]> Domos

Gaustadalléen 21 0349 NORWAY bjorn@domos.no

Domos

Gaustadalléen 21 0349 NORWAY magnus@domos.no

Transport IP Performance Measurement Quality Attenuation Application Outcomes Quality of Outcome Performance monitoring Network quality This document introduces the Quality of Outcome (QoO) framework, a novel 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 unified method for defining and evaluating application-specific network requirements while ensuring actionable insights for network optimization and simple quality scores for end-users. About This Document The latest revision of this draft can be found at . Status information for this document may be found at . Discussion of this document takes place on the IP Performance Measurement Working Group mailing list (), which is archived at . Subscribe at . Source for this draft and an issue tracker can be found at .

Introduction This document introduces the Quality of Outcome network score. Quality of Outcome is a network quality score designed to be easy to understand, while at the same time being objective, adaptable to different network quality needs, and allowing advanced analysis to identify the root cause of network problems. We define a network requirement framework that allows application developers to specify their network requirements, along with a way to create a simple user-facing metric based on comparing application requirements to measurements of network performance. The framework builds on Quality Attenuation , enabling network operators to achieve fault isolation and effective network planning through composability. Quality attenuation is a network quality metric that meets most of the criteria set out in the requirements; it can capture the probability of a network satisfying application requirements, it is composable, and it can be compared to a variety of application requirements. The part that is yet missing is how to present quality attenuation results to end-users and application developers in an understandable way. We believe a per-application, per application-type, or per-SLA approach is appropriate here. The challenge lies in specifying how to simplify enough without losing too much in terms of precision and accuracy. We believe the probabilistic approach is key as the network stack and application's network quality adaptation can be highly complex. Applications and the underlying networking protocols make separate optimizations based on their perceived network quality over time and saying something about an outcome with absolute certainty will be practically impossible. We can, however, make educated guesses on the probability of outcomes. We propose representing network quality as a minimum required throughput and set of latency and loss percentiles. Application developers, regulatory bodies and other interested parties can describe network requirements in the same manner. We propose a formula for a distance measure between perfect and unusable. This distance measure can, with some assumptions, calculate something that can be simplified into statements such as “A Video Conference has a 93% chance of being lag free on this network” all while making it possible to use the framework both for end-to-end test and analysis from within the network. The work proposes a minimum viable framework, and often trades precision for simplicity. The justification for this is to ensure adoption and usability in many different contexts such as active testing from applications and monitoring from network equipment. To counter the loss of precision, we require some parameters that allow for analysis of the precision.

Motivation This section describes the features and attributes a network quality framework must have to be useful for different stakeholders: application developers, end-users, and network operators/vendors. At a high level, end-users need an understandable network metric. Application developers require a network metric that allows them to evaluate how well their application is likely to perform given the measured network performance. Network operators and vendors need a metric that facilitates troubleshooting and optimization of their networks. Existing network quality metrics and frameworks typically address the needs of one or two of these stakeholders, but we have yet to find one that bridges the needs of all three. A key motivation for the Quality of Outcome (QoO) framework is to bridge the gap between the technical aspects of network performance and the practical needs of those who depend on it. While solutions exist for many of the problems causing high and unstable latency in the Internet, the incentives to deploy them have remained relatively weak. By creating a unifying framework for assessing network quality, we aim to strengthen these incentives significantly. Bandwidth alone is necessary but not sufficient for high-quality modern network experiences. Idle latency, working latency, jitter, and unmitigated packet loss are major causes of poor application outcomes. The impact of latency is widely recognized in network engineering circles , 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 and Responsiveness make significant strides toward creating a network quality metric that is far 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 , “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 operators have little incentive to improve network quality beyond increasing throughput. Despite the availability of open-source solutions, vendors rarely implement them. The root cause lies in the absence of a universally accepted network quality framework that captures how well applications are likely to perform. A recent 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" . Therefore, one critical requirement of a meaningful framework is its ability to answer the question, "Will an application work properly?" Answering this question requires several considerations. First, the Internet is inherently stochastic from the perspective of any given client, so certainty i unattainable. Second, different applications have varying needs and adapt differently to network conditions. A framework aiming to answer this question must accommodate 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 framework's design.

Design Goal The overall goal is to describe the requirements for an objective network quality framework and metric that is useful for end-users, application developers, and network operators/vendors alike.

Requirements This section outlines the three main requirements and their motivation. In general, all stakeholders ultimately care about the success of applications running over the network. Application success depends not just on bandwidth but also on the delay 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 sharing the network resources. Different applications have different needs from the network, and they place different patterns of load on it. To determine whether applications will work well or fail, a network quality framework must compare measurements of network performance to a wide variety of application requirements. Flexibility in describing application requirements and the ability to capture the delay characteristics of the network in sufficient detail are necessary to compute application success with satisfactory accuracy and precision. How can operators take action when measurements show that applications fail too often? The framework must support spatial composition , to answer this question. 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 succeed. To summarize, the framework and "meaningful metric" we're looking for should have the following properties:

1. Capture the information necessary to compute the probability that applications will work well. (Useful for end-users and application developers.)
2. Compare meaningfully to different application requirements.
3. Compose. Allow operators to isolate and quantify the contributions of different sub-outcomes and sub-paths of the network. (Useful for operators and vendors.)

Requirements for end-users The quality framework should facilitate a metric that is objective, relatable, and relatively understandable for an end-user. We are looking for a middle ground between objective QoS metrics (Throughput, packet loss, jitter, average latency) and subjective but understandable QoE metrics (MOS, 5-star ratings). The ideal framework should be objective, like QoS metrics, and understandable, like QoE metrics. If these requirements are met, the end-user can understand if a network can reliably deliver 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 lag. Each end user will have an individual tolerance of session quality, 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 quality of experience framework to place the responsibility for sourcing and representing end-user requirements onto the application developer. Application developers should 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 end-user quality threshold for an acceptable subset of their end users. Some real world examples where 'acceptable levels' have been derived by application developers include (note: developers of similar applications may have arrived at different figures):

• Remote music collaboration: 28ms latency note-to-ear for direct monitoring, <2ms jitter
• Online gaming: 6Mb/s downlink throughput and 30ms RTT to join a multiplayer game
• Virtual reality: <20ms RTT from head motion to rendered update in VR

Performing this UAT helps the developer understand what likelihood a new end-user has of an acceptable Quality of Experience based on the application's existing requirements towards the network. These requirements can evolve and improve based on feedback from end users, and in turn better inform the application's requirements towards the network.

Requirements from Application and Platform Developers The framework needs to give developers the ability to describe the network requirements of their applications. The format for specifying network requirements must include all relevant dimensions of network quality so that different applications which are sensitive to different network quality dimensions can all evaluate the network accurately. We can only expect some developers to have network expertise, so to make it easy for developers to use the framework, developers must be able to specify network requirements approximately. Therefore, it must be possible to describe both simple and complex network requirements. The framework also needs to be flexible so that it can be used with different kinds of traffic and that extreme network requirements which far exceed the needs of today's applications can also be articulated. If these requirements are met, developers of applications or platforms can state or test their network requirements and evaluate if the network is sufficient for a great application outcome. Both the application developers with networking expertise and those without can use the framework.

Requirements for Network Operators and Network Solution Vendors From an operator perspective, the key is to have a framework that lets operators find the network quality bottlenecks and objectively compare different networks and technologies. The framework must support mathematically sound compositionality ('addition' and 'subtraction') to achieve this. Why? 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, we could measure end-to-end (e.g., a-b-c-d-e) and not-end-to-end (e.g., b-c-d-e) and subtract, we can isolate the areas outside the influence of the network operator. In other words, we could get the network quality of a-b and b-c-d-e separately. 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 add them together to understand the end-to-end network quality. For another example where spatial composition is useful, we can look at a typical web page load sequence. If we measure web page load times and find they are too often too slow, we may then separately measure DNS resolution time, TCP round-trip time, and the time it takes to establish TLS connections 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 quality framework must be applicable in both lab testing and monitoring of production networks. It must be useful on different time scales, and it can't have a dependency on network technology or OSI layer. If these requirements are met, a network operator can monitor and test their network and understand where the true bottlenecks are, regardless of network technology.

Background The foundation of the framework is Quality Attenuation . This work will not go into detail about how to measure Quality Attenuation, but some relevant techniques are:

• Active probing with TWAMP Light / STAMP / IRTT
• Varying Latency Under Load Tests
• Varying Speed Tests with latency measures
• Simulating real traffic
• End-to-end measurements of real traffic
• TCP SYN ACK / DNS Lookup RTT Capture
• Estimation

Quality Attenuation represents quality measurements as distributions. Using Latency distributions to measure network quality is nothing new and has been proposed by various researchers/practitioners . The novelty of the Quality Attenuation metric is to view packet loss as infinite (or too late to be of use e.g. > 3 seconds) latency . Latency Distributions can be gathered via both passive monitoring and active testing. The active testing can use any type of IP traffic, such as TCP, UDP, or QUIC. It is OSI Layer and network technology independent, meaning it can be gathered in an end-user application, within some network equipment, or anywhere in between. A key assumption behind the choice of latency distribution is that different applications and application categories fail at different points of the latency distribution. Some applications, typically downloads, have lenient latency requirements. Video Conferences typically are sensitive to high 90th percentile latency and to the difference between the 90th and the 99th percentile. Online gaming typically has a low tolerance for high 99th percentile latency. All applications require a minumum level of throughput and a maximum packet loss rate. A network quality metric that aims to generalize network quality must take the latency distribution, throughput, and packet loss into consideration. Two distributions can be composed using convolution .

Discussion of other performance metrics Many network performance metrics and frameworks for reasoning about them have been proposed, used, and abused throughout the years. We present a brief description of some of the most relevant metrics. For each of the metrics below, we discuss whether or not they meet each of the three criteria set out in the requirements.

Average Peak 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 compute the probability of application success or failure 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 we know that enough throughput is available. Throughput cannot be composed.

Average Latency Average latency relates to user-observable application outcomes in the sense that the average latency must be low enough to support a good experience. However, it is not possible to conclude that a general application will work well based on the fact that the average latency is good enough . Average latency can be composed. If the average latency of links a-b and b-c is known, then the average latency of the composition a-b-c is the sum of a-b and b-c.

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.

Variance of latency The variance of latency can be calculated from any collection of samples, but network latency is not necessarily normally distributed, and so 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. If the variance of links a-b and b-c is known, then the variance of the composition a-b-c is the sum of the variances a-b and b-c.

Inter-Packet Delay Variation (IPDV) The most common definition of IPDV measures the difference in one-delay between subsequent packets. Some applications are very sensitive to this 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 compute the probability that a wide range of applications will work well. IPDV cannot be composed.

Packet Delay Variation (PDV) The most common definition of PDV measures the difference in one-delay between the smallest recorded latency and each value in a sample. PDV cannot be composed.

Trimmed Mean of Latency The trimmed mean of latency is the average 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 average latency. In cases where the trimmed mean removes real measurements, the trimming operation introduces errors that may compound when composed.

Round-trips Per Minute Round-trips per minute 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. It, therefore, 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) well-suited to detecting bufferbloat . RPM is not composable.

Quality Attenuation Quality Attenuation is a network performance metric that combines latency and packet loss into a single variable . Quality Attenuation relates to user-observable outcomes in the sense that user-observable outcomes 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 we know the quality attenuation of each sub-goal required to reach the desired outcome . Quality Attenuation is composable because the convolution of quality attenuation values allows us to compute the time it takes to reach specific outcomes given the quality attenuation of each sub-goal .

Summary of performance metrics This table summarizes the properties of each of the metrics we have surveyed. The column "Capture probability of general applications working well" records whether each metric can, in principle, capture the information necessary to compute the probability that a general application will work well. We assume measurements capture the properties of the end-to-end network path that the application is using.

Metric	Capture probability of general applications working well	Easy to articulate Application requirements	Composable
Average latency	Yes for some applications	Yes	Yes
Variance of latency	No	No	Yes
IPDV	Yes for some applications	No	No
PDV	Yes for some applications	No	No
Average Peak Throughput	Yes for some applications	Yes	No
99th Percentile of Latency	No	No	No
Trimmed mean of latency	Yes for some applications	Yes	No
Round Trips Per Minute	Yes for some applications	Yes	No
Quality Attenuation	Yes	No	Yes

Explanations:

• "Captures probability" refers to whether the metric captures enough information to compute the likelihood of an application succeeding.
• "Articulate requirements" refers to the ease with which application-specific requirements can be expressed using the metric.
• "Composable" means whether the metric supports mathematical composition to allow for detailed network analysis.

Sampling requirements To reach the design goal of being useful in the contexts laid out in the Motivation section, this work imposes no requirement on the time period or the network loading situation. This choice has pros and cons. Latency under load is extremely important, but average or median latency has a role too. However, a network quality metric that does not take latency under load into account is bound to fail at predicting application outcome. This framework only requires a latency distribution. If the sampling is done while the network is loaded, latency under load will be part of the distribution, which is encouraged, but is not always possible, for example when passively monitoring the latency of real traffic. It takes quite a few samples to have a statistically significant distribution. Modeling a distribution may be a challenging software engineering task, hence we need to sample the latency distribution at certain percentiles. A list of 10 percentiles in a logarithmic-esque fashion has already been suggested in industry [0th, 10th, 25th, 50th, 75th, 90th, 95th, 99th, 99.9th, 100th] and seems adequate. We propose to define a shared set of percentile values to report. The framework is flexible when it comes to the direction of traffic that is being sampled, but does require that it is noted whether the latency distribution is measured one-way or two-way. The framework does not require an explicit throughput measurement, but does require a note on the maximal observed throughput in the time period. By not requiring a specific number of samples, this framework allows taking 10 samples and calling it a distribution, which of course is not ideal. On the other hand, making the framework overly complex and difficult to adhere to using real-world equipment and applications is likely to reduce willingness to adopt the framework. Constraints will vary for different network equipment and applications. To make sure we can trust measurements from others and analyze their precision, we require:

• Timestamp of first sample
• Duration of the sampling period
• Number of samples
• Type of measurement:
  • Cyclic (a sample every Nth ms) - Specify N
  • Bursts (X samples every Nth ms) - Specify X and N
  • Passive (observing traffic and therefore unevenly sampled)

By requiring these values, the network measurements can be analyzed for precision and confidence. In particular, the simulation study demonstrates that lower sampling frequencies and short measurement durations can lead to overly optimistic or imprecise QoO scores.

Describing Network Requirements This work builds upon the work already proposed in the Broadband Forum standard called Quality of Experience Delivered (QED/TR-452) , which defines the Quality Attenuation metric. In essence, QoO describes network requirements as a list of percentile and latency requirement tuples. In other words, a network requirement may be expressed as: The network requirement for this app quality level/app/app category/SLA is “at 4 Mbps, 90% of packets needs to arrive within 100 ms, 100% of packets needs to arrive within 200ms”. This list can be as simple as “100% of packets need to arrive within 200ms” or as long as you would like. For the sake of simplicity, the requirements percentiles must match one or more of the percentiles defined in the measurements, i.e., one can set requirements at the [0th, 10th, 25th, 50th, 75th, 90th, 95th, 99th, 99.9th, 100th] percentiles. Packet loss must be reported as a separate value. Applications do of course have throughput requirements. We cannot rely on monitoring latency exclusively, because low bandwidth may give poor application outcomes without necessarily inducing a lot of latency. Therefore, the network requirements should 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. Whether the requirements are one-way or two-way must be specified. Where the requirement is one-way, the direction (uplink or downlink) must be specified. If two-way, a decomposition into uplink and downlink measurements may be specified. Until now, network requirements and measurements are what is already standardized in the BBF TR-452 (aka QED) framework . The novel part of this work is what comes next. A method for going from Network Requirements and Network Measurements to probabilities of good application outcomes. To do that we need to make articulating the network requirements a little bit more complicated. A key design goal was to have a distance measure between perfect and unusable, and have a way of quantifying what is ‘better’. We extend the requirements to include the quality required for perfection and a quality threshold beyond which the application is considered unusable. This is named Network Requirements for Perfection (NRP). As an example: At 4 Mbps, 99% of packets need to arrive within 100ms, 99.9% within 200ms (implying that 0.1% packet loss is acceptable) for the outcome to be perfect. Network Requirements Point of Unusability (NRPoU): If 99% of the packets have not arrived after 200ms, or 99.9% within 300ms, the outcome will be unusable. Where the NRPoU percentiles and NRP are a required pair then neither should define a percentile not included in the other - i.e., if the 99.9th percentile is part of the NRPoU then the NRP must also include the 99.9th percentile.

Calculating Quality of Outcome (QoO) The QoO metric calculates the likelihood of application success based on network performance, incorporating both latency and packet loss. There are three key scenarios:

• The network meets all the requirements for perfection. Probability of success: 100%.
• The network fails one or more criteria at the Point of Unusableness (NRPoU). Probability of success: 0%.
• The network performance falls between perfection and unusable. In this case, a QoO score is computed. The QoO score is calculated by taking the worst score derived from latency and packet loss.

Latency Component The latency-based QoO score is computed as follows: QoO_latency = min_{i}(min(max((1 - ((ML_i - NRP_i) / (NRPoU_i - NRP_i))) * 100, 0), 100)) Where:

• ML_i is the Measured Latency at percentile i.
• NRP_i is the Network Requirement for Perfection at percentile i.
• NRPoU_i is the Network Requirement Point of Unusableness at percentile i.

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 NRP and NRPoU: QoO_loss = min(max((1 - ((MLoss - NRP_Loss) / (NRPoU_Loss - NRP_Loss))) * 100, 0), 100) Where:

• MLoss is the Measured Packet Loss.
• NRP_Loss is the acceptable packet loss for perfection.
• NRPoU_Loss is the packet loss threshold beyond which the application becomes unusable.

Final QoO Calculation The overall QoO score is the minimum of the latency and packet loss scores: QoO = min(QoO_latency, QoO_loss) Example Requirements and Measured Data:

• NRP: 4 Mbps {99%, 250 ms, 0.1% loss}, {99.9%, 350 ms, 0.1% loss}
• NRPoU: {99%, 400 ms, 1% loss}, {99.9%, 401 ms, 1% loss}
• Measured Latency: 99% = 350 ms, 99.9% = 352 ms
• Measured Packet Loss: 0.5%
• Measured Minimum Bandwidth: 32 Mbps / 28 Mbps

Then the QoO is defined: QoO_latency = min( (min(max((1 - (350 ms - 250 ms) / (400 ms - 250 ms)) * 100, 0), 100), (min(max((1 - (352 ms - 350 ms) / (401 ms - 350 ms)) * 100, 0), 100)) ) = min(33.33, 96.08) = 33.33 QoO_loss = min(max((1 - (0.5% - 0.1%) / (1% - 0.1%)) * 100, 0), 100) = 55.56 Finally, the overall QoO score is: QoO = min(33.33, 55.56) = 33.33 In this example, the application has a 33% chance of meeting the quality expectations on this network, considering both latency and packet loss. Implementation Note: Sensitivity to Sampling Accuracy Based on the simulation results in , 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. Users of this framework should consider hardware/software measurement jitter, clock offset, or other system-level noise sources when collecting data, and configure sampling frequency and duration to suit their specific needs.

How to find network requirements A key advantage of having a measurement that stretches between perfect and unusable, as opposed to having a binary (Good/Bad) or other low resolution (Terrible/Bad/OK/Great/Excellent) metrics, is that we have some leeway. The leeway is useful, for instance: a lower than 20% chance of lag free experience is intuitively not good and a greater than 90% chance of lag free experience is intuitively good --- meaning we don’t have to find perfection for making the QoO metric useful. Nevertheless we have to find some points for unusableness and perfection. There is no strict definition of when the network is so bad that the application is unusable. For perfect, we may have a definition for some apps, but for apps like web browsing and gaming, lower latency is simply better. But to assist those who wish to make a requirement, we can say that if the end-user experience does not change when reducing the latency, the network quality is sufficient for the Network Requirements for Perfection (NRP) . Someone who wishes to make a network requirement for an application in the simplest possible way, should do something along these lines.

• Simulate increasing levels of latency
• Observe the application and note the threshold where the application stops working perfectly
• Observe the application and note the threshold where the application stops being useful at all

Someone who wishes to find sophisticated network requirements might proceed in this way

• Set thresholds for acceptable fps, animation fluidity, i/o latency (voice, video, actions), or other metrics capturing outcomes that directly affects the user experience
• Create a tool for measuring these user-facing metrics
• Simulate varying latency distribution with increasing levels of latency while measuring the user facing metrics.

A QoO score at 94 can be communicated as "John's smartphone has a 94% chance of lag-free Video Conferencing", however, this does not mean that at any point of time there is a 6% chance of lag. It means there is a 6% chance of experiencing lag during the entire session/time-period, and the network requirements should be adjusted accordingly. The reason for making the QoO metric for a session is to make it understandable for an end-user, an end-user should not have to relate to the time period the metric is for.

An example Example.com's video-conferencing service requirements can be translated into the QoO Framework. For best performance for video meetings, they specify 4/4 Mbps, 100 ms latency, <1% packet loss, and <30 ms jitter. This can be translated to an NRP: NRP example.com video conferencing service: At minimum 4/4 Mbps. {0p=70ms,99p=100ms} For minimum requirements example.com does not specify anything, but at 500ms latency or 1000ms 99p latency, a video conference is very unlikely to work in a remotely satisfactory way. NRPoU {0p=500,99p=1000ms} Of course, it is possible to specify network requirements for Example.com with multiple NRP/NRPoU, for different quality levels, one/two way video, and so on. Then one can calculate the QoO at each level.

Insights from Simulation Results While the QoO framework itself places no strict requirement on sampling patterns or measurement technology, a recent simulation study examined the metric’s real-world applicability under varying conditions of:

1. Sampling Frequency: Slow sampling rates (e.g., <1 Hz) 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 (NRP, NRPoU) 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 practice, these findings mean:

• Calibrate the combination of sampling rate and total measurement period to capture fat-tailed distributions of latency with sufficient accuracy.
• Avoid significant measurement noise where possible (e.g., by calibrating time sources, accounting for clock drift).
• Thorougly 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.

Insights from user testing A study involving 25 participants tested the Quality of Outcome (QoO) framework in a real-world settings . Participants used specially equipped routers in their homes for 10 days, providing both network performance data and feedback through pre- and post-trial surveys. Participants found QoO metrics 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 provide supporting evidence for QoO's value as a user-focused tool, bridging technical metrics with real-world application performance to enhance end-user satisfaction.

Known Weaknesses and open questions We have described a way of simplifying how the network requirements of applications can be compared to quality attenuation measurements. The simplification introduces several artifacts that may or may not be significant. If new information emerges that indicate other tradeoffs are more fit for our purpose, we should switch before this Internet Draft moves further. In this section we discuss some known limitations. 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 cell, or undergoing cell handover, the interference and fading from the local environment, and any switch between radio bearers with differing signal bandwidth and transmission-time intervals (e.g. 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 indiviudal terminals, and how terminal-hosted applications can trigger a quality attenuation query, is an open question.

Missing Temporal Information in Distributions. These two latency series: 1,200,1,200,1,200,1,200,1,200 and 1,1,1,1,1,200,200,200,200,200 will 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 perfectly capture outcomes we are getting into extreme computational complexity. As an application's performance is bound by how the developers react to varying network performance, meaning nearly all different series of latencies may have different application outcomes. It will most likely be necessary to add a time-scale to the application requirement specifications.

Subsampling the real distribution Additionally, we cannot capture latency on every packet that is sent. We can probe and sample, but there will always be unknowns. We are now in the realm of probability. Perfection is impossible, but instead of denying this, we should embrace it, which is why talking about the probability of outcomes is the way forward.

Assuming Linear Relationship between Perfect and Unusable (and that it is not really a probability) 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 requirements and finding a distance measure (between requirement and actual measured capability).

Binary Bandwidth threshold Choosing this is to reduce complexity, but we do acknowledge that the applications are not that simple. The defence for this trade off is that insufficient bandwidth will cause queues and therefore latency, and it should be possible to see this. Additionally, network requirements can be set up per quality level (resolution, fps etc.) for the application. However, having too many network requirements also increases the complexity for users of the framework, and it is still unclear if this is the optimal tradeoff.

Low resolution on Packet Loss To ensure simplicity, packet loss is described as infinite latency and the resolution will be bound to the percentiles we chose to sample. There is a good argument that some applications need higher resolution on packet loss for sufficiently describing application outcomes. If this good evidence is presented for this, packet loss should be measured separately and added to the QoO formula.

Arbitrary selection of percentiles There is a need for a selection of percentiles, as we in the name of simplicity can’t use them all. But how should we select them? The 0th (minimal) and 50th (median) percentile have implicit usage by themselves. discusses that the 90th, 98th and 99th percentiles are key for some apps. In general the wisdom is that the higher percentiles are more useful for interactive applications, but only to a certain point. At this point an application sees it as packet loss and may adapt to it. Should we pick the 95th, 96th percentile, the 96.5th or the 97th? We don’t know, and as this is likely not universal across applications and applications classes, we simply have to choose arbitrarily, and to the best of our knowledge.

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 . 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 , "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".

qoo-c

• Link to the open-source repository: https://github.com/domoslabs/qoo-c
• The organization responsible for the implementation: Domos
• A brief general description: A C library for calculating Quality of Outcome
• The implementation's level of maturity: A complete implentation of the specification described in this document
• Coverage: The library is tested with unit tests
• Licensing: GPL 2.0
• Implementation experience: Tested by the author. Needs additional testing by third parties.
• Contact information: Bjørn Ivar Teigen: bjorn@domos.no
• The date when information about this particular implementation was last updated: 10th of January 2024

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: In active development
• Coverage: The QoO part is tested with unit tests
• Licensing: GPL 2.0
• Implementation experience: Needs testing by third parties
• Contact information: Bjørn Ivar Teigen: bjorn@domos.no William Hawkins III: hawkinwh@ucmail.uc.edu
• The date when information about this particular implementation was last updated: 10th of January 2024

Conventions and Definitions The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 when, and only when, they appear in all capitals, as shown here.

Security Considerations TODO Security

IANA Considerations This document has no IANA actions.

References Normative References In many standards track documents several words are used to signify the requirements in the specification. These words are often capitalized. This document defines these words as they should be interpreted in IETF documents. This document specifies an Internet Best Current Practices for the Internet Community, and requests discussion and suggestions for improvements. RFC 2119 specifies common key words that may be used in protocol specifications. This document aims to reduce the ambiguity by clarifying that only UPPERCASE usage of the key words have the defined special meanings. Informative References This document describes a simple process that allows authors of Internet-Drafts to record the status of known implementations by including an Implementation Status section. 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. This process is not mandatory. Authors of Internet-Drafts are encouraged to consider using the process for their documents, and working groups are invited to think about applying the process to all of their protocol specifications. This document obsoletes RFC 6982, advancing it to a Best Current Practice. Broadband Forum BITAG Packet delay variation metrics appear in many different standards documents. The metric definition in RFC 3393 has considerable flexibility, and it allows multiple formulations of delay variation through the specification of different packet selection functions. Although flexibility provides wide coverage and room for new ideas, it can make comparisons of independent implementations more difficult. Two different formulations of delay variation have come into wide use in the context of active measurements. This memo examines a range of circumstances for active measurements of delay variation and their uses, and recommends which of the two forms is best matched to particular conditions and tasks. This memo provides information for the Internet community. This memo utilizes IP performance metrics that are applicable to both complete paths and sub-paths, and it defines relationships to compose a complete path metric from the sub-path metrics with some accuracy with regard to the actual metrics. This is called "spatial composition" in RFC 2330. The memo refers to the framework for metric composition, and provides background and motivation for combining metrics to derive others. The descriptions of several composed metrics and statistics follow. [STANDARDS-TRACK] This document describes a framework and a process for developing Performance Metrics of protocols and applications transported over IETF-specified protocols. These metrics can be used to characterize traffic on live networks and services. This memo documents an Internet Best Current Practice. The Measuring Network Quality for End-Users workshop was held virtually by the Internet Architecture Board (IAB) on September 14-16, 2021. This report summarizes the workshop, the topics discussed, and some preliminary conclusions drawn at the end of the workshop. Note that this document is a report on the proceedings of the workshop. The views and positions documented in this report are those of the workshop participants and do not necessarily reflect IAB views and positions. n.d. n.d. n.d. The One-way Active Measurement Protocol (OWAMP), specified in RFC 4656, provides a common protocol for measuring one-way metrics between network devices. OWAMP can be used bi-directionally to measure one-way metrics in both directions between two network elements. However, it does not accommodate round-trip or two-way measurements. This memo specifies a Two-Way Active Measurement Protocol (TWAMP), based on the OWAMP, that adds two-way or round-trip measurement capabilities. The TWAMP measurement architecture is usually comprised of two hosts with specific roles, and this allows for some protocol simplifications, making it an attractive alternative in some circumstances. [STANDARDS-TRACK] This document describes the Simple Two-way Active Measurement Protocol (STAMP), which enables the measurement of both one-way and round-trip performance metrics, like delay, delay variation, and packet loss. n.d. n.d. The purpose of this informational document is to establish test and evaluation methodology and measurement techniques for physical network equipment in the data center. RFC 8238 is a prerequisite for this document, as it contains terminology that is considered normative. Many of these terms and methods may be applicable beyond the scope of this document as the technologies originally applied in the data center are deployed elsewhere. n.d. n.d.

Acknowledgments TODO acknowledge.