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IAB workshop report: Measuring Network Quality for End-Users

The information below is for an old version of the document.
Document Type
This is an older version of an Internet-Draft that was ultimately published as RFC 9318.
Authors Wes Hardaker , Omer Shapira
Last updated 2022-03-07
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Network Working Group                                        W. Hardaker
Internet-Draft                                                   USC/ISI
Intended status: Informational                                O. Shapira
Expires: 8 September 2022                                          Apple
                                                            7 March 2022

      IAB workshop report: Measuring Network Quality for End-Users


   The Measuring Network Quality for End-Users workshop was held
   virtually by the Internet Architecture Board (IAB) from September
   14-16, 2021.  This report summarizes the workshop, the topics
   discussed, and some preliminary conclusions drawn at the end of the

Status of This Memo

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   This Internet-Draft will expire on 8 September 2022.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
     1.1.  Problem space . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Workshop Agenda . . . . . . . . . . . . . . . . . . . . . . .   4
   3.  Position Papers . . . . . . . . . . . . . . . . . . . . . . .   5
   4.  Workshop Topics and Discussion  . . . . . . . . . . . . . . .   7
     4.1.  Introduction and overviews  . . . . . . . . . . . . . . .   7
       4.1.1.  Key points from the keynote by Vint Cerf  . . . . . .   8
       4.1.2.  Introductory talks  . . . . . . . . . . . . . . . . .   9
       4.1.3.  Introductory talks - key points . . . . . . . . . . .  11
     4.2.  Metrics considerations  . . . . . . . . . . . . . . . . .  11
       4.2.1.  Common performance metrics  . . . . . . . . . . . . .  11
       4.2.2.  Availability metrics  . . . . . . . . . . . . . . . .  14
       4.2.3.  Capacity metrics  . . . . . . . . . . . . . . . . . .  15
       4.2.4.  Latency metrics . . . . . . . . . . . . . . . . . . .  15
       4.2.5.  Measurement case studies  . . . . . . . . . . . . . .  17
       4.2.6.  Metrics Key Points  . . . . . . . . . . . . . . . . .  18
     4.3.  Cross-layer Considerations  . . . . . . . . . . . . . . .  19
       4.3.1.  Separation of Concerns  . . . . . . . . . . . . . . .  20
       4.3.2.  Security and Privacy Considerations . . . . . . . . .  21
       4.3.3.  Concrete Suggestions  . . . . . . . . . . . . . . . .  21
       4.3.4.  Towards Future Cross-layer Observability  . . . . . .  22
       4.3.5.  Efficient Collaboration Between Hardware and Transport
               Protocols . . . . . . . . . . . . . . . . . . . . . .  22
       4.3.6.  Cross-Layer Key Points  . . . . . . . . . . . . . . .  23
     4.4.  Synthesis . . . . . . . . . . . . . . . . . . . . . . . .  23
       4.4.1.  Synthesis Key Points  . . . . . . . . . . . . . . . .  23
   5.  Conclusions . . . . . . . . . . . . . . . . . . . . . . . . .  23
     5.1.  General statements  . . . . . . . . . . . . . . . . . . .  24
     5.2.  Specific statements about detailed protocols/
           techniques  . . . . . . . . . . . . . . . . . . . . . . .  24
     5.3.  Problem statements and concerns . . . . . . . . . . . . .  25
     5.4.  No-consensus reached statements . . . . . . . . . . . . .  25
   6.  Follow-on work  . . . . . . . . . . . . . . . . . . . . . . .  26
   7.  Security considerations . . . . . . . . . . . . . . . . . . .  26
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  26
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .  26
     8.2.  Informative References  . . . . . . . . . . . . . . . . .  26
   Appendix A.  Participants List  . . . . . . . . . . . . . . . . .  32
   Appendix B.  IAB Members at the Time of Approval  . . . . . . . .  34
   Appendix C.  Acknowledgements . . . . . . . . . . . . . . . . . .  34
     C.1.  Draft contributors  . . . . . . . . . . . . . . . . . . .  34
     C.2.  Workshop Chairs . . . . . . . . . . . . . . . . . . . . .  35
     C.3.  Program Committee . . . . . . . . . . . . . . . . . . . .  35
   Appendix D.  Github Version of this document  . . . . . . . . . .  35
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  35

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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) in September
   14-16, 2021.  This report summarizes the workshop, the topics
   discussed, and some preliminary conclusions drawn at the end of the

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.  And, 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 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 the often sole measure of the quality
   of Internet connectivity.  With such narrow focus, the Measuring
   Network Quality for End-Users workshop aimed to discuss various

   *  What is user latency under typical working conditions?

   *  How reliable is connectivity across longer time periods?

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   *  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

   *  To what degree are the privacy, confidentiality, integrity, and
      authenticity of user communications guarded?

   *  Improving these aspects of network quality will likely depend on
      measurement 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 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 those 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, further discussion in Section 4:

   *  Introduction overviews and a keynote by Vint Cerf

   *  Metrics considerations

   *  Cross-layer considerations

   *  Synthesis

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   *  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

   *  Anna Brunstrom.  "Measuring newtork quality - the MONROE

   *  Bob Briscoe, Greg White, Vidhi Goel and Koen De Schepper.  "A
      single common metric to characterize varying packet delay"

   *  Brandon Schlinker.  "Internet's performance from Facebook's edge"

   *  Christoph Paasch, Kristen McIntyre, Randall Meyer, Stuart
      Cheshire, Omer Shapira.  "An end-user approach to the Internet
      Score" [McIntyre2021]

   *  Christoph Paasch, Randall Meyer, Stuart Cheshire, Omer Shapira.
      "Responsiveness under Working Conditions" [Paasch2021]

   *  Dave Reed, Levi Perigo.  "Measuring ISP Performance in Broadband
      America: a Study of Latency Under Load" [Reed2021]

   *  Eve M.  Schooler, 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, Liuyan Han. "Error
      Performance Measurement in Packet-Switched Networks" [Mirsky2021]

   *  Jana Iyengar.  "The Internet Exists In Its Use" [Iyengar2021]

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   *  Jari Arkko, Mirja Kuehlewind.  "Observability is needed to improve
      network quality" [Arkko2021]

   *  Joachim Fabini.  "Objective and subjective network quality"

   *  Jonathan Foulkes.  "Metrics helpful in assessing Internet Quality"

   *  Kalevi Kilkki, Benajamin Finley.  "In Search of Lost QoS"

   *  Karthik Sundaresan, Greg White, 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, Djamel
      Bousaber.  "State of Wi-Fi Reporting" [Kerpez2021]

   *  Kenjiro Cho. "Access Network Quality as Fitness for Purpose"

   *  Koen De Schepper, Olivier Tilmans, Gino Dion.  "Challenges and
      opportunities of hardware support for Low Queuing Latency without
      Packet Loss" [DeSchepper2021]

   *  Kyle MacMillian, Nick Feamster.  "Beyond Speed Test: Measuring
      Latency Under Load Across Different Speed Tiers" [MacMillian2021]

   *  Lucas Pardue, 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, Lisong Xu.  "User-Perceived Latency to
      measure CCAs" [Zhang2021]

   *  Neil Davies, Peter Thompson.  "Measuring Network Impact on
      Application Outcomes using Quality Attenuation" [Davies2021]

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   *  Olivier Bonaventure, 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.  "Measuring & Improving QoE on the Xfinity Wi-Fi
      Network" [Ghai2021]

   *  Robin Marx, Joris Herbots.  "Merge Those Metrics: Towards Holistic
      (Protocol) Logging" [Marx2021]

   *  Sandor Laki, Szilveszter Nadas, Balazs Varga, Luis M.  Contreras.
      "Incentive-Based Traffic Management and QoS Measurements"

   *  Satadal Sengupta, Hyojoon Kim, Jennifer Rexford.  "Fine-Grained
      RTT Monitoring Inside the Network" [Sengupta2021]

   *  Stuart Cheshire.  "The Internet is a Shared Network"

   *  Toerless Eckert, Alex Clemm. "network-quality-eckert-clemm-00.4"

   *  Vijay Sivaraman, Sharat Madanapalli, Himal Kumar.  "Measuring
      Network Experience Meaningfully, Accurately, and Scalably"

   *  Yaakov (J) Stein.  "The Futility of QoS" [Stein2021]

4.  Workshop Topics and Discussion

   The three day workshop was broken into four separate sections,
   including introductory material, that each played a role in framing
   the discussions.  This was followed by a discussion about 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) 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.

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   The introduction presentations by participants 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.  Some existing measurement work was also presented.  Vint
   Cerf provided a key note describing the history and importance of the

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
   re-considering not only the importance of one metric over the other,
   but also re-considering the entire metaphor.

   It is time for the experts to look at not only at adjusting TCP, but
   also at exploring other protocols, such as QUIC and others as well.
   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 later with better properties benefiting users.

   A suggestion: we should consider desirable properties exercises.  As
   we are looking at the parametric spaces, one can identify "desirable
   properties", as opposed to "fundamental properties".  Among such
   properties, there may be a low-latency property.  An example coming
   from ARPA: you want to know where the missile is now, not where it
   was.  Understanding what is driving the particular parameter in the
   design space.

   When the parameter values are changed in extreme, such as
   connectiveness, some other designs will emerge.  One case study is
   the interplanetary protocol, where "ping" is no long 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.  This is
   too late for IPv6, but this problem space is not too late for the
   future laying problems.

   IPv6 is still not implemented fully everywhere.  It's been a long
   road 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 started and lots
   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.

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   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 hop at most away from each other (e.g.
   Google, Facebook, Amazon).  The fundamental architecture of the
   Internet is changing.

4.1.2.  Introductory talks

   The Internet is a shared network, built on the 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.  On the other hand, the lack
   of in-network regulation made it difficult to ensure the best
   experience for every user.

   As the 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
   complex, and difficult to measure ways.  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

   Quality of Service (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.

   Quality of Experience (( QoE)) initiatives attempted to integrate the
   psychological aspects of how quality is perceived, and created
   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

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   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 Internet Service Provider.  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 the other important core metric:
   latency.  As a result, end-users whose experience is negatively
   affected by high lateness 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 the end user more throughput, while
   neglecting the latency metric, application designs started to employ
   various latency and short service disruption hiding techniques.  For
   example, user experience of web browser performance is closely tired
   to the content in the local cache.  While such techniques can clearly
   improve the user experience when using stale data is acceptable, this
   development is further decoupling user experience from the core

   In the most recent 10 years, efforts by Dave Taht and the bufferbloat
   society had led to significant progress updating queuing algorithms
   to reduce latencies under load compared to simipler FIFO queues.
   Unfortunately, the home router industry has yet to implement these
   algorithms, mostly due to marketing and cost reasons.  Most home
   router manufacturers depend on System on a Chip (SoC) acceleration to
   to make products with a desired throughput.  The 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 between different high
   throughput devices, the perception that a higher throughput leads to
   higher a quality of service 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, its important to
   appeal to the application vendors that they should adopt solutions
   that stress lower latencies.  Unfortunately, while bandwidth is
   straightforward to measure, responsiveness is trickier.  Many
   applications have found a set of metrics which are helpful to their
   realm, but these are not generalizable and universally applicable.
   Furthermore, due to the highly competitive application space, vendors
   may have economic reasons to avoid sharing their most useful metrics.

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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.  The 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 the users to
       these other factors will help user's understand their performance
       state.  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

   The workshop continued to discuss various 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 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
   the availability directly by simply attempting connections from the
   client-side to locations of interest.  For example,
   [tools.ookla_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] uses thousands of off-the shelf routers, called
   "Whiteboxes", with measurement software developed by
   [tools.samknows].  These Whiteboxes perform an array of network tests

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   and report availability based whether test connections were
   successful or not.

   Measuring available capacity can be helpful to the 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 the
   available capacity.  The ability to measure available capacity in
   real-time allows leveraging the different adaptive bitrate (ABR)
   compression algorithms to ensure the best possible user experience.
   Measuring the aggregated capacity demand allows Internet Service
   Provider's 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 other seasons.  For end-users,
   knowledge of their capacity needs can help them choose a data plan
   best suited for them.  In many cases, however, end-users have more
   than enough capacity, and adding more bandwidth will not improve
   their experience as 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 the time that it takes a
   network packet to traverse the path from one end to the other through
   the network.  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 the quality of experience.  The
   established latency metric is a round-trip time (RTT), commonly
   measured in milliseconds.  However, users often find the RTT
   unintuitive since, unlike other performance metrics, high RTT values
   indicate poor latency.  [Paasch2021] and [Mathis2021] presented an
   inverse metric, called "Round-trips per minute" (RPM).

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   There is an essential distinction between the "idle latency" and
   "latency under working conditions."  The former is measured when the
   network is not used and reflects the best-case scenario.  The latter
   is measured when the network is under a typical workload.  Until
   recently, the typical case was to present the idle latency.  However,
   these numbers can be misleading.  For example, data presented at the
   workshop shows that the idle latency can be up to 25 times lower than
   the latency under typical working conditions.  Because of that, when
   presenting latency to the end-user, it is essential to make a clear
   distinction between the two.

   Data shows that rapid changes in capacity affect latency.
   [Foulkes2021] attempts to quantify how often a rapid change in
   capacity can cause connectivity to become "unstable", i.e., having
   high latency but very little throughput.  Such changes in capacity
   can be caused by infrastructure failures, but are much more often
   caused by in-network phenomena, such 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 (WiFi vs. Ethernet),
   competing LAN traffic, device load/configuration, time of day and
   local loop/backhaul capacity.  These factors make measuring capacity
   only using an end-user device or network difficult.  A network router
   that sees 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.  And even then, various factors might limit the accuracy of
   said test.  Accurate capacity measurement requires a multiple

   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 pre-fetching their "feed" items.  These techniques make
   the core in-network metrics less indicative of the users' experience
   and necessitates collecting data in-application.

   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

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   investments, such as buiding entire datacenters closer to their
   users.  These data centers highlight the economic benefits that
   having fewer latency spikes outweigh the associated deployment cost.
   On the other hand, applications that are more tolerant to latency
   spikes can sometimes operate reasonably well through short spikes.
   Yet even those applications can benefit from consistently low
   latency.  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 the applications continue to evolve, in-application
   metrics are gaining in importance.  Using VOD as an example, one can
   assess the quality of experience by checking whether the video player
   can use the highest possible resolution, whether the video is smooth
   or freezing, and other similar metrics.  Then, the application
   developer can effectively use these metrics to prioritize future
   work.  All popular video platforms (Youtube, Instagram, Netflix, and
   others) have developed frameworks to collect and analyze such metrics
   at scale.  One example is the Scuba framework used by Meta

   Unfortunately, the in-application metrics can be challenging to use
   for comparative research purposes.  Firstly, different applications
   often use different metrics to measure the same phenomena.  For
   example, application A can measure the smoothness of video via "mean
   time to re-buffer."  In contrast, application B can rely on the
   "probability of re-buffering per second" for the same purpose.  A
   different challenge with using in-application metrics is that at the
   time of the workshop, VOD is a significant source of revenue for
   companies such as YouTube, Facebook, and Netflix, which places
   proprietary incentives against exchanging the in-application data.
   Finally, in-application metrics can also accurately describe the
   activities and preferences of an individual end-user, leading to
   privacy infringements.

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 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, something in the middle of the network or was it the service
   with which the user was attempting to communicate.

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4.2.3.  Capacity metrics

   If the network capacity does not meet the 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 accross multiple

   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

   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

   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
   applicaitons require limited capacity to function correclty.  Video-
   conferencing 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:

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   1.  The propagation delay, which reflects the path distance and the
       individual link technologies (e.g. fibre 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 spend 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

   3.  The transport protocol delays, which reflects 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 sumbissions have explicitly called out the
       application delay, which reflects the inefficiencies in the
       application layer.

   Traditionally, end-to-end latency is measured when the network is
   idle.  Results of such measurements reflect mostly 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

   Data presented at the workshop reveals a substantial difference
   between the idle latency and the working latency.  Depending on the
   traffic direciton and the technology type, the working latency is
   between 6 to 25 times higher than the idle latency:

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   | Direction  | Technology |Working | Idle    | Working -  |Working /|
   |            | type       |latency | latency | Idle       |Idle     |
   |            |            |        |         | difference |ratio    |
   | Downstream | FTTH       |148     | 10      | 138        |15       |
   | Dowstream  | 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.

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 Monterey OS.

   [Mathis2021] have applied the RPM metric to the results of more than
   4 billion download tests that M-Lab performed in 2010-2021.  During
   this time frame, the M-Lab measurement platform underwent several
   upgrades which 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 Cubic CCA leads to
   increased working latency, which is attributed to its use of larger

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   [Schlinker2019] presented a large-scale study that aimed to establish
   a correlation between goodput and quality of experience 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

   [Reed2021] presented the analysis of working latency measurements
   collected as part of the FCC's "Measuring Broadband America" (MBA)
   program.  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 the
   working latency.  At the time of this report, many home router
   manufacturers relied on hardware-accelerated routing which used FIFO
   queues.  Focusing the working latency measurements on those devices,
   and making the consumer aware of the effect of chosing one
   manufacturer vs. other can help improving the home router situation.
   The ideal test would be able to identify the working latency, and to
   pinpoint to the source of delay (home router, ISP, server side, or
   some network node in between).

   Another source of high working latency comes from network routers
   that are 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 the infrastructure.

4.2.6.  Metrics Key Points

   The metrics for network quality can be roughly grouped into:

   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.

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   3.  Latency metrics, indicating if the user gets the data in a timely

   4.  Higher-order metrics, which include both the network metrics,
       such as inter-packet arrival time, and the applicaiton metrics,
       such as the mean time between rebuffering for video streaming.

   The availabiltiy metrics can be seen as 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:

   1.  Availability and capacity are "hygienic factors" - unless an
       application is capable of using extra capacity, end-users will
       see little benefit from using overprovisioned lines.

   2.  The working latency has stronger correlation with user experience
       than latency under an idle network load.  The 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 can be effective to communicate latency to the end-

   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 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 section participants presented material and
   discussed how to accurately measure exactly where problems occur.
   The discussion showed how difficult it is to achieve accuracy when
   many components of a network connection affects the measurements.
   Discussion centered especially on the differences between physically
   wired and wireless connections and the difficulties of accurately
   determining problem spots when multiple different network types are
   responsible for the quality.  As an example, [Kerpez2021] showed that
   as Internet access becomes the norm, the limited bandwidth of 2.4Ghz
   wifi is most frequently the bottleneck.  In comparison, the wider
   bandwidth of the 5Ghz WiFi have only been the bottleneck in 20% of

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   the 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.

   *  The 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, and are not able to
      act on the limited information about the network performance.

   *  Internet service providers 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 the
      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 need for an accompanying set of tools to analyze the data.

   *  A transparent model for giving the different actors on the network
      connection an incentive to share the performance data they

   *  Preservation of end-user privacy.  In particular, federated
      learning approaches, where no centralized entity has the access to
      the whole picture, should be preferred.

   *  The data exchange format should include precautions against data
      manipulations, so that the different actors won't be tempted to
      game the mechanism.

4.3.1.  Separation of Concerns

   Commonly, there's a tight coupling between

   1.  collecting performance metrics,

   2.  interpreting those metrics and

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   3.  and acting upon the intrepretation of the metrics.

   Unfortunately, such model is not the best for successfully exchanging
   cross-layer data:

   *  The actors that have the ability 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 for the interpretation do not necessarily have the abilty
      to control the behavior of network / application.

   *  The actors that can control the behavior of network / application
      typically do not have access to the 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 / act upon should publish their measured data.

   *  The actors that have the expertise in interpreting and
      synthesizing the performance data will be able to publish the
      results of any interpretation.

4.3.2.  Security and Privacy Considerations

   Preserving the privacy of the 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 their privacy in doing so.

   Participants agreed that observability across multiple layers is
   necessary for an accurate measurement of the network quality.

4.3.3.  Concrete Suggestions

   *  The TCP protocol makes several metrics available for passive
      measurement, and the following metrics have been found to be

      -  TCP connection latency measured using SACK/ACK timing, as well
         as the timing between TCP retransmission events, are good
         proxies for end-to-end RTT measurements.

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      -  On the Linux platform, the tcp_info structure is the de-facto
         standard for an application to introspect the performance of
         kernel-space networking.  However, there is no equivalent de-
         facto standard for the user-space networking.

   *  The QUIC and MASQUE protocols make passive performance
      measurements more challenging.

      -  An approach that uses federated measurement / hierarchical
         aggregation appears more valuable for these protocols.

      -  The QLOG format seems to be the most mature candidate for such
         an exchange.

4.3.4.  Towards Future Cross-layer Observability

   The ownership of the Internet is spread across multiple
   administrative domains, making measuring performance data difficult.
   Furthermore, the immense scale of the Internet makes aggregation and
   analysis of such data difficult.  [Marx2021] presented a simple
   logging format that could potentially be used to collect and
   aggregate data from different layers.

   Another aspect of 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 can be more diligent in identifying a protocol's key
   performance indicators, and exposing those as part of the protocol

   Despite all the 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.  Recent development of
   federated learning algorithms suggests that it might be possible to
   perform cross-layer performance measurements while preserving user

4.3.5.  Efficient Collaboration Between Hardware and Transport Protocols

   With the advent of the 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 used a single
   FIFO queue, 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

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   of CCAs can operate on much smaller queues.  To fully support
   latencies less than 1ms, 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 that's 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 tranfer frame.  However, this
   aggregation can add up to 10ms 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 transport

4.3.6.  Cross-Layer Key Points


4.4.  Synthesis

   Finally, in the Synthesis section 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 make subscription

   (this section is TBD)

4.4.1.  Synthesis Key Points

   (this section is TBD)

5.  Conclusions

   During the final hour of the workshop we gathered statements that the
   group thought were summary statements from the 3 day event.  We later
   discarded any that were in contention (listed further below for
   completeness).  For this document, the editor 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.

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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 and archivable and
       queriable - include 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 incentive goodness -
       good metrics should be actionable to help drive industries toward

   7.  A lower latency Internet, however achieved would benefit all end

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

   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.

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   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

5.3.  Problem statements and concerns

   1.  Latency mean and medians are distractions from better

   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 toward driving
       competitive market incentive.

   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 recorded that did not have consensus of
   the group at the time, but we list them here for completeness about
   the fact they were discussed:

   1.  We do not have incontrovertible evidence that buffer bloat 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

       *  Need more than just English - different localization concerns.

   3.  Stakeholder incentives aren't aligned for easy wins in this

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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.  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.

   *  How oversubscribed networks can essentially be viewed as a DDoS

8.  References

8.1.  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,

   [RFC4035]  Arends, R., Austein, R., Larson, M., Massey, D., and S.
              Rose, "Protocol Modifications for the DNS Security
              Extensions", RFC 4035, DOI 10.17487/RFC4035, March 2005,

   [RFC5155]  Laurie, B., Sisson, G., Arends, R., and D. Blacka, "DNS
              Security (DNSSEC) Hashed Authenticated Denial of
              Existence", RFC 5155, DOI 10.17487/RFC5155, March 2008,

8.2.  Informative References

              Aldabbagh, A., "Regulatory perspective on measuring
              network quality for end users",
              presentationt-to-IAB-1v00-1.pdf , September 2021.

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              Arkko, J. and M. Kühlewind, "Observability is needed to
              improve network quality",
              IAB-uploads/2021/09/iab-position-paper-observability.pdf ,
              August 2021.

              Balasubramanian, P., "Transport Layer Statistics for
              Network Quality",
              uploads/2021/09/transportstatsquality.pdf , February 2021.

              Briscoe, B., White, G., Goel, V., and K. De Schepper, "A
              Single Common Metric to Characterize Varying Packet
              uploads/2021/09/single-delay-metric-1.pdf , September

              Casas, P., "10 Years of Internet-QoE Measurements. Video,
              Cloud, Conferencing, Web and Apps. What do we need from
              the Network Side?",
              uploads/2021/09/net_quality_internet_qoe_CASAS.pdf ,
              August 2021.

              Cheshire, S., "The Internet is a Shared Network",
              cheshire-internet-is-shared-00b.pdf , February 2021.

              Davies, N. and P. Thompson, "Measuring Network Impact on
              Application Outcomes using Quality Attenuation",
              Users-1.pdf , September 2021.

              De Schepper, K., Tilmans, O., and G. Dion, "Challenges and
              opportunities of hardware support for Low Queuing Latency
              without Packet Loss",
              Latency-measurement-workshop-20210802.pdf , February 2021.

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   [Dion2021] Dion, G., "Focusing on latency, not throughput, to provide
              a better internet experience and network quality",
              latency-.pdf , August 2021.

              Fabini, J., "Network Quality from an End User
              uploads/2021/09/Fabini-IAB-NetworkQuality.txt , February

   [FB_Scuba] "Facebook Scuba", n.d.,

   [FCC_MBA]  "Measuring Broadband America",

              "Measuring Broadband America - Open Methodology",
              open-methodology , n.d..

              Foulkes, J., "Metrics helpful in assessing Internet
              IAB_Metrics_helpful_in_assessing_Internet_Quality.pdf ,
              September 2021.

   [Ghai2021] Ghai, R., "Using TCP Connect Latency for Measuring CX and
              Network Optimization",
              uploads/2021/09/xfinity-wifi-ietf-iab-v2-1.pdf , February

              Iyengar, J., "The Internet Exists In Its Use",
              Internet-Exists-In-Its-Use.pdf , August 2021.

              Shafiei, J., Kerpez, K., Cioffi, J., Chow, P., and D.
              Bousaber, "Wi-Fi and Broadband Data",
              wp-content/IAB-uploads/2021/09/Wi-Fi-Report-ASSIA.pdf ,
              September 2021.

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              Kilkki, K. and B. Finley, "In Search of Lost QoS",
              In-Search-of-Lost-QoS.pdf , February 2021.

   [Laki2021] Nadas, S., Varga, B., Contreras, L.M., and S. Laki,
              "Incentive-Based Traffic Management and QoS Measurements",
              IAB_user_meas_WS_Nadas_et_al_IncentiveBasedTMwQoS.pdf ,
              February 2021.

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Appendix A.  Participants List

   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

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   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
   Koen De Schepper
   David Schinazi
   Brandon Schlinker
   Eve Schooler
   Satadal Sengupta
   Jinous Shafiei
   Omer Shapira
   Dan Siemon
   Vijay Sivaraman
   Karthik Sundaresan
   Dave Taht
   Rick Taylor

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   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

Appendix B.  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
   Ben Campbell
   Lars Eggert
   Wes Hardaker
   Cullen Jennings
   Mirja Kühlewind
   Zhenbin Li
   Jared Mauch
   Tommy Pauly
   Colin Perkins
   David Schinazi
   Russ White
   Jiankang Yao

Appendix C.  Acknowledgements

   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.

C.1.  Draft contributors

   Thank you to the people that contributed edits to this draft:

   Erik Auerswald
   Simon Leinen
   Brian Trammell

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C.2.  Workshop Chairs

   The workshop chairs consisted of:

   Wes Hardaker
   Evgeny Khorov
   Omer Shapira

C.3.  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 Kuehlewind
   Jason Livingood
   Matt Mathis
   Randall Meyer
   Kathleen Nichols
   Christoph Paasch
   Tommy Pauly
   Greg White
   Keith Winstein

Appendix D.  Github Version of this document

   While this document is under development, it can be viewed and
   tracked here:

Authors' Addresses

   Wes Hardaker

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   Omer Shapira

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