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Internet Architecture Board (IAB) W. Hardaker
Request for Comments: 9318
Category: Informational O. Shapira
ISSN: 2070-1721 October 2022
IAB Workshop Report: Measuring Network Quality for End-Users
Abstract
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.
Status of This Memo
This document is not an Internet Standards Track specification; it is
published for informational purposes.
This document is a product of the Internet Architecture Board (IAB)
and represents information that the IAB has deemed valuable to
provide for permanent record. It represents the consensus of the
Internet Architecture Board (IAB). Documents approved for
publication by the IAB are not candidates for any level of Internet
Standard; see Section 2 of RFC 7841.
Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
https://www.rfc-editor.org/info/rfc9318.
Copyright Notice
Copyright (c) 2022 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(https://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
to this document.
Table of Contents
1. Introduction
1.1. Problem Space
2. Workshop Agenda
3. Position Papers
4. Workshop Topics and Discussion
4.1. Introduction and Overviews
4.1.1. Key Points from the Keynote by Vint Cerf
4.1.2. Introductory Talks
4.1.3. Introductory Talks - Key Points
4.2. Metrics Considerations
4.2.1. Common Performance Metrics
4.2.2. Availability Metrics
4.2.3. Capacity Metrics
4.2.4. Latency Metrics
4.2.5. Measurement Case Studies
4.2.6. Metrics Key Points
4.3. Cross-Layer Considerations
4.3.1. Separation of Concerns
4.3.2. Security and Privacy Considerations
4.3.3. Metric Measurement Considerations
4.3.4. Towards Improving Future Cross-Layer Observability
4.3.5. Efficient Collaboration between Hardware and Transport
Protocols
4.3.6. Cross-Layer Key Points
4.4. Synthesis
4.4.1. Measurement and Metrics Considerations
4.4.2. End-User Metrics Presentation
4.4.3. Synthesis Key Points
5. Conclusions
5.1. General Statements
5.2. Specific Statements about Detailed Protocols/Techniques
5.3. Problem Statements and Concerns
5.4. No-Consensus-Reached Statements
6. Follow-On Work
7. IANA Considerations
8. Security Considerations
9. Informative References
Appendix A. Program Committee
Appendix B. Workshop Chairs
Appendix C. Workshop Participants
IAB Members at the Time of Approval
Acknowledgments
Contributors
Authors' Addresses
1. Introduction
The Internet Architecture Board (IAB) holds occasional workshops
designed to consider long-term issues and strategies for the
Internet, and to suggest future directions for the Internet
architecture. This long-term planning function of the IAB is
complementary to the ongoing engineering efforts performed by working
groups of the Internet Engineering Task Force (IETF).
The Measuring Network Quality for End-Users workshop [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.
1.1. Problem Space
The Internet in 2021 is quite different from what it was 10 years
ago. Today, it is a crucial part of everyone's daily life. People
use the Internet for their social life, for their daily jobs, for
routine shopping, and for keeping up with major events. An
increasing number of people can access a gigabit connection, which
would be hard to imagine a decade ago. Additionally, thanks to
improvements in security, people trust the Internet for financial
banking transactions, purchasing goods, and everyday bill payments.
At the same time, some aspects of the end-user experience have not
improved as much. Many users have typical connection latencies that
remain at decade-old levels. Despite significant reliability
improvements in data center environments, end users also still often
see interruptions in service. Despite algorithmic advances in the
field of control theory, one still finds that the queuing delays in
the last-mile equipment exceeds the accumulated transit delays.
Transport improvements, such as QUIC, Multipath TCP, and TCP Fast
Open, are still not fully supported in some networks. Likewise,
various advances in the security and privacy of user data are not
widely supported, such as encrypted DNS to the local resolver.
Some of the major factors behind this lack of progress is the popular
perception that throughput is often the sole measure of the quality
of Internet connectivity. With such a narrow focus, the Measuring
Network Quality for End-Users workshop aimed to discuss various
topics:
* What is user latency under typical working conditions?
* How reliable is connectivity across longer time periods?
* Do networks allow the use of a broad range of protocols?
* What services can be run by network clients?
* What kind of IPv4, NAT, or IPv6 connectivity is offered, and are
there firewalls?
* What security mechanisms are available for local services, such as
DNS?
* To what degree are the privacy, confidentiality, integrity, and
authenticity of user communications guarded?
* Improving these aspects of network quality will likely depend on
measuring and exposing metrics in a meaningful way to all involved
parties, including to end users. Such measurement and exposure of
the right metrics will allow service providers and network
operators to concentrate focus on their users' experience and will
simultaneously empower users to choose the Internet Service
Providers (ISPs) that can deliver the best experience based on
their needs.
* What are the fundamental properties of a network that contributes
to a good user experience?
* What metrics quantify these properties, and how can we collect
such metrics in a practical way?
* What are the best practices for interpreting those metrics and
incorporating them in a decision-making process?
* What are the best ways to communicate these properties to service
providers and network operators?
* How can these metrics be displayed to users in a meaningful way?
2. Workshop Agenda
The Measuring Network Quality for End-Users workshop was divided into
the following main topic areas; see further discussion in Sections 4
and 5:
* Introduction overviews and a keynote by Vint Cerf
* Metrics considerations
* Cross-layer considerations
* Synthesis
* Group conclusions
3. Position Papers
The following position papers were received for consideration by the
workshop attendees. The workshop's web page [WORKSHOP] contains
archives of the papers, presentations, and recorded videos.
* Ahmed Aldabbagh. "Regulatory perspective on measuring network
quality for end users" [Aldabbagh2021]
* Al Morton. "Dream-Pipe or Pipe-Dream: What Do Users Want (and how
can we assure it)?" [Morton2021]
* Alexander Kozlov. "The 2021 National Internet Segment Reliability
Research"
* Anna Brunstrom. "Measuring network quality - the MONROE
experience"
* Bob Briscoe, Greg White, Vidhi Goel, and Koen De Schepper. "A
Single Common Metric to Characterize Varying Packet Delay"
[Briscoe2021]
* Brandon Schlinker. "Internet Performance from Facebook's Edge"
[Schlinker2019]
* Christoph Paasch, Kristen McIntyre, Randall Meyer, Stuart
Cheshire, and Omer Shapira. "An end-user approach to the Internet
Score" [McIntyre2021]
* Christoph Paasch, Randall Meyer, Stuart Cheshire, and Omer
Shapira. "Responsiveness under Working Conditions" [Paasch2021]
* Dave Reed and Levi Perigo. "Measuring ISP Performance in
Broadband America: A Study of Latency Under Load" [Reed2021]
* Eve M. Schooler and Rick Taylor. "Non-traditional Network
Metrics"
* Gino Dion. "Focusing on latency, not throughput, to provide
better internet experience and network quality" [Dion2021]
* Gregory Mirsky, Xiao Min, Gyan Mishra, and Liuyan Han. "The error
performance metric in a packet-switched network" [Mirsky2021]
* Jana Iyengar. "The Internet Exists In Its Use" [Iyengar2021]
* Jari Arkko and Mirja Kuehlewind. "Observability is needed to
improve network quality" [Arkko2021]
* Joachim Fabini. "Network Quality from an End User Perspective"
[Fabini2021]
* Jonathan Foulkes. "Metrics helpful in assessing Internet Quality"
[Foulkes2021]
* Kalevi Kilkki and Benajamin Finley. "In Search of Lost QoS"
[Kilkki2021]
* Karthik Sundaresan, Greg White, and Steve Glennon. "Latency
Measurement: What is latency and how do we measure it?"
* Keith Winstein. "Five Observations on Measuring Network Quality
for Users of Real-Time Media Applications"
* Ken Kerpez, Jinous Shafiei, John Cioffi, Pete Chow, and Djamel
Bousaber. "Wi-Fi and Broadband Data" [Kerpez2021]
* Kenjiro Cho. "Access Network Quality as Fitness for Purpose"
* Koen De Schepper, Olivier Tilmans, and Gino Dion. "Challenges and
opportunities of hardware support for Low Queuing Latency without
Packet Loss" [DeSchepper2021]
* Kyle MacMillian and Nick Feamster. "Beyond Speed Test: Measuring
Latency Under Load Across Different Speed Tiers" [MacMillian2021]
* Lucas Pardue and Sreeni Tellakula. "Lower-layer performance not
indicative of upper-layer success" [Pardue2021]
* Matt Mathis. "Preliminary Longitudinal Study of Internet
Responsiveness" [Mathis2021]
* Michael Welzl. "A Case for Long-Term Statistics" [Welzl2021]
* Mikhail Liubogoshchev. "Cross-layer Cooperation for Better
Network Service" [Liubogoshchev2021]
* Mingrui Zhang, Vidhi Goel, and Lisong Xu. "User-Perceived Latency
to Measure CCAs" [Zhang2021]
* Neil Davies and Peter Thompson. "Measuring Network Impact on
Application Outcomes Using Quality Attenuation" [Davies2021]
* Olivier Bonaventure and Francois Michel. "Packet delivery time as
a tie-breaker for assessing Wi-Fi access points" [Michel2021]
* Pedro Casas. "10 Years of Internet-QoE Measurements. Video,
Cloud, Conferencing, Web and Apps. What do we Need from the
Network Side?" [Casas2021]
* Praveen Balasubramanian. "Transport Layer Statistics for Network
Quality" [Balasubramanian2021]
* Rajat Ghai. "Using TCP Connect Latency for measuring CX and
Network Optimization" [Ghai2021]
* Robin Marx and Joris Herbots. "Merge Those Metrics: Towards
Holistic (Protocol) Logging" [Marx2021]
* Sandor Laki, Szilveszter Nadas, Balazs Varga, and Luis M.
Contreras. "Incentive-Based Traffic Management and QoS
Measurements" [Laki2021]
* Satadal Sengupta, Hyojoon Kim, and Jennifer Rexford. "Fine-
Grained RTT Monitoring Inside the Network" [Sengupta2021]
* Stuart Cheshire. "The Internet is a Shared Network"
[Cheshire2021]
* Toerless Eckert and Alex Clemm. "network-quality-eckert-clemm-
00.4"
* Vijay Sivaraman, Sharat Madanapalli, and Himal Kumar. "Measuring
Network Experience Meaningfully, Accurately, and Scalably"
[Sivaraman2021]
* Yaakov (J) Stein. "The Futility of QoS" [Stein2021]
4. Workshop Topics and Discussion
The agenda for the three-day workshop was broken into four separate
sections that each played a role in framing the discussions. The
workshop started with a series of introduction and problem space
presentations (Section 4.1), followed by metrics considerations
(Section 4.2), cross-layer considerations (Section 4.3), and a
synthesis discussion (Section 4.4). After the four subsections
concluded, a follow-on discussion was held to draw conclusions that
could be agreed upon by workshop participants (Section 5).
4.1. Introduction and Overviews
The workshop started with a broad focus on the state of user Quality
of Service (QoS) and Quality of Experience (QoE) on the Internet
today. The goal of the introductory talks was to set the stage for
the workshop by describing both the problem space and the current
solutions in place and their limitations.
The introduction presentations provided views of existing QoS and QoE
measurements and their effectiveness. Also discussed was the
interaction between multiple users within the network, as well as the
interaction between multiple layers of the OSI stack. Vint Cerf
provided a keynote describing the history and importance of the
topic.
4.1.1. Key Points from the Keynote by Vint Cerf
We may be operating in a networking space with dramatically different
parameters compared to 30 years ago. This differentiation justifies
reconsidering not only the importance of one metric over the other
but also reconsidering the entire metaphor.
It is time for the experts to look at not only adjusting TCP but also
exploring other protocols, such as QUIC has done lately. It's
important that we feel free to consider alternatives to TCP. TCP is
not a teddy bear, and one should not be afraid to replace it with a
transport layer with better properties that better benefit its users.
A suggestion: we should consider exercises to identify desirable
properties. As we are looking at the parametric spaces, one can
identify "desirable properties", as opposed to "fundamental
properties", for example, a low-latency property. An example coming
from the Advanced Research Projects Agency (ARPA): you want to know
where the missile is now, not where it was. Understanding drives
particular parameter creation and selection in the design space.
When parameter values are changed in extreme, such as connectiveness,
alternative designs will emerge. One case study of note is the
interplanetary protocol, where "ping" is no longer indicative of
anything useful. While we look at responsiveness, we should not
ignore connectivity.
Unfortunately, maintaining backward compatibility is painful. The
work on designing IPv6 so as to transition from IPv4 could have been
done better if the backward compatibility was considered. It is too
late for IPv6, but it is not too late to consider this issue for
potential future problems.
IPv6 is still not implemented fully everywhere. It's been a long
road to deployment since starting work in 1996, and we are still not
there. In 1996, the thinking was that it was quite easy to implement
IPv6, but that failed to hold true. In 1996, the dot-com boom began,
where a lot of money was spent quickly, and the moment was not caught
in time while the market expanded exponentially. This should serve
as a cautionary tale.
One last point: consider performance across multiple hops in the
Internet. We've not seen many end-to-end metrics, as successfully
developing end-to-end measurements across different network and
business boundaries is quite hard to achieve. A good question to ask
when developing new protocols is "will the new protocol work across
multiple network hops?"
Multi-hop networks are being gradually replaced by humongous, flat
networks with sufficient connectivity between operators so that
systems become 1 hop, or 2 hops at most, away from each other (e.g.,
Google, Facebook, and Amazon). The fundamental architecture of the
Internet is changing.
4.1.2. Introductory Talks
The Internet is a shared network built on IP protocols using packet
switching to interconnect multiple autonomous networks. The
Internet's departure from circuit-switching technologies allowed it
to scale beyond any other known network design. On the other hand,
the lack of in-network regulation made it difficult to ensure the
best experience for every user.
As Internet use cases continue to expand, it becomes increasingly
more difficult to predict which network characteristics correlate
with better user experiences. Different application classes, e.g.,
video streaming and teleconferencing, can affect user experience in
ways that are complex and difficult to measure. Internet utilization
shifts rapidly during the course of each day, week, and year, which
further complicates identifying key metrics capable of predicting a
good user experience.
QoS initiatives attempted to overcome these difficulties by strictly
prioritizing different types of traffic. However, QoS metrics do not
always correlate with user experience. The utility of the QoS metric
is further limited by the difficulties in building solutions with the
desired QoS characteristics.
QoE initiatives attempted to integrate the psychological aspects of
how quality is perceived and create statistical models designed to
optimize the user experience. Despite these high modeling efforts,
the QoE approach proved beneficial in certain application classes.
Unfortunately, generalizing the models proved to be difficult, and
the question of how different applications affect each other when
sharing the same network remains an open problem.
The industry's focus on giving the end user more throughput/bandwidth
led to remarkable advances. In many places around the world, a home
user enjoys gigabit speeds to their ISP. This is so remarkable that
it would have been brushed off as science fiction a decade ago.
However, the focus on increased capacity came at the expense of
neglecting another important core metric: latency. As a result, end
users whose experience is negatively affected by high latency were
advised to upgrade their equipment to get more throughput instead.
[MacMillian2021] showed that sometimes such an upgrade can lead to
latency improvements, due to the economical reasons of overselling
the "value-priced" data plans.
As the industry continued to give end users more throughput, while
mostly neglecting latency concerns, application designs started to
employ various latency and short service disruption hiding
techniques. For example, a user's web browser performance experience
is closely tied to the content in the browser's local cache. While
such techniques can clearly improve the user experience when using
stale data is possible, this development further decouples user
experience from core metrics.
In the most recent 10 years, efforts by Dave Taht and the bufferbloat
society have led to significant progress in updating queuing
algorithms to reduce latencies under load compared to simpler FIFO
queues. Unfortunately, the home router industry has yet to implement
these algorithms, mostly due to marketing and cost concerns. Most
home router manufacturers depend on System on a Chip (SoC)
acceleration to create products with a desired throughput. SoC
manufacturers opt for simpler algorithms and aggressive aggregation,
reasoning that a higher-throughput chip will have guaranteed demand.
Because consumers are offered choices primarily among different high-
throughput devices, the perception that a higher throughput leads to
higher a QoS continues to strengthen.
The home router is not the only place that can benefit from clearer
indications of acceptable performance for users. Since users
perceive the Internet via the lens of applications, it is important
that we call upon application vendors to adopt solutions that stress
lower latencies. Unfortunately, while bandwidth is straightforward
to measure, responsiveness is trickier. Many applications have found
a set of metrics that are helpful to their realm but do not
generalize well and cannot become universally applicable.
Furthermore, due to the highly competitive application space, vendors
may have economic reasons to avoid sharing their most useful metrics.
4.1.3. Introductory Talks - Key Points
1. Measuring bandwidth is necessary but is not alone sufficient.
2. In many cases, Internet users don't need more bandwidth but
rather need "better bandwidth", i.e., they need other
connectivity improvements.
3. Users perceive the quality of their Internet connection based on
the applications they use, which are affected by a combination of
factors. There's little value in exposing a typical user to the
entire spectrum of possible reasons for the poor performance
perceived in their application-centric view.
4. Many factors affecting user experience are outside the users'
sphere of control. It's unclear whether exposing users to these
other factors will help them understand the state of their
network performance. In general, users prefer simple,
categorical choices (e.g., "good", "better", and "best" options).
5. The Internet content market is highly competitive, and many
applications develop their own "secret sauce".
4.2. Metrics Considerations
In the second agenda section, the workshop continued its discussion
about metrics that can be used instead of or in addition to available
bandwidth. Several workshop attendees presented deep-dive studies on
measurement methodology.
4.2.1. Common Performance Metrics
Losing Internet access entirely is, of course, the worst user
experience. Unfortunately, unless rebooting the home router restores
connectivity, there is little a user can do other than contacting
their service provider. Nevertheless, there is value in the
systematic collection of availability metrics on the client side;
these can help the user's ISP localize and resolve issues faster
while enabling users to better choose between ISPs. One can measure
availability directly by simply attempting connections from the
client side to distant locations of interest. For example, Ookla's
[Speedtest] uses a large number of Android devices to measure network
and cellular availability around the globe. Ookla collects hundreds
of millions of data points per day and uses these for accurate
availability reporting. An alternative approach is to derive
availability from the failure rates of other tests. For example,
[FCC_MBA] and [FCC_MBA_methodology] use thousands of off-the-shelf
routers, with measurement software developed by [SamKnows]. These
routers perform an array of network tests and report availability
based on whether test connections were successful or not.
Measuring available capacity can be helpful to end users, but it is
even more valuable for service providers and application developers.
High-definition video streaming requires significantly more capacity
than any other type of traffic. At the time of the workshop, video
traffic constituted 90% of overall Internet traffic and contributed
to 95% of the revenues from monetization (via subscriptions, fees, or
ads). As a result, video streaming services, such as Netflix, need
to continuously cope with rapid changes in available capacity. The
ability to measure available capacity in real time leverages the
different adaptive bitrate (ABR) compression algorithms to ensure the
best possible user experience. Measuring aggregated capacity demand
allows ISPs to be ready for traffic spikes. For example, during the
end-of-year holiday season, the global demand for capacity has been
shown to be 5-7 times higher than during other seasons. For end
users, knowledge of their capacity needs can help them select the
best data plan given their intended usage. In many cases, however,
end users have more than enough capacity, and adding more bandwidth
will not improve their experience -- after a point, it is no longer
the limiting factor in user experience. Finally, the ability to
differentiate between the "throughput" and the "goodput" can be
helpful in identifying when the network is saturated.
In measuring network quality, latency is defined as the time it takes
a packet to traverse a network path from one end to the other. At
the time of this report, users in many places worldwide can enjoy
Internet access that has adequately high capacity and availability
for their current needs. For these users, latency improvements,
rather than bandwidth improvements, can lead to the most significant
improvements in QoE. The established latency metric is a round-trip
time (RTT), commonly measured in milliseconds. However, users often
find RTT values unintuitive since, unlike other performance metrics,
high RTT values indicate poor latency and users typically understand
higher scores to be better. To address this, [Paasch2021] and
[Mathis2021] present an inverse metric, called "Round-trips Per
Minute" (RPM).
There is an important distinction between "idle latency" and "latency
under working conditions". The former is measured when the network
is underused and reflects a best-case scenario. The latter is
measured when the network is under a typical workload. Until
recently, typical tools reported a network's idle latency, which can
be misleading. For example, data presented at the workshop shows
that idle latencies can be up to 25 times lower than the latency
under typical working loads. Because of this, it is essential to
make a clear distinction between the two when presenting latency to
end users.
Data shows that rapid changes in capacity affect latency.
[Foulkes2021] attempts to quantify how often a rapid change in
capacity can cause network connectivity to become "unstable" (i.e.,
having high latency with very little throughput). Such changes in
capacity can be caused by infrastructure failures but are much more
often caused by in-network phenomena, like changing traffic
engineering policies or rapid changes in cross-traffic.
Data presented at the workshop shows that 36% of measured lines have
capacity metrics that vary by more than 10% throughout the day and
across multiple days. These differences are caused by many
variables, including local connectivity methods (Wi-Fi vs. Ethernet),
competing LAN traffic, device load/configuration, time of day, and
local loop/backhaul capacity. These factor variations make measuring
capacity using only an end-user device or other end-network
measurement difficult. A network router seeing aggregated traffic
from multiple devices provides a better vantage point for capacity
measurements. Such a test can account for the totality of local
traffic and perform an independent capacity test. However, various
factors might still limit the accuracy of such a test. Accurate
capacity measurement requires multiple samples.
As users perceive the Internet through the lens of applications, it
may be difficult to correlate changes in capacity and latency with
the quality of the end-user experience. For example, web browsers
rely on cached page versions to shorten page load times and mitigate
connectivity losses. In addition, social networking applications
often rely on prefetching their "feed" items. These techniques make
the core in-network metrics less indicative of the users' experience
and necessitates collecting data from the end-user applications
themselves.
It is helpful to distinguish between applications that operate on a
"fixed latency budget" from those that have more tolerance to latency
variance. Cloud gaming serves as an example application that
requires a "fixed latency budget", as a sudden latency spike can
decide the "win/lose" ratio for a player. Companies that compete in
the lucrative cloud gaming market make significant infrastructure
investments, such as building entire data centers closer to their
users. These data centers highlight the economic benefit that lower
numbers of latency spikes outweigh the associated deployment costs.
On the other hand, applications that are more tolerant to latency
spikes can continue to operate reasonably well through short spikes.
Yet, even those applications can benefit from consistently low
latency depending on usage shifts. For example, Video-on-Demand
(VOD) apps can work reasonably well when the video is consumed
linearly, but once the user tries to "switch a channel" or to "skip
ahead", the user experience suffers unless the latency is
sufficiently low.
Finally, as applications continue to evolve, in-application metrics
are gaining in importance. For example, VOD applications can assess
the QoE by application-specific metrics, such as whether the video
player is able to use the highest possible resolution, identifying
when the video is smooth or freezing, or other similar metrics.
Application developers can then effectively use these metrics to
prioritize future work. All popular video platforms (YouTube,
Instagram, Netflix, and others) have developed frameworks to collect
and analyze VOD metrics at scale. One example is the Scuba framework
used by Meta [Scuba].
Unfortunately, in-application metrics can be challenging to use for
comparative research purposes. First, different applications often
use different metrics to measure the same phenomena. For example,
application A may measure the smoothness of video via "mean time to
rebuffer", while application B may rely on the "probability of
rebuffering per second" for the same purpose. A different challenge
with in-application metrics is that VOD is a significant source of
revenue for companies, such as YouTube, Facebook, and Netflix,
placing a proprietary incentive against exchanging the in-application
data. A final concern centers on the privacy issues resulting from
in-application metrics that accurately describe the activities and
preferences of an individual end user.
4.2.2. Availability Metrics
Availability is simply defined as whether or not a packet can be sent
and then received by its intended recipient. Availability is naively
thought to be the simplest to measure, but it is more complex when
considering that continual, instantaneous measurements would be
needed to detect the smallest of outages. Also difficult is
determining the root cause of infallibility: was the user's line
down, was something in the middle of the network, or was it the
service with which the user was attempting to communicate?
4.2.3. Capacity Metrics
If the network capacity does not meet user demands, the network
quality will be impacted. Once the capacity meets the demands,
increasing capacity won't lead to further quality improvements.
The actual network connection capacity is determined by the equipment
and the lines along the network path, and it varies throughout the
day and across multiple days. Studies involving DSL lines in North
America indicate that over 30% of the DSL lines have capacity metrics
that vary by more than 10% throughout the day and across multiple
days.
Some factors that affect the actual capacity are:
1. Presence of a competing traffic, either in the LAN or in the WAN
environments. In the LAN setting, the competing traffic reflects
the multiple devices that share the Internet connection. In the
WAN setting, the competing traffic often originates from the
unrelated network flows that happen to share the same network
path.
2. Capabilities of the equipment along the path of the network
connection, including the data transfer rate and the amount of
memory used for buffering.
3. Active traffic management measures, such as traffic shapers and
policers that are often used by the network providers.
There are other factors that can negatively affect the actual line
capacities.
The user demands of the traffic follow the usage patterns and
preferences of the particular users. For example, large data
transfers can use any available capacity, while the media streaming
applications require limited capacity to function correctly.
Videoconferencing applications typically need less capacity than
high-definition video streaming.
4.2.4. Latency Metrics
End-to-end latency is the time that a particular packet takes to
traverse the network path from the user to their destination and
back. The end-to-end latency comprises several components:
1. The propagation delay, which reflects the path distance and the
individual link technologies (e.g., fiber vs. satellite). The
propagation doesn't depend on the utilization of the network, to
the extent that the network path remains constant.
2. The buffering delay, which reflects the time segments spent in
the memory of the network equipment that connect the individual
network links, as well as in the memory of the transmitting
endpoint. The buffering delay depends on the network
utilization, as well as on the algorithms that govern the queued
segments.
3. The transport protocol delays, which reflect the time spent in
retransmission and reassembly, as well as the time spent when the
transport is "head-of-line blocked".
4. Some of the workshop submissions that have explicitly called out
the application delay, which reflects the inefficiencies in the
application layer.
Typically, end-to-end latency is measured when the network is idle.
Results of such measurements mostly reflect the propagation delay but
not other kinds of delay. This report uses the term "idle latency"
to refer to results achieved under idle network conditions.
Alternatively, if the latency is measured when the network is under
its typical working conditions, the results reflect multiple types of
delays. This report uses the term "working latency" to refer to such
results. Other sources use the term "latency under load" (LUL) as a
synonym.
Data presented at the workshop reveals a substantial difference
between the idle latency and the working latency. Depending on the
traffic direction and the technology type, the working latency is
between 6 to 25 times higher than the idle latency:
+============+============+========+=========+============+=========+
| Direction | Technology |Working | Idle | Working - |Working /|
| | Type |Latency | Latency | Idle |Idle |
| | | | | Difference |Ratio |
+============+============+========+=========+============+=========+
| Downstream | FTTH |148 | 10 | 138 |15 |
+------------+------------+--------+---------+------------+---------+
| Downstream | Cable |103 | 13 | 90 |8 |
+------------+------------+--------+---------+------------+---------+
| Downstream | DSL |194 | 10 | 184 |19 |
+------------+------------+--------+---------+------------+---------+
| Upstream | FTTH |207 | 12 | 195 |17 |
+------------+------------+--------+---------+------------+---------+
| Upstream | Cable |176 | 27 | 149 |6 |
+------------+------------+--------+---------+------------+---------+
| Upstream | DSL |686 | 27 | 659 |25 |
+------------+------------+--------+---------+------------+---------+
Table 1
While historically the tooling available for measuring latency
focused on measuring the idle latency, there is a trend in the
industry to start measuring the working latency as well, e.g.,
Apple's [NetworkQuality].
4.2.5. Measurement Case Studies
The participants have proposed several concrete methodologies for
measuring the network quality for the end users.
[Paasch2021] introduced a methodology for measuring working latency
from the end-user vantage point. The suggested method incrementally
adds network flows between the user device and a server endpoint
until a bottleneck capacity is reached. From these measurements, a
round-trip latency is measured and reported to the end user. The
authors chose to report results with the RPM metric. The methodology
had been implemented in Apple's macOS Monterey.
[Mathis2021] applied the RPM metric to the results of more than 4
billion download tests that M-Lab performed from 2010-2021. During
this time frame, the M-Lab measurement platform underwent several
upgrades that allowed the research team to compare the effect of
different TCP congestion control algorithms (CCAs) on the measured
end-to-end latency. The study showed that the use of cubic CCA leads
to increased working latency, which is attributed to its use of
larger queues.
[Schlinker2019] presented a large-scale study that aimed to establish
a correlation between goodput and QoE on a large social network. The
authors performed the measurements at multiple data centers from
which video segments of set sizes were streamed to a large number of
end users. The authors used the goodput and throughput metrics to
determine whether particular paths were congested.
[Reed2021] presented the analysis of working latency measurements
collected as part of the Measuring Broadband America (MBA) program by
the Federal Communication Commission (FCC). The FCC does not include
working latency in its yearly report but does offer it in the raw
data files. The authors used a subset of the raw data to identify
important differences in the working latencies across different ISPs.
[MacMillian2021] presented analysis of working latency across
multiple service tiers. They found that, unsurprisingly, "premium"
tier users experienced lower working latency compared to a "value"
tier. The data demonstrated that working latency varies
significantly within each tier; one possible explanation is the
difference in equipment deployed in the homes.
These studies have stressed the importance of measurement of working
latency. At the time of this report, many home router manufacturers
rely on hardware-accelerated routing that uses FIFO queues. Focusing
on measuring the working latency measurements on these devices and
making the consumer aware of the effect of choosing one manufacturer
vs. another can help improve the home router situation. The ideal
test would be able to identify the working latency and pinpoint the
source of the delay (home router, ISP, server side, or some network
node in between).
Another source of high working latency comes from network routers
exposed to cross-traffic. As [Schlinker2019] indicated, these can
become saturated during the peak hours of the day. Systematic
testing of the working latency in routers under load can help improve
both our understanding of latency and the impact of deployed
infrastructure.
4.2.6. Metrics Key Points
The metrics for network quality can be roughly grouped into the
following:
1. Availability metrics, which indicate whether the user can access
the network at all.
2. Capacity metrics, which indicate whether the actual line capacity
is sufficient to meet the user's demands.
3. Latency metrics, which indicate if the user gets the data in a
timely fashion.
4. Higher-order metrics, which include both the network metrics,
such as inter-packet arrival time, and the application metrics,
such as the mean time between rebuffering for video streaming.
The availability metrics can be seen as a derivative of either the
capacity (zero capacity leading to zero availability) or the latency
(infinite latency leading to zero availability).
Key points from the presentations and discussions included the
following:
1. Availability and capacity are "hygienic factors" -- unless an
application is capable of using extra capacity, end users will
see little benefit from using over-provisioned lines.
2. Working latency has a stronger correlation with the user
experience than latency under an idle network load. Working
latency can exceed the idle latency by order of magnitude.
3. The RPM metric is a stable metric, with positive values being
better, that may be more effective when communicating latency to
end users.
4. The relationship between throughput and goodput can be effective
in finding the saturation points, both in client-side
[Paasch2021] and server-side [Schlinker2019] settings.
5. Working latency depends on the algorithm choice for addressing
endpoint congestion control and router queuing.
Finally, it was commonly agreed to that the best metrics are those
that are actionable.
4.3. Cross-Layer Considerations
In the cross-layer segment of the workshop, participants presented
material on and discussed how to accurately measure exactly where
problems occur. Discussion centered especially on the differences
between physically wired and wireless connections and the
difficulties of accurately determining problem spots when multiple
different types of network segments are responsible for the quality.
As an example, [Kerpez2021] showed that a limited bandwidth of 2.4
Ghz Wi-Fi bottlenecks the most frequently. In comparison, the wider
bandwidth of the 5 Ghz Wi-Fi has only bottlenecked in 20% of
observations.
The participants agreed that no single component of a network
connection has all the data required to measure the effects of the
network performance on the quality of the end-user experience.
* Applications that are running on the end-user devices have the
best insight into their respective performance but have limited
visibility into the behavior of the network itself and are unable
to act based on their limited perspective.
* ISPs have good insight into QoS considerations but are not able to
infer the effect of the QoS metrics on the quality of end-user
experiences.
* Content providers have good insight into the aggregated behavior
of the end users but lack the insight on what aspects of network
performance are leading indicators of user behavior.
The workshop had identified the need for a standard and extensible
way to exchange network performance characteristics. Such an
exchange standard should address (at least) the following:
* A scalable way to capture the performance of multiple (potentially
thousands of) endpoints.
* The data exchange format should prevent data manipulation so that
the different participants won't be able to game the mechanisms.
* Preservation of end-user privacy. In particular, federated
learning approaches should be preferred so that no centralized
entity has the access to the whole picture.
* A transparent model for giving the different actors on a network
connection an incentive to share the performance data they
collect.
* An accompanying set of tools to analyze the data.
4.3.1. Separation of Concerns
Commonly, there's a tight coupling between collecting performance
metrics, interpreting those metrics, and acting upon the
interpretation. Unfortunately, such a model is not the best for
successfully exchanging cross-layer data, as:
* actors that are able to collect particular performance metrics
(e.g., the TCP RTT) do not necessarily have the context necessary
for a meaningful interpretation,
* the actors that have the context and the computational/storage
capacity to interpret metrics do not necessarily have the ability
to control the behavior of the network/application, and
* the actors that can control the behavior of networks and/or
applications typically do not have access to complete measurement
data.
The participants agreed that it is important to separate the above
three aspects, so that:
* the different actors that have the data, but not the ability to
interpret and/or act upon it, should publish their measured data
and
* the actors that have the expertise in interpreting and
synthesizing performance data should publish the results of their
interpretations.
4.3.2. Security and Privacy Considerations
Preserving the privacy of Internet end users is a difficult
requirement to meet when addressing this problem space. There is an
intrinsic trade-off between collecting more data about user
activities and infringing on their privacy while doing so.
Participants agreed that observability across multiple layers is
necessary for an accurate measurement of the network quality, but
doing so in a way that minimizes privacy leakage is an open question.
4.3.3. Metric Measurement Considerations
* The following TCP protocol metrics have been found to be effective
and are available for passive measurement:
- TCP connection latency measured using selective acknowledgment
(SACK) or acknowledgment (ACK) timing, as well as the timing
between TCP retransmission events, are good proxies for end-to-
end RTT measurements.
- On the Linux platform, the tcp_info structure is the de facto
standard for an application to inspect the performance of
kernel-space networking. However, there is no equivalent de
facto standard for user-space networking.
* The QUIC and MASQUE protocols make passive performance
measurements more challenging.
- An approach that uses federated measurement/hierarchical
aggregation may be more valuable for these protocols.
- The QLOG format seems to be the most mature candidate for such
an exchange.
4.3.4. Towards Improving Future Cross-Layer Observability
The ownership of the Internet is spread across multiple
administrative domains, making measurement of end-to-end performance
data difficult. Furthermore, the immense scale of the Internet makes
aggregation and analysis of this difficult. [Marx2021] presented a
simple logging format that could potentially be used to collect and
aggregate data from different layers.
Another aspect of the cross-layer collaboration hampering measurement
is that the majority of current algorithms do not explicitly provide
performance data that can be used in cross-layer analysis. The IETF
community could be more diligent in identifying each protocol's key
performance indicators and exposing them as part of the protocol
specification.
Despite all these challenges, it should still be possible to perform
limited-scope studies in order to have a better understanding of how
user quality is affected by the interaction of the different
components that constitute the Internet. Furthermore, recent
development of federated learning algorithms suggests that it might
be possible to perform cross-layer performance measurements while
preserving user privacy.
4.3.5. Efficient Collaboration between Hardware and Transport Protocols
With the advent of the low latency, low loss, and scalable throughput
(L4S) congestion notification and control, there is an even higher
need for the transport protocols and the underlying hardware to work
in unison.
At the time of the workshop, the typical home router uses a single
FIFO queue that is large enough to allow amortizing the lower-layer
header overhead across multiple transport PDUs. These designs worked
well with the cubic congestion control algorithm, yet the newer
generation of algorithms can operate on much smaller queues. To
fully support latencies less than 1 ms, the home router needs to work
efficiently on sequential transmissions of just a few segments vs.
being optimized for large packet bursts.
Another design trait common in home routers is the use of packet
aggregation to further amortize the overhead added by the lower-layer
headers. Specifically, multiple IP datagrams are combined into a
single, large transfer frame. However, this aggregation can add up
to 10 ms to the packet sojourn delay.
Following the famous "you can't improve what you don't measure"
adage, it is important to expose these aggregation delays in a way
that would allow identifying the source of the bottlenecks and making
hardware more suitable for the next generation of transport
protocols.
4.3.6. Cross-Layer Key Points
* Significant differences exist in the characteristics of metrics to
be measured and the required optimizations needed in wireless vs.
wired networks.
* Identification of an issue's root cause is hampered by the
challenges in measuring multi-segment network paths.
* No single component of a network connection has all the data
required to measure the effects of the complete network
performance on the quality of the end-user experience.
* Actionable results require both proper collection and
interpretation.
* Coordination among network providers is important to successfully
improve the measurement of end-user experiences.
* Simultaneously providing accurate measurements while preserving
end-user privacy is challenging.
* Passive measurements from protocol implementations may provide
beneficial data.
4.4. Synthesis
Finally, in the synthesis section of the workshop, the presentations
and discussions concentrated on the next steps likely needed to make
forward progress. Of particular concern is how to bring forward
measurements that can make sense to end users trying to select
between various networking subscription options.
4.4.1. Measurement and Metrics Considerations
One important consideration is how decisions can be made and what
actions can be taken based on collected metrics. Measurements must
be integrated with applications in order to get true application
views of congestion, as measurements over different infrastructure or
via other applications may return incorrect results. Congestion
itself can be a temporary problem, and mitigation strategies may need
to be different depending on whether it is expected to be a short-
term or long-term phenomenon. A significant challenge exists in
measuring short-term problems, driving the need for continuous
measurements to ensure critical moments and long-term trends are
captured. For short-term problems, workshop participants debated
whether an issue that goes away is indeed a problem or is a sign that
a network is properly adapting and self-recovering.
Important consideration must be taken when constructing metrics in
order to understand the results. Measurements can also be affected
by individual packet characteristics -- differently sized packets
typically have a linear relationship with their delay. With this in
mind, measurements can be divided into a delay based on geographical
distances, a packet-size serialization delay, and a variable (noise)
delay. Each of these three sub-component delays can be different and
individually measured across each segment in a multi-hop path.
Variable delay can also be significantly impacted by external
factors, such as bufferbloat, routing changes, network load sharing,
and other local or remote changes in performance. Network
measurements, especially load-specific tests, must also be run long
enough to ensure that any problems associated with buffering,
queuing, etc. are captured. Measurement technologies should also
distinguish between upstream and downstream measurements, as well as
measure the difference between end-to-end paths and sub-path
measurements.
4.4.2. End-User Metrics Presentation
Determining end-user needs requires informative measurements and
metrics. How do we provide the users with the service they need or
want? Is it possible for users to even voice their desires
effectively? Only high-level, simplistic answers like "reliability",
"capacity", and "service bundling" are typical answers given in end-
user surveys. Technical requirements that operators can consume,
like "low-latency" and "congestion avoidance", are not terms known to
and used by end users.
Example metrics useful to end users might include the number of users
supported by a service and the number of applications or streams that
a network can support. An example solution to combat networking
issues include incentive-based traffic management strategies (e.g.,
an application requesting lower latency may also mean accepting lower
bandwidth). User-perceived latency must be considered, not just
network latency -- user experience in-application to in-server
latency and network-to-network measurements may only be studying the
lowest-level latency. Thus, picking the right protocol to use in a
measurement is critical in order to match user experience (for
example, users do not transmit data over ICMP, even though it is a
common measurement tool).
In-application measurements should consider how to measure different
types of applications, such as video streaming, file sharing, multi-
user gaming, and real-time voice communications. It may be that
asking users for what trade-offs they are willing to accept would be
a helpful approach: would they rather have a network with low latency
or a network with higher bandwidth? Gamers may make different
decisions than home office users or content producers, for example.
Furthermore, how can users make these trade-offs in a fair manner
that does not impact other users? There is a tension between
solutions in this space vs. the cost associated with solving these
problems, as well as which customers are willing to front these
improvement costs.
Challenges in providing higher-priority traffic to users centers
around the ability for networks to be willing to listen to client
requests for higher incentives, even though commercial interests may
not flow to them without a cost incentive. Shared mediums in general
are subject to oversubscribing, such that the number of users a
network can support is either accurate on an underutilized network or
may assume an average bandwidth or other usage metric that fails to
be accurate during utilization spikes. Individual metrics are also
affected by in-home devices from cheap routers to microwaves and by
(multi-)user behaviors during tests. Thus, a single metric alone or
a single reading without context may not be useful in assisting a
user or operator to determine where the problem source actually is.
User comprehension of a network remains a challenging problem.
Multiple workshop participants argued for a single number
(potentially calculated with a weighted aggregation formula) or a
small number of measurements per expected usage (e.g., a "gaming"
score vs. a "content producer" score). Many agreed that some users
may instead prefer to consume simplified or color-coded ratings
(e.g., good/better/best, red/yellow/green, or bronze/gold/platinum).
4.4.3. Synthesis Key Points
* Some proposed metrics:
- Round-trips Per Minute (RPM)
- users per network
- latency
- 99% latency and bandwidth
* Median and mean measurements are distractions from the real
problems.
* Shared network usage greatly affects quality.
* Long measurements are needed to capture all facets of potential
network bottlenecks.
* Better-funded research in all these areas is needed for progress.
* End users will best understand a simplified score or ranking
system.
5. Conclusions
During the final hour of the three-day workshop, statements that the
group deemed to be summary statements were gathered. Later, any
statements that were in contention were discarded (listed further
below for completeness). For this document, the authors took the
original list and divided it into rough categories, applied some
suggested edits discussed on the mailing list, and further edited for
clarity and to provide context.
5.1. General Statements
1. Bandwidth is necessary but not alone sufficient.
2. In many cases, Internet users don't need more bandwidth but
rather need "better bandwidth", i.e., they need other
improvements to their connectivity.
3. We need both active and passive measurements -- passive
measurements can provide historical debugging.
4. We need passive measurements to be continuous, archivable, and
queriable, including reliability/connectivity measurements.
5. 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.
6. A useful metric for goodness must actually incentivize goodness
-- good metrics should be actionable to help drive industries
towards improvement.
7. A lower-latency Internet, however achieved, would benefit all end
users.
5.2. Specific Statements about Detailed Protocols/Techniques
1. Round-trips Per Minute (RPM) is a useful, consumable metric.
2. We need a usable tool that fills the current gap between network
reachability, latency, and speed tests.
3. End users that want to be involved in QoS decisions should be
able to voice their needs and desires.
4. Applications are needed that can perform and report good quality
measurements in order to identify insufficient points in network
access.
5. Research done by regulators indicate that users/consumers prefer
a simple metric per application, which frequently resolves to
whether the application will work properly or not.
6. New measurements and QoS or QoE techniques should not rely only
or depend on reading TCP headers.
7. It is clear from developers of interactive applications and from
network operators that lower latency is a strong factor in user
QoE. However, metrics are lacking to support this statement
directly.
5.3. Problem Statements and Concerns
1. Latency mean and medians are distractions from better
measurements.
2. It is frustrating to only measure network services without
simultaneously improving those services.
3. Stakeholder incentives aren't aligned for easy wins in this
space. Incentives are needed to motivate improvements in public
network access. Measurements may be one step towards driving
competitive market incentives.
4. For future-proof networking, it is important to measure the
ecological impact of material and energy usage.
5. We do not have incontrovertible evidence that any one metric
(e.g., latency or speed) is more important than others to
persuade device vendors to concentrate on any one optimization.
5.4. No-Consensus-Reached Statements
Additional statements were discussed and recorded that did not have
consensus of the group at the time, but they are listed here for
completeness:
1. We do not have incontrovertible evidence that bufferbloat is a
prevalent problem.
2. The measurement needs to support reporting localization in order
to find problems. Specifically:
* Detecting a problem is not sufficient if you can't find the
location.
* Need more than just English -- different localization
concerns.
3. Stakeholder incentives aren't aligned for easy wins in this
space.
6. Follow-On Work
There was discussion during the workshop about where future work
should be performed. The group agreed that some work could be done
more immediately within existing IETF working groups (e.g., IPPM,
DetNet, and RAW), while other longer-term research may be needed in
IRTF groups.
7. IANA Considerations
This document has no IANA actions.
8. Security Considerations
A few security-relevant topics were discussed at the workshop,
including but not limited to:
* what prioritization techniques can work without invading the
privacy of the communicating parties and
* how oversubscribed networks can essentially be viewed as a DDoS
attack.
9. Informative References
[Aldabbagh2021]
Aldabbagh, A., "Regulatory perspective on measuring
network quality for end-users", September 2021,
<https://www.iab.org/wp-content/IAB-
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to-IAB-1v00-1.pdf>.
[Arkko2021]
Arkko, J. and M. Kühlewind, "Observability is needed to
improve network quality", August 2021,
<https://www.iab.org/wp-content/IAB-uploads/2021/09/iab-
position-paper-observability.pdf>.
[Balasubramanian2021]
Balasubramanian, P., "Transport Layer Statistics for
Network Quality", February 2021, <https://www.iab.org/wp-
content/IAB-uploads/2021/09/transportstatsquality.pdf>.
[Briscoe2021]
Briscoe, B., White, G., Goel, V., and K. De Schepper, "A
Single Common Metric to Characterize Varying Packet
Delay", September 2021, <https://www.iab.org/wp-content/
IAB-uploads/2021/09/single-delay-metric-1.pdf>.
[Casas2021]
Casas, P., "10 Years of Internet-QoE Measurements Video,
Cloud, Conferencing, Web and Apps. What do we need from
the Network Side?", August 2021, <https://www.iab.org/wp-
content/IAB-uploads/2021/09/
net_quality_internet_qoe_CASAS.pdf>.
[Cheshire2021]
Cheshire, S., "The Internet is a Shared Network", August
2021, <https://www.iab.org/wp-content/IAB-uploads/2021/09/
draft-cheshire-internet-is-shared-00b.pdf>.
[Davies2021]
Davies, N. and P. Thompson, "Measuring Network Impact on
Application Outcomes Using Quality Attenuation", September
2021, <https://www.iab.org/wp-content/IAB-uploads/2021/09/
PNSol-et-al-Submission-to-Measuring-Network-Quality-for-
End-Users-1.pdf>.
[DeSchepper2021]
De Schepper, K., Tilmans, O., and G. Dion, "Challenges and
opportunities of hardware support for Low Queuing Latency
without Packet Loss", February 2021, <https://www.iab.org/
wp-content/IAB-uploads/2021/09/Nokia-IAB-Measuring-
Network-Quality-Low-Latency-measurement-workshop-
20210802.pdf>.
[Dion2021] Dion, G., De Schepper, K., and O. Tilmans, "Focusing on
latency, not throughput, to provide a better internet
experience and network quality", August 2021,
<https://www.iab.org/wp-content/IAB-uploads/2021/09/Nokia-
IAB-Measuring-Network-Quality-Improving-and-focusing-on-
latency-.pdf>.
[Fabini2021]
Fabini, J., "Network Quality from an End User
Perspective", February 2021, <https://www.iab.org/wp-
content/IAB-uploads/2021/09/Fabini-IAB-
NetworkQuality.txt>.
[FCC_MBA] FCC, "Measuring Broadband America",
<https://www.fcc.gov/general/measuring-broadband-america>.
[FCC_MBA_methodology]
FCC, "Measuring Broadband America - Open Methodology",
<https://www.fcc.gov/general/measuring-broadband-america-
open-methodology>.
[Foulkes2021]
Foulkes, J., "Metrics helpful in assessing Internet
Quality", September 2021, <https://www.iab.org/wp-content/
IAB-uploads/2021/09/
IAB_Metrics_helpful_in_assessing_Internet_Quality.pdf>.
[Ghai2021] Ghai, R., "Using TCP Connect Latency for measuring CX and
Network Optimization", February 2021,
<https://www.iab.org/wp-content/IAB-uploads/2021/09/
xfinity-wifi-ietf-iab-v2-1.pdf>.
[Iyengar2021]
Iyengar, J., "The Internet Exists In Its Use", August
2021, <https://www.iab.org/wp-content/IAB-uploads/2021/09/
The-Internet-Exists-In-Its-Use.pdf>.
[Kerpez2021]
Shafiei, J., Kerpez, K., Cioffi, J., Chow, P., and D.
Bousaber, "Wi-Fi and Broadband Data", September 2021,
<https://www.iab.org/wp-content/IAB-uploads/2021/09/Wi-Fi-
Report-ASSIA.pdf>.
[Kilkki2021]
Kilkki, K. and B. Finley, "In Search of Lost QoS",
February 2021, <https://www.iab.org/wp-content/IAB-
uploads/2021/09/Kilkki-In-Search-of-Lost-QoS.pdf>.
[Laki2021] Nadas, S., Varga, B., Contreras, L.M., and S. Laki,
"Incentive-Based Traffic Management and QoS Measurements",
February 2021, <https://www.iab.org/wp-content/IAB-
uploads/2021/11/CamRdy-
IAB_user_meas_WS_Nadas_et_al_IncentiveBasedTMwQoS.pdf>.
[Liubogoshchev2021]
Liubogoshchev, M., "Cross-layer Cooperation for Better
Network Service", February 2021, <https://www.iab.org/wp-
content/IAB-uploads/2021/09/Cross-layer-Cooperation-for-
Better-Network-Service-2.pdf>.
[MacMillian2021]
MacMillian, K. and N. Feamster, "Beyond Speed Test:
Measuring Latency Under Load Across Different Speed
Tiers", February 2021, <https://www.iab.org/wp-content/
IAB-uploads/2021/09/2021_nqw_lul.pdf>.
[Marx2021] Marx, R. and J. Herbots, "Merge Those Metrics: Towards
Holistic (Protocol) Logging", February 2021,
<https://www.iab.org/wp-content/IAB-uploads/2021/09/
MergeThoseMetrics_Marx_Jul2021.pdf>.
[Mathis2021]
Mathis, M., "Preliminary Longitudinal Study of Internet
Responsiveness", August 2021, <https://www.iab.org/wp-
content/IAB-uploads/2021/09/Preliminary-Longitudinal-
Study-of-Internet-Responsiveness-1.pdf>.
[McIntyre2021]
Paasch, C., McIntyre, K., Shapira, O., Meyer, R., and S.
Cheshire, "An end-user approach to an Internet Score",
September 2021, <https://www.iab.org/wp-content/IAB-
uploads/2021/09/Internet-Score-2.pdf>.
[Michel2021]
Michel, F. and O. Bonaventure, "Packet delivery time as a
tie-breaker for assessing Wi-Fi access points", February
2021, <https://www.iab.org/wp-content/IAB-uploads/2021/09/
camera_ready_Packet_delivery_time_as_a_tie_breaker_for_ass
essing_Wi_Fi_access_points.pdf>.
[Mirsky2021]
Mirsky, G., Min, X., Mishra, G., and L. Han, "The error
performance metric in a packet-switched network", February
2021, <https://www.iab.org/wp-content/IAB-uploads/2021/09/
IAB-worshop-Error-performance-measurement-in-packet-
switched-networks.pdf>.
[Morton2021]
Morton, A. C., "Dream-Pipe or Pipe-Dream: What Do Users
Want (and how can we assure it)?", Work in Progress,
Internet-Draft, draft-morton-ippm-pipe-dream-01, 6
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draft-morton-ippm-pipe-dream-01>.
[NetworkQuality]
Apple, "Network Quality",
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Appendix A. Program Committee
The program committee consisted of:
Jari Arkko
Olivier Bonaventure
Vint Cerf
Stuart Cheshire
Sam Crowford
Nick Feamster
Jim Gettys
Toke Hoiland-Jorgensen
Geoff Huston
Cullen Jennings
Katarzyna Kosek-Szott
Mirja Kühlewind
Jason Livingood
Matt Mathis
Randall Meyer
Kathleen Nichols
Christoph Paasch
Tommy Pauly
Greg White
Keith Winstein
Appendix B. Workshop Chairs
The workshop chairs consisted of:
Wes Hardaker
Evgeny Khorov
Omer Shapira
Appendix C. Workshop Participants
The following is a list of participants who attended the workshop
over a remote connection:
Ahmed Aldabbagh
Jari Arkko
Praveen Balasubramanian
Olivier Bonaventure
Djamel Bousaber
Bob Briscoe
Rich Brown
Anna Brunstrom
Pedro Casas
Vint Cerf
Stuart Cheshire
Kenjiro Cho
Steve Christianson
John Cioffi
Alexander Clemm
Luis M. Contreras
Sam Crawford
Neil Davies
Gino Dion
Toerless Eckert
Lars Eggert
Joachim Fabini
Gorry Fairhurst
Nick Feamster
Mat Ford
Jonathan Foulkes
Jim Gettys
Rajat Ghai
Vidhi Goel
Wes Hardaker
Joris Herbots
Geoff Huston
Toke Høiland-Jørgensen
Jana Iyengar
Cullen Jennings
Ken Kerpez
Evgeny Khorov
Kalevi Kilkki
Joon Kim
Zhenbin Li
Mikhail Liubogoshchev
Jason Livingood
Kyle MacMillan
Sharat Madanapalli
Vesna Manojlovic
Robin Marx
Matt Mathis
Jared Mauch
Kristen McIntyre
Randall Meyer
François Michel
Greg Mirsky
Cindy Morgan
Al Morton
Szilveszter Nadas
Kathleen Nichols
Lai Yi Ohlsen
Christoph Paasch
Lucas Pardue
Tommy Pauly
Levi Perigo
David Reed
Alvaro Retana
Roberto
Koen De Schepper
David Schinazi
Brandon Schlinker
Eve Schooler
Satadal Sengupta
Jinous Shafiei
Shapelez
Omer Shapira
Dan Siemon
Vijay Sivaraman
Karthik Sundaresan
Dave Taht
Rick Taylor
Bjørn Ivar Teigen
Nicolas Tessares
Peter Thompson
Balazs Varga
Bren Tully Walsh
Michael Welzl
Greg White
Russ White
Keith Winstein
Lisong Xu
Jiankang Yao
Gavin Young
Mingrui Zhang
IAB Members at the Time of Approval
Internet Architecture Board members at the time this document was
approved for publication were:
Jari Arkko
Deborah Brungard
Lars Eggert
Wes Hardaker
Cullen Jennings
Mallory Knodel
Mirja Kühlewind
Zhenbin Li
Tommy Pauly
David Schinazi
Russ White
Qin Wu
Jiankang Yao
Acknowledgments
The authors would like to thank the workshop participants, the
members of the IAB, and the program committee for creating and
participating in many interesting discussions.
Contributors
Thank you to the people that contributed edits to this document:
Erik Auerswald
Simon Leinen
Brian Trammell
Authors' Addresses
Wes Hardaker
Email: ietf@hardakers.net
Omer Shapira
Email: omer_shapira@apple.com