path: root/doc/rfc/rfc9318.txt

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.

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   Copyright (c) 2022 IETF Trust and the persons identified as the
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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-
              uploads/2021/09/2021-09-07-Aldabbagh-Ofcom-presentationt-
              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
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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