Quality of Outcome
draft-ietf-ippm-qoo-01
| Document | Type | Active Internet-Draft (ippm WG) | |
|---|---|---|---|
| Authors | Bjørn Ivar Teigen , Magnus Olden | ||
| Last updated | 2024-09-20 | ||
| Replaces | draft-olden-ippm-qoo | ||
| RFC stream | Internet Engineering Task Force (IETF) | ||
| Intended RFC status | (None) | ||
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| Additional resources | Mailing list discussion | ||
| Stream | WG state | WG Document | |
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draft-ietf-ippm-qoo-01
IP Performance Measurement B. I. Teigen
Internet-Draft M. Olden
Intended status: Informational Domos
Expires: 24 March 2025 20 September 2024
Quality of Outcome
draft-ietf-ippm-qoo-01
Abstract
This document describes a new network quality framework named Quality
of Outcome (QoO). The QoO framework is unique among network quality
frameworks because it is designed to meet the needs of application
developers, users and operators. This is achieved by basing the
framework on Quality Attenuation, defining a simple format for
specifying application requirements, and defining a way to compute a
simple and intuitive user-facing metric.
The framework proposes a way of sampling network quality, setting
network quality requirements and a formula for calculating the
probability for the sampled network to satisfy network requirements.
About This Document
This note is to be removed before publishing as an RFC.
The latest revision of this draft can be found at
https://github.com/domoslabs/QoOID. 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/domoslabs/QoOID.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . 5
2.2. Design Goal . . . . . . . . . . . . . . . . . . . . . . . 6
2.3. Requirements . . . . . . . . . . . . . . . . . . . . . . 6
2.3.1. Requirements for end-users . . . . . . . . . . . . . 7
2.3.2. Requirements from Application and Platform
Developers . . . . . . . . . . . . . . . . . . . . . 8
2.3.3. Requirements for Network Operators and Network Solution
Vendors . . . . . . . . . . . . . . . . . . . . . . . 9
3. Background . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1. Discussion of other performance metrics . . . . . . . . . 10
3.1.1. Average Peak Throughput . . . . . . . . . . . . . . . 11
3.1.2. Average Latency . . . . . . . . . . . . . . . . . . . 11
3.1.3. 99th Percentile of Latency . . . . . . . . . . . . . 11
3.1.4. Variance of latency . . . . . . . . . . . . . . . . . 12
3.1.5. Inter-Packet Delay Variation (IPDV) . . . . . . . . . 12
3.1.6. Packet Delay Variation (PDV) . . . . . . . . . . . . 12
3.1.7. Trimmed Mean of Latency . . . . . . . . . . . . . . . 12
3.1.8. Round-trips Per Minute . . . . . . . . . . . . . . . 13
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3.1.9. Quality Attenuation . . . . . . . . . . . . . . . . . 13
3.1.10. Summary of performance metrics . . . . . . . . . . . 13
4. Sampling requirements . . . . . . . . . . . . . . . . . . . . 15
5. Describing Network Requirements . . . . . . . . . . . . . . . 16
6. Calculating Quality of Outcome (QoO) . . . . . . . . . . . . 17
7. How to find network requirements . . . . . . . . . . . . . . 19
7.1. An example . . . . . . . . . . . . . . . . . . . . . . . 20
8. Known Weaknesses and open questions . . . . . . . . . . . . . 21
8.1. Missing Temporal Information in Distributions. . . . . . 21
8.2. Subsampling the real distribution . . . . . . . . . . . . 22
8.3. Assuming Linear Relationship between Perfect and Unusable
(and that it is not really a probability) . . . . . . . . 22
8.4. Binary Bandwidth threshold . . . . . . . . . . . . . . . 22
8.5. Low resolution on Packet Loss . . . . . . . . . . . . . . 22
8.6. Arbitrary selection of percentiles . . . . . . . . . . . 22
9. Implementation status . . . . . . . . . . . . . . . . . . . . 23
9.1. qoo-c . . . . . . . . . . . . . . . . . . . . . . . . . . 23
9.2. goresponsiveness . . . . . . . . . . . . . . . . . . . . 24
10. Conventions and Definitions . . . . . . . . . . . . . . . . . 25
11. Security Considerations . . . . . . . . . . . . . . . . . . . 25
12. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 25
13. References . . . . . . . . . . . . . . . . . . . . . . . . . 25
13.1. Normative References . . . . . . . . . . . . . . . . . . 25
13.2. Informative References . . . . . . . . . . . . . . . . . 26
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 27
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 27
1. Introduction
This document explores how the quality attenuation metric and
framework [TR-452.1] can be extended to meet the full set of
requirements described in the Motivation section. The goal is to
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. Basing this
framework on quality attenuation [TR-452.1] ensures that network
operators get the fault-isolation and network planning benefits of
composability.
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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.
2. Motivation
This section describes the features and attributes a network quality
framework must have to be useful for different stakeholders. The
stakeholders included are Application developers, End-Users, and
Network Operators and Vendors. At a high level, End-Users need an
understandable network metric. Application developers need 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
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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.
2.1. Introduction
This section aims to describe the features a network performance
framework must have to be understandable to end-users, useful for
application developers, and actionable for network operators. One of
the key motivations behind this initiative 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 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 [BITAG]. Unfortunately, it is complicated to
benchmark the quality of network transport. 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 [RPM]
make huge strides in making a better network quality metric that is
far closer to application outcomes than bandwidth is, and the latter
is successful at being relatively relatable/understandable to end-
users.
As pointed out in [RPM], “Our networks remain unresponsive, not from
a lack of technical solutions, but rather a lack of awareness of the
problem.” The lack of awareness means a lack of incentives for
operators to invest in improving network quality (beyond increasing
the throughput). While Open Source solutions exist, vendors rarely
implement them. And it all boils down to the lack of a universally
accepted network quality framework that captures how well
applications are likely to work.
A recent IAB workshop on measuring internet quality for end users
identified this important point: Users mostly care about application
performance (as opposed to network performance). Among the
conclusions is 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]. One of
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the requirements we set out here is, therefore, to be able to answer
this question: "Will an application work properly?". An answer to
this question requires a few things; First, we must acknowledge that
the internet is stochastic (from the point-of-view of any given
client), and we can never answer this question with certainty.
Second, different applications have different needs and adapt
differently to varying network conditions. Any framework aiming to
answer this question must be able to cater to the needs of different
applications. Thirdly, end users are individuals with different
perception of, and levels of tolerance for, degradation of network
conditions and the resulting effect on application experience.
2.2. 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.
2.3. Requirements
This section describes the three main requirements and the motivation
for each.
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 the network links and
computational steps involved in making the application function.
These delays in turn 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 put different patterns of load on the network. To provide an
answer to whether or not applications will work well or fail, a
network quality framework must therefore be able to compare
measurements of network performance to many different application
requirements.
Flexibility in describing application requirements and the ability to
capture the delay characteristics of the network in enough detail to
compute how likely application success is with satisfactory accuracy
and precision are necessary conditions.
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How can operators take action when measurements show that
applications fail too often? We can answer this question if the
measured metric(s) support spatial composition [RFC6049], [RFC6390].
Spatial composition gives us the ability to divide results into sub-
results, each measuring the performance of a required sub-milestone
that must be reached in time for the application to succeed.
To summarise, 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. So that operators can isolate and quantify the
contributions of different sub-outcomes and sub-paths of the
network. (Useful for Operators and Vendors)
2.3.1. 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.
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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.
2.3.2. 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.
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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.
2.3.3. 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.
3. Background
The foundation of the framework is Quality Attenuation [TR-452.1].
This work will not go into detail about how to measure Quality
Attenuation, but some relevant techniques are:
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* Active probing with TWAMP Light [RFC5357] / STAMP [RFC8762] / IRTT
[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
[Kelly][RFC8239][RFC6049]. 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 [TR-452.1].
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 [TR-452.1].
3.1. 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.
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For each of the metrics below, we discuss whether or not they meet
each of the three criteria set out in the requirements.
3.1.1. 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.
3.1.2. 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 [BITAG].
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.
3.1.3. 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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3.1.4. 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.
3.1.5. Inter-Packet Delay Variation (IPDV)
The most common definition of IPDV [RFC5481] 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.
3.1.6. Packet Delay Variation (PDV)
The most common definition of PDV [RFC5481] measures the difference
in one-delay between the smallest recorded latency and each value in
a sample.
PDV cannot be composed.
3.1.7. 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.
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3.1.8. 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. 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
[Bufferbloat].
RPM is not composable.
3.1.9. Quality Attenuation
Quality Attenuation is a network performance metric that combines
latency and packet loss into a single variable [TR-452.1].
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 [Haeri22].
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
[Haeri22].
3.1.10. 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.
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+=============+========================+==============+============+
| Metric | Capture probability of | Easy to | Composable |
| | general applications | articulate | |
| | working well | Application | |
| | | requirements | |
+=============+========================+==============+============+
| Average | Yes for some | Yes | Yes |
| latency | applications | | |
+-------------+------------------------+--------------+------------+
| Variance of | No | No | Yes |
| latency | | | |
+-------------+------------------------+--------------+------------+
| IPDV | Yes for some | No | No |
| | applications | | |
+-------------+------------------------+--------------+------------+
| PDV | Yes for some | No | No |
| | applications | | |
+-------------+------------------------+--------------+------------+
| Average | Yes for some | Yes | No |
| Peak | applications | | |
| Throughput | | | |
+-------------+------------------------+--------------+------------+
| 99th | No | No | No |
| Percentile | | | |
| of Latency | | | |
+-------------+------------------------+--------------+------------+
| 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 | | | |
+-------------+------------------------+--------------+------------+
Table 1
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.
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* "Composable" means whether the metric supports mathematical
composition to allow for detailed network analysis.
4. 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
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* 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 the report of these variables, we ensure that the
network measurements can be analyzed for precision and confidence.
5. Describing Network Requirements
This work builds upon the work already proposed in the Broadband
Forum standard called Quality of Experience Delivered (QED/TR-452)
[TR-452.1], 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.
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Until now, network requirements and measurements are what is already
standardized in the BBF TR-452 (aka QED) framework [TR-452.1]. 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 Requirement Point of Unusableness
(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.
6. 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))
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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:
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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.
7. 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 (Superbad/Bad/OK/Great/Supergreat) 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
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* 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.
7.1. 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.
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8. 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.
8.1. 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.
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8.2. 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.
8.3. 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).
8.4. 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.
8.5. 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.
8.6. 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. [BITAG] 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
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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.
9. Implementation status
Note to RFC Editor: This section MUST be removed before publication
of the document.
This section records the status of known implementations of the
protocol defined by this specification at the time of posting of this
Internet-Draft, and is based on a proposal described in [RFC7942].
The description of implementations in this section is intended to
assist the IETF in its decision processes in progressing drafts to
RFCs. Please note that the listing of any individual implementation
here does not imply endorsement by the IETF. Furthermore, no effort
has been spent to verify the information presented here that was
supplied by IETF contributors. This is not intended as, and must not
be construed to be, a catalog of available implementations or their
features. Readers are advised to note that other implementations may
exist.
According to [RFC7942], "this will allow reviewers and working groups
to assign due consideration to documents that have the benefit of
running code, which may serve as evidence of valuable experimentation
and feedback that have made the implemented protocols more mature.
It is up to the individual working groups to use this information as
they see fit".
9.1. qoo-c
* Link to the open-source repository:
https://github.com/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
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* 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
9.2. goresponsiveness
* Link to the open-source repository:
https://github.com/network-quality/goresponsiveness
The specific pull-request: https://github.com/network-
quality/goresponsiveness/pull/56
* The organization responsible for the implementation:
University of Cincinatti for goresponsiveness as a whole, Domos
for the QoO part.
* A brief general description:
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
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* 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
10. 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 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
11. Security Considerations
TODO Security
12. IANA Considerations
This document has no IANA actions.
13. References
13.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/rfc/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/rfc/rfc8174>.
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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>.
[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>.
[IRTT] "Isochronous Round-Trip Tester", n.d.,
<https://github.com/heistp/irtt>.
[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>.
[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>.
[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>.
[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>.
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[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>.
[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>.
[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>.
[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/>.
[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>.
Acknowledgments
TODO acknowledge.
Authors' Addresses
Bjørn Ivar Teigen
Domos
Gaustadalléen 21
0349
Norway
Email: bjorn@domos.no
Magnus Olden
Domos
Gaustadalléen 21
0349
Norway
Email: magnus@domos.no
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