Internet DRAFT - draft-ietf-tsvwg-aqm-dualq-coupled

draft-ietf-tsvwg-aqm-dualq-coupled







Transport Area working group (tsvwg)                      K. De Schepper
Internet-Draft                                           Nokia Bell Labs
Intended status: Experimental                            B. Briscoe, Ed.
Expires: 2 March 2023                                        Independent
                                                                G. White
                                                               CableLabs
                                                          29 August 2022


  DualQ Coupled AQMs for Low Latency, Low Loss and Scalable Throughput
                                 (L4S)
                 draft-ietf-tsvwg-aqm-dualq-coupled-25

Abstract

   This specification defines a framework for coupling the Active Queue
   Management (AQM) algorithms in two queues intended for flows with
   different responses to congestion.  This provides a way for the
   Internet to transition from the scaling problems of standard TCP
   Reno-friendly ('Classic') congestion controls to the family of
   'Scalable' congestion controls.  These are designed for consistently
   very Low queuing Latency, very Low congestion Loss and Scaling of
   per-flow throughput (L4S) by using Explicit Congestion Notification
   (ECN) in a modified way.  Until the Coupled DualQ, these scalable L4S
   congestion controls could only be deployed where a clean-slate
   environment could be arranged, such as in private data centres.

   The specification first explains how a Coupled DualQ works.  It then
   gives the normative requirements that are necessary for it to work
   well.  All this is independent of which two AQMs are used, but
   pseudocode examples of specific AQMs are given in appendices.

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
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   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 2 March 2023.



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Copyright Notice

   Copyright (c) 2022 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents (https://trustee.ietf.org/
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   Please review these documents carefully, as they describe your rights
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   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
     1.1.  Outline of the Problem  . . . . . . . . . . . . . . . . .   3
     1.2.  Context, Scope & Applicability  . . . . . . . . . . . . .   6
     1.3.  Terminology . . . . . . . . . . . . . . . . . . . . . . .   7
     1.4.  Features  . . . . . . . . . . . . . . . . . . . . . . . .   9
   2.  DualQ Coupled AQM . . . . . . . . . . . . . . . . . . . . . .  11
     2.1.  Coupled AQM . . . . . . . . . . . . . . . . . . . . . . .  11
     2.2.  Dual Queue  . . . . . . . . . . . . . . . . . . . . . . .  12
     2.3.  Traffic Classification  . . . . . . . . . . . . . . . . .  12
     2.4.  Overall DualQ Coupled AQM Structure . . . . . . . . . . .  13
     2.5.  Normative Requirements for a DualQ Coupled AQM  . . . . .  17
       2.5.1.  Functional Requirements . . . . . . . . . . . . . . .  17
         2.5.1.1.  Requirements in Unexpected Cases  . . . . . . . .  18
       2.5.2.  Management Requirements . . . . . . . . . . . . . . .  19
         2.5.2.1.  Configuration . . . . . . . . . . . . . . . . . .  19
         2.5.2.2.  Monitoring  . . . . . . . . . . . . . . . . . . .  21
         2.5.2.3.  Anomaly Detection . . . . . . . . . . . . . . . .  22
         2.5.2.4.  Deployment, Coexistence and Scaling . . . . . . .  22
   3.  IANA Considerations (to be removed by RFC Editor) . . . . . .  22
   4.  Security Considerations . . . . . . . . . . . . . . . . . . .  22
     4.1.  Low Delay without Requiring Per-Flow Processing . . . . .  22
     4.2.  Handling Unresponsive Flows and Overload  . . . . . . . .  23
       4.2.1.  Unresponsive Traffic without Overload . . . . . . . .  24
       4.2.2.  Avoiding Short-Term Classic Starvation: Sacrifice L4S
               Throughput or Delay?  . . . . . . . . . . . . . . . .  25
       4.2.3.  L4S ECN Saturation: Introduce Drop or Delay?  . . . .  26
         4.2.3.1.  Protecting against Overload by Unresponsive
                 ECN-Capable Traffic . . . . . . . . . . . . . . . .  28
   5.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  28
     5.1.  Normative References  . . . . . . . . . . . . . . . . . .  28
     5.2.  Informative References  . . . . . . . . . . . . . . . . .  29
   Appendix A.  Example DualQ Coupled PI2 Algorithm  . . . . . . . .  35



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     A.1.  Pass #1: Core Concepts  . . . . . . . . . . . . . . . . .  35
     A.2.  Pass #2: Edge-Case Details  . . . . . . . . . . . . . . .  46
   Appendix B.  Example DualQ Coupled Curvy RED Algorithm  . . . . .  51
     B.1.  Curvy RED in Pseudocode . . . . . . . . . . . . . . . . .  51
     B.2.  Efficient Implementation of Curvy RED . . . . . . . . . .  57
   Appendix C.  Choice of Coupling Factor, k . . . . . . . . . . . .  59
     C.1.  RTT-Dependence  . . . . . . . . . . . . . . . . . . . . .  59
     C.2.  Guidance on Controlling Throughput Equivalence  . . . . .  60
   Acknowledgements  . . . . . . . . . . . . . . . . . . . . . . . .  64
   Contributors  . . . . . . . . . . . . . . . . . . . . . . . . . .  64
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  65

1.  Introduction

   This document specifies a framework for DualQ Coupled AQMs, which can
   serve as the network part of the L4S
   architecture [I-D.ietf-tsvwg-l4s-arch].  A Coupled DualQ AQM consists
   of two queues; L4S and Classic.  The L4S queue is intended for
   Scalable congestion controls that can maintain very low queuing
   latency (sub-millisecond on average) and high throughput at the same
   time.  The Coupled DualQ acts like a semi-permeable membrane: the L4S
   queue isolates the sub-millisecond average queuing delay of L4S from
   Classic latency; while the coupling between the queues pools the
   capacity between both queues so that ad hoc numbers of capacity-
   seeking applications all sharing the same capacity can have roughly
   equivalent throughput per flow, whichever queue they use.  The DualQ
   achieves this indirectly, without having to inspect transport layer
   flow identifiers and without compromising the performance of the
   Classic traffic, relative to a single queue.  The DualQ design has
   low complexity and requires no configuration for the public Internet.

1.1.  Outline of the Problem

   Latency is becoming the critical performance factor for many (most?)
   applications on the public Internet, e.g. interactive Web, Web
   services, voice, conversational video, interactive video, interactive
   remote presence, instant messaging, online gaming, remote desktop,
   cloud-based applications, and video-assisted remote control of
   machinery and industrial processes.  Once access network bit rates
   reach levels now common in the developed world, further increases
   offer diminishing returns unless latency is also addressed
   [Dukkipati06].  In the last decade or so, much has been done to
   reduce propagation time by placing caches or servers closer to users.
   However, queuing remains a major intermittent component of latency.

   Traditionally very low latency has only been available for a few
   selected low rate applications, that confine their sending rate
   within a specially carved-off portion of capacity, which is



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   prioritized over other traffic, e.g. Diffserv EF [RFC3246].  Up to
   now it has not been possible to allow any number of low latency, high
   throughput applications to seek to fully utilize available capacity,
   because the capacity-seeking process itself causes too much queuing
   delay.

   To reduce this queuing delay caused by the capacity seeking process,
   changes either to the network alone or to end-systems alone are in
   progress.  L4S involves a recognition that both approaches are
   yielding diminishing returns:

   *  Recent state-of-the-art active queue management (AQM) in the
      network, e.g. FQ-CoDel [RFC8290], PIE [RFC8033], Adaptive
      RED [ARED01] ) has reduced queuing delay for all traffic, not just
      a select few applications.  However, no matter how good the AQM,
      the capacity-seeking (sawtoothing) rate of TCP-like congestion
      controls represents a lower limit that will either cause queuing
      delay to vary or cause the link to be under-utilized.  These AQMs
      are tuned to allow a typical capacity-seeking Reno-friendly flow
      to induce an average queue that roughly doubles the base RTT,
      adding 5-15 ms of queuing on average (cf. 500 microseconds with
      L4S for the same mix of long-running and web traffic).  However,
      for many applications low delay is not useful unless it is
      consistently low.  With these AQMs, 99th percentile queuing delay
      is 20-30 ms (cf. 2 ms with the same traffic over L4S).

   *  Similarly, recent research into using e2e congestion control
      without needing an AQM in the network (e.g. BBR
      [I-D.cardwell-iccrg-bbr-congestion-control]) seems to have hit a
      similar lower limit to queuing delay of about 20ms on average, but
      there are also regular 25ms delay spikes due to bandwidth probes
      and 60ms spikes due to flow-starts.

   L4S learns from the experience of Data Center TCP [RFC8257], which
   shows the power of complementary changes both in the network and on
   end-systems.  DCTCP teaches us that two small but radical changes to
   congestion control are needed to cut the two major outstanding causes
   of queuing delay variability:

   1.  Far smaller rate variations (sawteeth) than Reno-friendly
       congestion controls;

   2.  A shift of smoothing and hence smoothing delay from network to
       sender.

   Without the former, a 'Classic' (e.g. Reno-friendly) flow's round
   trip time (RTT) varies between roughly 1 and 2 times the base RTT
   between the machines in question.  Without the latter a 'Classic'



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   flow's response to changing events is delayed by a worst-case
   (transcontinental) RTT, which could be hundreds of times the actual
   smoothing delay needed for the RTT of typical traffic from localized
   CDNs.

   These changes are the two main features of the family of so-called
   'Scalable' congestion controls (which includes DCTCP, TCP Prague and
   SCReAM).  Both these changes only reduce delay in combination with a
   complementary change in the network and they are both only feasible
   with ECN, not drop, for the signalling:

   1.  The smaller sawteeth allow an extremely shallow ECN packet-
       marking threshold in the queue.

   2.  And no smoothing in the network means that every fluctuation of
       the queue is signalled immediately.

   Without ECN, either of these would lead to very high loss levels.
   But, with ECN, the resulting high marking levels are just signals,
   not impairments.  (Note that BBRv2 [BBRv2] combines the best of both
   worlds - it works as a scalable congestion control when ECN is
   available, but also aims to minimize delay when it isn't.)

   However, until now, Scalable congestion controls (like DCTCP) did not
   co-exist well in a shared ECN-capable queue with existing Classic
   (e.g. Reno [RFC5681] or Cubic [RFC8312]) congestion controls --
   Scalable controls are so aggressive that these 'Classic' algorithms
   would drive themselves to a small capacity share.  Therefore, until
   now, L4S controls could only be deployed where a clean-slate
   environment could be arranged, such as in private data centres (hence
   the name DCTCP).

   One way to solve the problem of coexistence between Scalable and
   Classic flows is to use a per-flow-queuing approach such as FQ-
   CoDel [RFC8290].  It classifies packets by flow identifier into
   separate queues in order to isolate sparse flows from the higher
   latency in the queues assigned to heavier flows.  However, if a
   Classic flow needs both low delay and high throughput, having a queue
   to itself does not isolate it from the harm it causes to itself.
   Also FQ approaches need to inspect flow identifiers, which is not
   always practical.

   In summary, Scalable congestion controls address the root cause of
   the latency, loss and scaling problems with Classic congestion
   controls.  Both FQ and DualQ AQMs can be enablers for this smooth low
   latency scalable behaviour.  The DualQ approach is particularly
   useful because identifying flows is sometimes not practical or
   desirable.



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1.2.  Context, Scope & Applicability

   L4S involves complementary changes in the network and on end-systems:

   Network:  A DualQ Coupled AQM (defined in the present document) or a
      modification to flow-queue AQMs (described in section 4.2.b of the
      L4S architecture [I-D.ietf-tsvwg-l4s-arch]);

   End-system:  A Scalable congestion control (defined in section 4 of
      the L4S ECN protocol [I-D.ietf-tsvwg-ecn-l4s-id]).

   Packet identifier:  The network and end-system parts of L4S can be
      deployed incrementally, because they both identify L4S packets
      using the experimentally assigned explicit congestion notification
      (ECN) codepoints in the IP header: ECT(1) and CE [RFC8311]
      [I-D.ietf-tsvwg-ecn-l4s-id].

   Data Center TCP (DCTCP [RFC8257]) is an example of a Scalable
   congestion control for controlled environments that has been deployed
   for some time in Linux, Windows and FreeBSD operating systems.
   During the progress of this document through the IETF a number of
   other Scalable congestion controls were implemented, e.g. TCP Prague
   [I-D.briscoe-iccrg-prague-congestion-control] [PragueLinux], BBRv2
   [BBRv2], [I-D.cardwell-iccrg-bbr-congestion-control], QUIC Prague and
   the L4S variant of SCREAM for real-time media [RFC8298].

   The focus of this specification is to enable deployment of the
   network part of the L4S service.  Then, without any management
   intervention, applications can exploit this new network capability as
   their operating systems migrate to Scalable congestion controls,
   which can then evolve _while_ their benefits are being enjoyed by
   everyone on the Internet.

   The DualQ Coupled AQM framework can incorporate any AQM designed for
   a single queue that generates a statistical or deterministic mark/
   drop probability driven by the queue dynamics.  Pseudocode examples
   of two different DualQ Coupled AQMs are given in the appendices.  In
   many cases the framework simplifies the basic control algorithm, and
   requires little extra processing.  Therefore, it is believed the
   Coupled AQM would be applicable and easy to deploy in all types of
   buffers; buffers in cost-reduced mass-market residential equipment;
   buffers in end-system stacks; buffers in carrier-scale equipment
   including remote access servers, routers, firewalls and Ethernet
   switches; buffers in network interface cards, buffers in virtualized
   network appliances, hypervisors, and so on.






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   For the public Internet, nearly all the benefit will typically be
   achieved by deploying the Coupled AQM into either end of the access
   link between a 'site' and the Internet, which is invariably the
   bottleneck (see section 6.4 of[I-D.ietf-tsvwg-l4s-arch] about
   deployment, which also defines the term 'site' to mean a home, an
   office, a campus or mobile user equipment).

   Latency is not the only concern of L4S:

   *  The "Low Loss" part of the name denotes that L4S generally
      achieves zero congestion loss (which would otherwise cause
      retransmission delays), due to its use of ECN.

   *  The "Scalable throughput" part of the name denotes that the per-
      flow throughput of Scalable congestion controls should scale
      indefinitely, avoiding the imminent scaling problems with 'TCP-
      Friendly' congestion control algorithms [RFC3649].

   The former is clearly in scope of this AQM document.  However, the
   latter is an outcome of the end-system behaviour, and therefore
   outside the scope of this AQM document, even though the AQM is an
   enabler.

   The overall L4S architecture [I-D.ietf-tsvwg-l4s-arch] gives more
   detail, including on wider deployment aspects such as backwards
   compatibility of Scalable congestion controls in bottlenecks where a
   DualQ Coupled AQM has not been deployed.  The supporting papers
   [DualPI2Linux], [PI2], [DCttH19] and [PI2param] give the full
   rationale for the AQM's design, both discursively and in more precise
   mathematical form, as well as the results of performance evaluations.
   The main results have been validated independently when using the
   Prague congestion control [Boru20] (experiments are run using Prague
   and DCTCP, but only the former are relevant for validation, because
   Prague fixes a number of problems with the Linux DCTCP code that make
   it unsuitable for the public Internet).

1.3.  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in [RFC2119] [RFC8174]
   when, and only when, they appear in all capitals, as shown here.

   The DualQ Coupled AQM uses two queues for two services.  Each of the
   following terms identifies both the service and the queue that
   provides the service:

   Classic service/queue:  The Classic service is intended for all the



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      congestion control behaviours that co-exist with Reno [RFC5681]
      (e.g. Reno itself, Cubic [RFC8312], TFRC [RFC5348]).

   Low-Latency, Low-Loss Scalable throughput (L4S) service/queue:  The
      'L4S' service is intended for traffic from scalable congestion
      control algorithms, such as TCP Prague
      [I-D.briscoe-iccrg-prague-congestion-control], which was derived
      from Data Center TCP [RFC8257].  The L4S service is for more
      general traffic than just TCP Prague -- it allows the set of
      congestion controls with similar scaling properties to Prague to
      evolve, such as the examples of Scalable congestion controls
      listed below (Relentless, SCReAM, etc.).

   Classic Congestion Control:  A congestion control behaviour that can
      co-exist with standard TCP Reno [RFC5681] without causing
      significantly negative impact on its flow rate [RFC5033].  With
      Classic congestion controls, such as Reno or Cubic, because flow
      rate has scaled since TCP congestion control was first designed in
      1988, it now takes hundreds of round trips (and growing) to
      recover after a congestion signal (whether a loss or an ECN mark)
      as shown in the examples in section 5.1 of the L4S
      architecture [I-D.ietf-tsvwg-l4s-arch] and in [RFC3649].
      Therefore, control of queuing and utilization becomes very slack,
      and the slightest disturbances (e.g. from new flows starting)
      prevent a high rate from being attained.

   Scalable Congestion Control:  A congestion control where the average
      time from one congestion signal to the next (the recovery time)
      remains invariant as the flow rate scales, all other factors being
      equal.  This maintains the same degree of control over queueing
      and utilization whatever the flow rate, as well as ensuring that
      high throughput is robust to disturbances.  For instance, DCTCP
      averages 2 congestion signals per round-trip whatever the flow
      rate, as do other recently developed scalable congestion controls,
      e.g. Relentless TCP [I-D.mathis-iccrg-relentless-tcp], TCP Prague
      [I-D.briscoe-iccrg-prague-congestion-control], [PragueLinux],
      BBRv2 [BBRv2], [I-D.cardwell-iccrg-bbr-congestion-control] and the
      L4S variant of SCREAM for real-time media [SCReAM], [RFC8298]).
      For the public Internet a Scalable transport has to comply with
      the requirements in Section 4 of [I-D.ietf-tsvwg-ecn-l4s-id]
      (aka. the 'Prague L4S requirements').

   C:  Abbreviation for Classic, e.g. when used as a subscript.

   L:  Abbreviation for L4S, e.g. when used as a subscript.






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      The terms Classic or L4S can also qualify other nouns, such as
      'codepoint', 'identifier', 'classification', 'packet', 'flow'.
      For example: an L4S packet means a packet with an L4S identifier
      sent from an L4S congestion control.

      Both Classic and L4S services can cope with a proportion of
      unresponsive or less-responsive traffic as well, but in the L4S
      case its rate has to be smooth enough or low enough not to build a
      queue (e.g. DNS, VoIP, game sync datagrams, etc.).  The DualQ
      Coupled AQM behaviour is defined to be similar to a single FIFO
      queue with respect to unresponsive and overload traffic.

   Reno-friendly:  The subset of Classic traffic that is friendly to the
      standard Reno congestion control defined for TCP in [RFC5681].
      Reno-friendly is used in place of 'TCP-friendly', given the latter
      has become imprecise, because the TCP protocol is now used with so
      many different congestion control behaviours, and Reno is used in
      non-TCP transports such as QUIC.

   Classic ECN:  The original Explicit Congestion Notification (ECN)
      protocol [RFC3168], which requires ECN signals to be treated the
      same as drops, both when generated in the network and when
      responded to by the sender.

      For L4S, the names used for the four codepoints of the 2-bit IP-
      ECN field are unchanged from those defined in [RFC3168]: Not ECT,
      ECT(0), ECT(1) and CE, where ECT stands for ECN-Capable Transport
      and CE stands for Congestion Experienced.  A packet marked with
      the CE codepoint is termed 'ECN-marked' or sometimes just 'marked'
      where the context makes ECN obvious.

1.4.  Features

   The AQM couples marking and/or dropping from the Classic queue to the
   L4S queue in such a way that a flow will get roughly the same
   throughput whichever it uses.  Therefore, both queues can feed into
   the full capacity of a link and no rates need to be configured for
   the queues.  The L4S queue enables Scalable congestion controls like
   DCTCP or TCP Prague to give very low and predictably low latency,
   without compromising the performance of competing 'Classic' Internet
   traffic.

   Thousands of tests have been conducted in a typical fixed residential
   broadband setting.  Experiments used a range of base round trip
   delays up to 100ms and link rates up to 200 Mb/s between the data
   centre and home network, with varying amounts of background traffic
   in both queues.  For every L4S packet, the AQM kept the average
   queuing delay below 1ms (or 2 packets where serialization delay



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   exceeded 1ms on slower links), with 99th percentile no worse than
   2ms.  No losses at all were introduced by the L4S AQM.  Details of
   the extensive experiments are available [DualPI2Linux], [PI2],
   [DCttH19].  Subjective testing using very demanding high bandwidth
   low latency applications over a single shared access link is also
   described in [L4Sdemo16] and summarized in the section about
   applications in the L4S architecture [I-D.ietf-tsvwg-l4s-arch] .

   In all these experiments, the host was connected to the home network
   by fixed Ethernet, in order to quantify the queuing delay that can be
   achieved by a user who cares about delay.  It should be emphasized
   that L4S support at the bottleneck link cannot 'undelay' bursts
   introduced by another link on the path, for instance by legacy Wi-Fi
   equipment.  However, if L4S support is added to the queue feeding the
   _outgoing_ WAN link of a home gateway, it would be counterproductive
   not to also reduce the burstiness of the _incoming_ Wi-Fi.  Also,
   trials of Wi-Fi equipment with an L4S DualQ Coupled AQM on the
   _outgoing_ Wi-Fi interface are in progress, and early results of an
   L4S DualQ Coupled AQM in a 5G radio access network testbed with
   emulated outdoor cell edge radio fading are given in [L4S_5G].

   Unlike Diffserv Expedited Forwarding, the L4S queue does not have to
   be limited to a small proportion of the link capacity in order to
   achieve low delay.  The L4S queue can be filled with a heavy load of
   capacity-seeking flows (TCP Prague etc.) and still achieve low delay.
   The L4S queue does not rely on the presence of other traffic in the
   Classic queue that can be 'overtaken'.  It gives low latency to L4S
   traffic whether or not there is Classic traffic.  The tail latency of
   traffic served by the Classic AQM is sometimes a little better
   sometimes a little worse, when a proportion of the traffic is L4S.

   The two queues are only necessary because:

   *  the large variations (sawteeth) of Classic flows need roughly a
      base RTT of queuing delay to ensure full utilization

   *  Scalable flows do not need a queue to keep utilization high, but
      they cannot keep latency predictably low if they are mixed with
      Classic traffic,

   The L4S queue has latency priority within sub-round trip timescales,
   but over longer periods the coupling from the Classic to the L4S AQM
   (explained below) ensures that it does not have bandwidth priority
   over the Classic queue.







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2.  DualQ Coupled AQM

   There are two main aspects to the approach:

   *  The Coupled AQM that addresses throughput equivalence between
      Classic (e.g. Reno, Cubic) flows and L4S flows (that satisfy the
      Prague L4S requirements).

   *  The Dual Queue structure that provides latency separation for L4S
      flows to isolate them from the typically large Classic queue.

2.1.  Coupled AQM

   In the 1990s, the `TCP formula' was derived for the relationship
   between the steady-state congestion window, cwnd, and the drop
   probability, p of standard Reno congestion control [RFC5681].  To a
   first order approximation, the steady-state cwnd of Reno is inversely
   proportional to the square root of p.

   The design focuses on Reno as the worst case, because if it does no
   harm to Reno, it will not harm Cubic or any traffic designed to be
   friendly to Reno.  TCP Cubic implements a Reno-compatibility mode,
   which is relevant for typical RTTs under 20ms as long as the
   throughput of a single flow is less than about 350Mb/s.  In such
   cases it can be assumed that Cubic traffic behaves similarly to Reno.
   The term 'Classic' will be used for the collection of Reno-friendly
   traffic including Cubic and potentially other experimental congestion
   controls intended not to significantly impact the flow rate of Reno.

   A supporting paper [PI2] includes the derivation of the equivalent
   rate equation for DCTCP, for which cwnd is inversely proportional to
   p (not the square root), where in this case p is the ECN marking
   probability.  DCTCP is not the only congestion control that behaves
   like this, so the term 'Scalable' will be used for all similar
   congestion control behaviours (see examples in Section 1.2).  The
   term 'L4S' is used for traffic driven by a Scalable congestion
   control that also complies with the additional 'Prague L4S'
   requirements [I-D.ietf-tsvwg-ecn-l4s-id].

   For safe co-existence, under stationary conditions, a Scalable flow
   has to run at roughly the same rate as a Reno TCP flow (all other
   factors being equal).  So the drop or marking probability for Classic
   traffic, p_C has to be distinct from the marking probability for L4S
   traffic, p_L.  The original ECN specification [RFC3168] required
   these probabilities to be the same, but [RFC8311] updates RFC 3168 to
   enable experiments in which these probabilities are different.





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   Also, to remain stable, Classic sources need the network to smooth
   p_C so it changes relatively slowly.  It is hard for a network node
   to know the RTTs of all the flows, so a Classic AQM adds a _worst-
   case_ RTT of smoothing delay (about 100-200 ms).  In contrast, L4S
   shifts responsibility for smoothing ECN feedback to the sender, which
   only delays its response by its _own_ RTT, as well as allowing a more
   immediate response if necessary.

   The Coupled AQM achieves safe coexistence by making the Classic drop
   probability p_C proportional to the square of the coupled L4S
   probability p_CL. p_CL is an input to the instantaneous L4S marking
   probability p_L but it changes as slowly as p_C.  This makes the Reno
   flow rate roughly equal the DCTCP flow rate, because the squaring of
   p_CL counterbalances the square root of p_C in the 'TCP formula' of
   Classic Reno congestion control.

   Stating this as a formula, the relation between Classic drop
   probability, p_C, and the coupled L4S probability p_CL needs to take
   the form:

       p_C = ( p_CL / k )^2                  (1)

   where k is the constant of proportionality, which is termed the
   coupling factor.

2.2.  Dual Queue

   Classic traffic needs to build a large queue to prevent under-
   utilization.  Therefore, a separate queue is provided for L4S
   traffic, and it is scheduled with priority over the Classic queue.
   Priority is conditional to prevent starvation of Classic traffic in
   certain conditions (see Section 2.4).

   Nonetheless, coupled marking ensures that giving priority to L4S
   traffic still leaves the right amount of spare scheduling time for
   Classic flows to each get equivalent throughput to DCTCP flows (all
   other factors such as RTT being equal).

2.3.  Traffic Classification

   Both the Coupled AQM and DualQ mechanisms need an identifier to
   distinguish L4S (L) and Classic (C) packets.  Then the coupling
   algorithm can achieve coexistence without having to inspect flow
   identifiers, because it can apply the appropriate marking or dropping
   probability to all flows of each type.  A separate
   specification [I-D.ietf-tsvwg-ecn-l4s-id] requires the network to
   treat the ECT(1) and CE codepoints of the ECN field as this
   identifier.  An additional process document has proved necessary to



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   make the ECT(1) codepoint available for experimentation [RFC8311].

   For policy reasons, an operator might choose to steer certain packets
   (e.g. from certain flows or with certain addresses) out of the L
   queue, even though they identify themselves as L4S by their ECN
   codepoints.  In such cases, the L4S ECN
   protocol [I-D.ietf-tsvwg-ecn-l4s-id] says that the device "MUST NOT
   alter the end-to-end L4S ECN identifier", so that it is preserved
   end-to-end.  The aim is that each operator can choose how it treats
   L4S traffic locally, but an individual operator does not alter the
   identification of L4S packets, which would prevent other operators
   downstream from making their own choices on how to treat L4S traffic.

   In addition, an operator could use other identifiers to classify
   certain additional packet types into the L queue that it deems will
   not risk harm to the L4S service.  For instance addresses of specific
   applications or hosts; specific Diffserv codepoints such as EF
   (Expedited Forwarding), Voice-Admit or the Non-Queue-Building (NQB)
   per-hop behaviour; or certain protocols (e.g. ARP, DNS) (see
   Section 5.4.1 of [I-D.ietf-tsvwg-ecn-l4s-id]).  Note that the
   mechanism only reads these identifiers.  [I-D.ietf-tsvwg-ecn-l4s-id]
   says it "MUST NOT alter these non-ECN identifiers".  Thus, the L
   queue is not solely an L4S queue, it can be considered more generally
   as a low latency queue.

2.4.  Overall DualQ Coupled AQM Structure

   Figure 1 shows the overall structure that any DualQ Coupled AQM is
   likely to have.  This schematic is intended to aid understanding of
   the current designs of DualQ Coupled AQMs.  However, it is not
   intended to preclude other innovative ways of satisfying the
   normative requirements in Section 2.5 that minimally define a DualQ
   Coupled AQM.  Also, the schematic only illustrates operation under
   normally expected circumstances; behaviour under overload or with
   operator-specific classifiers is deferred to Section 2.5.1.1.

   The classifier on the left separates incoming traffic between the two
   queues (L and C).  Each queue has its own AQM that determines the
   likelihood of marking or dropping (p_L and p_C).  It has been
   proved [PI2] that it is preferable to control load with a linear
   controller, then square the output before applying it as a drop
   probability to Reno-friendly traffic (because Reno congestion control
   decreases its load proportional to the square-root of the increase in
   drop).  So, the AQM for Classic traffic needs to be implemented in
   two stages: i) a base stage that outputs an internal probability p'
   (pronounced p-prime); and ii) a squaring stage that outputs p_C,
   where




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       p_C = (p')^2.                         (2)

   Substituting for p_C in Eqn (1) gives:

       p' = p_CL / k

   So the slow-moving input to ECN marking in the L queue (the coupled
   L4S probability) is:

       p_CL = k*p'.                          (3)

   The actual ECN marking probability p_L that is applied to the L queue
   needs to track the immediate L queue delay under L-only congestion
   conditions, as well as track p_CL under coupled congestion
   conditions.  So the L queue uses a native AQM that calculates a
   probability p'_L as a function of the instantaneous L queue delay.
   And, given the L queue has conditional priority over the C queue,
   whenever the L queue grows, the AQM ought to apply marking
   probability p'_L, but p_L ought not to fall below p_CL.  This
   suggests:

       p_L = max(p'_L, p_CL),                (4)

   which has also been found to work very well in practice.

   The two transformations of p' in equations (2) and (3) implement the
   required coupling given in equation (1) earlier.

   The constant of proportionality or coupling factor, k, in equation
   (1) determines the ratio between the congestion probabilities (loss
   or marking) experienced by L4S and Classic traffic.  Thus, k
   indirectly determines the ratio between L4S and Classic flow rates,
   because flows (assuming they are responsive) adjust their rate in
   response to congestion probability.  Appendix C.2 gives guidance on
   the choice of k and its effect on relative flow rates.
















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                           _________
                                  | |    ,------.
                    L4S (L) queue | |===>| ECN  |
                       ,'| _______|_|    |marker|\
                     <'  |         |     `------'\\
                      //`'         v        ^ p_L \\
                     //       ,-------.     |      \\
                    //        |Native |p'_L |       \\,.
                   //         |  L4S  |--->(MAX)    <  |   ___
      ,----------.//          |  AQM  |     ^ p_CL   `\|.'Cond-`.
      |  IP-ECN  |/           `-------'     |          / itional \
   ==>|Classifier|            ,-------.   (k*p')       [ priority]==>
      |          |\           |  Base |     |          \scheduler/
      `----------'\\          |  AQM  |---->:        ,'|`-.___.-'
                   \\         |       |p'   |      <'  |
                    \\        `-------'   (p'^2)    //`'
                     \\            ^        |      //
                      \\,.         |        v p_C //
                      <  | _________     .------.//
                       `\|   |      |    | Drop |/
                 Classic (C) |queue |===>|/mark |
                           __|______|    `------'

                   Figure 1: DualQ Coupled AQM Schematic

   Legend: ===> traffic flow; ---> control dependency.

   After the AQMs have applied their dropping or marking, the scheduler
   forwards their packets to the link.  Even though the scheduler gives
   priority to the L queue, it is not as strong as the coupling from the
   C queue.  This is because, as the C queue grows, the base AQM applies
   more congestion signals to L traffic (as well as C).  As L flows
   reduce their rate in response, they use less than the scheduling
   share for L traffic.  So, because the scheduler is work preserving,
   it schedules any C traffic in the gaps.

   Giving priority to the L queue has the benefit of very low L queue
   delay, because the L queue is kept empty whenever L traffic is
   controlled by the coupling.  Also, there only has to be a coupling in
   one direction - from Classic to L4S.  Priority has to be conditional
   in some way to prevent the C queue being starved in the short-term
   (see Section 4.2.2) to give C traffic a means to push in, as
   explained next.  With normal responsive L traffic, the coupled ECN
   marking gives C traffic the ability to push back against even strict
   priority, by congestion marking the L traffic to make it yield some
   space.  However, if there is just a small finite set of C packets
   (e.g. a DNS request or an initial window of data) some Classic AQMs
   will not induce enough ECN marking in the L queue, no matter how long



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   the small set of C packets waits.  Then, if the L queue happens to
   remain busy, the C traffic would never get a scheduling opportunity
   from a strict priority scheduler.  Ideally the Classic AQM would be
   designed to increase the coupled marking the longer that C packets
   have been waiting, but this is not always practical - hence the need
   for L priority to be conditional.  Giving a small weight or limited
   waiting time for C traffic improves response times for short Classic
   messages, such as DNS requests, and improves Classic flow startup
   because immediate capacity is available.

   Example DualQ Coupled AQM algorithms called DualPI2 and Curvy RED are
   given in Appendix A and Appendix B.  Either example AQM can be used
   to couple packet marking and dropping across a dual Q.

   DualPI2 uses a Proportional-Integral (PI) controller as the Base AQM.
   Indeed, this Base AQM with just the squared output and no L4S queue
   can be used as a drop-in replacement for PIE [RFC8033], in which case
   it is just called PI2 [PI2].  PI2 is a principled simplification of
   PIE that is both more responsive and more stable in the face of
   dynamically varying load.

   Curvy RED is derived from RED [RFC2309], except its configuration
   parameters are delay-based to make them insensitive to link rate and
   it requires fewer operations per packet than RED.  However, DualPI2
   is more responsive and stable over a wider range of RTTs than Curvy
   RED.  As a consequence, at the time of writing, DualPI2 has attracted
   more development and evaluation attention than Curvy RED, leaving the
   Curvy RED design not so fully evaluated.

   Both AQMs regulate their queue against targets configured in units of
   time rather than bytes.  As already explained, this ensures
   configuration can be invariant for different drain rates.  With AQMs
   in a dualQ structure this is particularly important because the drain
   rate of each queue can vary rapidly as flows for the two queues
   arrive and depart, even if the combined link rate is constant.

   It would be possible to control the queues with other alternative
   AQMs, as long as the normative requirements (those expressed in
   capitals) in Section 2.5 are observed.

   The two queues could optionally be part of a larger queuing
   hierarchy, such as the initial example ideas in
   [I-D.briscoe-tsvwg-l4s-diffserv].








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2.5.  Normative Requirements for a DualQ Coupled AQM

   The following requirements are intended to capture only the essential
   aspects of a DualQ Coupled AQM.  They are intended to be independent
   of the particular AQMs implemented for each queue, but to still
   define the DualQ framework built around those AQMs.

2.5.1.  Functional Requirements

   A Dual Queue Coupled AQM implementation MUST comply with the
   prerequisite L4S behaviours for any L4S network node (not just a
   DualQ) as specified in section 5 of [I-D.ietf-tsvwg-ecn-l4s-id].
   These primarily concern classification and remarking as briefly
   summarized in Section 2.3 earlier.  But there is also a subsection
   (5.5) giving guidance on reducing the burstiness of the link
   technology underlying any L4S AQM.

   A Dual Queue Coupled AQM implementation MUST utilize two queues, each
   with an AQM algorithm.

   The AQM algorithm for the low latency (L) queue MUST be able to apply
   ECN marking to ECN-capable packets.

   The scheduler draining the two queues MUST give L4S packets priority
   over Classic, although priority MUST be bounded in order not to
   starve Classic traffic (see Section 4.2.2).  The scheduler SHOULD be
   work-conserving, or otherwise close to work-conserving.  This is
   because Classic traffic needs to be able to efficiently fill any
   space left by L4S traffic even though the scheduler would otherwise
   allocate it to L4S.

   [I-D.ietf-tsvwg-ecn-l4s-id] defines the meaning of an ECN marking on
   L4S traffic, relative to drop of Classic traffic.  In order to ensure
   coexistence of Classic and Scalable L4S traffic, it says, "The
   likelihood that an AQM drops a Not-ECT Classic packet (p_C) MUST be
   roughly proportional to the square of the likelihood that it would
   have marked it if it had been an L4S packet (p_L)."  The term
   'likelihood' is used to allow for marking and dropping to be either
   probabilistic or deterministic.

   For the current specification, this translates into the following
   requirement.  A DualQ Coupled AQM MUST apply ECN marking to traffic
   in the L queue that is no lower than that derived from the likelihood
   of drop (or ECN marking) in the Classic queue using Eqn.  (1).

   The constant of proportionality, k, in Eqn (1) determines the
   relative flow rates of Classic and L4S flows when the AQM concerned
   is the bottleneck (all other factors being equal).  The L4S ECN



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   protocol [I-D.ietf-tsvwg-ecn-l4s-id] says, "The constant of
   proportionality (k) does not have to be standardised for
   interoperability, but a value of 2 is RECOMMENDED."

   Assuming Scalable congestion controls for the Internet will be as
   aggressive as DCTCP, this will ensure their congestion window will be
   roughly the same as that of a standards track TCP Reno congestion
   control (Reno) [RFC5681] and other Reno-friendly controls, such as
   TCP Cubic in its Reno-compatibility mode.

   The choice of k is a matter of operator policy, and operators MAY
   choose a different value using the guidelines in Appendix C.2.

   If multiple customers or users share capacity at a bottleneck
   (e.g. in the Internet access link of a campus network), the
   operator's choice of k will determine capacity sharing between the
   flows of different customers.  However, on the public Internet,
   access network operators typically isolate customers from each other
   with some form of layer-2 multiplexing (OFDM(A) in DOCSIS3.1, CDMA in
   3G, SC-FDMA in LTE) or L3 scheduling (WRR in DSL), rather than
   relying on host congestion controls to share capacity between
   customers [RFC0970].  In such cases, the choice of k will solely
   affect relative flow rates within each customer's access capacity,
   not between customers.  Also, k will not affect relative flow rates
   at any times when all flows are Classic or all flows are L4S, and it
   will not affect the relative throughput of small flows.


2.5.1.1.  Requirements in Unexpected Cases

   The flexibility to allow operator-specific classifiers (Section 2.3)
   leads to the need to specify what the AQM in each queue ought to do
   with packets that do not carry the ECN field expected for that queue.
   It is expected that the AQM in each queue will inspect the ECN field
   to determine what sort of congestion notification to signal, then it
   will decide whether to apply congestion notification to this
   particular packet, as follows:

   *  If a packet that does not carry an ECT(1) or CE codepoint is
      classified into the L queue:

      -  if the packet is ECT(0), the L AQM SHOULD apply CE-marking
         using a probability appropriate to Classic congestion control
         and appropriate to the target delay in the L queue







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      -  if the packet is Not-ECT, the appropriate action depends on
         whether some other function is protecting the L queue from
         misbehaving flows (e.g. per-flow queue protection
         [I-D.briscoe-docsis-q-protection] or latency policing):

         o  If separate queue protection is provided, the L AQM SHOULD
            ignore the packet and forward it unchanged, meaning it
            should not calculate whether to apply congestion
            notification and it should neither drop nor CE-mark the
            packet (for instance, the operator might classify EF traffic
            that is unresponsive to drop into the L queue, alongside
            responsive L4S-ECN traffic)

         o  if separate queue protection is not provided, the L AQM
            SHOULD apply drop using a drop probability appropriate to
            Classic congestion control and appropriate to the target
            delay in the L queue

   *  If a packet that carries an ECT(1) codepoint is classified into
      the C queue:

      -  the C AQM SHOULD apply CE-marking using the coupled AQM
         probability p_CL (= k*p').

   The above requirements are worded as "SHOULDs", because operator-
   specific classifiers are for flexibility, by definition.  Therefore,
   alternative actions might be appropriate in the operator's specific
   circumstances.  An example would be where the operator knows that
   certain legacy traffic marked with one codepoint actually has a
   congestion response associated with another codepoint.

   If the DualQ Coupled AQM has detected overload, it MUST introduce
   Classic drop to both types of ECN-capable traffic until the overload
   episode has subsided.  Introducing drop if ECN marking is
   persistently high is recommended by Section 7 of the ECN
   specification [RFC3168] and Section 4.2.1 of the AQM
   Recommendations [RFC7567].

2.5.2.  Management Requirements


2.5.2.1.  Configuration

   By default, a DualQ Coupled AQM SHOULD NOT need any configuration for
   use at a bottleneck on the public Internet [RFC7567].  The following
   parameters MAY be operator-configurable, e.g. to tune for non-
   Internet settings:




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   *  Optional packet classifier(s) to use in addition to the ECN field
      (see Section 2.3);

   *  Expected typical RTT, which can be used to determine the queuing
      delay of the Classic AQM at its operating point, in order to
      prevent typical lone flows from under-utilizing capacity.  For
      example:

      -  for the PI2 algorithm (Appendix A) the queuing delay target is
         dependent on the typical RTT;

      -  for the Curvy RED algorithm (Appendix B) the queuing delay at
         the desired operating point of the curvy ramp is configured to
         encompass a typical RTT;

      -  if another Classic AQM was used, it would be likely to need an
         operating point for the queue based on the typical RTT, and if
         so it SHOULD be expressed in units of time.

      An operating point that is manually calculated might be directly
      configurable instead, e.g. for links with large numbers of flows
      where under-utilization by a single flow would be unlikely.

   *  Expected maximum RTT, which can be used to set the stability
      parameter(s) of the Classic AQM.  For example:

      -  for the PI2 algorithm (Appendix A), the gain parameters of the
         PI algorithm depend on the maximum RTT.

      -  for the Curvy RED algorithm (Appendix B) the smoothing
         parameter is chosen to filter out transients in the queue
         within a maximum RTT.

      Stability parameter(s) that are manually calculated assuming a
      maximum RTT might be directly configurable instead.

   *  Coupling factor, k (see Appendix C.2);

   *  A limit to the conditional priority of L4S.  This is scheduler-
      dependent, but it SHOULD be expressed as a relation between the
      max delay of a C packet and an L packet.  For example:

      -  for a WRR scheduler a weight ratio between L and C of w:1 means
         that the maximum delay to a C packet is w times that of an L
         packet.






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      -  for a time-shifted FIFO (TS-FIFO) scheduler (see Section 4.2.2)
         a time-shift of tshift means that the maximum delay to a C
         packet is tshift greater than that of an L packet. tshift could
         be expressed as a multiple of the typical RTT rather than as an
         absolute delay.

   *  The maximum Classic ECN marking probability, p_Cmax, before
      introducing drop.

2.5.2.2.  Monitoring

   An experimental DualQ Coupled AQM SHOULD allow the operator to
   monitor each of the following operational statistics on demand, per
   queue and per configurable sample interval, for performance
   monitoring and perhaps also for accounting in some cases:

   *  Bits forwarded, from which utilization can be calculated;

   *  Total packets in the three categories: arrived, presented to the
      AQM, and forwarded.  The difference between the first two will
      measure any non-AQM tail discard.  The difference between the last
      two will measure proactive AQM discard;

   *  ECN packets marked, non-ECN packets dropped, ECN packets dropped,
      which can be combined with the three total packet counts above to
      calculate marking and dropping probabilities;

   *  Queue delay (not including serialization delay of the head packet
      or medium acquisition delay) - see further notes below.

      Unlike the other statistics, queue delay cannot be captured in a
      simple accumulating counter.  Therefore, the type of queue delay
      statistics produced (mean, percentiles, etc.) will depend on
      implementation constraints.  To facilitate comparative evaluation
      of different implementations and approaches, an implementation
      SHOULD allow mean and 99th percentile queue delay to be derived
      (per queue per sample interval).  A relatively simple way to do
      this would be to store a coarse-grained histogram of queue delay.
      This could be done with a small number of bins with configurable
      edges that represent contiguous ranges of queue delay.  Then, over
      a sample interval, each bin would accumulate a count of the number
      of packets that had fallen within each range.  The maximum queue
      delay per queue per interval MAY also be recorded, to aid
      diagnosis of faults and anomalous events.







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2.5.2.3.  Anomaly Detection

   An experimental DualQ Coupled AQM SHOULD asynchronously report the
   following data about anomalous conditions:

   *  Start-time and duration of overload state.

      A hysteresis mechanism SHOULD be used to prevent flapping in and
      out of overload causing an event storm.  For instance, exit from
      overload state could trigger one report, but also latch a timer.
      Then, during that time, if the AQM enters and exits overload state
      any number of times, the duration in overload state is
      accumulated, but no new report is generated until the first time
      the AQM is out of overload once the timer has expired.

2.5.2.4.  Deployment, Coexistence and Scaling

   [RFC5706] suggests that deployment, coexistence and scaling should
   also be covered as management requirements.  The raison d'etre of the
   DualQ Coupled AQM is to enable deployment and coexistence of Scalable
   congestion controls - as incremental replacements for today's Reno-
   friendly controls that do not scale with bandwidth-delay product.
   Therefore, there is no need to repeat these motivating issues here
   given they are already explained in the Introduction and detailed in
   the L4S architecture [I-D.ietf-tsvwg-l4s-arch].

   The descriptions of specific DualQ Coupled AQM algorithms in the
   appendices cover scaling of their configuration parameters, e.g. with
   respect to RTT and sampling frequency.

3.  IANA Considerations (to be removed by RFC Editor)

   This specification contains no IANA considerations.

4.  Security Considerations


4.1.  Low Delay without Requiring Per-Flow Processing

   The L4S architecture [I-D.ietf-tsvwg-l4s-arch] compares the DualQ and
   per-flow-queuing (FQ) approaches to L4S.  The privacy considerations
   section in that document motivates the DualQ on the grounds that
   users who want to encrypt application flow identifiers, e.g. in IPSec
   or other encrypted VPN tunnels, don't have to sacrifice low delay
   ([RFC8404] encourages avoidance of such privacy compromises).






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   The security considerations section of the L4S architecture also
   includes subsections on policing of relative flow-rates (section 8.1)
   and on policing of flows that cause excessive queuing delay (section
   8.2).  It explains that the interests of users do not collide in the
   same way for delay as they do for bandwidth.  For someone to get more
   of the bandwidth of a shared link, someone else necessarily gets less
   (a 'zero-sum game'), whereas queuing delay can be reduced for
   everyone, without any need for someone else to lose out.  It also
   explains that, on the current Internet, scheduling usually enforces
   separation of bandwidth between 'sites' (e.g. households, businesses
   or mobile users), but it is not common to need to schedule or police
   the bandwidth used by individual application flows.

   By the above arguments, per-flow rate policing might not be necessary
   and in trusted environments (e.g. private data centres) it is
   certainly unlikely to be needed.  Therefore, because it is hard to
   avoid complexity and unintended side effects with per-flow rate
   policing, it needs to be separable from a basic AQM, as an option,
   under policy control.  On this basis, the DualQ Coupled AQM provides
   low delay without prejudging the question of per-flow rate policing.

   Nonetheless, the interests of users or flows might conflict, e.g. in
   case of accident or malice.  Then per-flow rate control could be
   necessary.  If flow-rate control is needed, it can be provided as a
   modular addition to a DualQ.  And similarly, if protection against
   excessive queue delay is needed, a per-flow queue protection option
   can be added to a DualQ (e.g. [I-D.briscoe-docsis-q-protection]).

4.2.  Handling Unresponsive Flows and Overload

   In the absence of any per-flow control, it is important that the
   basic DualQ Coupled AQM gives unresponsive flows no more throughput
   advantage than a single-queue AQM would, and that it at least handles
   overload situations.  Overload means that incoming load significantly
   or persistently exceeds output capacity, but it is not intended to be
   a precise term -- significant and persistent are matters of degree.

   A trade-off needs to be made between complexity and the risk of
   either traffic class harming the other.  In overloaded conditions the
   higher priority L4S service will have to sacrifice some aspect of its
   performance.  Depending on the degree of overload, alternative
   solutions may relax a different factor: e.g. throughput, delay, drop.
   These choices need to be made either by the developer or by operator
   policy, rather than by the IETF.  Subsequent subsections discuss
   aspects relating to handling of different degrees of overload:






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   *  Unresponsive flows (L and/or C) but not overloaded, i.e. the sum
      of unresponsive load before adding any responsive traffic is below
      capacity;

         This case is handled by the regular Coupled DualQ (Section 2.1)
         but not discussed there.  So below, Section 4.2.1 explains the
         design goal, and how it is achieved in practice;

   *  Unresponsive flows (L and/or C) causing persistent overload,
      i.e. the sum of unresponsive load even before adding any
      responsive traffic persistently exceeds capacity;

         This case is not covered by the regular Coupled DualQ mechanism
         (Section 2.1) but the last para in Section 2.5.1.1 sets out a
         requirement to handle the case where ECN-capable traffic could
         starve non-ECN-capable traffic.  Section 4.2.3 below discusses
         the general options and gives specific examples.

   *  Short-term overload that lies between the 'not overloaded' and
      'persistently overloaded' cases.

         For the period before overload is deemed persistent,
         Section 4.2.2 discusses options for more immediate mechanisms
         at the scheduler timescale.  These prevent short-term
         starvation of the C queue by making the priority of the L queue
         conditional, as required in Section 2.5.1.

4.2.1.  Unresponsive Traffic without Overload

   When one or more L flows and/or C flows are unresponsive, but their
   total load is within the link capacity so that they do not saturate
   the coupled marking (below 100%), the goal of a DualQ AQM is to
   behave no worse than a single-queue AQM.

   Tests have shown that this is indeed the case with no additional
   mechanism beyond the regular Coupled DualQ of Section 2.1 (see the
   results of 'overload experiments' in [DCttH19]).  Perhaps counter-
   intuitively, whether the unresponsive flow classifies itself into the
   L or the C queue, the DualQ system behaves as if it has subtracted
   from the overall link capacity.  Then, the coupling shares out the
   remaining capacity between any competing responsive flows (in either
   queue).  See also Section 4.2.2, which discusses scheduler-specific
   details.








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4.2.2.  Avoiding Short-Term Classic Starvation: Sacrifice L4S Throughput
        or Delay?

   Priority of L4S is required to be conditional (see Section 2.4 &
   Section 2.5.1) to avoid short-term starvation of Classic.  Otherwise,
   as explained in Section 2.4, even a lone responsive L4S flow could
   temporarily block a small finite set of C packets (e.g. an initial
   window or DNS request).  The blockage would only be brief, but it
   could be longer for certain AQM implementations that can only
   increase the congestion signal coupled from the C queue when C
   packets are actually being dequeued.  There is then the question of
   whether to sacrifice L4S throughput or L4S delay (or some other
   policy) to make the priority conditional:

   Sacrifice L4S throughput:  By using weighted round-robin as the
      conditional priority scheduler, the L4S service can sacrifice some
      throughput during overload.  This can either be thought of as
      guaranteeing a minimum throughput service for Classic traffic, or
      as guaranteeing a maximum delay for a packet at the head of the
      Classic queue.

      Cautionary note: a WRR scheduler can only guarantee Classic
      throughput if Classic sources are sending enough to use it --
      congestion signals can undermine scheduling because they determine
      how much responsive traffic of each class arrives for scheduling
      in the first place.  This is why scheduling is only relied on to
      handle short-term starvation; until congestion signals build up
      and the sources react.  Even during long-term overload (discussed
      more fully in Section 4.2.3), it's pragmatic to discard packets
      from both queues, which again thins the traffic before it reaches
      the scheduler.  This is because a scheduler cannot be relied on to
      handle long-term overload since the right scheduler weight cannot
      be known for every scenario.

      The scheduling weight of the Classic queue should be small
      (e.g. 1/16).  In most traffic scenarios the scheduler will not
      interfere and it will not need to, because the coupling mechanism
      and the end-systems will determine the share of capacity across
      both queues as if it were a single pool.  However, if L4S traffic
      is over-aggressive or unresponsive, the scheduler weight for
      Classic traffic will at least be large enough to ensure it does
      not starve in the short-term.

      Although WRR scheduling is only expected to address short-term
      overload, there are (somewhat rare) cases when WRR has an effect
      on capacity shares over longer time-scales.  But its effect is
      minor, and it certainly does no harm.  Specifically, in cases
      where the ratio of L4S to Classic flows (e.g. 19:1) is greater



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      than the ratio of their scheduler weights (e.g. 15:1), the L4S
      flows will get less than an equal share of the capacity, but only
      slightly.  For instance, with the example numbers given, each L4S
      flow will get (15/16)/19 = 4.9% when ideally each would get
      1/20=5%. In the rather specific case of an unresponsive flow
      taking up just less than the capacity set aside for L4S
      (e.g. 14/16 in the above example), using WRR could significantly
      reduce the capacity left for any responsive L4S flows.

      The scheduling weight of the Classic queue should not be too
      small, otherwise a C packet at the head of the queue could be
      excessively delayed by a continually busy L queue.  For instance
      if the Classic weight is 1/16, the maximum that a Classic packet
      at the head of the queue can be delayed by L traffic is the
      serialization delay of 15 MTU-sized packets.

   Sacrifice L4S Delay:  The operator could choose to control overload
      of the Classic queue by allowing some delay to 'leak' across to
      the L4S queue.  The scheduler can be made to behave like a single
      First-In First-Out (FIFO) queue with different service times by
      implementing a very simple conditional priority scheduler that
      could be called a "time-shifted FIFO" (see the Modifier Earliest
      Deadline First (MEDF) scheduler [MEDF]).  This scheduler adds
      tshift to the queue delay of the next L4S packet, before comparing
      it with the queue delay of the next Classic packet, then it
      selects the packet with the greater adjusted queue delay.

      Under regular conditions, this time-shifted FIFO scheduler behaves
      just like a strict priority scheduler.  But under moderate or high
      overload it prevents starvation of the Classic queue, because the
      time-shift (tshift) defines the maximum extra queuing delay of
      Classic packets relative to L4S.  This would control milder
      overload of responsive traffic by introducing delay to defer
      invoking the overload mechanisms in Section 4.2.3, particularly
      when close to the maximum congestion signal.

   The example implementations in Appendix A and Appendix B could both
   be implemented with either policy.

4.2.3.  L4S ECN Saturation: Introduce Drop or Delay?

   This section concerns persistent overload caused by unresponsive L
   and/or C flows.  To keep the throughput of both L4S and Classic flows
   roughly equal over the full load range, a different control strategy
   needs to be defined above the point where the L4S AQM persistently
   saturates to an ECN marking probability of 100% leaving no room to
   push back the load any harder.  L4S ECN marking will saturate first
   (assuming the coupling factor k>1), even though saturation could be



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   caused by the sum of unresponsive traffic in either or both queues
   exceeding the link capacity.

   The term 'unresponsive' includes cases where a flow becomes
   temporarily unresponsive, for instance, a real-time flow that takes a
   while to adapt its rate in response to congestion, or a standard Reno
   flow that is normally responsive, but above a certain congestion
   level it will not be able to reduce its congestion window below the
   allowed minimum of 2 segments [RFC5681], effectively becoming
   unresponsive.  (Note that L4S traffic ought to remain responsive
   below a window of 2 segments (see the L4S
   requirements [I-D.ietf-tsvwg-ecn-l4s-id]).

   Saturation raises the question of whether to relieve congestion by
   introducing some drop into the L4S queue or by allowing delay to grow
   in both queues (which could eventually lead to drop due to buffer
   exhaustion anyway):

   Drop on Saturation:  Persistent saturation can be defined by a
      maximum threshold for coupled L4S ECN marking (assuming k>1)
      before saturation starts to make the flow rates of the different
      traffic types diverge.  Above that, the drop probability of
      Classic traffic is applied to all packets of all traffic types.
      Then experiments have shown that queueing delay can be kept at the
      target in any overload situation, including with unresponsive
      traffic, and no further measures are required (Section 4.2.3.1).

   Delay on Saturation:  When L4S marking saturates, instead of
      introducing L4S drop, the drop and marking probabilities of both
      queues could be capped.  Beyond that, delay will grow either
      solely in the queue with unresponsive traffic (if WRR is used), or
      in both queues (if time-shifted FIFO is used).  In either case,
      the higher delay ought to control temporary high congestion.  If
      the overload is more persistent, eventually the combined DualQ
      will overflow and tail drop will control congestion.

   The example implementation in Appendix A solely applies the "drop on
   saturation" policy.  The DOCSIS specification of a DualQ Coupled
   AQM [DOCSIS3.1] also implements the 'drop on saturation' policy with
   a very shallow L buffer.  However, the addition of DOCSIS per-flow
   Queue Protection [I-D.briscoe-docsis-q-protection] turns this into
   'delay on saturation' by redirecting some packets of the flow(s) most
   responsible for L queue overload into the C queue, which has a higher
   delay target.  If overload continues, this again becomes 'drop on
   saturation' as the level of drop in the C queue rises to maintain the
   target delay of the C queue.





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4.2.3.1.  Protecting against Overload by Unresponsive ECN-Capable
          Traffic

   Without a specific overload mechanism, unresponsive traffic would
   have a greater advantage if it were also ECN-capable.  The advantage
   is undetectable at normal low levels of marking.  However, it would
   become significant with the higher levels of marking typical during
   overload, when it could evade a significant degree of drop.  This is
   an issue whether the ECN-capable traffic is L4S or Classic.

   This raises the question of whether and when to introduce drop of
   ECN-capable traffic, as required by both Section 7 of the ECN
   spec [RFC3168] and Section 4.2.1 of the AQM
   recommendations [RFC7567].

   As an example, experiments with the DualPI2 AQM (Appendix A) have
   shown that introducing 'drop on saturation' at 100% coupled L4S
   marking addresses this problem with unresponsive ECN as well as
   addressing the saturation problem.  At saturation, DualPI2 switches
   into overload mode, where the base AQM is driven by the max delay of
   both queues and it introduces probabilistic drop to both queues
   equally.  It leaves only a small range of congestion levels just
   below saturation where unresponsive traffic gains any advantage from
   using the ECN capability (relative to being unresponsive without
   ECN), and the advantage is hardly detectable (see [DualQ-Test] and
   section IV-E of [DCttH19].  Also overload with an unresponsive ECT(1)
   flow gets no more bandwidth advantage than with ECT(0).

5.  References

5.1.  Normative References

   [I-D.ietf-tsvwg-ecn-l4s-id]
              Schepper, K. D. and B. Briscoe, "Explicit Congestion
              Notification (ECN) Protocol for Very Low Queuing Delay
              (L4S)", Work in Progress, Internet-Draft, draft-ietf-
              tsvwg-ecn-l4s-id-28, 8 August 2022,
              <https://datatracker.ietf.org/api/v1/doc/document/draft-
              ietf-tsvwg-ecn-l4s-id/>.

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.







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   [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>.

   [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>.

5.2.  Informative References

   [Alizadeh-stability]
              Alizadeh, M., Javanmard, A., and B. Prabhakar, "Analysis
              of DCTCP: Stability, Convergence, and Fairness", ACM
              SIGMETRICS 2011 , June 2011,
              <https://dl.acm.org/citation.cfm?id=1993753>.

   [AQMmetrics]
              Kwon, M. and S. Fahmy, "A Comparison of Load-based and
              Queue- based Active Queue Management Algorithms", Proc.
              Int'l Soc. for Optical Engineering (SPIE) 4866:35--46 DOI:
              10.1117/12.473021, 2002,
              <https://www.cs.purdue.edu/homes/fahmy/papers/ldc.pdf>.

   [ARED01]   Floyd, S., Gummadi, R., and S. Shenker, "Adaptive RED: An
              Algorithm for Increasing the Robustness of RED's Active
              Queue Management", ACIRI Technical Report , August 2001,
              <https://www.icir.org/floyd/red.html>.

   [BBRv2]    Cardwell, N., "BRTCP BBR v2 Alpha/Preview Release", GitHub
              repository; Linux congestion control module,
              <https://github.com/google/bbr/blob/v2alpha/README.md>.

   [Boru20]   Boru Oljira, D., Grinnemo, K-J., Brunstrom, A., and J.
              Taheri, "Validating the Sharing Behavior and Latency
              Characteristics of the L4S Architecture", ACM CCR 
              50(2):37--44, May 2020,
              <https://dl.acm.org/doi/abs/10.1145/3402413.3402419>.

   [CCcensus19]
              Mishra, A., Sun, X., Jain, A., Pande, S., Joshi, R., and
              B. Leong, "The Great Internet TCP Congestion Control
              Census", Proc. ACM on Measurement and Analysis of
              Computing Systems 3(3), December 2019,
              <https://doi.org/10.1145/3366693>.





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   [CoDel]    Nichols, K. and V. Jacobson, "Controlling Queue Delay",
              ACM Queue 10(5), May 2012,
              <https://queue.acm.org/issuedetail.cfm?issue=2208917>.

   [CRED_Insights]
              Briscoe, B., "Insights from Curvy RED (Random Early
              Detection)", BT Technical Report TR-TUB8-2015-003
              arXiv:1904.07339 [cs.NI], July 2015,
              <https://arxiv.org/abs/1904.07339>.

   [DCttH19]  De Schepper, K., Bondarenko, O., Tilmans, O., and B.
              Briscoe, "`Data Centre to the Home': Ultra-Low Latency for
              All", Updated RITE project Technical Report , July 2019,
              <https://bobbriscoe.net/pubs.html#DCttH_TR>.

   [DOCSIS3.1]
              CableLabs, "MAC and Upper Layer Protocols Interface
              (MULPI) Specification, CM-SP-MULPIv3.1", Data-Over-Cable
              Service Interface Specifications DOCSIS® 3.1 Version i17
              or later, 21 January 2019, <https://specification-
              search.cablelabs.com/CM-SP-MULPIv3.1>.

   [DualPI2Linux]
              Albisser, O., De Schepper, K., Briscoe, B., Tilmans, O.,
              and H. Steen, "DUALPI2 - Low Latency, Low Loss and
              Scalable (L4S) AQM", Proc. Linux Netdev 0x13 , March 2019,
              <https://www.netdevconf.org/0x13/session.html?talk-
              DUALPI2-AQM>.

   [DualQ-Test]
              Steen, H., "Destruction Testing: Ultra-Low Delay using
              Dual Queue Coupled Active Queue Management", Master's
              Thesis, Dept of Informatics, Uni Oslo , May 2017,
              <https://www.duo.uio.no/bitstream/handle/10852/57424/
              thesis-henrste.pdf?sequence=1>.

   [Dukkipati06]
              Dukkipati, N. and N. McKeown, "Why Flow-Completion Time is
              the Right Metric for Congestion Control", ACM CCR 
              36(1):59--62, January 2006,
              <https://dl.acm.org/doi/10.1145/1111322.1111336>.

   [Heist21]  Heist, P. and J. Morton, "L4S Tests", GitHub README,
              August 2021, <https://github.com/heistp/l4s-
              tests/#underutilization-with-bursty-traffic>.






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   [I-D.briscoe-docsis-q-protection]
              Briscoe, B. and G. White, "The DOCSIS(r) Queue Protection
              Algorithm to Preserve Low Latency", Work in Progress,
              Internet-Draft, draft-briscoe-docsis-q-protection-06, 13
              May 2022,
              <https://datatracker.ietf.org/api/v1/doc/document/draft-
              briscoe-docsis-q-protection/>.

   [I-D.briscoe-iccrg-prague-congestion-control]
              Schepper, K. D., Tilmans, O., and B. Briscoe, "Prague
              Congestion Control", Work in Progress, Internet-Draft,
              draft-briscoe-iccrg-prague-congestion-control-01, 11 July
              2022, <https://datatracker.ietf.org/api/v1/doc/document/
              draft-briscoe-iccrg-prague-congestion-control/>.

   [I-D.briscoe-tsvwg-l4s-diffserv]
              Briscoe, B., "Interactions between Low Latency, Low Loss,
              Scalable Throughput (L4S) and Differentiated Services",
              Work in Progress, Internet-Draft, draft-briscoe-tsvwg-l4s-
              diffserv-02, 2 July 2018,
              <https://datatracker.ietf.org/api/v1/doc/document/draft-
              briscoe-tsvwg-l4s-diffserv/>.

   [I-D.cardwell-iccrg-bbr-congestion-control]
              Cardwell, N., Cheng, Y., Yeganeh, S. H., Swett, I., and V.
              Jacobson, "BBR Congestion Control", Work in Progress,
              Internet-Draft, draft-cardwell-iccrg-bbr-congestion-
              control-02, 7 March 2022,
              <https://datatracker.ietf.org/api/v1/doc/document/draft-
              cardwell-iccrg-bbr-congestion-control/>.

   [I-D.ietf-tsvwg-l4s-arch]
              Briscoe, B., Schepper, K. D., Bagnulo, M., and G. White,
              "Low Latency, Low Loss, Scalable Throughput (L4S) Internet
              Service: Architecture", Work in Progress, Internet-Draft,
              draft-ietf-tsvwg-l4s-arch-19, 27 July 2022,
              <https://datatracker.ietf.org/api/v1/doc/document/draft-
              ietf-tsvwg-l4s-arch/>.

   [I-D.mathis-iccrg-relentless-tcp]
              Mathis, M., "Relentless Congestion Control", Work in
              Progress, Internet-Draft, draft-mathis-iccrg-relentless-
              tcp-00, 4 March 2009, <https://www.ietf.org/archive/id/
              draft-mathis-iccrg-relentless-tcp-00.txt>.

   [L4Sdemo16]
              Bondarenko, O., De Schepper, K., Tsang, I., and B.
              Briscoe, "Ultra-Low Delay for All: Live Experience, Live



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              Analysis", Proc. MMSYS'16 pp33:1--33:4, May 2016,
              <https//dl.acm.org/citation.cfm?doid=2910017.2910633
              (videos of demos:
              https://riteproject.eu/dctth/#1511dispatchwg )>.

   [L4S_5G]   Willars, P., Wittenmark, E., Ronkainen, H., Östberg, C.,
              Johansson, I., Strand, J., Lédl, P., and D. Schnieders,
              "Enabling time-critical applications over 5G with rate
              adaptation", Ericsson - Deutsche Telekom White Paper BNEW-
              21:025455 Uen, May 2021, <https://www.ericsson.com/en/
              reports-and-papers/white-papers/enabling-time-critical-
              applications-over-5g-with-rate-adaptation>.

   [Labovitz10]
              Labovitz, C., Iekel-Johnson, S., McPherson, D., Oberheide,
              J., and F. Jahanian, "Internet Inter-Domain Traffic", Proc
              ACM SIGCOMM; ACM CCR 40(4):75--86, August 2010,
              <https://doi.org/10.1145/1851275.1851194>.

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

   [MEDF]     Menth, M., Schmid, M., Heiss, H., and T. Reim, "MEDF - a
              simple scheduling algorithm for two real-time transport
              service classes with application in the UTRAN", Proc. IEEE
              Conference on Computer Communications (INFOCOM'03) Vol.2
              pp.1116-1122, March 2003,
              <https://infocom2003.ieee-infocom.org/papers/27_04.PDF>.

   [PI2]      De Schepper, K., Bondarenko, O., Briscoe, B., and I.
              Tsang, "PI2: A Linearized AQM for both Classic and
              Scalable TCP", ACM CoNEXT'16 , December 2016,
              <https://riteproject.files.wordpress.com/2015/10/
              pi2_conext.pdf>.

   [PI2param] Briscoe, B., "PI2 Parameters", Technical Report TR-BB-
              2021-001 arXiv:2107.01003 [cs.NI], July 2021,
              <https://arxiv.org/abs/2107.01003>.

   [PragueLinux]
              Briscoe, B., De Schepper, K., Albisser, O., Misund, J.,
              Tilmans, O., Kühlewind, M., and A.S. Ahmed, "Implementing
              the `TCP Prague' Requirements for Low Latency Low Loss
              Scalable Throughput (L4S)", Proc. Linux Netdev 0x13 ,
              March 2019, <https://www.netdevconf.org/0x13/
              session.html?talk-tcp-prague-l4s>.



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   [RFC0970]  Nagle, J., "On Packet Switches With Infinite Storage",
              RFC 970, DOI 10.17487/RFC0970, December 1985,
              <https://www.rfc-editor.org/info/rfc970>.

   [RFC2309]  Braden, B., Clark, D., Crowcroft, J., Davie, B., Deering,
              S., Estrin, D., Floyd, S., Jacobson, V., Minshall, G.,
              Partridge, C., Peterson, L., Ramakrishnan, K., Shenker,
              S., Wroclawski, J., and L. Zhang, "Recommendations on
              Queue Management and Congestion Avoidance in the
              Internet", RFC 2309, DOI 10.17487/RFC2309, April 1998,
              <https://www.rfc-editor.org/info/rfc2309>.

   [RFC2914]  Floyd, S., "Congestion Control Principles", BCP 41,
              RFC 2914, DOI 10.17487/RFC2914, September 2000,
              <https://www.rfc-editor.org/info/rfc2914>.

   [RFC3246]  Davie, B., Charny, A., Bennet, J.C.R., Benson, K., Le
              Boudec, J.Y., Courtney, W., Davari, S., Firoiu, V., and D.
              Stiliadis, "An Expedited Forwarding PHB (Per-Hop
              Behavior)", RFC 3246, DOI 10.17487/RFC3246, March 2002,
              <https://www.rfc-editor.org/info/rfc3246>.

   [RFC3649]  Floyd, S., "HighSpeed TCP for Large Congestion Windows",
              RFC 3649, DOI 10.17487/RFC3649, December 2003,
              <https://www.rfc-editor.org/info/rfc3649>.

   [RFC5033]  Floyd, S. and M. Allman, "Specifying New Congestion
              Control Algorithms", BCP 133, RFC 5033,
              DOI 10.17487/RFC5033, August 2007,
              <https://www.rfc-editor.org/info/rfc5033>.

   [RFC5348]  Floyd, S., Handley, M., Padhye, J., and J. Widmer, "TCP
              Friendly Rate Control (TFRC): Protocol Specification",
              RFC 5348, DOI 10.17487/RFC5348, September 2008,
              <https://www.rfc-editor.org/info/rfc5348>.

   [RFC5681]  Allman, M., Paxson, V., and E. Blanton, "TCP Congestion
              Control", RFC 5681, DOI 10.17487/RFC5681, September 2009,
              <https://www.rfc-editor.org/info/rfc5681>.

   [RFC5706]  Harrington, D., "Guidelines for Considering Operations and
              Management of New Protocols and Protocol Extensions",
              RFC 5706, DOI 10.17487/RFC5706, November 2009,
              <https://www.rfc-editor.org/info/rfc5706>.







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   [RFC7567]  Baker, F., Ed. and G. Fairhurst, Ed., "IETF
              Recommendations Regarding Active Queue Management",
              BCP 197, RFC 7567, DOI 10.17487/RFC7567, July 2015,
              <https://www.rfc-editor.org/info/rfc7567>.

   [RFC8033]  Pan, R., Natarajan, P., Baker, F., and G. White,
              "Proportional Integral Controller Enhanced (PIE): A
              Lightweight Control Scheme to Address the Bufferbloat
              Problem", RFC 8033, DOI 10.17487/RFC8033, February 2017,
              <https://www.rfc-editor.org/info/rfc8033>.

   [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>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/info/rfc8174>.

   [RFC8257]  Bensley, S., Thaler, D., Balasubramanian, P., Eggert, L.,
              and G. Judd, "Data Center TCP (DCTCP): TCP Congestion
              Control for Data Centers", RFC 8257, DOI 10.17487/RFC8257,
              October 2017, <https://www.rfc-editor.org/info/rfc8257>.

   [RFC8290]  Hoeiland-Joergensen, T., McKenney, P., Taht, D., Gettys,
              J., and E. Dumazet, "The Flow Queue CoDel Packet Scheduler
              and Active Queue Management Algorithm", RFC 8290,
              DOI 10.17487/RFC8290, January 2018,
              <https://www.rfc-editor.org/info/rfc8290>.

   [RFC8298]  Johansson, I. and Z. Sarker, "Self-Clocked Rate Adaptation
              for Multimedia", RFC 8298, DOI 10.17487/RFC8298, December
              2017, <https://www.rfc-editor.org/info/rfc8298>.

   [RFC8312]  Rhee, I., Xu, L., Ha, S., Zimmermann, A., Eggert, L., and
              R. Scheffenegger, "CUBIC for Fast Long-Distance Networks",
              RFC 8312, DOI 10.17487/RFC8312, February 2018,
              <https://www.rfc-editor.org/info/rfc8312>.

   [RFC8404]  Moriarty, K., Ed. and A. Morton, Ed., "Effects of
              Pervasive Encryption on Operators", RFC 8404,
              DOI 10.17487/RFC8404, July 2018,
              <https://www.rfc-editor.org/info/rfc8404>.






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   [SCReAM]   Johansson, I., "SCReAM", GitHub repository; ,
              <https://github.com/EricssonResearch/scream/blob/master/
              README.md>.

   [SigQ-Dyn] Briscoe, B., "Rapid Signalling of Queue Dynamics",
              Technical Report TR-BB-2017-001 arXiv:1904.07044 [cs.NI],
              September 2017, <https://arxiv.org/abs/1904.07044>.

Appendix A.  Example DualQ Coupled PI2 Algorithm

   As a first concrete example, the pseudocode below gives the DualPI2
   algorithm.  DualPI2 follows the structure of the DualQ Coupled AQM
   framework in Figure 1.  A simple ramp function (configured in units
   of queuing time) with unsmoothed ECN marking is used for the Native
   L4S AQM.  The ramp can also be configured as a step function.  The
   PI2 algorithm [PI2] is used for the Classic AQM.  PI2 is an improved
   variant of the PIE AQM [RFC8033].

   The pseudocode will be introduced in two passes.  The first pass
   explains the core concepts, deferring handling of edge-cases like
   overload to the second pass.  To aid comparison, line numbers are
   kept in step between the two passes by using letter suffixes where
   the longer code needs extra lines.

   All variables are assumed to be floating point in their basic units
   (size in bytes, time in seconds, rates in bytes/second, alpha and
   beta in Hz, and probabilities from 0 to 1.  Constants expressed in k
   (kilo), M (mega), G (giga), u (micro), m (milli) , %, ... are assumed
   to be converted to their appropriate multiple or fraction to
   represent the basic units.  A real implementation that wants to use
   integer values needs to handle appropriate scaling factors and allow
   accordingly appropriate resolution of its integer types (including
   temporary internal values during calculations).

   A full open source implementation for Linux is available at:
   https://github.com/L4STeam/sch_dualpi2_upstream and explained in
   [DualPI2Linux].  The specification of the DualQ Coupled AQM for
   DOCSIS cable modems and CMTSs is available in [DOCSIS3.1] and
   explained in [LLD].

A.1.  Pass #1: Core Concepts

   The pseudocode manipulates three main structures of variables: the
   packet (pkt), the L4S queue (lq) and the Classic queue (cq).  The
   pseudocode consists of the following six functions:






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   *  The initialization function dualpi2_params_init(...) (Figure 2)
      that sets parameter defaults (the API for setting non-default
      values is omitted for brevity)

   *  The enqueue function dualpi2_enqueue(lq, cq, pkt) (Figure 3)

   *  The dequeue function dualpi2_dequeue(lq, cq, pkt) (Figure 4)

   *  The recurrence function recur(q, likelihood) for de-randomized ECN
      marking (shown at the end of Figure 4).

   *  The L4S AQM function laqm(qdelay) (Figure 5) used to calculate the
      ECN-marking probability for the L4S queue

   *  The base AQM function that implements the PI algorithm
      dualpi2_update(lq, cq) (Figure 6) used to regularly update the
      base probability (p'), which is squared for the Classic AQM as
      well as being coupled across to the L4S queue.

   It also uses the following functions that are not shown in full here:

   *  scheduler(), which selects between the head packets of the two
      queues; the choice of scheduler technology is discussed later;

   *  cq.byt() or lq.byt() returns the current length (aka. backlog) of
      the relevant queue in bytes;

   *  cq.len() or lq.len() returns the current length of the relevant
      queue in packets;

   *  cq.time() or lq.time() returns the current queuing delay of the
      relevant queue in units of time (see Note a);

   *  mark(pkt) and drop(pkt) for ECN-marking and dropping a packet;

   In experiments so far (building on experiments with PIE) on broadband
   access links ranging from 4 Mb/s to 200 Mb/s with base RTTs from 5 ms
   to 100 ms, DualPI2 achieves good results with the default parameters
   in Figure 2.  The parameters are categorised by whether they relate
   to the Base PI2 AQM, the L4S AQM or the framework coupling them
   together.  Constants and variables derived from these parameters are
   also included at the end of each category.  Each parameter is
   explained as it is encountered in the walk-through of the pseudocode
   below, and the rationale for the chosen defaults are given so that
   sensible values can be used in scenarios other than the regular
   public Internet.





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   1:  dualpi2_params_init(...) {         % Set input parameter defaults
   2:    % DualQ Coupled framework parameters
   5:    limit = MAX_LINK_RATE * 250 ms               % Dual buffer size
   3:    k = 2                                         % Coupling factor
   4:    % NOT SHOWN % scheduler-dependent weight or equival't parameter
   6:
   7:    % PI2 Classic AQM parameters
   8:    target = 15 ms                             % Queue delay target
   9:    RTT_max = 100 ms                      % Worst case RTT expected
   10:   % PI2 constants derived from above PI2 parameters
   11:   p_Cmax = min(1/k^2, 1)             % Max Classic drop/mark prob
   12:   Tupdate = min(target, RTT_max/3)        % PI sampling interval
   13:   alpha = 0.1 * Tupdate / RTT_max^2      % PI integral gain in Hz
   14:   beta = 0.3 / RTT_max               % PI proportional gain in Hz
   15:
   16:   % L4S ramp AQM parameters
   17:   minTh = 800 us        % L4S min marking threshold in time units
   18:   range = 400 us                % Range of L4S ramp in time units
   19:   Th_len = 1 pkt           % Min L4S marking threshold in packets
   20:   % L4S constants
   21:   p_Lmax = 1                               % Max L4S marking prob
   22: }

       Figure 2: Example Header Pseudocode for DualQ Coupled PI2 AQM

   The overall goal of the code is to apply the marking and dropping
   probabilities for L4S and Classic traffic (p_L and p_C).  These are
   derived from the underlying base probabilities p'_L and p' driven
   respectively by the traffic in the L and C queues.  The marking
   probability for the L queue (p_L) depends on both the base
   probability in its own queue (p'_L) and a probability called p_CL,
   which is coupled across from p' in the C queue (see Section 2.4 for
   the derivation of the specific equations and dependencies).

   The probabilities p_CL and p_C are derived in lines 4 and 5 of the
   dualpi2_update() function (Figure 6) then used in the
   dualpi2_dequeue() function where p_L is also derived from p_CL at
   line 6 (Figure 4).  The code walk-through below builds up to
   explaining that part of the code eventually, but it starts from
   packet arrival.











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   1:  dualpi2_enqueue(lq, cq, pkt) { % Test limit and classify lq or cq
   2:    if ( lq.byt() + cq.byt() + MTU > limit)
   3:      drop(pkt)                     % drop packet if buffer is full
   4:    timestamp(pkt)     % only needed if using the sojourn technique
   5:    % Packet classifier
   6:    if ( ecn(pkt) modulo 2 == 1 )         % ECN bits = ECT(1) or CE
   7:      lq.enqueue(pkt)
   8:    else                             % ECN bits = not-ECT or ECT(0)
   9:      cq.enqueue(pkt)
   10: }

       Figure 3: Example Enqueue Pseudocode for DualQ Coupled PI2 AQM

   1:  dualpi2_dequeue(lq, cq, pkt) {     % Couples L4S & Classic queues
   2:    while ( lq.byt() + cq.byt() > 0 ) {
   3:      if ( scheduler() == lq ) {
   4:        lq.dequeue(pkt)                      % Scheduler chooses lq
   5:        p'_L = laqm(lq.time())                        % Native LAQM
   6:        p_L = max(p'_L, p_CL)                  % Combining function
   7:        if ( recur(lq, p_L) )                      % Linear marking
   8:          mark(pkt)
   9:      } else {
   10:       cq.dequeue(pkt)                      % Scheduler chooses cq
   11:       if ( recur(cq, p_C) ) {            % probability p_C = p'^2
   12:         if ( ecn(pkt) == 0 ) {           % if ECN field = not-ECT
   13:           drop(pkt)                                % squared drop
   14:           continue        % continue to the top of the while loop
   15:         }
   16:         mark(pkt)                                  % squared mark
   17:       }
   18:     }
   19:     return(pkt)                      % return the packet and stop
   20:   }
   21:   return(NULL)                             % no packet to dequeue
   22: }

   23: recur(q, likelihood) {   % Returns TRUE with a certain likelihood
   24:   q.count += likelihood
   25:   if (q.count > 1) {
   26:     q.count -= 1
   27:     return TRUE
   28:   }
   29:   return FALSE
   30: }

       Figure 4: Example Dequeue Pseudocode for DualQ Coupled PI2 AQM





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   When packets arrive, first a common queue limit is checked as shown
   in line 2 of the enqueuing pseudocode in Figure 3.  This assumes a
   shared buffer for the two queues (Note b discusses the merits of
   separate buffers).  In order to avoid any bias against larger
   packets, 1 MTU of space is always allowed, and the limit is
   deliberately tested before enqueue.

   If limit is not exceeded, the packet is timestamped in line 4 (only
   if the sojourn time technique is being used to measure queue delay;
   see Note a for alternatives).

   At lines 5-9, the packet is classified and enqueued to the Classic or
   L4S queue dependent on the least significant bit of the ECN field in
   the IP header (line 6).  Packets with a codepoint having an LSB of 0
   (Not-ECT and ECT(0)) will be enqueued in the Classic queue.
   Otherwise, ECT(1) and CE packets will be enqueued in the L4S queue.
   Optional additional packet classification flexibility is omitted for
   brevity (see the L4S ECN protocol [I-D.ietf-tsvwg-ecn-l4s-id]).

   The dequeue pseudocode (Figure 4) is repeatedly called whenever the
   lower layer is ready to forward a packet.  It schedules one packet
   for dequeuing (or zero if the queue is empty) then returns control to
   the caller, so that it does not block while that packet is being
   forwarded.  While making this dequeue decision, it also makes the
   necessary AQM decisions on dropping or marking.  The alternative of
   applying the AQMs at enqueue would shift some processing from the
   critical time when each packet is dequeued.  However, it would also
   add a whole queue of delay to the control signals, making the control
   loop sloppier (for a typical RTT it would double the Classic queue's
   feedback delay).

   All the dequeue code is contained within a large while loop so that
   if it decides to drop a packet, it will continue until it selects a
   packet to schedule.  Line 3 of the dequeue pseudocode is where the
   scheduler chooses between the L4S queue (lq) and the Classic queue
   (cq).  Detailed implementation of the scheduler is not shown (see
   discussion later).

   *  If an L4S packet is scheduled, in lines 7 and 8 the packet is ECN-
      marked with likelihood p_L.  The recur() function at the end of
      Figure 4 is used, which is preferred over random marking because
      it avoids delay due to randomization when interpreting congestion
      signals, but it still desynchronizes the saw-teeth of the flows.
      Line 6 calculates p_L as the maximum of the coupled L4S
      probability p_CL and the probability from the native L4S AQM p'_L.
      This implements the max() function shown in Figure 1 to couple the
      outputs of the two AQMs together.  Of the two probabilities input
      to p_L in line 6:



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      -  p'_L is calculated per packet in line 5 by the laqm() function
         (see Figure 5),

      -  Whereas p_CL is maintained by the dualpi2_update() function
         which runs every Tupdate (Tupdate is set in line 12 of
         Figure 2).

   *  If a Classic packet is scheduled, lines 10 to 17 drop or mark the
      packet with probability p_C.

   The Native L4S AQM algorithm (Figure 5) is a ramp function, similar
   to the RED algorithm, but simplified as follows:

   *  The extent of the ramp is defined in units of queuing delay, not
      bytes, so that configuration remains invariant as the queue
      departure rate varies.

   *  It uses instantaneous queueing delay, which avoids the complexity
      of smoothing, but also avoids embedding a worst-case RTT of
      smoothing delay in the network (see Section 2.1).

   *  The ramp rises linearly directly from 0 to 1, not to an
      intermediate value of p'_L as RED would, because there is no need
      to keep ECN marking probability low.

   *  Marking does not have to be randomized.  Determinism is used
      instead of randomness; to reduce the delay necessary to smooth out
      the noise of randomness from the signal.

   The ramp function requires two configuration parameters, the minimum
   threshold (minTh) and the width of the ramp (range), both in units of
   queuing time, as shown in lines 17 & 18 of the initialization
   function in Figure 2.  The ramp function can be configured as a step
   (see Note c).

   Although the DCTCP paper [Alizadeh-stability] recommends an ECN
   marking threshold of 0.17*RTT_typ, it also shows that the threshold
   can be much shallower with hardly any worse under-utilization of the
   link (because the amplitude of DCTCP's sawteeth is so small).  Based
   on extensive experiments, for the public Internet the default minimum
   ECN marking threshold (target) in Figure 2 is considered a good
   compromise, even though it is significantly smaller fraction of
   RTT_typ.








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   1:  laqm(qdelay) {               % Returns native L4S AQM probability
   2:    if (qdelay >= maxTh)
   3:      return 1
   4:    else if (qdelay > minTh)
   5:      return (qdelay - minTh)/range  % Divide could use a bit-shift
   6:    else
   7:      return 0
   8:  }

            Figure 5: Example Pseudocode for the Native L4S AQM


   1:  dualpi2_update(lq, cq) {                % Update p' every Tupdate
   2:    curq = cq.time()  % use queuing time of first-in Classic packet
   3:    p' = p' + alpha * (curq - target) + beta * (curq - prevq)
   4:    p_CL = k * p'  % Coupled L4S prob = base prob * coupling factor
   5:    p_C = p'^2                       % Classic prob = (base prob)^2
   6:    prevq = curq
   7:  }

      Figure 6: Example PI-Update Pseudocode for DualQ Coupled PI2 AQM

   (Clamping p' within the range [0,1] omitted for clarity - see text)

   The coupled marking probability, p_CL depends on the base probability
   (p'), which is kept up to date by the core PI algorithm in Figure 6
   executed every Tupdate.

   Note that p' solely depends on the queuing time in the Classic queue.
   In line 2, the current queuing delay (curq) is evaluated from how
   long the head packet was in the Classic queue (cq).  The function
   cq.time() (not shown) subtracts the time stamped at enqueue from the
   current time (see Note a) and implicitly takes the current queuing
   delay as 0 if the queue is empty.

   The algorithm centres on line 3, which is a classical Proportional-
   Integral (PI) controller that alters p' dependent on: a) the error
   between the current queuing delay (curq) and the target queuing
   delay, 'target'; and b) the change in queuing delay since the last
   sample.  The name 'PI' represents the fact that the second factor
   (how fast the queue is growing) is _P_roportional to load while the
   first is the _I_ntegral of the load (so it removes any standing queue
   in excess of the target).

   The target parameter can be set based on local knowledge, but the aim
   is for the default to be a good compromise for anywhere in the
   intended deployment environment -- the public Internet.  According to
   [PI2param], the target queuing delay on line 9 of Figure 2 is related



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   to the typical base RTT worldwide, RTT_typ, by two factors: target =
   RTT_typ * g * f.  Below we summarize the rationale behind these
   factors and introduce a further adjustment.  The two factors ensure
   that, in a large proportion of cases (say 90%), the sawtooth
   variations in RTT of a single flow will fit within the buffer without
   underutilizing the link.  Frankly, these factors are educated
   guesses, but with the emphasis closer to 'educated' than to 'guess'
   (see [PI2param] for full background):

   *  RTT_typ is taken as 25 ms.  This is based on an average CDN
      latency measured in each country weighted by the number of
      Internet users in that country to produce an overall weighted
      average for the Internet [PI2param].  Countries were ranked by
      number of Internet users, and once 90% of Internet users were
      covered, smaller countries were excluded to avoid
      unrepresentatively small sample sizes.  Also, importantly, the
      data for the average CDN latency in China (with the largest number
      of Internet users) has been removed, because the CDN latency was a
      significant outlier and, on reflection, the experimental technique
      seemed inappropriate to the CDN market in China.

   *  g is taken as 0.38.  The factor g is a geometry factor that
      characterizes the shape of the sawteeth of prevalent Classic
      congestion controllers.  The geometry factor is the fraction of
      the amplitude of the sawtooth variability in queue delay that lies
      below the AQM's target.  For instance, at low bit rate, the
      geometry factor of standard Reno is 0.5, but at higher rates it
      tends to just under 1.  According to the census of congestion
      controllers conducted by Mishra et al. in Jul-Oct
      2019 [CCcensus19], most Classic TCP traffic uses Cubic.  And,
      according to the analysis in [PI2param], if running over a PI2
      AQM, a large proportion of this Cubic traffic would be in its
      Reno-Friendly mode, which has a geometry factor of ~0.39 (all
      known implementations).  The rest of the Cubic traffic would be in
      true Cubic mode, which has a geometry factor of ~0.36.  Without
      modelling the sawtooth profiles from all the other less prevalent
      congestion controllers, we estimate a 7:3 weighted average of
      these two, resulting in an average geometry factor of 0.38.

   *  f is taken as 2.  The factor f is a safety factor that increases
      the target queue to allow for the distribution of RTT_typ around
      its mean.  Otherwise, the target queue would only avoid
      underutilization for those users below the mean.  It also provides
      a safety margin for the proportion of paths in use that span
      beyond the distance between a user and their local CDN.
      Currently, no data is available on the variance of queue delay
      around the mean in each region, so there is plenty of room for
      this guess to become more educated.



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   *  [PI2param] recommends target = RTT_typ * g * f = 25ms * 0.38 * 2 =
      19 ms.  However, a further adjustment is warranted, because target
      is moving year-on-year.  The paper is based on data collected in
      2019, and it mentions evidence from speedtest.net that suggests
      RTT_typ reduced by 17% (fixed) or 12% (mobile) between 2020 and
      2021.  Therefore, we recommend a default of target = 15 ms at the
      time of writing (2021).

   Operators can always use the data and discussion in [PI2param] to
   configure a more appropriate target for their environment.  For
   instance, an operator might wish to question the assumptions called
   out in that paper, such as the goal of no underutilization for a
   large majority of single flow transfers (given many large transfers
   use multiple flows to avoid the scaling limitations of Classic
   flows).

   The two 'gain factors' in line 3 of Figure 6, alpha and beta,
   respectively weight how strongly each of the two elements (Integral
   and Proportional) alters p'.  They are in units of 'per second of
   delay' or Hz, because they transform differences in queueing delay
   into changes in probability (assuming probability has a value from 0
   to 1).

   Alpha and beta determine how much p' ought to change after each
   update interval (Tupdate).  For smaller Tupdate, p' should change by
   the same amount per second, but in finer more frequent steps.  So
   alpha depends on Tupdate (see line 13 of the initialization function
   in Figure 2).  It is best to update p' as frequently as possible, but
   Tupdate will probably be constrained by hardware performance.  As
   shown in line 13, the update interval should be frequent enough to
   update at least once in the time taken for the target queue to drain
   ('target') as long as it updates at least three times per maximum
   RTT.  Tupdate defaults to 16 ms in the reference Linux implementation
   because it has to be rounded to a multiple of 4 ms.  For link rates
   from 4 to 200 Mb/s and a maximum RTT of 100ms, it has been verified
   through extensive testing that Tupdate=16ms (as also recommended in
   the PIE spec [RFC8033]) is sufficient.

   The choice of alpha and beta also determines the AQM's stable
   operating range.  The AQM ought to change p' as fast as possible in
   response to changes in load without over-compensating and therefore
   causing oscillations in the queue.  Therefore, the values of alpha
   and beta also depend on the RTT of the expected worst-case flow
   (RTT_max).

   The maximum RTT of a PI controller (RTT_max in line 10 of Figure 2)
   is not an absolute maximum, but more instability (more queue
   variability) sets in for long-running flows with an RTT above this



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   value.  The propagation delay halfway round the planet and back in
   glass fibre is 200 ms.  However, hardly any traffic traverses such
   extreme paths and, since the significant consolidation of Internet
   traffic between 2007 and 2009 [Labovitz10], a high and growing
   proportion of all Internet traffic (roughly two-thirds at the time of
   writing) has been served from content distribution networks (CDNs) or
   'cloud' services distributed close to end-users.  The Internet might
   change again, but for now, designing for a maximum RTT of 100ms is a
   good compromise between faster queue control at low RTT and some
   instability on the occasions when a longer path is necessary.

   Recommended derivations of the gain constants alpha and beta can be
   approximated for Reno over a PI2 AQM as: alpha = 0.1 * Tupdate /
   RTT_max^2; beta = 0.3 / RTT_max, as shown in lines 14 & 15 of
   Figure 2.  These are derived from the stability analysis in [PI2].
   For the default values of Tupdate=16 ms and RTT_max = 100 ms, they
   result in alpha = 0.16; beta = 3.2 (discrepancies are due to
   rounding).  These defaults have been verified with a wide range of
   link rates, target delays and a range of traffic models with mixed
   and similar RTTs, short and long flows, etc.

   In corner cases, p' can overflow the range [0,1] so the resulting
   value of p' has to be bounded (omitted from the pseudocode).  Then,
   as already explained, the coupled and Classic probabilities are
   derived from the new p' in lines 4 and 5 of Figure 6 as p_CL = k*p'
   and p_C = p'^2.

   Because the coupled L4S marking probability (p_CL) is factored up by
   k, the dynamic gain parameters alpha and beta are also inherently
   factored up by k for the L4S queue.  So, the effective gain factor
   for the L4S queue is k*alpha (with defaults alpha = 0.16 Hz and k=2,
   effective L4S alpha = 0.32 Hz).

   Unlike in PIE [RFC8033], alpha and beta do not need to be tuned every
   Tupdate dependent on p'.  Instead, in PI2, alpha and beta are
   independent of p' because the squaring applied to Classic traffic
   tunes them inherently.  This is explained in [PI2], which also
   explains why this more principled approach removes the need for most
   of the heuristics that had to be added to PIE.

   Nonetheless, an implementer might wish to add selected details to
   either AQM.  For instance the Linux reference DualPI2 implementation
   includes the following (not shown in the pseudocode above):








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   *  Classic and coupled marking or dropping (i.e. based on p_C and
      p_CL from the PI controller) is not applied to a packet if the
      aggregate queue length in bytes is < 2 MTU (prior to enqueuing the
      packet or dequeuing it, depending on whether the AQM is configured
      to be applied at enqueue or dequeue);

   *  In the WRR scheduler, the 'credit' indicating which queue should
      transmit is only changed if there are packets in both queues
      (i.e. if there is actual resource contention).  This means that a
      properly paced L flow might never be delayed by the WRR.  The WRR
      credit is reset in favour of the L queue when the link is idle.

   An implementer might also wish to add other heuristics, e.g. burst
   protection [RFC8033] or enhanced burst protection [RFC8034].

   Notes:

   a.  The drain rate of the queue can vary if it is scheduled relative
       to other queues, or to cater for fluctuations in a wireless
       medium.  To auto-adjust to changes in drain rate, the queue needs
       to be measured in time, not bytes or packets [AQMmetrics],
       [CoDel].  Queuing delay could be measured directly as the sojourn
       time (aka.  service time) of the queue, by storing a per-packet
       time-stamp as each packet is enqueued, and subtracting this from
       the system time when the packet is dequeued.  If time-stamping is
       not easy to introduce with certain hardware, queuing delay could
       be predicted indirectly by dividing the size of the queue by the
       predicted departure rate, which might be known precisely for some
       link technologies (see for example in DOCSIS PIE [RFC8034]).

       However, sojourn time is slow to detect bursts.  For instance, if
       a burst arrives at an empty queue, the sojourn time only fully
       measures the burst's delay when its last packet is dequeued, even
       though the queue has known the size of the burst since its last
       packet was enqueued - so it could have signalled congestion
       earlier.  To remedy this, each head packet can be marked when it
       is dequeued based on the expected delay of the tail packet behind
       it, as explained below, rather than based on the head packet's
       own delay due to the packets in front of it. [Heist21] identifies
       a specific scenario where bursty traffic significantly hits
       utilization of the L queue.  If this effect proves to be more
       widely applicable, using the delay behind the head could improve
       performance.

       The delay behind the head can be implemented by dividing the
       backlog at dequeue by the link rate or equivalently multiplying
       the backlog by the delay per unit of backlog.  The implementation
       details will depend on whether the link rate is known; if it is



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       not, a moving average of the delay per unit backlog can be
       maintained.  This delay consists of serialization as well as
       media acquisition for shared media.  So the details will depend
       strongly on the specific link technology, This approach should be
       less sensitive to timing errors and cost less in operations and
       memory than the otherwise equivalent 'scaled sojourn time'
       metric, which is the sojourn time of a packet scaled by the ratio
       of the queue sizes when the packet departed and
       arrived [SigQ-Dyn].

   b.  Line 2 of the dualpi2_enqueue() function (Figure 3) assumes an
       implementation where lq and cq share common buffer memory.  An
       alternative implementation could use separate buffers for each
       queue, in which case the arriving packet would have to be
       classified first to determine which buffer to check for available
       space.  The choice is a trade-off; a shared buffer can use less
       memory whereas separate buffers isolate the L4S queue from tail-
       drop due to large bursts of Classic traffic (e.g. a Classic Reno
       TCP during slow-start over a long RTT).

   c.  There has been some concern that using the step function of DCTCP
       for the Native L4S AQM requires end-systems to smooth the signal
       for an unnecessarily large number of round trips to ensure
       sufficient fidelity.  A ramp is no worse than a step in initial
       experiments with existing DCTCP.  Therefore, it is recommended
       that a ramp is configured in place of a step, which will allow
       congestion control algorithms to investigate faster smoothing
       algorithms.

       A ramp is more general that a step, because an operator can
       effectively turn the ramp into a step function, as used by DCTCP,
       by setting the range to zero.  There will not be a divide by zero
       problem at line 5 of Figure 5 because, if minTh is equal to
       maxTh, the condition for this ramp calculation cannot arise.

A.2.  Pass #2: Edge-Case Details

   This section takes a second pass through the pseudocode adding
   details of two edge-cases: low link rate and overload.  Figure 7
   repeats the dequeue function of Figure 4, but with details of both
   edge-cases added.  Similarly, Figure 8 repeats the core PI algorithm
   of Figure 6, but with overload details added.  The initialization,
   enqueue, L4S AQM and recur functions are unchanged.

   The link rate can be so low that it takes a single packet queue
   longer to serialize than the threshold delay at which ECN marking
   starts to be applied in the L queue.  Therefore, a minimum marking
   threshold parameter in units of packets rather than time is necessary



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   (Th_len, default 1 packet in line 19 of Figure 2) to ensure that the
   ramp does not trigger excessive marking on slow links.  Where an
   implementation knows the link rate, it can set up this minimum at the
   time it is configured.  For instance, it would divide 1 MTU by the
   link rate to convert it into a serialization time, then if the lower
   threshold of the Native L AQM ramp was lower than this serialization
   time, it could increase the thresholds to shift the bottom of the
   ramp to 2 MTU.  This is the approach used in DOCSIS [DOCSIS3.1],
   because the configured link rate is dedicated to the DualQ.

   The pseudocode given here applies where the link rate is unknown,
   which is more common for software implementations that might be
   deployed in scenarios where the link is shared with other queues.  In
   lines 5a to 5d in Figure 7 the native L4S marking probability, p'_L,
   is zeroed if the queue is only 1 packet (in the default
   configuration).

   Linux implementation note:

   *  In Linux, the check that the queue exceeds Th_len before marking
      with the native L4S AQM is actually at enqueue, not dequeue,
      otherwise it would exempt the last packet of a burst from being
      marked.  The result of the check is conveyed from enqueue to the
      dequeue function via a boolean in the packet metadata.

   Persistent overload is deemed to have occurred when Classic drop/
   marking probability reaches p_Cmax.  Above this point, the Classic
   drop probability is applied to both L and C queues, irrespective of
   whether any packet is ECN-capable.  ECT packets that are not dropped
   can still be ECN-marked.

   In line 10 of the initialization function (Figure 2), the maximum
   Classic drop probability p_Cmax = min(1/k^2, 1) or 1/4 for the
   default coupling factor k=2.  In practice, 25% has been found to be a
   good threshold to preserve fairness between ECN capable and non ECN
   capable traffic.  This protects the queues against both temporary
   overload from responsive flows and more persistent overload from any
   unresponsive traffic that falsely claims to be responsive to ECN.

   When the Classic ECN marking probability reaches the p_Cmax threshold
   (1/k^2), the marking probability coupled to the L4S queue, p_CL will
   always be 100% for any k (by equation (1) in Section 2).  So, for
   readability, the constant p_Lmax is defined as 1 in line 22 of the
   initialization function (Figure 2).  This is intended to ensure that
   the L4S queue starts to introduce dropping once ECN-marking saturates
   at 100% and can rise no further.  The 'Prague L4S'
   requirements [I-D.ietf-tsvwg-ecn-l4s-id] state that, when an L4S
   congestion control detects a drop, it falls back to a response that



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   coexists with 'Classic' Reno congestion control.  So it is correct
   that, when the L4S queue drops packets, it drops them proportional to
   p'^2, as if they are Classic packets.

   The two queues each test for overload in lines 4b and 12b of the
   dequeue function (Figure 7).  Lines 8c to 8g drop L4S packets with
   probability p'^2.  Lines 8h to 8i mark the remaining packets with
   probability p_CL.  Given p_Lmax = 1, all remaining packets will be
   marked because, to have reached the else block at line 8b, p_CL >= 1.

   Line 2a in the core PI algorithm (Figure 8) deals with overload of
   the L4S queue when there is little or no Classic traffic.  This is
   necessary, because the core PI algorithm maintains the appropriate
   drop probability to regulate overload, but it depends on the length
   of the Classic queue.  If there is little or no Classic queue the
   naive PI update function in Figure 6 would drop nothing, even if the
   L4S queue were overloaded - so tail drop would have to take over
   (lines 2 and 3 of Figure 3).

   Instead, line 2a of the full PI update function in Figure 8 ensures
   that the base PI AQM in line 3 is driven by whichever of the two
   queue delays is greater, but line 3 still always uses the same
   Classic target (default 15 ms).  If L queue delay is greater just
   because there is little or no Classic traffic, normally it will still
   be well below the base AQM target.  This is because L4S traffic is
   also governed by the shallow threshold of its own native AQM (lines 5
   and 6 of the dequeue algorithm in Figure 7).  So the base AQM will be
   driven to zero and not contribute.  However, if the L queue is
   overloaded by traffic that is unresponsive to its marking, the max()
   in line 2 enables the L queue to smoothly take over driving the base
   AQM into overload mode even if there is little or no Classic traffic.
   Then the base AQM will keep the L queue to the Classic target
   (default 15 ms) by shedding L packets.


















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   1:  dualpi2_dequeue(lq, cq, pkt) {     % Couples L4S & Classic queues
   2:    while ( lq.byt() + cq.byt() > 0 ) {
   3:      if ( scheduler() == lq ) {
   4a:       lq.dequeue(pkt)                             % L4S scheduled
   4b:       if ( p_CL < p_Lmax ) {      % Check for overload saturation
   5a:         if (lq.len()>Th_len)                   % >1 packet queued
   5b:           p'_L = laqm(lq.time())                    % Native LAQM
   5c:         else
   5d:           p'_L = 0                 % Suppress marking 1 pkt queue
   6:          p_L = max(p'_L, p_CL)                % Combining function
   7:          if ( recur(lq, p_L)                       %Linear marking
   8a:           mark(pkt)
   8b:       } else {                              % overload saturation
   8c:         if ( recur(lq, p_C) ) {          % probability p_C = p'^2
   8e:           drop(pkt)      % revert to Classic drop due to overload
   8f:           continue        % continue to the top of the while loop
   8g:         }
   8h:         if ( recur(lq, p_CL) )        % probability p_CL = k * p'
   8i:           mark(pkt)         % linear marking of remaining packets
   8j:       }
   9:      } else {
   10:       cq.dequeue(pkt)                         % Classic scheduled
   11:       if ( recur(cq, p_C) ) {            % probability p_C = p'^2
   12a:        if ( (ecn(pkt) == 0)                % ECN field = not-ECT
   12b:             OR (p_C >= p_Cmax) ) {       % Overload disables ECN
   13:           drop(pkt)                     % squared drop, redo loop
   14:           continue        % continue to the top of the while loop
   15:         }
   16:         mark(pkt)                                  % squared mark
   17:       }
   18:     }
   19:     return(pkt)                      % return the packet and stop
   20:   }
   21:   return(NULL)                             % no packet to dequeue
   22: }

       Figure 7: Example Dequeue Pseudocode for DualQ Coupled PI2 AQM
                      (Including Code for Edge-Cases)

   1:  dualpi2_update(lq, cq) {                % Update p' every Tupdate
   2a:   curq = max(cq.time(), lq.time())    % use greatest queuing time
   3:    p' = p' + alpha * (curq - target) + beta * (curq - prevq)
   4:    p_CL = p' * k  % Coupled L4S prob = base prob * coupling factor
   5:    p_C = p'^2                       % Classic prob = (base prob)^2
   6:    prevq = curq
   7:  }





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      Figure 8: Example PI-Update Pseudocode for DualQ Coupled PI2 AQM
                         (Including Overload Code)


   The choice of scheduler technology is critical to overload protection
   (see Section 4.2.2).

   *  A well-understood weighted scheduler such as weighted round-robin
      (WRR) is recommended.  As long as the scheduler weight for Classic
      is small (e.g. 1/16), its exact value is unimportant because it
      does not normally determine capacity shares.  The weight is only
      important to prevent unresponsive L4S traffic starving Classic
      traffic in the short term (see Section 4.2.2).  This is because
      capacity sharing between the queues is normally determined by the
      coupled congestion signal, which overrides the scheduler, by
      making L4S sources leave roughly equal per-flow capacity available
      for Classic flows.

   *  Alternatively, a time-shifted FIFO (TS-FIFO) could be used.  It
      works by selecting the head packet that has waited the longest,
      biased against the Classic traffic by a time-shift of tshift.  To
      implement time-shifted FIFO, the scheduler() function in line 3 of
      the dequeue code would simply be implemented as the scheduler()
      function at the bottom of Figure 10 in Appendix B.  For the public
      Internet a good value for tshift is 50ms.  For private networks
      with smaller diameter, about 4*target would be reasonable.  TS-
      FIFO is a very simple scheduler, but complexity might need to be
      added to address some deficiencies (which is why it is not
      recommended over WRR):

      -  TS-FIFO does not fully isolate latency in the L4S queue from
         uncontrolled bursts in the Classic queue;

      -  Using sojourn time for TS-FIFO is only appropriate if time-
         stamping of packets is feasible;

      -  Even if time-stamping is supported, the sojourn time of the
         head packet is always stale, so a more instantaneous measure of
         queue delay could be used (see Note a in Appendix A.1).

   *  A strict priority scheduler would be inappropriate as discussed in
      Section 4.2.2.









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Appendix B.  Example DualQ Coupled Curvy RED Algorithm

   As another example of a DualQ Coupled AQM algorithm, the pseudocode
   below gives the Curvy RED based algorithm.  Although the AQM was
   designed to be efficient in integer arithmetic, to aid understanding
   it is first given using floating point arithmetic (Figure 10).  Then,
   one possible optimization for integer arithmetic is given, also in
   pseudocode (Figure 11).  To aid comparison, the line numbers are kept
   in step between the two by using letter suffixes where the longer
   code needs extra lines.

B.1.  Curvy RED in Pseudocode

   The pseudocode manipulates three main structures of variables: the
   packet (pkt), the L4S queue (lq) and the Classic queue (cq) and
   consists of the following five functions:

   *  The initialization function cred_params_init(...) (Figure 2) that
      sets parameter defaults (the API for setting non-default values is
      omitted for brevity);

   *  The dequeue function cred_dequeue(lq, cq, pkt) (Figure 4);

   *  The scheduling function scheduler(), which selects between the
      head packets of the two queues.

   It also uses the following functions that are either shown elsewhere,
   or not shown in full here:

   *  The enqueue function, which is identical to that used for DualPI2,
      dualpi2_enqueue(lq, cq, pkt) in Figure 3;

   *  mark(pkt) and drop(pkt) for ECN-marking and dropping a packet;

   *  cq.byt() or lq.byt() returns the current length (aka. backlog) of
      the relevant queue in bytes;

   *  cq.time() or lq.time() returns the current queuing delay of the
      relevant queue in units of time (see Note a in Appendix A.1).












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   Because Curvy RED was evaluated before DualPI2, certain improvements
   introduced for DualPI2 were not evaluated for Curvy RED.  In the
   pseudocode below, the straightforward improvements have been added on
   the assumption they will provide similar benefits, but that has not
   been proven experimentally.  They are: i) a conditional priority
   scheduler instead of strict priority ii) a time-based threshold for
   the native L4S AQM; iii) ECN support for the Classic AQM.  A recent
   evaluation has proved that a minimum ECN-marking threshold (minTh)
   greatly improves performance, so this is also included in the
   pseudocode.

   Overload protection has not been added to the Curvy RED pseudocode
   below so as not to detract from the main features.  It would be added
   in exactly the same way as in Appendix A.2 for the DualPI2
   pseudocode.  The native L4S AQM uses a step threshold, but a ramp
   like that described for DualPI2 could be used instead.  The scheduler
   uses the simple TS-FIFO algorithm, but it could be replaced with WRR.

   The Curvy RED algorithm has not been maintained or evaluated to the
   same degree as the DualPI2 algorithm.  In initial experiments on
   broadband access links ranging from 4 Mb/s to 200 Mb/s with base RTTs
   from 5 ms to 100 ms, Curvy RED achieved good results with the default
   parameters in Figure 9.

   The parameters are categorised by whether they relate to the Classic
   AQM, the L4S AQM or the framework coupling them together.  Constants
   and variables derived from these parameters are also included at the
   end of each category.  These are the raw input parameters for the
   algorithm.  A configuration front-end could accept more meaningful
   parameters (e.g. RTT_max and RTT_typ) and convert them into these raw
   parameters, as has been done for DualPI2 in Appendix A.  Where
   necessary, parameters are explained further in the walk-through of
   the pseudocode below.


















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   1:  cred_params_init(...) {            % Set input parameter defaults
   2:    % DualQ Coupled framework parameters
   3:    limit = MAX_LINK_RATE * 250 ms               % Dual buffer size
   4:    k' = 1                        % Coupling factor as a power of 2
   5:    tshift = 50 ms                % Time shift of TS-FIFO scheduler
   6:    % Constants derived from Classic AQM parameters
   7:    k = 2^k'                    % Coupling factor from Equation (1)
   6:
   7:    % Classic AQM parameters
   8:    g_C = 5            % EWMA smoothing parameter as a power of 1/2
   9:    S_C = -1          % Classic ramp scaling factor as a power of 2
   10:   minTh = 500 ms    % No Classic drop/mark below this queue delay
   11:   % Constants derived from Classic AQM parameters
   12:   gamma = 2^(-g_C)                     % EWMA smoothing parameter
   13:   range_C = 2^S_C                         % Range of Classic ramp
   14:
   15:   % L4S AQM parameters
   16:   T = 1 ms             % Queue delay threshold for native L4S AQM
   17:   % Constants derived from above parameters
   18:   S_L = S_C - k'        % L4S ramp scaling factor as a power of 2
   19:   range_L = 2^S_L                             % Range of L4S ramp
   20: }

    Figure 9: Example Header Pseudocode for DualQ Coupled Curvy RED AQM



























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   1:  cred_dequeue(lq, cq, pkt) {       % Couples L4S & Classic queues
   2:    while ( lq.byt() + cq.byt() > 0 ) {
   3:      if ( scheduler() == lq ) {
   4:        lq.dequeue(pkt)                            % L4S scheduled
   5a:       p_CL = (Q_C - minTh) / range_L
   5b:       if (  ( lq.time() > T )
   5c:          OR ( p_CL > maxrand(U) ) )
   6:          mark(pkt)
   7:      } else {
   8:        cq.dequeue(pkt)                        % Classic scheduled
   9a:       Q_C = gamma * cq.time() + (1-gamma) * Q_C % Classic Q EWMA
   10a:      sqrt_p_C = (Q_C - minTh) / range_C
   10b:      if ( sqrt_p_C > maxrand(2*U) ) {
   11:         if ( (ecn(pkt) == 0)  {            % ECN field = not-ECT
   12:           drop(pkt)                    % Squared drop, redo loop
   13:           continue       % continue to the top of the while loop
   14:         }
   15:         mark(pkt)
   16:       }
   17:     }
   18:     return(pkt)                % return the packet and stop here
   19:   }
   20:   return(NULL)                            % no packet to dequeue
   21: }

   22: maxrand(u) {                % return the max of u random numbers
   23:   maxr=0
   24:   while (u-- > 0)
   25:     maxr = max(maxr, rand())                   % 0 <= rand() < 1
   26:   return(maxr)
   27: }

   28: scheduler() {
   29:   if ( lq.time() + tshift >= cq.time() )
   30:     return lq;
   31:   else
   32:     return cq;
   33: }

   Figure 10: Example Dequeue Pseudocode for DualQ Coupled Curvy RED AQM

   The dequeue pseudocode (Figure 10) is repeatedly called whenever the
   lower layer is ready to forward a packet.  It schedules one packet
   for dequeuing (or zero if the queue is empty) then returns control to
   the caller, so that it does not block while that packet is being
   forwarded.  While making this dequeue decision, it also makes the
   necessary AQM decisions on dropping or marking.  The alternative of
   applying the AQMs at enqueue would shift some processing from the



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   critical time when each packet is dequeued.  However, it would also
   add a whole queue of delay to the control signals, making the control
   loop very sloppy.

   The code is written assuming the AQMs are applied on dequeue (Note
   1).  All the dequeue code is contained within a large while loop so
   that if it decides to drop a packet, it will continue until it
   selects a packet to schedule.  If both queues are empty, the routine
   returns NULL at line 20.  Line 3 of the dequeue pseudocode is where
   the conditional priority scheduler chooses between the L4S queue (lq)
   and the Classic queue (cq).  The time-shifted FIFO scheduler is shown
   at lines 28-33, which would be suitable if simplicity is paramount
   (see Note 2).

   Within each queue, the decision whether to forward, drop or mark is
   taken as follows (to simplify the explanation, it is assumed that
   U=1):

   L4S:  If the test at line 3 determines there is an L4S packet to
      dequeue, the tests at lines 5b and 5c determine whether to mark
      it.  The first is a simple test of whether the L4S queue delay
      (lq.time()) is greater than a step threshold T (Note 3).  The
      second test is similar to the random ECN marking in RED, but with
      the following differences: i) marking depends on queuing time, not
      bytes, in order to scale for any link rate without being
      reconfigured; ii) marking of the L4S queue depends on a logical OR
      of two tests; one against its own queuing time and one against the
      queuing time of the _other_ (Classic) queue; iii) the tests are
      against the instantaneous queuing time of the L4S queue, but a
      smoothed average of the other (Classic) queue; iv) the queue is
      compared with the maximum of U random numbers (but if U=1, this is
      the same as the single random number used in RED).

      Specifically, in line 5a the coupled marking probability p_CL is
      set to the amount by which the averaged Classic queueing delay Q_C
      exceeds the minimum queuing delay threshold (minTh) all divided by
      the L4S scaling parameter range_L. range_L represents the queuing
      delay (in seconds) added to minTh at which marking probability
      would hit 100%. Then in line 5c (if U=1) the result is compared
      with a uniformly distributed random number between 0 and 1, which
      ensures that, over range_L, marking probability will linearly
      increase with queueing time.

   Classic:  If the scheduler at line 3 chooses to dequeue a Classic
      packet and jumps to line 7, the test at line 10b determines
      whether to drop or mark it.  But before that, line 9a updates Q_C,
      which is an exponentially weighted moving average (Note 4) of the
      queuing time of the Classic queue, where cq.time() is the current



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      instantaneous queueing time of the packet at the head of the
      Classic queue (zero if empty) and gamma is the EWMA constant
      (default 1/32, see line 12 of the initialization function).

      Lines 10a and 10b implement the Classic AQM.  In line 10a the
      averaged queuing time Q_C is divided by the Classic scaling
      parameter range_C, in the same way that queuing time was scaled
      for L4S marking.  This scaled queuing time will be squared to
      compute Classic drop probability so, before it is squared, it is
      effectively the square root of the drop probability, hence it is
      given the variable name sqrt_p_C.  The squaring is done by
      comparing it with the maximum out of two random numbers (assuming
      U=1).  Comparing it with the maximum out of two is the same as the
      logical `AND' of two tests, which ensures drop probability rises
      with the square of queuing time.

   The AQM functions in each queue (lines 5c & 10b) are two cases of a
   new generalization of RED called Curvy RED, motivated as follows.
   When the performance of this AQM was compared with FQ-CoDel and PIE,
   their goal of holding queuing delay to a fixed target seemed
   misguided [CRED_Insights].  As the number of flows increases, if the
   AQM does not allow host congestion controllers to increase queuing
   delay, it has to introduce abnormally high levels of loss.  Then loss
   rather than queuing becomes the dominant cause of delay for short
   flows, due to timeouts and tail losses.

   Curvy RED constrains delay with a softened target that allows some
   increase in delay as load increases.  This is achieved by increasing
   drop probability on a convex curve relative to queue growth (the
   square curve in the Classic queue, if U=1).  Like RED, the curve hugs
   the zero axis while the queue is shallow.  Then, as load increases,
   it introduces a growing barrier to higher delay.  But, unlike RED, it
   requires only two parameters, not three.  The disadvantage of Curvy
   RED (compared to a PI controller for example) is that it is not
   adapted to a wide range of RTTs.  Curvy RED can be used as is when
   the RTT range to be supported is limited, otherwise an adaptation
   mechanism is needed.

   From our limited experiments with Curvy RED so far, recommended
   values of these parameters are: S_C = -1; g_C = 5; T = 5 * MTU at the
   link rate (about 1ms at 60Mb/s) for the range of base RTTs typical on
   the public Internet.  [CRED_Insights] explains why these parameters
   are applicable whatever rate link this AQM implementation is deployed
   on and how the parameters would need to be adjusted for a scenario
   with a different range of RTTs (e.g. a data centre).  The setting of
   k depends on policy (see Section 2.5 and Appendix C.2 respectively
   for its recommended setting and guidance on alternatives).




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   There is also a cUrviness parameter, U, which is a small positive
   integer.  It is likely to take the same hard-coded value for all
   implementations, once experiments have determined a good value.  Only
   U=1 has been used in experiments so far, but results might be even
   better with U=2 or higher.

   Notes:

   1.  The alternative of applying the AQMs at enqueue would shift some
       processing from the critical time when each packet is dequeued.
       However, it would also add a whole queue of delay to the control
       signals, making the control loop sloppier (for a typical RTT it
       would double the Classic queue's feedback delay).  On a platform
       where packet timestamping is feasible, e.g. Linux, it is also
       easiest to apply the AQMs at dequeue because that is where
       queuing time is also measured.

   2.  WRR better isolates the L4S queue from large delay bursts in the
       Classic queue, but it is slightly less simple than TS-FIFO.  If
       WRR were used, a low default Classic weight (e.g. 1/16) would
       need to be configured in place of the time shift in line 5 of the
       initialization function (Figure 9).

   3.  A step function is shown for simplicity.  A ramp function (see
       Figure 5 and the discussion around it in Appendix A.1) is
       recommended, because it is more general than a step and has the
       potential to enable L4S congestion controls to converge more
       rapidly.

   4.  An EWMA is only one possible way to filter bursts; other more
       adaptive smoothing methods could be valid and it might be
       appropriate to decrease the EWMA faster than it increases,
       e.g. by using the minimum of the smoothed and instantaneous queue
       delays, min(Q_C, qc.time()).

B.2.  Efficient Implementation of Curvy RED

   Although code optimization depends on the platform, the following
   notes explain where the design of Curvy RED was particularly
   motivated by efficient implementation.











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   The Classic AQM at line 10b calls maxrand(2*U), which gives twice as
   much curviness as the call to maxrand(U) in the marking function at
   line 5c.  This is the trick that implements the square rule in
   equation (1) (Section 2.1).  This is based on the fact that, given a
   number X from 1 to 6, the probability that two dice throws will both
   be less than X is the square of the probability that one throw will
   be less than X.  So, when U=1, the L4S marking function is linear and
   the Classic dropping function is squared.  If U=2, L4S would be a
   square function and Classic would be quartic.  And so on.

   The maxrand(u) function in lines 16-21 simply generates u random
   numbers and returns the maximum.  Typically, maxrand(u) could be run
   in parallel out of band.  For instance, if U=1, the Classic queue
   would require the maximum of two random numbers.  So, instead of
   calling maxrand(2*U) in-band, the maximum of every pair of values
   from a pseudorandom number generator could be generated out-of-band,
   and held in a buffer ready for the Classic queue to consume.

   1:  cred_dequeue(lq, cq, pkt) {       % Couples L4S & Classic queues
   2:    while ( lq.byt() + cq.byt() > 0 ) {
   3:      if ( scheduler() == lq ) {
   4:        lq.dequeue(pkt)                            % L4S scheduled
   5:        if ((lq.time() > T) OR (Q_C >> (S_L-2) > maxrand(U)))
   6:          mark(pkt)
   7:      } else {
   8:        cq.dequeue(pkt)                        % Classic scheduled
   9:        Q_C += (qc.ns() - Q_C) >> g_C             % Classic Q EWMA
   10:       if ( (Q_C >> (S_C-2) ) > maxrand(2*U) ) {
   11:         if ( (ecn(pkt) == 0)  {            % ECN field = not-ECT
   12:           drop(pkt)                    % Squared drop, redo loop
   13:           continue       % continue to the top of the while loop
   14:         }
   15:         mark(pkt)
   16:       }
   17:     }
   18:     return(pkt)                % return the packet and stop here
   19:   }
   20:   return(NULL)                            % no packet to dequeue
   21: }

     Figure 11: Optimised Example Dequeue Pseudocode for DualQ Coupled
                        AQM using Integer Arithmetic

   The two ranges, range_L and range_C are expressed as powers of 2 so
   that division can be implemented as a right bit-shift (>>) in lines 5
   and 10 of the integer variant of the pseudocode (Figure 11).





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   For the integer variant of the pseudocode, an integer version of the
   rand() function used at line 25 of the maxrand(function) in Figure 10
   would be arranged to return an integer in the range 0 <= maxrand() <
   2^32 (not shown).  This would scale up all the floating point
   probabilities in the range [0,1] by 2^32.

   Queuing delays are also scaled up by 2^32, but in two stages: i) In
   line 9 queuing time qc.ns() is returned in integer nanoseconds,
   making the value about 2^30 times larger than when the units were
   seconds, ii) then in lines 5 and 10 an adjustment of -2 to the right
   bit-shift multiplies the result by 2^2, to complete the scaling by
   2^32.

   In line 8 of the initialization function, the EWMA constant gamma is
   represented as an integer power of 2, g_C, so that in line 9 of the
   integer code the division needed to weight the moving average can be
   implemented by a right bit-shift (>> g_C).

Appendix C.  Choice of Coupling Factor, k


C.1.  RTT-Dependence

   Where Classic flows compete for the same capacity, their relative
   flow rates depend not only on the congestion probability, but also on
   their end-to-end RTT (= base RTT + queue delay).  The rates of
   Reno [RFC5681] flows competing over an AQM are roughly inversely
   proportional to their RTTs.  Cubic exhibits similar RTT-dependence
   when in Reno-compatibility mode, but it is less RTT-dependent
   otherwise.

   Until the early experiments with the DualQ Coupled AQM, the
   importance of the reasonably large Classic queue in mitigating RTT-
   dependence when the base RTT is low had not been appreciated.
   Appendix A.1.6 of the L4S ECN protocol [I-D.ietf-tsvwg-ecn-l4s-id]
   uses numerical examples to explain why bloated buffers had concealed
   the RTT-dependence of Classic congestion controls before that time.
   Then it explains why, the more that queuing delays have reduced, the
   more that RTT-dependence has surfaced as a potential starvation
   problem for long RTT flows, when competing against very short RTT
   flows.

   Given that congestion control on end-systems is voluntary, there is
   no reason why it has to be voluntarily RTT-dependent.  The RTT-
   dependence of existing Classic traffic cannot be 'undeployed'.
   Therefore, [I-D.ietf-tsvwg-ecn-l4s-id] requires L4S congestion
   controls to be significantly less RTT-dependent than the standard
   Reno congestion control [RFC5681], at least at low RTT.  Then RTT-



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   dependence ought to be no worse than it is with appropriately sized
   Classic buffers.  Following this approach means there is no need for
   network devices to address RTT-dependence, although there would be no
   harm if they did, which per-flow queuing inherently does.

C.2.  Guidance on Controlling Throughput Equivalence

   The coupling factor, k, determines the balance between L4S and
   Classic flow rates (see Section 2.5.2.1 and equation (1)).

   For the public Internet, a coupling factor of k=2 is recommended, and
   justified below.  For scenarios other than the public Internet, a
   good coupling factor can be derived by plugging the appropriate
   numbers into the same working.

   To summarize the maths below, from equation (7) it can be seen that
   choosing k=1.64 would theoretically make L4S throughput roughly the
   same as Classic, _if their actual end-to-end RTTs were the same_.
   However, even if the base RTTs are the same, the actual RTTs are
   unlikely to be the same, because Classic traffic needs a fairly large
   queue to avoid under-utilization and excess drop.  Whereas L4S does
   not.

   Therefore, to determine the appropriate coupling factor policy, the
   operator needs to decide at what base RTT it wants L4S and Classic
   flows to have roughly equal throughput, once the effect of the
   additional Classic queue on Classic throughput has been taken into
   account.  With this approach, a network operator can determine a good
   coupling factor without knowing the precise L4S algorithm for
   reducing RTT-dependence - or even in the absence of any algorithm.

   The following additional terminology will be used, with appropriate
   subscripts:

   r:  Packet rate [pkt/s]

   R:  RTT [s/round]

   p:  ECN marking probability []

   On the Classic side, we consider Reno as the most sensitive and
   therefore worst-case Classic congestion control.  We will also
   consider Cubic in its Reno-friendly mode ('CReno'), as the most
   prevalent congestion control, according to the references and
   analysis in [PI2param].  In either case, the Classic packet rate in
   steady state is given by the well-known square root formula for Reno
   congestion control:




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       r_C = 1.22 / (R_C * p_C^0.5)          (5)

   On the L4S side, we consider the Prague congestion
   control [I-D.briscoe-iccrg-prague-congestion-control] as the
   reference for steady-state dependence on congestion.  Prague conforms
   to the same equation as DCTCP, but we do not use the equation derived
   in the DCTCP paper, which is only appropriate for step marking.  The
   coupled marking, p_CL, is the appropriate one when considering
   throughput equivalence with Classic flows.  Unlike step marking,
   coupled markings are inherently spaced out, so we use the formula for
   DCTCP packet rate with probabilistic marking derived in Appendix A of
   [PI2].  We use the equation without RTT-independence enabled, which
   will be explained later.

       r_L = 2 / (R_L * p_CL)                (6)

   For packet rate equivalence, we equate the two packet rates and
   rearrange into the same form as Equation (1), so the two can be
   equated and simplified to produce a formula for a theoretical
   coupling factor, which we shall call k*:

       r_c = r_L
   =>  p_C = (p_CL/1.64 * R_L/R_C)^2

       p_C = ( p_CL / k )^2                  (1)

       k* = 1.64 * (R_C / R_L)               (7)

   We say that this coupling factor is theoretical, because it is in
   terms of two RTTs, which raises two practical questions: i) for
   multiple flows with different RTTs, the RTT for each traffic class
   would have to be derived from the RTTs of all the flows in that class
   (actually the harmonic mean would be needed); ii) a network node
   cannot easily know the RTT of the flows anyway.

   RTT-dependence is caused by window-based congestion control, so it
   ought to be reversed there, not in the network.  Therefore, we use a
   fixed coupling factor in the network, and reduce RTT-dependence in
   L4S senders.  We cannot expect Classic senders to all be updated to
   reduce their RTT-dependence.  But solely addressing the problem in
   L4S senders at least makes RTT-dependence no worse - not just between
   L4S senders, but also between L4S and Classic senders.

   Traditionally, throughput equivalence has been defined for flows
   under comparable conditions, including with the same base
   RTT [RFC2914].  So if we assume the same base RTT, R_b, for
   comparable flows, we can put both R_C and R_L in terms of R_b.




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   We can approximate the L4S RTT to be hardly greater than the base
   RTT, i.e. R_L ~= R_b.  And we can replace R_C with (R_b + q_C), where
   the Classic queue, q_C, depends on the target queue delay that the
   operator has configured for the Classic AQM.

   Taking PI2 as an example Classic AQM, it seems that we could just
   take R_C = R_b + target (recommended 15 ms by default in
   Appendix A.1).  However, target is roughly the queue depth reached by
   the tips of the sawteeth of a congestion control, not the average
   [PI2param].  That is R_max = R_b + target.

   The position of the average in relation to the max depends on the
   amplitude and geometry of the sawteeth.  We consider two examples:
   Reno [RFC5681], as the most sensitive worst-case, and Cubic [RFC8312]
   in its Reno-friendly mode ('CReno') as the most prevalent congestion
   control algorithm on the Internet according to the references in
   [PI2param].  Both are AIMD, so we will generalize using b as the
   multiplicative decrease factor (b_r = 0.5 for Reno, b_c = 0.7 for
   CReno).  Then:

     R_C  = (R_max + b*R_max) / 2
          = R_max * (1+b)/2

   R_reno = 0.75 * (R_b + target);     R_creno = 0.85 * (R_b + target).
                                                                    (8)

   Plugging all this into equation (7) we get a fixed coupling factor
   for each:

   k_reno = 1.64*0.75*(R_b+target)/R_b
          = 1.23*(1 + target/R_b);     k_creno = 1.39 * (1 + target/R_b)

   An operator can then choose the base RTT at which it wants throughput
   to be equivalent.  For instance, if we recommend that the operator
   chooses R_b = 25 ms, as a typical base RTT between Internet users and
   CDNs [PI2param], then these coupling factors become:

   k_reno = 1.23 * (1 + 15/25)        k_creno  = 1.39 * (1 + 15/25)
          = 1.97                               = 2.22
          ~= 2                                 ~= 2                  (9)

   The approximation is relevant to any of the above example DualQ
   Coupled algorithms, which use a coupling factor that is an integer
   power of 2 to aid efficient implementation.  It also fits best to the
   worst case (Reno).






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   To check the outcome of this coupling factor, we can express the
   ratio of L4S to Classic throughput by substituting from their rate
   equations (5) and (6), then also substituting for p_C in terms of
   p_CL, using equation (1) with k=2 as just determined for the
   Internet:

   r_L / r_C  = 2 (R_C * p_C^0.5) / 1.22 (R_L * p_CL)
              = (R_C * p_CL) / (1.22 * R_L * p_CL)
              = R_C / (1.22 * R_L)                                  (10)

   As an example, we can then consider single competing CReno and Prague
   flows, by expressing both their RTTs in (10) in terms of their base
   RTTs, R_bC and R_bL.  So R_C is replaced by equation (8) for CReno.
   And R_L is replaced by the max() function below, which represents the
   effective RTT of the current Prague congestion
   control [I-D.briscoe-iccrg-prague-congestion-control] in its
   (default) RTT-independent mode, because it sets a floor to the
   effective RTT that it uses for additive increase:

             ~= 0.85 * (R_bC + target) / (1.22 * max(R_bL, R_typ))
             ~= (R_bC + target) / (1.4 * max(R_bL, R_typ))

   It can be seen that, for base RTTs below target (15 ms), both the
   numerator and the denominator plateau, which has the desired effect
   of limiting RTT-dependence.

   At the start of the above derivations, an explanation was promised
   for why the L4S throughput equation in equation (6) did not need to
   model RTT-independence.  This is because we only use one point - at
   the typical base RTT where the operator chooses to calculate the
   coupling factor.  Then, throughput equivalence will at least hold at
   that chosen point.  Nonetheless, assuming Prague senders implement
   RTT-independence over a range of RTTs below this, the throughput
   equivalence will then extend over that range as well.

   Congestion control designers can choose different ways to reduce RTT-
   dependence.  And each operator can make a policy choice to decide on
   a different base RTT, and therefore a different k, at which it wants
   throughput equivalence.  Nonetheless, for the Internet, it makes
   sense to choose what is believed to be the typical RTT most users
   experience, because a Classic AQM's target queuing delay is also
   derived from a typical RTT for the Internet.

   As a non-Internet example, for localized traffic from a particular
   ISP's data centre, using the measured RTTs, it was calculated that a
   value of k = 8 would achieve throughput equivalence, and experiments
   verified the formula very closely.




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   But, for a typical mix of RTTs across the general Internet, a value
   of k=2 is recommended as a good workable compromise.

Acknowledgements

   Thanks to Anil Agarwal, Sowmini Varadhan, Gabi Bracha, Nicolas Kuhn,
   Greg Skinner, Tom Henderson, David Pullen, Mirja Kuehlewind, Gorry
   Fairhurst, Pete Heist, Ermin Sakic and Martin Duke for detailed
   review comments particularly of the appendices and suggestions on how
   to make the explanations clearer.  Thanks also to Tom Henderson for
   insights on the choice of schedulers and queue delay measurement
   techniques.  And thanks to the area reviewers Christer Holmberg, Lars
   Eggert and Roman Danyliw.

   The early contributions of Koen De Schepper, Bob Briscoe, Olga
   Bondarenko and Inton Tsang were part-funded by the European Community
   under its Seventh Framework Programme through the Reducing Internet
   Transport Latency (RITE) project (ICT-317700).  Contributions of Koen
   De Schepper and Olivier Tilmans were also part-funded by the 5Growth
   and DAEMON EU H2020 projects.  Bob Briscoe's contribution was also
   part-funded by the Comcast Innovation Fund and the Research Council
   of Norway through the TimeIn project.  The views expressed here are
   solely those of the authors.

Contributors

   The following contributed implementations and evaluations that
   validated and helped to improve this specification:

      Olga Albisser <olga@albisser.org> of Simula Research Lab, Norway
      (Olga Bondarenko during early drafts) implemented the prototype
      DualPI2 AQM for Linux with Koen De Schepper and conducted
      extensive evaluations as well as implementing the live performance
      visualization GUI [L4Sdemo16].

      Olivier Tilmans <olivier.tilmans@nokia-bell-labs.com> of Nokia
      Bell Labs, Belgium prepared and maintains the Linux implementation
      of DualPI2 for upstreaming.

      Shravya K.S. wrote a model for the ns-3 simulator based on the -01
      version of this Internet-Draft.  Based on this initial work, Tom
      Henderson <tomh@tomh.org> updated that earlier model and created a
      model for the DualQ variant specified as part of the Low Latency
      DOCSIS specification, as well as conducting extensive evaluations.

      Ing Jyh (Inton) Tsang of Nokia, Belgium built the End-to-End Data
      Centre to the Home broadband testbed on which DualQ Coupled AQM
      implementations were tested.



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Authors' Addresses

   Koen De Schepper
   Nokia Bell Labs
   Antwerp
   Belgium
   Email: koen.de_schepper@nokia.com
   URI:   https://www.bell-labs.com/about/researcher-profiles/
   koende_schepper/


   Bob Briscoe (editor)
   Independent
   United Kingdom
   Email: ietf@bobbriscoe.net
   URI:   https://bobbriscoe.net/


   Greg White
   CableLabs
   Louisville, CO,
   United States of America
   Email: G.White@CableLabs.com




























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