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Transport Area Working Group                                    G. White
Internet-Draft                                             K. Sundaresan
Intended status: Informational                                B. Briscoe
Expires: September 12, 2019                                    CableLabs
                                                          March 11, 2019


                Low Latency DOCSIS - Technology Overview
                        draft-white-tsvwg-lld-00

Abstract

   NOTE: This document is a reformatted version of [LLD-white-paper].

   The evolution of the bandwidth capabilities - from kilobits per
   second to gigabits - across generations of DOCSIS cable broadband
   technology has paved the way for the applications that today form our
   digital lives.  Along with increased bandwidth, or "speed", the
   latency performance of DOCSIS technology has also improved in recent
   years.  Although it often gets less attention, latency performance
   contributes as much or more to the broadband experience and the
   feasibility of future applications as does speed.

   Low Latency DOCSIS technology (LLD) is a specification developed by
   CableLabs in collaboration with DOCSIS vendors and cable operators
   that tackles the two main causes of latency in the network: queuing
   delay and media acquisition delay.  LLD introduces an approach
   wherein data traffic from applications that aren't causing latency
   can take a different logical path through the DOCSIS network without
   getting hung up behind data from applications that are causing
   latency, as is the case in today's Internet architectures.  This
   mechanism doesn't interfere with the way applications share the total
   bandwidth of the connection, and it doesn't reduce one application's
   latency at the expense of others.  In addition, LLD improves the
   DOCSIS upstream media acquisition delay with a faster request-grant
   loop and a new proactive scheduling mechanism.  LLD makes the
   internet experience better for latency sensitive applications without
   any negative impact on other applications.

   The latest generation of DOCSIS equipment that has been deployed in
   the field - DOCSIS 3.1 - experiences typical latency performance of
   around 10 milliseconds (ms) on the Access Network link.  However,
   under heavy load, the link can experience delay spikes of 100 ms or
   more.  LLD systems can deliver a consistent 1 ms delay on the DOCSIS
   network for traffic that isn't causing latency, imperceptible for
   nearly all applications.  The experience will be more consistent with
   much smaller delay variation.




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   LLD can be deployed by field-upgrading DOCSIS 3.1 cable modem and
   cable modem termination system devices with new software.  The
   technology includes tools that enable automatic provisioning of these
   new services, and it also introduces new tools to report statistics
   of latency performance to the operator.

   Cable operators, DOCSIS equipment manufacturers, and application
   providers will all have to act in order to take advantage of LLD.
   This white paper explains the technology and describes the role that
   each of these parties plays in making LLD a reality.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
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   Drafts is at https://datatracker.ietf.org/drafts/current/.

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on September 12, 2019.

Copyright Notice

   Copyright (c) 2019 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (https://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect
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   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.









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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Latency in DOCSIS Networks  . . . . . . . . . . . . . . . . .   4
   3.  New Dual-Queue Approach . . . . . . . . . . . . . . . . . . .   7
     3.1.  Low-Latency Aggregate Service Flows . . . . . . . . . . .   8
     3.2.  Identifying NQB Packets - Default Classifiers . . . . . .   9
     3.3.  Coupled AQM . . . . . . . . . . . . . . . . . . . . . . .  10
     3.4.  Queue Protection  . . . . . . . . . . . . . . . . . . . .  11
   4.  Upstream Scheduling Improvements  . . . . . . . . . . . . . .  12
     4.1.  Faster Request Grant Loop . . . . . . . . . . . . . . . .  12
     4.2.  Proactive Grant Service . . . . . . . . . . . . . . . . .  13
   5.  Low Latency DOCSIS Performance  . . . . . . . . . . . . . . .  13
   6.  Deployment Considerations . . . . . . . . . . . . . . . . . .  16
     6.1.  Device Support  . . . . . . . . . . . . . . . . . . . . .  16
     6.2.  Packet Marking  . . . . . . . . . . . . . . . . . . . . .  17
     6.3.  Provisioning Mechanisms . . . . . . . . . . . . . . . . .  18
       6.3.1.  Aggregate QoS Profiles  . . . . . . . . . . . . . . .  18
       6.3.2.  Migration Using Existing Configuration File and
               Service Class Name  . . . . . . . . . . . . . . . . .  18
       6.3.3.  Explicit Definition of ASF in the Configuration File   19
     6.4.  Latency Histogram Reporting . . . . . . . . . . . . . . .  19
   7.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .  19
   8.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  20
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  20
   10. Security Considerations . . . . . . . . . . . . . . . . . . .  20
   11. Informative References  . . . . . . . . . . . . . . . . . . .  20
   Appendix A.  Low Latency and High Bandwidth: L4S  . . . . . . . .  22
   Appendix B.  Simulation Details . . . . . . . . . . . . . . . . .  24
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  24

1.  Introduction

   Let's begin with bandwidth (or "speed"): the amount of data that can
   be delivered across a network connection over a period of time.
   Sometimes bandwidth is very important to the broadband experience,
   particularly when an application is trying to send or receive large
   amounts of data, such as watching videos on Netflix, downloading
   videos/music, syncing file-shares or email clients, uploading a video
   to YouTube or Instagram, or downloading a new application or system
   update.  Other times, bandwidth (or bandwidth alone) isn't enough,
   and latency has a big effect on the user experience.

   Latency is the time that it takes for a short message (a packet, in
   networking terminology) to make it across the network from the sender
   to the receiver and for a response to come back.  Network latency is
   commonly measured as round-trip-time and is sometimes referred to as
   "ping time."  Applications that are more interactive or real-time,



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   like web browsing, online gaming, and video conferencing/chatting,
   perform the best when latency is kept low, and adding more bandwidth
   without addressing latency doesn't make things better.

   When multiple applications share the broadband connection of one
   household (e.g., several users doing different activities at the same
   time), each of those applications can have an impact on the
   performance of the others.  They all share the total bandwidth of the
   connection (so more active applications mean less bandwidth for each
   one), and they can all cause the latency of the connection to
   increase.

   It turns out that applications today that want to send a lot of data
   all at once do a reasonably good job of sharing the bandwidth in a
   fair manner, but they actually cause a pretty big latency problem
   when they do it because they send data too quickly and expect the
   network to queue it up.  We call these applications "queue-building"
   applications, e.g., video streaming (Netflix).  There are also plenty
   of other applications that don't send data too quickly, so they don't
   cause latency.  We call these "non-queue-building" applications,
   e.g., video chatting (FaceTime).

   LLD separates these two types of traffic into two logical queues,
   which greatly improves the latency experienced by the non-queue-
   building applications (many of which may be latency-sensitive)
   without having any downside for the queue-building applications.  In
   addition, two queues allow LLD to support a next-generation
   application protocol that can scale up to sending data at 10 Gbps and
   beyond while maintaining ultra-low queuing delay, which means that in
   the future, there may not be queue-building applications at all.

   As of the writing of this document, the Low Latency DOCSIS
   specifications have just been published ([DOCSIS-MULPIv3.1],
   [DOCSIS-CCAP-OSSIv3.1], [DOCSIS-CM-OSSIv3.1]), and DOCSIS equipment
   manufacturers are working on building support for the functionality.
   In addition, work is underway in the Internet Engineering Task Force
   to standardize low-latency architectures across the broader Internet
   ecosystem.

2.  Latency in DOCSIS Networks

   Low Latency DOCSIS technology is the next step in a progression of
   latency improvements that have been made to the DOCSIS specifications
   by CableLabs in recent years.  Table 1 provides a snapshot of the
   milestones in round-trip latency performance with DOCSIS technology
   from the first DOCSIS 3.0 equipment to DOCSIS 3.1 equipment that
   supports [RFC8034] Active Queue Management, and finally the new Low
   Latency DOCSIS, which achieves ~1 ms of round-trip latency.  The



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   table references three metrics that describe the range of latencies
   added by the DOCSIS network link that would be experienced by a
   broadband user.  The first, "When Idle," refers to a broadband
   connection that is not being actively used by the customer.  The
   second, "Under Load," represents average latency while the user is
   actively using the service (e.g., streaming video).  Finally, the
   third, "99th Percentile," gives an indication of the maximum latency
   that a customer would commonly experience in real usage scenarios.
   The table uses order-of-magnitude numbers because the actual
   performance will vary because of a number of factors including DOCSIS
   channel configuration and actual application usage pattern.

   For latency-sensitive applications, the 99th percentile value has the
   most impact on user experience.

   TABLE 1.  EVOLUTION OF LATENCY PERFORMANCE IN DOCSIS NETWORKS (ROUND-
            TRIP TIME IN MILLISECONDS BETWEEN THE CM AND CMTS)

   +-------------------------------+--------+----------+---------------+
   |                               | When   | Under    | 99th          |
   |                               | Idle   | Load     | Percentile    |
   +-------------------------------+--------+----------+---------------+
   | DOCSIS 3.0 Early Equipment    | ~10 ms | ~1000 ms | ~1000 ms      |
   | DOCSIS 3.0 w/ Buffer Control  | ~10 ms | ~100 ms  | ~100 ms       |
   | DOCSIS 3.1 Active Queue       | ~10 ms | ~10 ms   | ~100 ms       |
   | Management                    |        |          |               |
   | Low Latency DOCSIS 3.1        | ~1 ms  | ~1 ms    | ~1 ms         |
   +-------------------------------+--------+----------+---------------+

                                  Table 1

   The latency described in Table 1 is caused by a series of factors in
   the DOCSIS cable modem (CM) and cable modem termination system
   (CMTS).  Figure 1 in [LLD-white-paper] illustrates the range of
   latencies caused by those factors in DOCSIS 3.1 networks.

   The lowest two latency sources in Figure 1 in [LLD-white-paper] have
   minor impacts on overall latency.

   The "Switching/Forwarding" delay represents the amount of time it
   takes for the CM and CMTS to make the decision to forward a packet.
   This has a very minor impact on overall latency.

   The "Propagation" delay (the amount of time it takes for a signal to
   travel on the HFC plant) is set by the speed of light and the
   distance from CM to CMTS.  Not much can be done to affect latency
   from this source.




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   Of the sources in Figure 1 in [LLD-white-paper], the top three
   significantly drive latency performance.

   The range of the "Serialization/Encoding" delay comes from the
   upstream and downstream channel configuration options available to
   the operator.  Some of these configurations provide significant
   robustness benefits at the expense of latency, whereas others may be
   less robust to noise but provide very low latency.  The LLD
   specification does not modify the set of options available to the
   operator.  Rather, operators should be encouraged to use the lowest
   latency channel configurations that they can, given the plant
   conditions.

   The "Media Acquisition" delay is a result of the shared-medium
   scheduling currently provided by DOCSIS technology, in which the CMTS
   arbitrates access to the upstream channel via a request-grant
   mechanism.

   The "Queuing" delay is mainly caused by the current TCP protocol and
   its variants.  Applications today that need to seek out as much
   bandwidth as possible use a transport protocol like TCP (or the TCP-
   replacement known as QUIC), which uses a "congestion control"
   algorithm (such as Reno, Cubic, or BBR) to adjust to the available
   bandwidth at the bottleneck link through the network.  Typically,
   this will be the last mile link - the DOCSIS link for cable customers
   - where the bandwidth available for each application often varies
   rapidly as the activity of all the devices in the household varies.

   With today's congestion control algorithms, the sender ramps up the
   sending rate until it's sending data faster than the bottleneck link
   can support.  Packets then start queuing in a buffer at the entrance
   to the link, i.e. the CM or CMTS.  This queue of packets grows
   quickly until the device decides to discard some newly arriving
   packets, which triggers the sender to pause for a bit in order to
   allow the buffer to drain somewhat before resuming sending.  This
   process is an inherent feature of the TCP family of Internet
   transport protocols, and it repeats over and over again until the
   file transfer completes.  In doing so, it causes latency and packet
   loss for all of the traffic that shares the broadband link.

   LLD tackles the two main causes of latency in the network: queuing
   delay and media acquisition delay.

   o  LLD addresses Queueing Delay by allowing non-queue-building
      applications to avoid waiting behind the delays caused by the
      current TCP or its variants.  At a high level, the low-latency
      architecture consists of a dual-queue approach that treats both
      queues as a single pool of bandwidth.



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   o  LLD cuts Media Acquisition Delay by using a faster request-grant
      loop and by adding support for a new proactive scheduler that can
      provide extremely low latency service.

   In addition, LLD introduces detailed statistics on queueing delay via
   histogram calculations performed by the CM (for upstream) and CMTS
   (for downstream).  Furthermore, CableLabs is working with a broad
   cross-section of stakeholders in the IETF to standardize an end-to-
   end service architecture that can leverage LLD to enable even high
   bandwidth TCP flows to achieve ultra-low queuing delay.  This
   technology will be important for future, interactive high-data-rate
   applications like holographic light field experiences, as well as for
   enabling higher performance versions of today's applications like web
   and video conferencing.

   The sections below describe these features in more detail.

3.  New Dual-Queue Approach

   Of all the features of LLD, the dual-queue mechanism has by far the
   greatest impact on round-trip latency and latency variation.  The
   concept of the dual-queue approach is that the majority of the
   applications that use the internet can be divided into two
   categories:

   o  Queue-Building Applications: These application traffic flows
      frequently send data faster than the path between sender and
      receiver can support.  The most common instance of queue-building
      flows are flows that use the current TCP or QUIC protocols.  As
      discussed above, these capacity-seeking protocols use a legacy
      congestion control algorithm that probes for available capacity on
      the path by sending data faster than the path can support and
      expecting the network to queue the excess data in internal
      buffers.  The majority of traffic (by volume) today is queue-
      building.  Some examples of queue-building applications are video
      streaming (e.g., Netflix, YouTube) and application downloads.

   o  Non-Queue-Building Applications: These application traffic flows
      very rarely send data faster than the path can support.  They come
      in two subcategories:

      *  Today's self-limited, non-capacity-seeking apps, such as
         multiplayer online games and IP communication apps (such as
         Skype or FaceTime).  These applications send data at a
         relatively low data rate and generally space their packets out
         in a manner that does not cause a queue to form in the network.





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      *  Future capacity-seeking TCP/QUIC applications that adopt the
         new L4S congestion control algorithm (see Appendix A) and so
         can immediately respond to fast congestion signals sent by the
         network.  These applications are still in development, as
         networks must first support L4S before applications are able to
         take advantage, but some prime candidates are web browsing,
         cloud VR, and interactive light field experiences.

   Queue-building (QB) application flows are the source of queuing
   delay, and today's non-queue-building (NQB) apps typically suffer
   from the latency caused by the QB flows.

   The purpose of the dual-queue mechanism is to segment queue-building
   traffic from non-queue-building traffic in a manner that can be
   readily implemented in DOCSIS 3.1 equipment and that doesn't alter
   the overall bandwidth of the broadband service.

   By segmenting these two types of applications into separate queues,
   each can get optimal performance.  The QB traffic can build a queue
   and achieve the necessary and expected throughput performance, and
   the NQB traffic can take advantage of the available lower latencies
   by avoiding the delay caused by the QB flows.  It is important to
   note that this segmentation of traffic isn't for purposes of giving
   one class of traffic benefits at the expense of the other - it isn't
   a high-priority queue and a low-priority queue.  Instead, each queue
   is optimized for the distinct features and requirements of the two
   classes of traffic, enabling increased functionality and adding value
   for the broadband user.  This is smart network management at work.

3.1.  Low-Latency Aggregate Service Flows

   DOCSIS 3.1 equipment, like equipment built against earlier versions
   of the specification, supports a number of upstream and downstream
   Service Flows (SFs).  These Service Flows are logical pipes that are
   defined by their configured Quality of Service (QoS) parameters (most
   commonly, the rate shaping parameters [MULPIv3.1] that specify the
   speed of user connections) and that carry a subset of the traffic to/
   from a particular CM, as specified by a set of packet classifiers
   configured by the operator.  Traditionally, each Service Flow
   provides near-complete isolation of its traffic from the traffic
   transiting other Service Flows (those on the same CM as well as those
   on other CMs) - each Service Flow has its own buffer and queue and is
   scheduled independently by the CMTS.

   Typically, the operator defines a service offering via the
   configuration of a single upstream Service Flow and a single
   downstream Service Flow with rate shaping enabled, and all of the
   user's traffic transits these two Service Flows.



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   The DOCSIS 3.1 specification already includes optional support in the
   CMTS for a mechanism to group any number of the Service Flows serving
   a particular CM.  LLD leverages and extends this "Aggregate Service
   Flow" (ASF) feature to establish (and group) a pair of Service Flows
   in each direction specifically to enable low-latency services.  One
   of the Service Flows in the pair (the "Low Latency Service Flow")
   will carry NQB traffic, and the other Service Flow (the "Classic
   Service Flow") will carry QB traffic.  The Aggregate Service Flow is
   configured for the service's rate shaping setting, and the two
   constituent Service Flows inside the Aggregate have rate shaping
   disabled.  The result is that the operator can configure the total
   aggregate rate of the service offering in each direction and does not
   have to configure (or even consider) how much of the user's traffic
   is likely to be NQB vs QB.

   Figure 2 in [LLD-white-paper] illustrates an example configuration of
   broadband service as it might look in a current DOCSIS deployment, as
   well as how it would look with Low Latency DOCSIS.  In the
   traditional configuration, there is a single downstream Service Flow
   with a rate of 100 Mbps and a single upstream Service Flow with a
   rate of 20 Mbps.  In the LLD configuration, there is a single
   downstream Aggregate Service Flow with a rate of 100 Mbps, containing
   two individual Service Flows, one for Low Latency traffic and one for
   Classic traffic.  Similarly, there is single upstream Aggregate
   Service Flow with a rate of 20 Mbps, containing two individual
   Service Flows for Low Latency and Classic traffic.

   The CMTS will enforce the Aggregate "Max Sustained Traffic Rate"
   (AMSR), and the end-user's applications determine how much of the
   aggregate bandwidth they consume irrespective of which SF they use -
   just as they do today with a single DOCSIS SF.

   As described later, Inter-Service-Flow scheduling is arranged to make
   the ASF function as a single pool of bandwidth.

3.2.  Identifying NQB Packets - Default Classifiers

   By default, the traffic within an Aggregate Service Flow is segmented
   into the two constituent Service Flows by a set of packet classifiers
   (see Figure 3 in [LLD-white-paper]) that examine the Differentiated
   Services (DiffServ) Field and the Explicit Congestion Notification
   (ECN) Field, which are standard elements of the IPv4/IPv6 header
   [RFC3168].  Specifically, packets with an NQB DiffServ value or an
   ECN field indicating either ECN Capable Transport 1 (ECT(1)) or
   Congestion Experienced (CE) will get mapped to the Low Latency
   Service Flow, and the rest of the traffic will get mapped to the
   Classic Service Flow.




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   As of the writing of this draft, it is proposed that the DiffServ
   value 0x2A be standardized in IETF/IANA to indicate NQB
   [I-D.white-tsvwg-nqb].  Certain existing DiffServ values may also be
   classified as NQB by default, such as Expedited Forwarding (EF).

   The expectation is that non-queue-building traffic sources
   (applications) will either mark their packets with an NQB DiffServ
   value or support ECN.

   Although the DiffServ Field is being used to indicate NQB behavior,
   that does not imply adoption of the Differentiated Services
   architecture as it is typically understood.  In the traditional
   DiffServ architecture, applications indicate a desire for a
   particular treatment of their packets - often implemented as a
   priority level - which in essence conveys a value judgement as to the
   importance of that traffic relative to the traffic of other
   applications.  Such an architecture can work just fine in a managed
   environment where all applications conform to a common view of their
   relative priority levels and so can be trusted to mark their packets
   appropriately.  It fails, however, when applications need to send
   packets across trust boundaries between networks, where there would
   be no common view on their relative importance.  As a result, the
   DiffServ architecture is often used within managed networks
   (corporate networks, campus networks, etc.) but is not used on the
   Internet.

   LLD's usage of the DiffServ Field to indicate NQB sidesteps this
   fundamental problem by eliminating the subjective value judgement on
   the relative importance of applications.  Instead, this usage of the
   DiffServ Field describes objectively verifiable behavior on the part
   of the application - that it will not build a queue.  Therefore,
   networks can verify that the marking has been applied properly before
   a packet is allowed into the Low Latency Service Flow queue (see
   Section 3.4).

   The ECN classifiers enable LLD's support of the IETF's Low Latency
   Low Loss Scalable throughput (L4S) service
   [I-D.ietf-tsvwg-ecn-l4s-id], which is an evolution of the original
   ECN facility to support applications needing both high bandwidth and
   low latency (see Appendix A).

3.3.  Coupled AQM

   To manage queuing delay, both the Low Latency Service Flow queue and
   the Classic Service Flow queue support Active Queue Management (AQM)
   (see Figure 4 in [LLD-white-paper]).





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   In the case of the Classic Service Flow, the queue implements the
   same state-of-the-art Active Queue Management techniques used in
   today's DOCSIS 3.1 networks.  For upstream Classic Service Flows, the
   DOCSIS 3.1 specification mandates that the CM implement the DOCSIS-
   PIE (Proportional-Integral-Enhanced AQM Algorithm), which introduces
   packet drops at an appropriate rate to drive the queue delay to the
   default target value of 10 ms.  For downstream Classic Service Flows,
   the AQM in the CMTS is still vendor specific.

   In the case of the Low Latency Service Flow, the queue supports L4S
   congestion controllers by implementing an Immediate Active Queue
   Management algorithm that utilizes ECN marking instead of packet
   drops.  By default, the algorithm does not mark the packet if the
   queuing delay is less than 0.475 milliseconds and always marks the
   packet if the delay is greater than 1 ms.  Between those configurable
   values, the algorithm marks at a rate that ramps up from 0% to 100%
   over the range.  In addition, per [I-D.ietf-tsvwg-aqm-dualq-coupled],
   the Immediate AQM in the Low Latency Queue is coupled to the Classic
   Queue AQM so that congestion in the Classic Queue will induce ECN
   marking in the Low Latency Queue that will act to balance the per-
   flow throughput across all of the flows in both queues.  L4S
   congestion control and the role of the dual-queue-coupled-aqm in
   providing flow balance is described further in Appendix A.

   To enable the Low Latency Queue to rapidly dequeue an arrived burst
   of traffic, the Inter-Service-Flow scheduler gives a higher weight to
   the Low Latency Queue than it does to the Classic Queue.  The
   coupling to the Low Latency AQM counterbalances the weighted
   scheduler by making low-latency applications leave space for Classic
   traffic.  This ensures that the weighted scheduler does not give
   priority over bandwidth, as a traditional weighted scheduler would.

3.4.  Queue Protection

   Because of the small buffer size of the Low Latency Queue, classic
   TCP flows or other queue-building flows would see poor performance
   (due to high packet loss) if they were to end up in the Low Latency
   Queue.  In addition, they would destroy the latency performance for
   the non-queue-building flows, negating the primary benefits of LLD.

   To prevent this situation, the packets that are classified to the Low
   Latency queue pass through a "Queue Protection" function (see
   Figure 5 in [LLD-white-paper]), which scores each flow's contribution
   to the growth of the queue.  If the queue delay exceeds a threshold,
   the Queue Protection function identifies the flow or flows that have
   contributed most to the growth of the queue delay, and it redirects
   future packets from those flows to the Classic Service Flow.  This




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   mechanism is performed objectively and statistically, without
   examining the identifiers or contents of the data being transmitted.

4.  Upstream Scheduling Improvements

   The DOCSIS upstream Media Access Control (MAC) Layer uses a request-
   grant mechanism.  When data to be transmitted arrive at the CM, a
   request message is sent from the CM to the CMTS.  The CMTS schedules
   the individual transmission bursts for all the CMs and communicates
   this via a bandwidth allocation map (MAP) message.  Each MAP message
   describes the upstream transmission opportunities (grants) for a time
   interval and is sent shortly before the interval to which it applies.

   When a CM has data to send, it waits for a "contention request"
   transmission opportunity.  During that opportunity, it sends a short
   request message indicating the amount of data it has to send.  It
   then waits for a subsequent MAP message granting it a transmission
   opportunity in which to send its data.  This time interval between
   the arrival of the packet at the CM and the time at which the data
   arrives at the CMTS on the upstream channel is known as the Request-
   Grant Delay (see Figure 6 in [LLD-white-paper]).  In the absence of
   queuing delay, this delay is generally 2-8 ms.

4.1.  Faster Request Grant Loop

   LLD lowers the request-grant delay by requiring support for a shorter
   MAP Interval and a shorter MAP Processing Time (see Figure 7 in
   [LLD-white-paper]).

   The MAP interval is the amount of time that each MAP message
   describes.  The MAP interval is also the time interval between
   consecutive MAP messages.  Reducing the MAP interval means that the
   CMTS processes incoming requests more frequently, thus shortening the
   amount of time that a request might wait at the CMTS before being
   processed.  A shorter MAP interval also means that grants are not
   scheduled as far into the future within each MAP message.

   The MAP Processing Time is the amount of time the CMTS uses to
   perform its scheduling calculations.  With a shorter MAP Processing
   Time, there is less delay between a request being received at the
   CMTS and the resulting grant being scheduled.

   The LLD specification requires support for a nominal MAP interval of
   1 ms or less for OFDMA upstream channels, in place of the 2-4 ms used
   previously.  In certain configurations, a 1 ms MAP interval may
   introduce tradeoffs such as upstream and/or downstream inefficiency
   that will need to be weighed against the latency improvement.




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4.2.  Proactive Grant Service

   DOCSIS scheduling services are designed to customize the behavior of
   the request-grant process for particular traffic types.  LLD
   introduces a new scheduling service called Proactive Grant Service
   (PGS), which can eliminate the request-grant loop entirely (see
   Figure 8 in [LLD-white-paper]).

   In PGS, a CMTS proactively schedules a stream of grants to a Service
   Flow at a rate that is intended to match or exceed the instantaneous
   demand.  In doing so, the vast majority of packets carried by the
   Service Flow can be transmitted without being delayed by the Request-
   Grant process.  During periods when the CMTS estimates no demand for
   bandwidth for a particular PGS Service Flow, it can conserve
   bandwidth by providing periodic unicast request opportunities rather
   than a stream of grants.

   The service parameters that are specific to PGS are Guaranteed Grant
   Interval (GGI), Guaranteed Grant Rate (GGR), and Guaranteed Request
   Interval (GRI).  In addition, the traditional rate-shaping
   parameters, such as Maximum Sustained Traffic Rate and Peak Rate,
   serve as an upper bound on the grants that can be provided to a PGS
   Service Flow.

   PGS can eliminate the delay caused by the Request-Grant loop, but it
   comes at the price of efficiency.  Inevitably, the CMTS will not be
   able to exactly predict the instantaneous demand for the Service
   Flow, so it may overestimate the capacity needed.  When the shared
   channel is fully utilized, this could reduce the capacity available
   to other Service Flows.

   The PGS scheduling type may appear at first to be similar to an
   existing DOCSIS upstream scheduling type "UGS/AD."  The main
   differences with PGS are that it sets a minimum floor on the level of
   granting (minimum grant spacing and minimum granted bandwidth) rather
   than setting a fixed grant pattern (fixed grant size and precise
   grant spacing), it supports the "Continuous Concatenation and
   Fragmentation" method of filling grants (where a contiguous sequence
   of bytes are dequeued to fill the grant, regardless of packet
   boundaries) rather than only carrying a single packet in each grant,
   and the CM is expected to continue to send Requests to the CMTS to
   inform it of packets that might be waiting in the queue.

5.  Low Latency DOCSIS Performance

   CableLabs has developed a simulator using the NS3 platform
   (<https://www.nsnam.org>) in order to evaluate the performance of
   different aspects of LLD.  The simulator models a DOCSIS 3.1 link



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   (OFDM/A channel types) between the CM and the CMTS and can be
   configured to enable or disable various components of the technology.

   Because the latency performance of the service depends on the mix of
   applications in use by the customer, we have developed a set of 10
   traffic mix scenarios that represent what we believe to be common
   busy-hour behaviors for a cable customer.  All traffic mixes include
   two bidirectional UDP sessions that are modeled after online games,
   but they could also represent VoIP or video conferencing/chatting
   applications.  One of the sessions has its packets marked as NQB and
   the other does not, allowing us to see the benefit that the low-
   latency queue provides.

   In addition, each traffic mix has a set of other applications that
   create background load, as summarized in Table 2 (see Appendix B for
   details on the traffic types).  All of this background load traffic
   utilizes the classic queue.

   Some of these traffic mixes represent behaviors that may be very
   common for broadband users during busy hour, whereas others represent
   more extreme behaviors that users may occasionally engage in.  When
   generating an overall view of the performance across all of the
   traffic mixes, we model the fact that they may not all be equally
   likely to occur by giving the more common mixes (1, 2, and 8) ten
   times the weight that we give to each of the other less common mixes.

                     TABLE2.  BACKGROUND TRAFFIC MIXES

   +----------------+--------------------------------------------------+
   | Traffic Mix 1  | 1 web user                                       |
   | Traffic Mix 2  | 1 web user, 1 video streaming user               |
   | Traffic Mix 3  | 1 web user, 1 FTP upstream                       |
   | Traffic Mix 4  | 1 web user, 1 FTP downstream                     |
   | Traffic Mix 5  | 1 web user, 1 FTP upstream and 1 FTP downstream  |
   | Traffic Mix 6  | 1 web user, 5 FTP upstream and 5 FTP downstream  |
   | Traffic Mix 7  | 1 web user, 5 FTP up, 5 FTP down, and 2 video    |
   |                | streaming users                                  |
   | Traffic Mix 8  | 5 web users                                      |
   | Traffic Mix 9  | 16 TCP down (speedtest)                          |
   | Traffic Mix 10 | 8 TCP up (speedtest)                             |
   +----------------+--------------------------------------------------+

                                  Table 2

   Table 3 summarizes the 99th percentile per-packet latency for the
   NQB-marked game traffic across all ten traffic mixes, as well as the
   weighted overall performance, for four different systems:




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   1.  a legacy DOCSIS 3.1 system with AQM disabled, 2 ms MAP interval;

   2.  a legacy DOCSIS 3.1 system with AQM enabled, 2 ms MAP interval;

   3.  a Low Latency DOCSIS 3.1 system without PGS, 1 ms MAP interval;
       and

   4.  a Low Latency DOCSIS 3.1 system with PGS configured for 5 Mbps
       GGR, 1 ms MAP interval.

   We include LLD with and without PGS because some network operators
   may wish to deploy LLD without the overhead that comes with PGS
   scheduling.

    TABLE 3.  99TH PERCENTILE ROUND-TRIP LATENCY FOR NQB-MARKED TRAFFIC
                          BETWEEN THE CM AND CMTS

   +-----------+-------------+------------+--------------+-------------+
   |           |    Legacy   |   Legacy   | Low Latency  | Low Latency |
   |           |  DOCSIS 3.1 | DOCSIS 3.1 | DOCSIS with  | DOCSIS with |
   |           | with no AQM |  with AQM  |    no PGS    |     PGS     |
   +-----------+-------------+------------+--------------+-------------+
   | Traffic   |    7.7 ms   |   7.7 ms   |    4.7 ms    |    0.9 ms   |
   | Mix 1     |             |            |              |             |
   | Traffic   |    7.7 ms   |   7.7 ms   |    4.8 ms    |    0.9 ms   |
   | Mix 2     |             |            |              |             |
   | Traffic   |   159.5 ms  |  36.6 ms   |    4.7 ms    |    0.9 ms   |
   | Mix 3     |             |            |              |             |
   | Traffic   |    7.8 ms   |   7.9 ms   |    4.7 ms    |    0.9 ms   |
   | Mix 4     |             |            |              |             |
   | Traffic   |   159.6 ms  |  57.4 ms   |    4.7 ms    |    0.9 ms   |
   | Mix 5     |             |            |              |             |
   | Traffic   |   253.7 ms  |  96.7 ms   |    4.7 ms    |    0.9 ms   |
   | Mix 6     |             |            |              |             |
   | Traffic   |   253.9 ms  |  74.7 ms   |    4.7 ms    |    0.9 ms   |
   | Mix 7     |             |            |              |             |
   | Traffic   |    7.7 ms   |   7.7 ms   |    4.7 ms    |    0.9 ms   |
   | Mix 8     |             |            |              |             |
   | Traffic   |   259.3 ms  |  52.1 ms   |    4.8 ms    |    0.9 ms   |
   | Mix 9     |             |            |              |             |
   | Traffic   |   254.0 ms  |  34.1 ms   |    4.8 ms    |    0.9 ms   |
   | Mix 10    |             |            |              |             |
   | Weighted  |   250.5 ms  |  32.4 ms   |    4.7 ms    |    0.9 ms   |
   | Overall   |             |            |              |             |
   | P99       |             |            |              |             |
   +-----------+-------------+------------+--------------+-------------+

                                  Table 3



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   As can be seen in this table, there are several traffic mixes
   (notably 1, 2, 4, and 8) for which the relatively light traffic load
   doesn't create the conditions for TCP to cause significant queuing
   delay, so even the "Legacy DOCSIS 3.1 with no AQM" system results in
   fairly low latency.  However, in the heavier traffic mixes, the
   benefit of AQM can be seen and the benefit of the dual-queue
   mechanism in LLD becomes very apparent.  By separating the NQB-marked
   traffic from the queue-building traffic, the NQB-marked traffic is
   isolated from the delay created by the TCP flows entirely, and very
   reliable low latency is achieved.  The right-most system, which
   additionally implements PGS, can eliminate the request-grant delay
   for the NQB traffic and thereby drive the round-trip latency below 1
   ms at 99th percentile.

   Figure 9 in [LLD-white-paper] illustrates the weighted overall
   latency performance across all ten traffic mixes.  The plot is a log-
   log complementary cumulative distribution function, with the y-axis
   labeled with the equivalent quantile values.

   Focusing, for instance, on the horizontal through the 99th percentile
   (P99), it can be seen that LLD with PGS holds delay below 0.9 ms for
   99% of packets.  In contrast, a DOCSIS 3.1 network without AQM can
   only hold delay below 250 ms for 99% of packets.  So, P99 delay is
   more than 250 times better with LLD.  We therefore see that LLD will
   bring a consistent, low-latency, responsive quality to cable
   broadband performance and user experiences for NBQ traffic.

6.  Deployment Considerations

6.1.  Device Support

   Deploying LLD in the MSO network can be accomplished via software-
   only upgrades to the existing DOCSIS 3.1 CMs and CMTSs.  Table 4
   shows which LLD features need implementation on the CM side, the CMTS
   side, or both.  The Dual Queue feature in the upstream requires an
   upgrade to the CM as well as to the CMTS.  The other features (Dual
   Queue in Downstream, Upstream Scheduling improvements) only require
   upgrades on the CMTS, so they can be deployed to CMs that don't
   support LLD (including DOCSIS 3.0 modems).












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              TABLE 4.  DEVICE DEPENDENCIES FOR LLD FEATURES

   +------------+------------+-------------+-------------+-------------+
   | LLD        | Downstream | Downstream  | Upstream    | Upstream    |
   | Feature    | Latency Im | Latency Imp | Latency Imp | Latency Imp |
   |            | provements | rovements - | rovements - | rovements - |
   |            | - CMTS     | CM upgrade? | CMTS        | CM upgrade? |
   |            | upgrade?   |             | upgrade?    |             |
   +------------+------------+-------------+-------------+-------------+
   | Dual Queue | Required   | Not         | Required    | Required    |
   | (ASF,      |            | required    |             |             |
   | Coupled    |            |             |             |             |
   | AQM, QP)   |            |             |             |             |
   | Upstream   | Not        | Not         | Required    | Not         |
   | Scheduling | applicable | applicable  |             | required    |
   | (Faster    |            |             |             |             |
   | Req-Grant  |            |             |             |             |
   | Loop, PGS) |            |             |             |             |
   +------------+------------+-------------+-------------+-------------+

                                  Table 4

6.2.  Packet Marking

   The design of LLD takes the approach that applications are in the
   best position to determine which flows or which packets are non-
   queue-building.  Thus, applications such as online games will be able
   to tag their packets with the NQB DiffServ value to indicate that
   they behave in a non-queue-building way, so that LLD will be able to
   classify them into the Low Latency Service Flow.

   For these packet markings to be useful for the LLD classifiers, they
   will need to survive the journey from the application source to the
   CM or CMTS.  In some cases, operators today clear the DiffServ Field
   in packets entering their network from an interconnecting network,
   which would prevent the markings making their way to the CMTS.  This
   practice is presumably driven by the view that DiffServ Field usage
   is defined by each operator for use within its network, in which case
   preserving another network's markings has no value.  As was described
   in Section 3.2, it is proposed that a single globally standard value
   be chosen to indicate NQB so that operators that intend to support
   LLD can ensure that this specific value traverses their inbound
   interconnects and their network and then arrives at the CMTS intact.

   Although application marking is preferable, some network operators
   might want to provide immediate benefits to applications that behave
   in a non-queue-building way, in advance of application developers
   introducing support for NQB tagging.  It might be possible to



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   repurpose the queue protection function to identify NQB behavior even
   if the packets are not tagged as NQB, e.g., by assuming that all non-
   TCP traffic is likely to be NQB and relying on queue protection to
   redirect the QB flows.  This is currently an area of active research.

   Further, it is possible that intermediary software or devices (either
   installed by the user or provided by the operator) could identify
   flows that are expected to be NQB and mark the packets on behalf of
   the application.

6.3.  Provisioning Mechanisms

   The LLD specifications include provisioning mechanisms to allow an
   MSO to deploy low-latency features with minimal operational impact.
   Figure 10 in [LLD-white-paper] shows all the pieces needed to build a
   low-latency service in the upstream and downstream direction.
   Although it is possible to define a Low Latency ASF, its constituent
   Classic and Low Latency SFs, and the associated classifiers
   explicitly in the CM's configuration file, a new feature known as the
   Aggregate QoS Profile can make this configuration automatic in many
   cases.  Default classifiers will be created and default parameters
   for AQM and queue protection will be used, or any of these can be
   overridden by the operator as needed.

6.3.1.  Aggregate QoS Profiles

   Similar to Service Class Names that are expanded by the CMTS into a
   set of QoS parameters for a Service Flow during the registration
   process, an operator can create an Aggregate QoS Profile (AQP) on the
   CMTS to describe the parameters of an Aggregate Service Flow, its
   constituent Service Flows, and the classifiers used to identify NQB
   traffic.

   Just like with Service Class Names, the operator can also provide
   explicit values in the configuration file for any ASF or SF
   parameters that they wish to "override".

6.3.2.  Migration Using Existing Configuration File and Service Class
        Name

   One very straightforward way to migrate to LLD configurations may not
   involve any changes to the CM configuration file.  This method
   involves the automatic expansion of a Service Flow definition to a
   Low Latency ASF via the use of a Service Class Name and matching AQP
   definition.

   When the CMTS sees a Service Class Name in a Service Flow definition
   from the CM's config file, if the CM indicates support for LLD, then



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   the CMTS will first use the Service Class Name as an AQP Name and
   look for a matching entry in the AQP Table.  If it finds a matching
   entry, it will automatically expand the Service Flow into an ASF and
   two Service Flows.

   This mechanism allows the operator to deploy LLD by simply updating
   the CMTS to support the feature and configuring AQP entries that
   match the Service Class Names in use in CM config files.  Then, as
   CMs are updated over time to include support for LLD, they will
   automatically start being configured with a Low Latency ASF.

6.3.3.  Explicit Definition of ASF in the Configuration File

   An operator can also encode a Low Latency ASF in a CM configuration
   file directly using an Aggregate Service Flow TLV (70 or 71).  The
   ASF TLV could have an AQP Name that is used by the CMTS to look up a
   definition of the ASF in its AQP Table.  It could also have ASF
   parameters that would explicitly define the ASF or would override the
   AQP parameters.  A configuration could also have explicit individual
   Service Flow TLVs (24 or 25) that are linked to the ASF via the
   Aggregate Service Flow Reference TLV.

6.4.  Latency Histogram Reporting

   As part of the AQM operation, CMs and CMTSs generate estimates of the
   queuing latency for the upstream and downstream Service Flows,
   respectively.  The latency histogram reporting function exposes these
   estimates to the operator to provide information that can be utilized
   to characterize network performance, optimize configurations, or
   troubleshoot problems in the field.

   This latency histogram reporting can be enabled via a configuration
   file setting or can be initiated by setting a MIB object on the
   device.  The operator configures the bins of the histogram, and the
   CM or the CMTS logs the number of packets with recorded latencies
   into each of the bins.  The CM implements histograms for upstream
   Service Flows, and the CMTS implements histograms for downstream
   Service Flows.  (This function can be enabled even for Service Flows
   for which AQM is disabled.)  The latency estimates from the AQM are
   represented in the form of a histogram as well as a maximum latency
   value.  See Figure 11 in [LLD-white-paper].

7.  Conclusion

   LLD enables a huge leap in latency performance and will improve the
   Internet experience overall.  With LLD, online gaming will become
   more responsive and video chats will cease to be "choppy."  This
   technology will enable a range of new applications that require real-



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   time interface between the cyber and physical worlds, such as
   vehicular communications and remote health care services.

   To realize the benefits of LLD, a number of parties need to take
   action.  DOCSIS equipment manufacturers will need to develop and
   integrate the LLD features into software updates for CMTSs and CMs.
   Cable operators need to plan the roll-out of software updates and
   configurations to DOCSIS equipment and set up the network to support
   those services (e.g., carrying DiffServ/ECN markings through the
   network).  Application and operating system vendors will need to
   adopt packet marking for NQB traffic and/or adopt the L4S congestion
   controller.  Each element of the Internet ecosystem will make these
   decisions independently; the faster that all take the necessary
   steps, the more quickly the user experience will improve.

   The cable industry has provisioned its network with substantial
   bandwidth and is poised to take another leap forward with its 10G
   networks.  But more bandwidth is only part of the broadband
   performance story.  Latency is becoming crucial to the evolution of
   broadband.  That is why LLD is a cornerstone of cable's 10G future.

8.  Acknowledgements

   CableLabs would like to thank the participants of the Low Latency
   DOCSIS Working Group, representing ARRIS, Broadcom, Casa, Charter,
   Cisco, Comcast, Cox Communications, Huawei, Intel, Liberty Global,
   Nokia, Rogers, Shaw, Videotron

9.  IANA Considerations

   None

10.  Security Considerations

   TBD

11.  Informative References

   [DOCSIS-CCAP-OSSIv3.1]
              Cable Television Laboratories, Inc., "DOCSIS 3.1 CCAP
              Operations Support System Interface Specification, CM-SP-
              CCAP-OSSIv3.1-I14-190121", January 21, 2019,
              <https://specification-search.cablelabs.com/
              CM-SP-CCAP-OSSIv3.1>.







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   [DOCSIS-CM-OSSIv3.1]
              Cable Television Laboratories, Inc., "DOCSIS 3.1 Cable
              Modem Operations Support System Interface Specification,
              CM-SP-CM-OSSIv3.1-I14-190121", January 21, 2019,
              <https://specification-search.cablelabs.com/
              CM-SP-CM-OSSIv3.1>.

   [DOCSIS-MULPIv3.1]
              Cable Television Laboratories, Inc., "MAC and Upper Layer
              Protocols Interface Specification, CM-SP-
              MULPIv3.1-I17-190121", January 21, 2019,
              <https://specification-search.cablelabs.com/
              CM-SP-MULPIv3.1>.

   [I-D.ietf-tsvwg-aqm-dualq-coupled]
              Schepper, K., Briscoe, B., Bondarenko, O., and I. Tsang,
              "DualQ Coupled AQMs for Low Latency, Low Loss and Scalable
              Throughput (L4S)", draft-ietf-tsvwg-aqm-dualq-coupled-08
              (work in progress), November 2018.

   [I-D.ietf-tsvwg-ecn-l4s-id]
              Schepper, K. and B. Briscoe, "Identifying Modified
              Explicit Congestion Notification (ECN) Semantics for
              Ultra-Low Queuing Delay (L4S)", draft-ietf-tsvwg-ecn-l4s-
              id-05 (work in progress), November 2018.

   [I-D.ietf-tsvwg-l4s-arch]
              Briscoe, B., Schepper, K., and M. Bagnulo, "Low Latency,
              Low Loss, Scalable Throughput (L4S) Internet Service:
              Architecture", draft-ietf-tsvwg-l4s-arch-03 (work in
              progress), October 2018.

   [I-D.white-tsvwg-nqb]
              White, G., "Identifying and Handling Non Queue Building
              Flows in a Bottleneck Link", draft-white-tsvwg-nqb-00
              (work in progress), October 2018.

   [LLD-white-paper]
              White, G., Sundaresan, K., and B. Briscoe, "Low Latency
              DOCSIS: Technology Overview", February 2019,
              <https://cablela.bs/
              low-latency-docsis-technology-overview-february-2019>.

   [RFC3168]  Ramakrishnan, K., Floyd, S., and D. Black, "The Addition
              of Explicit Congestion Notification (ECN) to IP",
              RFC 3168, DOI 10.17487/RFC3168, September 2001,
              <https://www.rfc-editor.org/info/rfc3168>.




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   [RFC8034]  White, G. and R. Pan, "Active Queue Management (AQM) Based
              on Proportional Integral Controller Enhanced PIE) for
              Data-Over-Cable Service Interface Specifications (DOCSIS)
              Cable Modems", RFC 8034, DOI 10.17487/RFC8034, February
              2017, <https://www.rfc-editor.org/info/rfc8034>.

   [RFC8311]  Black, D., "Relaxing Restrictions on Explicit Congestion
              Notification (ECN) Experimentation", RFC 8311,
              DOI 10.17487/RFC8311, January 2018,
              <https://www.rfc-editor.org/info/rfc8311>.

   [web-user-model]
              3GPP, "3GPP2-TSGC5, HTTP, FTP and TCP models for 1xEV-DV
              simulations", 2001.

Appendix A.  Low Latency and High Bandwidth: L4S

   How can LLD support applications that want maximum speed, and low
   latency too?  CableLabs is working with the Internet Engineering Task
   Force to make this a reality through a new technology called L4S: Low
   Latency Low Loss Scalable throughput [I-D.ietf-tsvwg-l4s-arch].

   L4S improves many of today's applications (e.g., video chat,
   everything on the web), but it will also enable future applications
   that will need both high bandwidth and low delay, such as HD video
   conferencing, cloud-rendered interactive video, cloud-rendered
   virtual reality, augmented reality, remote presence with remote
   control, interactive light field experiences, and others yet to be
   invented.

   L4S involves incremental changes to the congestion controller on the
   sender and to the AQM at the bottleneck.  The key is to indicate
   congestion by marking packets using Explicit Congestion Notification
   (ECN) rather than discarding packets.  L4S uses the 2-bit ECN field
   in the IP header (v4 or v6) and defines each marked packet to
   represent a lower strength of congestion signal [RFC8311] than the
   original ECN standard.  All the benefits of L4S follow from that.

   o  Low Latency: The sender's L4S congestion controller makes small
      but frequent rate adjustments dependent on the proportion of ECN
      marked packets, and the L4S AQM starts applying ECN-marks to
      packets at a very shallow buffer threshold.  This means an L4S
      queue can ripple at the very bottom of the buffer with sub-
      millisecond queuing delay but still fully utilize the link.
      Small, frequent adjustments could not even be considered if packet
      discards were used instead of ECN - they would induce a
      prohibitively high loss level.  Further, AQMs could not consider a




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      very shallow threshold if small adjustments were not used, as
      severe link under-utilization would result.

   o  Low Loss: By definition, using ECN eliminates packet discard.  In
      turn, that eliminates retransmission delays, which particularly
      impact the responsiveness of short web-like exchanges of data.
      Using ECN eliminates both the round-trip delay repairing a loss
      and the delay while detecting a loss.  In addition, an L4S AQM can
      immediately signal queue growth using ECN, catching queue growth
      early.  In contrast, classic AQMs hold back from discarding a
      packet for 100-200 ms because if a burst subsides of its own
      accord, a loss in itself could cause more harm than the good it
      would do as a signal to slow down.  Furthermore, eliminating
      packet discard eliminates the collateral damage caused to flows
      that were not significantly contributing to congestion.

   o  Scalable Throughput: Existing congestion control algorithms don't
      scale, so applications need to open many simultaneous connections
      to fully utilize today's broadband connections.  An L4S congestion
      controller can rapidly ramp up its sending rate to match any link
      capacity.  This is because L4S uses a "scalable congestion
      controller" that maintains the same frequency of control signals
      (2 ECN marks per round trip on average) regardless of flow rate.
      With classic congestion controllers, the faster they try to go,
      the longer they run blind without any control signals.

   The technology behind L4S isn't new; it is based on a scalable
   congestion control called Data Center TCP (DCTCP) that is currently
   used in data centers to get very high throughputs with ultra-low
   delay and loss.  What is new is the development of a way that
   scalable traffic can coexist with the existing TCP and QUIC traffic
   on the Internet - the key that unlocks a transition to L4S.  Until
   now, DCTCP has been confined to data centers because it would starve
   any classic flows sharing a link.

   Separation into two queues serves two purposes: (1) it isolates L4S
   flows from the queuing of classic TCP and QUIC and (2) it sends each
   type of traffic appropriately scaled congestion signals.  This
   results in any number of application flows (of either type) all
   getting roughly equal bandwidth each, as if there were just one
   aggregate pool of bandwidth, with no division between the Service
   Flows.

   The approach couples the levels of ECN and drop signaling, as shown
   in Figure 12 in [LLD-white-paper].  The packet rate of today's
   classic congestion controls conforms to the well-known square-root
   rule (on the left of the figure).  So, the classic AQM applies a drop
   level to Classic traffic that is coupled to the square of the ECN



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   marking level being applied to Low Latency traffic.  The squaring in
   the network counterbalances the square root at the sender, so the
   packet rates of the two types of flow turn out roughly the same.

   Supporting L4S in LLD is relatively straightforward.  All that is
   needed is to classify L4S flows into the Low Latency SF and support
   the logic in the Low Latency SF to perform immediate ECN marking of
   packets (see Section 3.2).

Appendix B.  Simulation Details

   For the results reported in this paper, we set up the following
   network with 5 types of client devices behind the CM and a set of
   servers north of the CMTS.  See Figure 13 in [LLD-white-paper].  The
   link delays shown are 1-way values.  The DOCSIS link is configured in
   the most latency-efficient manner (short interleavers, small OFDMA
   frame sizes) and models a plant distance of 8 km.  The service is
   configured with a Maximum Sustained Traffic Rate (rate limit) of 50
   Mbps in the upstream direction and 200 Mbps in the downstream
   direction.

   The upstream game traffic model involves normally distributed packet
   interarrival times (mu=33 ms, sigma=3 ms) and normally distributed
   packet sizes (mu=110 bytes, sigma=20 bytes) constrained to discard
   draws of packet size <32 bytes or >188 bytes.  The downstream game
   traffic model involves normally distributed packet interarrival times
   (mu=33 ms, sigma=5 ms) and normally distributed packet sizes (mu=432
   bytes, sigma=20 bytes) constrained to discard draws of packet size
   <32 bytes or >832 bytes.

   The background load traffic is configured as follows.  The web user
   is based on the 3GPP standardized web user model [web-user-model].
   The video streaming model is an abstracted model of a Dynamic
   Adaptive Streaming over HTTP (DASH) streaming video user where the
   video stream is 6 Mbps and is implemented as a 3.75 MB file download
   every 5 seconds.  Each FTP session involves the sender selecting a
   file size using a log-normal random variable (mu=14.8, sigma=2.0,
   leading to a median file size of 2.7 MB), opening a TCP connection,
   sending the file, closing the TCP connection, then pausing for 100 ms
   before repeating the process.  Although we refer to this model as an
   FTP model, the intention is that it models TCP usage across all
   applications other than web browsing and video streaming.

Authors' Addresses







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   Greg White
   CableLabs
   858 Coal Creek Circle
   Louisville, CO  80027
   US

   Email: g.white@cablelabs.com


   Karthik Sundaresan
   CableLabs
   858 Coal Creek Circle
   Louisville, CO  80027
   US

   Email: k.sundaresan@cablelabs.com


   Bob Briscoe
   CableLabs
   UK

   Email: b.briscoe-contractor@cablelabs.com




























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