quality,
   not subjective user experience.

   It is important to note that the QoO framework itself does not define
   where QoO scores fall on the spectrum between QoS and QoE metrics.
   The position on this spectrum depends primarily on how the ROP and



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   CPUP thresholds are chosen.  With appropriate threshold selection
   based on user-acceptance testing and application performance
   analysis, QoO scores can likely be tuned to be close to (if not
   identical to) QoE metrics, while still maintaining the objectivity
   and composability benefits of QoS metrics.

   The remainder of this document outlines requirements for the QoO
   framework (Section 4), defines the core QoO framework (Section 5),
   discusses operational considerations (Section 6) and known weaknesses
   and open questions (Section 7), and compares QoO with existing
   quality metrics (Section 10).

4.  Requirements

   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
   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.











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   Bandwidth 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
   bandwidth alone.  The latter, 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 bandwidth.
   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
   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.



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4.1.  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 (Section 4.2), application developers
   (Section 4.3), and network operators (Section 4.4).

   In general, all stakeholders ultimately care about the performance of
   applications running over a network.  Application performance does
   not only depend on bandwidth 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 6.3).  Flexibility in
   describing application requirements and the ability to capture the
   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:





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   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 (Section 4.2), application
   developers (Section 4.3), and network operators (Section 4.4).

4.2.  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.






















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   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
   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)

   Note that developers of similar applications may have arrived at
   different figures.

4.3.  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
   that it can be used with different kinds of traffic and that extreme
   network performance requirements which far exceed the needs of



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   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.

4.4.  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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5.  The QoO Framework

   The QoO framework builds upon the conceptual foundation of the
   Quality Attenuation metric as proposed in the Broadband Forum
   standard called Quality of Experience Delivered (QED) [TR-452.1].
   Quality Attenuation represents quality measurements as distributions.
   Using latency distributions to measure network quality has been
   proposed by various researchers and practitioners (e.g., [Kelly],
   [RFC6049], and [RFC8239]).  Quality Attenuation also incorporates a
   packet loss distribution, treating packet loss as infinite (or too
   late to be of use, e.g., > 5 seconds) latency [TR-452.1], 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 novelty of Quality Attenuation lies in
   its unified treatment of latency and loss within a single
   distributional framework, enabling mathematical composition of
   network segments as two distributions can be composed using
   convolution [TR-452.1].

   In line with Quality Attenuation, QoO focuses on latency
   distributions and packet loss rates as the basis for network quality
   assessment, with the assumption that additional factors which could
   in principle be measured but are not explicitly captured will
   inevitably influence the observed latency and packet loss behavior,
   so that QoO indirectly accounts for their effects through the
   measured distributions.  The results of corresponding network
   performance measurements are compared to pre-defined, application-
   specific network performance requirements, which ultimately yield
   per-application QoO scores.  These scores express network quality as
   a relative position (percentage) on a linear scale between two
   performance thresholds: one for optimal performance and one for
   unacceptable performance.

   The following sections first discuss how network conditions can be
   measured (Section 5.1), then describe how QoO defines per-application
   network performance requirements (Section 5.2), and finally explain
   how QoO scores are calculated (Section 5.3) with an example provided
   in Section 5.4

5.1.  Measuring Network Conditions

   One of the key motivations for the QoO framework is that 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



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   considerations apply for a variety of other factors with applications
   usually requiring some minimum level of throughput and tolerating
   some maximum packet loss rate.  Measurement techniques used for QoO
   must be able to capture these characteristics with sufficient
   resolution to respect the data granularities needed by the different
   application classes.

   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.

   This document does not mandate the use of specific measurement
   approaches.  However, 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]

   *  Latency Under Load Tests

   *  Speed Tests with latency measures

   *  Simulating real traffic

   *  End-to-end measurements of real traffic

   *  TCP SYN ACK or DNS Lookup RTT Capture

   *  On-Path Telemetry methods (IOAM [RFC9197], AltMark [RFC9341])

   *  Estimation

   Modeling full latency distributions 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



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   only possible 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 framework is agnostic to traffic direction but mandates that
   measurements specify whether latency is one-way or round-trip.

   To ensure broad applicability across diverse use cases, the QoO
   framework deliberately avoids prescribing specific conditions for
   sampling, such as fixed time intervals or defined network load
   levels.  This flexibility enables deployment in both controlled and
   production environments.

   When measurements are taken during periods of network load, the
   result naturally includes latency under load.  In scenarios such as
   passive monitoring of production traffic, capturing artificially
   loaded conditions may not always be feasible, whereas passively
   observing the actual network load may be possible.

   Importantly, the framework does not enforce a minimum sample count.
   This means that even a small number of samples (e.g., 10) could
   technically constitute a distribution but such cases are clearly
   insufficient for statistical confidence.  The intent is to balance
   rigor with practicality, recognizing that constraints vary across
   devices, applications, and deployment environments.

   To support reproducibility and enable confidence analysis, each
   measurement must be accompanied by the following metadata:

   *  Description of the measurement path, including the endpoints
      (source and destination), network segments traversed, measurement
      points (if applicable), and direction (uplink, downlink, or
      bidirectional)

   *  Timestamp of first sample

   *  Total duration of the sampling period

   *  Number of samples collected

   *  Sampling method, including:

      -  Cyclic: One sample every N milliseconds (specify N)

      -  Burst: X samples every N milliseconds (specify X and N)




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      -  Passive: Opportunistic sampling of live traffic (non-uniform
         intervals)

   These metadata elements are essential for interpreting the precision
   and reliability of the measurements.  As demonstrated in
   [QoOSimStudy], 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:

   *  The repeatability of measurements under similar network conditions

   *  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.

5.2.  Describing Network Performance Requirements

   The QoO framework expresses network performance requirements as a set
   of percentile-latency tuples with corresponding packet loss
   thresholds and a minimum required throughput.  For example, a
   requirement might state: at 4Mbps, 90% of packets must arrive within
   100ms, and 100% within 200ms, implying 0% packet loss.  This list can
   be minimal (e.g., 100% within 200ms) or extended as needed and
   different percentiles may be used to characterize different
   applications.  Still, it might be beneficial for future
   standardization activities to converge on a fixed set of general
   percentiles or for specific applications / application classes to
   make QoO measurements between different networks or providers more
   comparable.  For the sake of simplicity, this document only states
   that the latency percentiles specified in the requirements must match
   the information provided by the measurements.  This means that when
   the measurements report full distributions, requirements can use
   arbitrary percentiles while the requirement percentiles need to match
   one or more of the percentiles reported by the measurements in case



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   the simplification described in Section 5.1 is used, i.e., one can
   set requirements at the [0th, 10th, 25th, 50th, 75th, 90th, 95th,
   99th, 99.9th, 100th] percentiles.  Packet loss rates and bandwidth
   must be reported as separate values.

   Applications do of course have throughput requirements, and thus a
   complete framework for application-level network quality must also
   take capacity into account.  Insufficient bandwidth may give
   unacceptable application outcomes without necessarily inducing a lot
   of latency or packet loss.  Therefore, the network requirements must
   include a minimum throughput requirement.

   A fully specified requirement can be thought of as specifying 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.  This may be achieved by running the
   actual application and measuring delay and loss alongside it, or by
   generating artificial traffic to a level at least equivalent to the
   application traffic load.  The QoO framework focuses on latency,
   packet loss, and throughput 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 through
   the measured distributions.

   Whether the requirements are one-way or two-way must be specified.
   Where the requirement is one-way, the direction (user-to-network or
   network-to-user) must be specified.  In case of a two-way
   requirement, a decomposition into uplink and downlink components may
   be specified.

   Network performance requirements and measurements are already
   standardized in the QED framework [TR-452.1].  This document extends
   the QED framework with a method that translates the network
   performance requirements and measurements into a network quality
   score that quantifies how close the provided network conditions are
   to the optimal conditions specified by the requirements.

   To that aim, first recall the key design goal of establishing a
   quantifiable distance between optimal and unacceptable network
   conditions, thereby enabling an objective assessment of relative
   quality.  Accordingly, the requirements specification is extended to
   define both the network performance required for achieving optimal
   application performance and the lower network performance threshold
   below which the application performance is considered unacceptable.






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   The two ends of the distance measure correspond to the Requirements
   for Optimal Performance (ROP) and the Conditions at the Point of
   Unacceptable Performance (CPUP).  For example, ROP could be defined
   as: at 4Mbps, 99% of packets need to arrive within 100ms, 99.9%
   within 200ms, and 0.1% packet loss is acceptable for the outcome to
   be as intended.  Similarly, CPUP could be defined as: if 99% of the
   packets have not arrived after 200ms, or 99.9% within 300ms, the
   perceived service will be unacceptable.

   If a latency percentile is included in the ROP, it must also be
   defined in the CPUP, and vice versa, i.e., neither specification
   should define a percentile that is not present in the other.  For
   example, if the 99.9th percentile is part of the CPUP then the ROP
   must also include the 99.9th percentile.

   The derivation of ROP and CPUP values requires standardized testing
   conditions to ensure consistency and accuracy.  Application
   developers should publish their testing methodologies, including the
   network conditions, hardware configurations, and measurement
   procedures used to establish these thresholds.  Without such
   standardization, the overall accuracy and precision of QoO scores may
   be reduced due to variations in testing approaches across different
   applications and developers.

   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.

   This document does not define a standardized approach for creating a
   quality-focused network performance requirement specification with
   spec-creation only discussing general considerations for creating a
   specification.

5.3.  Calculating QoO

   The QoO score compares the results of network performance
   measurements to application-specific network performance requirements
   by assessing how close the measured network performance is to the
   network conditions needed for optimal application performance,
   incorporating both latency and packet loss.  There are three key
   scenarios:

   *  The network meets all requirements for optimal performance (ROP).
      QoO Score: 100%.



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   *  The network fails one or more criteria for conditions at the point
      of unacceptable performance (CPUP).  QoO Score: 0%.

   *  The network performance falls between optimal and unacceptable.
      In this case, a continuous QoO score between 0% and 100% is
      computed by taking the worst score derived from latency and packet
      loss.

   Note that the QoO score should reflect the directionality of the
   measurements (one-way or round-trip) as specified in the network
   performance requirements.  When comparing measurements to
   requirements, both must use the same directionality and, for one-way
   measurements, the same direction (uplink or downlink).

5.3.1.  Overall QoO Calculation

   The overall QoO score is the minimum of a latency (QoO_latency) and a
   packet loss (QoO_loss) score:

   QoO = min(QoO_latency, QoO_loss)

   with QoO_latency and QoO_loss as defined in the following.

5.3.2.  Latency Component

   The QoO latency score bases on linear interpolations of the latency
   values at all latency percentiles defined in ROP / CPUP and
   represents the minimum value for all percentiles:

   for i in latency_percentiles:
     m = (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 as indicated in the requirement for optimal
      performance at percentile latency_percentiles[i].

   *  CPUP[i] is the latency as indicated in the condition at the point
      of unacceptable performance at percentile latency_percentiles[i].




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5.3.3.  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 total measured packet loss (MLoss) and the
   packet loss thresholds defined in the ROP and CPUP:

   m = (M_Loss - ROP_Loss) / (CPUP_Loss - ROP_Loss)
   QoO_loss = clamp(0, m, 1) * 100

   Where:

   *  M_Loss is the measured packet loss.

   *  ROP_Loss is the acceptable packet loss for optimal performance.

   *  CPUP_Loss is the packet loss threshold beyond which the
      application becomes unacceptable.

5.4.  Example

   The following example illustrates the QoO calculations.

   Example requirements:

   *  Minimum bandwidth: 4 Mbps

   *  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 packet loss: 2%

   *  Measured minimum bandwidth: 32Mbps / 28Mbps

   Latency component:

   m1 = (350ms - 200ms) / (500ms - 200ms)
   m2 = (375ms - 300ms) / (600ms - 300ms)
   metrics = [clamp(0, m1, 1), clamp(0, m2, 1)]
   QoO_latency = find_min(metrics) * 100 = 50.00

   Packet loss component:



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   m = (2% - 1%) / (5% - 1%)
   QoO_loss = clamp(0, m, 1) * 100 = 75.00

   Overall QoO score:

   QoO = min(QoO_latency, QoO_loss) = min(50.00, 75.00) = 50.00

   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 and considering both latency and packet loss.
   The score implies that the latency impact dominates the packet loss
   impact and that the network overall provides conditions at the midway
   point of the performance range.

6.  Operational Considerations

   This document focuses on the core QoO framework and describes how
   quality-focused network performance requirements and corresponding
   network performance measurements can be used to calculate QoO network
   quality scores.  Aiming to ensure broad and easy applicability of the
   QoO framework across diverse use cases, the document does not impose
   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
   standardization activities are expected to capture and refine best
   practices once more operational experience has been gained.

6.1.  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.

   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.





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   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.

6.2.  Composability and Use Cases

   One of the key strengths of the QoO framework is the mathematical
   composability of the underlying Quality Attenuation metric, allowing
   network quality measurements to be combined across segments using
   convolution [TR-452.1].  This approach requires sharing the measured
   distributions for the involved segments among relevant stakeholders,
   which can be challenging across different operators or networks.









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   However, even without sharing distributions 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.

6.3.  Deployment Considerations

   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

   *  The measurement methodology accounts for any traffic
      prioritization, differentiated services, or quality-of-service
      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.

6.4.  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 bandwidth 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 usability.

   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



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   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.

6.5.  Sensitity 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.

   2.  Measurement Noise: Measurement errors on the same scale as the
       thresholds (ROP, CPUP) can distort high-percentile latencies and
       cause artificially lower QoO.

   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, we deduce the following guidelines for practical
   application:





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   *  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 application requirement thresholds so that the
      resulting QoO scores accurately reflect application performance.

   These guidelines are non-normative but reflect empirical evidence on
   how QoO performs.

6.6.  Insights From User Testing

   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.

7.  Known Weaknesses and Open Questions

   The described QoO framework simplifies the comparison between network
   performance requirements from applications and Quality Attenuation
   measurements.  This simplification introduces several artifacts, the
   significance of which may vary depending on the context.  The
   following section discusses some known limitations.  A general
   assumption underlying the framework is that factors which could in
   principle be measured but are not explicitly captured by QoO (such as
   temporal packet ordering, fine-grained bandwidth variations, or the
   full shape of the latency distribution) will inevitably influence the



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   observed latency and packet loss behavior, so that QoO indirectly
   accounts for their effects through the measured distributions.

7.1.  Volatile Networks

   Volatile networks - in particular, mobile cellular networks - pose a
   challenge for network quality prediction, with the level of assurance
   of the prediction likely to decrease as session duration increases.
   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
   signal bandwidth available to 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 signal bandwidth and transmission-time
   intervals (e.g., 3GPP 4G and 5G).  This suggests a requirement for
   measuring Quality Attenuation 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 Quality Attenuation query, is an open
   question.

7.2.  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
   different application outcomes.

7.3.  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.






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7.4.  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 here
   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).

7.5.  Binary Bandwidth Threshold

   Choosing a binary bandwidth 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.

7.6.  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
   arbitrarily, based on the best available knowledge and the intended
   use case.

8.  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.





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8.1.  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.

8.2.  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.

   *  Transparency in how QoO scores are derived and what assumptions
      are made.

8.3.  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.

   To mitigate these risks, the following is recommended:




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   *  Implement rate-limiting and access control for active measurement
      tools.

   *  Ensure that measurement traffic does not interfere with critical
      services.

   *  Monitor for abnormal measurement patterns that may indicate abuse.

8.4.  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.

9.  Privacy Considerations

   QoO measurements may involve collecting detailed performance data
   from end-user devices or applications.  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.

   *  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)).







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10.  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
   Section 4.1.  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 mathematical composition to enable
   detailed network analysis.

10.1.  Throughput

   Throughput is related to user-observable application outcomes because
   there must be enough bandwidth available.  Adding extra bandwidth
   above a certain threshold will, at best, receive diminishing returns
   (and any returns are often due to reduced latency).  It is not
   possible to assess optimal or unacceptable application performance
   based on throughput alone for most applications.  Throughput can be
   compared to a variety of application requirements, but since there is
   no direct correlation between throughput and application performance,
   it is not possible to conclude that an application will work well
   even if it is known that enough throughput is available.

   Throughput cannot be composed.

10.2.  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.

10.3.  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.







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10.4.  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.

10.5.  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.

10.6.  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.

10.7.  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.








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10.8.  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.

10.9.  Quality Attenuation

   Quality Attenuation is a network quality metric that combines
   dedicated latency and packet loss distributions 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].

   Quality Attenuation is composable because the convolution of Quality
   Attenuation values 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].

10.10.  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 rates,
   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, as latency distributions can be composed
   using 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.





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11.  IANA Considerations

   This document has no IANA actions.

12.  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".

12.1.  qoo-c

   *  Link to the open-source repository:

      https://github.com/getCUJO/qoo-c

   *  The organization responsible for the implementation:

      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




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   *  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

12.2.  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:

      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




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   *  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

13.  References

13.1.  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>.

13.2.  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>.





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   [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-opsawg-rfc5706bis]
              Claise, B., Clarke, J., Farrel, A., Barguil, S.,
              Pignataro, C., and R. Chen, "Guidelines for Considering
              Operations and Management in IETF Specifications", Work in
              Progress, Internet-Draft, draft-ietf-opsawg-rfc5706bis-01,
              17 December 2025, <https://datatracker.ietf.org/doc/html/
              draft-ietf-opsawg-rfc5706bis-01>.

   [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>.

   [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>.




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   [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>.

   [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>.

   [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>.

   [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>.







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   [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>.

   [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>.

   [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>.





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   [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>.

   [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>.

   [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>.





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   [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>.

   [RRUL]     "Real-time response under load test specification", n.d.,
              <https://www.bufferbloat.net/projects/bloat/wiki/
              RRUL_Spec/>.

   [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>.

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 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





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   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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