Transport Area working group (tsvwg)                      K. De Schepper
Internet-Draft                                           Nokia Bell Labs
Intended status: Experimental                            B. Briscoe, Ed.
Expires: September 6, 2018                                     CableLabs
                                                           O. Bondarenko
                                                     Simula Research Lab
                                                                I. Tsang
                                                           March 5, 2018

  DualQ Coupled AQMs for Low Latency, Low Loss and Scalable Throughput


   Data Centre TCP (DCTCP) was designed to provide predictably low
   queuing latency, near-zero loss, and throughput scalability using
   explicit congestion notification (ECN) and an extremely simple
   marking behaviour on switches.  However, DCTCP does not co-exist with
   existing TCP traffic---DCTCP is so aggressive that existing TCP
   algorithms approach starvation.  So, until now, DCTCP could only be
   deployed where a clean-slate environment could be arranged, such as
   in private data centres.  This specification defines `DualQ Coupled
   Active Queue Management (AQM)' to allow scalable congestion controls
   like DCTCP to safely co-exist with classic Internet traffic.  The
   Coupled AQM ensures that a flow runs at about the same rate whether
   it uses DCTCP or TCP Reno/Cubic, but without inspecting transport
   layer flow identifiers.  When tested in a residential broadband
   setting, DCTCP achieved sub-millisecond average queuing delay and
   zero congestion loss under a wide range of mixes of DCTCP and
   `Classic' broadband Internet traffic, without compromising the
   performance of the Classic traffic.  The solution also reduces
   network complexity and eliminates network configuration.

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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   Internet-Drafts are draft documents valid for a maximum of six months
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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 September 6, 2018.

Copyright Notice

   Copyright (c) 2018 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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   publication of this document.  Please review these documents
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   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
     1.1.  Problem and Scope . . . . . . . . . . . . . . . . . . . .   3
     1.2.  Terminology . . . . . . . . . . . . . . . . . . . . . . .   5
     1.3.  Features  . . . . . . . . . . . . . . . . . . . . . . . .   6
   2.  DualQ Coupled AQM . . . . . . . . . . . . . . . . . . . . . .   7
     2.1.  Coupled AQM . . . . . . . . . . . . . . . . . . . . . . .   7
     2.2.  Dual Queue  . . . . . . . . . . . . . . . . . . . . . . .   8
     2.3.  Traffic Classification  . . . . . . . . . . . . . . . . .   8
     2.4.  Overall DualQ Coupled AQM Structure . . . . . . . . . . .   9
     2.5.  Normative Requirements for a DualQ Coupled AQM  . . . . .  11
       2.5.1.  Functional Requirements . . . . . . . . . . . . . . .  11
       2.5.2.  Management Requirements . . . . . . . . . . . . . . .  12
   3.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  13
   4.  Security Considerations . . . . . . . . . . . . . . . . . . .  13
     4.1.  Overload Handling . . . . . . . . . . . . . . . . . . . .  13
       4.1.1.  Avoiding Classic Starvation: Sacrifice L4S Throughput
               or Delay? . . . . . . . . . . . . . . . . . . . . . .  14
       4.1.2.  Congestion Signal Saturation: Introduce L4S Drop or
               Delay?  . . . . . . . . . . . . . . . . . . . . . . .  15
       4.1.3.  Protecting against Unresponsive ECN-Capable Traffic .  16
   5.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  16
   6.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  16
     6.1.  Normative References  . . . . . . . . . . . . . . . . . .  17
     6.2.  Informative References  . . . . . . . . . . . . . . . . .  17
   Appendix A.  Example DualQ Coupled PI2 Algorithm  . . . . . . . .  20
     A.1.  Pass #1: Core Concepts  . . . . . . . . . . . . . . . . .  20

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     A.2.  Pass #2: Overload Details . . . . . . . . . . . . . . . .  26
   Appendix B.  Example DualQ Coupled Curvy RED Algorithm  . . . . .  28
   Appendix C.  Guidance on Controlling Throughput Equivalence . . .  34
   Appendix D.  Open Issues  . . . . . . . . . . . . . . . . . . . .  35
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  36

1.  Introduction

1.1.  Problem and Scope

   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.  In the developed world, further
   increases in access network bit-rate offer diminishing returns,
   whereas latency is still a multi-faceted problem.  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
   component of latency.

   The Diffserv architecture provides Expedited Forwarding [RFC3246], so
   that low latency traffic can jump the queue of other traffic.
   However, on access links dedicated to individual sites (homes, small
   enterprises or mobile devices), often all traffic at any one time
   will be latency-sensitive and, if all the traffic on a link is marked
   as EF, Diffserv cannot reduce the delay of any of it.  In contrast,
   the Low Latency Low Loss Scalable throughput (L4S) approach removes
   the causes of any unnecessary queuing delay.

   The bufferbloat project has shown that excessively-large buffering
   (`bufferbloat') has been introducing significantly more delay than
   the underlying propagation time.  These delays appear only
   intermittently--only when a capacity-seeking (e.g.  TCP) flow is long
   enough for the queue to fill the buffer, making every packet in other
   flows sharing the buffer sit through the queue.

   Active queue management (AQM) was originally developed to solve this
   problem (and others).  Unlike Diffserv, which gives low latency to
   some traffic at the expense of others, AQM controls latency for _all_
   traffic in a class.  In general, AQMs introduce an increasing level
   of discard from the buffer the longer the queue persists above a
   shallow threshold.  This gives sufficient signals to capacity-seeking
   (aka. greedy) flows to keep the buffer empty for its intended
   purpose: absorbing bursts.  However, RED [RFC2309] and other
   algorithms from the 1990s were sensitive to their configuration and
   hard to set correctly.  So, AQM was not widely deployed.

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   More recent state-of-the-art AQMs, e.g. fq_CoDel [RFC8290],
   PIE [RFC8033], Adaptive RED [ARED01], are easier to configure,
   because they define the queuing threshold in time not bytes, so it is
   invariant for different link rates.  However, no matter how good the
   AQM, the sawtoothing rate of TCP will either cause queuing delay to
   vary or cause the link to be under-utilized.  Even with a perfectly
   tuned AQM, the additional queuing delay will be of the same order as
   the underlying speed-of-light delay across the network.  Flow-queuing
   can isolate one flow from another, but it cannot isolate a TCP flow
   from the delay variations it inflicts on itself, and it has other
   problems - it overrides the flow rate decisions of variable rate
   video applications, it does not recognise the flows within IPSec VPN
   tunnels and it is relatively expensive to implement.

   It seems that further changes to the network alone will now yield
   diminishing returns.  Data Centre TCP (DCTCP [RFC8257]) teaches us
   that a small but radical change to TCP is needed to cut two major
   outstanding causes of queuing delay variability:

   1.  the `sawtooth' varying rate of TCP itself;

   2.  the smoothing delay deliberately introduced into AQMs to permit
       bursts without triggering losses.

   The former causes a flow's round trip time (RTT) to vary from about 1
   to 2 times the base RTT between the machines in question.  The latter
   delays the system's response to change by a worst-case
   (transcontinental) RTT, which could be hundreds of times the actual
   RTT of typical traffic from localized CDNs.

   Latency is not our only concern:

   3.  It was known when TCP was first developed that it would not scale
       to high bandwidth-delay products.

   Given regular broadband bit-rates over WAN distances are
   already [RFC3649] beyond the scaling range of `classic' TCP Reno,
   `less unscalable' Cubic [I-D.ietf-tcpm-cubic] and
   Compound [I-D.sridharan-tcpm-ctcp] variants of TCP have been
   successfully deployed.  However, these are now approaching their
   scaling limits.  Unfortunately, fully scalable TCPs such as DCTCP
   cause `classic' TCP to starve itself, which is why they have been
   confined to private data centres or research testbeds (until now).

   This document specifies a `DualQ Coupled AQM' extension that solves
   the problem of coexistence between scalable and classic flows,
   without having to inspect flow identifiers.  The AQM is not like
   flow-queuing approaches [RFC8290] that classify packets by flow

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   identifier into numerous separate queues in order to isolate sparse
   flows from the higher latency in the queues assigned to heavier flow.
   In contrast, the AQM exploits the behaviour of scalable congestion
   controls like DCTCP so that every packet in every flow sharing the
   queue for DCTCP-like traffic can be served with very low latency.

   This AQM extension can be combined with any single queue AQM that
   generates a statistical or deterministic mark/drop probability driven
   by the queue dynamics.  In many cases it 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.

   The overall L4S architecture is described in
   [I-D.ietf-tsvwg-l4s-arch].  The supporting papers [PI2] and [DCttH15]
   give the full rationale for the AQM's design, both discursively and
   in more precise mathematical form.

1.2.  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   document are to be interpreted as described in [RFC2119].  In this
   document, these words will appear with that interpretation only when
   in ALL CAPS.  Lower case uses of these words are not to be
   interpreted as carrying RFC-2119 significance.

   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 (denoted by subscript C):  The `Classic' service is intended
      for all the behaviours that currently co-exist with TCP Reno (TCP
      Cubic, Compound, SCTP, etc).

   Low-Latency, Low-Loss and Scalable (L4S, denoted by subscript L):
      The `L4S' service is intended for a set of congestion controls
      with scalable properties such as DCTCP (e.g.
      Relentless [Mathis09]).

   Either service can cope with a proportion of unresponsive or less-
   responsive traffic as well (e.g.  DNS, VoIP, etc), just as a single
   queue AQM can.  The DualQ Coupled AQM behaviour is similar to a
   single FIFO queue with respect to unresponsive and overload traffic.

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1.3.  Features

   The AQM couples marking and/or dropping across the two queues such
   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 to give stunningly 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.  Typical
   experiments used base round trip delays up to 100ms between the data
   centre and home network, and large amounts of background traffic in
   both queues.  For every L4S packet, the AQM kept the average queuing
   delay below 1ms (or 2 packets if serialization delay is bigger for
   slow links), and no losses at all were introduced by the AQM.
   Details of the extensive experiments will be made available [PI2]

   Subjective testing was also conducted using a demanding panoramic
   interactive video application run over a stack with DCTCP enabled and
   deployed on the testbed.  Each user could pan or zoom their own high
   definition (HD) sub-window of a larger video scene from a football
   match.  Even though the user was also downloading large amounts of
   L4S and Classic data, latency was so low that the picture appeared to
   stick to their finger on the touchpad (all the L4S data achieved the
   same ultra-low latency).  With an alternative AQM, the video
   noticeably lagged behind the finger gestures.

   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 like DCTCP 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, and the latency of
   Classic traffic does not suffer when a proportion of the traffic is
   L4S.  The two queues are only necessary because DCTCP-like flows
   cannot keep latency predictably low and keep utilization high if they
   are mixed with legacy TCP flows,

   The experiments used the Linux implementation of DCTCP that is
   deployed in private data centres, without any modification despite
   its known deficiencies.  Nonetheless, certain modifications will be
   necessary before DCTCP is safe to use on the Internet, which are
   recorded in Appendix A of [I-D.ietf-tsvwg-ecn-l4s-id].  However, the
   focus of this specification is to get the network service in place.
   Then, without any management intervention, applications can exploit
   it by migrating to scalable controls like DCTCP, which can then

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   evolve _while_ their benefits are being enjoyed by everyone on the

2.  DualQ Coupled AQM

   There are two main aspects to the approach:

   o  the Coupled AQM that addresses throughput equivalence between
      Classic (e.g.  Reno, Cubic) flows and L4S (e.g.  DCTCP) flows

   o  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 TCP's congestion window, cwnd, and its drop probability, p.
   To a first order approximation, cwnd of TCP Reno is inversely
   proportional to the square root of p.

   TCP Cubic implements a Reno-compatibility mode, which is the only
   relevant mode for typical RTTs under 20ms as long as the throughput
   of a single flow is less than about 500Mb/s.  Therefore it can be
   assumed that Cubic traffic behaves similarly to Reno (but with a
   slightly different constant of proportionality), and the term
   'Classic' will be used for the collection of Reno-friendly traffic
   including Cubic in Reno mode.

   The 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 'L4S' traffic will be used for all similar

   In order to make a DCTCP flow run at roughly the same rate as a Reno
   TCP flow (all other factors being equal), the drop or marking
   probability for Classic traffic, p_C has to be distinct from the
   marking probability for L4S traffic, p_L (in contrast to RFC3168
   which requires them to be the same).  It is necessary to make the
   Classic drop probability p_C proportional to the square of the L4S
   marking probability p_L.  This makes the Reno flow rate roughly equal
   the DCTCP flow rate, because it squares the square root of p_C in the
   Reno rate equation to make it proportional to the straight p_L in the
   DCTCP rate equation.

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   Stating this as a formula, the relation between Classic drop
   probability, p_C, and L4S marking probability, p_L needs to take the

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

   where k is the constant of proportionality.

2.2.  Dual Queue

   Classic traffic typically builds a large queue to prevent under-
   utilization.  Therefore a separate queue is provided for L4S traffic,
   and it is scheduled with priority over Classic.  Priority is
   conditional to prevent starvation of Classic traffic.

   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).  The algorithm achieves this
   without having to inspect flow identifiers.

2.3.  Traffic Classification

   Both the Coupled AQM and DualQ mechanisms need an identifier to
   distinguish L and C packets.  A separate draft
   [I-D.ietf-tsvwg-ecn-l4s-id] recommends using the ECT(1) codepoint of
   the ECN field as this identifier, having assessed various
   alternatives.  An additional process document has proved necessary to
   make the ECT(1) codepoint available for experimentation [RFC8311].

   In addition (not instead), other identifiers could be used to
   classify certain additional packet types into the L queue, that are
   deemed not to risk harming the L4S service.  For instance addresses
   of specific applications or hosts (see [I-D.ietf-tsvwg-ecn-l4s-id]),
   specific Diffserv codepoints such as EF (Expedited Forwarding), CS5
   (Application Signalling) and Voice-Admit service classes (see
   [I-D.briscoe-tsvwg-l4s-diffserv]) or certain protocols (e.g.  ARP,

   Note that the DualQ Coupled AQM only reads these classifiers, it MUST
   NOT re-mark or alter these identifiers (except for marking the ECN
   field with the CE codepoint - with increasing frequency to indicate
   increasing congestion).

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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.

   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 dropping or marking (p_L and p_C).  Nonetheless, the
   AQM for Classic traffic is 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

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

   This allows p_L to be coupled to p_C by marking L4S traffic
   proportionately to the intermediate output from the first stage.
   Specifically, the output of the base AQM is coupled across to the L
   queue in proportion to the output of the base AQM:

       p_CL = k*p',                         (3)

   where k is the constant coupling factor (see Appendix C) and p_CL is
   the output from the coupling between the C queue and the L queue.

   It can be seen in the following that these two transformations of p'
   implement the required coupling given in equation (1) earlier.
   Substituting for p' from equation (3) into (2):

      p_C = ( p_CL / k )^2.

   The actual L4S marking probability p_L is the maximum of the coupled
   output (p_CL) and the output of a native L4S AQM (p'L), shown as
   '(MAX)' in the schematic.  While the output of the Native L4S AQM is
   high (p'L > p_CL) it will dominate the way L traffic is marked.  When
   the native L4S AQM output is lower, the way L traffic is marked will
   be driven by the coupling, that is p_L = p_CL.  So, whenever the
   coupling is needed, as required from equation (1):

      p_C = ( p_L / k )^2.

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                                  | |    ,------.
                        L4S 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 |queue |===>|/mark |
                           __|______|    `------'

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

                   Figure 1: DualQ Coupled AQM Schematic

   After the AQMs have applied their dropping or marking, the scheduler
   forwards their packets to the link, giving priority to L4S traffic.
   Priority has to be conditional in some way (see Section 4.1).  Simple
   strict priority is inappropriate otherwise it could lead the L4S
   queue to starve the Classic queue.  For example, consider the case
   where a continually busy L4S queue blocks a DNS request in the
   Classic queue, arbitrarily delaying the start of a new Classic flow.

   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
   we call it just PI2 [PI2].  PI2 is a principled simplification of PIE
   that is both more responsive and more stable in the face of
   dynamically varying load.

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   Curvy RED is derived from RED [RFC2309], but its configuration
   parameters are insensitive to link rate and it requires less
   operations per packet.  However, DualPI2 is more responsive and
   stable over a wider range of RTTs than Curvy RED.  As a consequence,
   DualPI2 has attracted more development attention than Curvy RED,
   leaving the Curvy RED design incomplete and not so fully evaluated.

   Both AQMs regulate their queue in units of time not 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.

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 used for each queue.

2.5.1.  Functional Requirements

   In the Dual Queue, L4S packets MUST be given priority over Classic,
   although priority MUST be bounded in order not to starve Classic

   All L4S traffic MUST be ECN-capable.  Some Classic traffic might also
   be ECN-capable.

   Whatever identifier is used for L4S experiments,
   [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
   prevent starvation of Classic traffic by 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)."  In
   other words, in any DualQ Coupled AQM, the power to which p_L is
   raised in Eqn. (1) MUST be 2.  The term 'likelihood' is used to allow
   for marking and dropping to be either probabilistic or deterministic.

   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).
   [I-D.ietf-tsvwg-ecn-l4s-id] says, "The constant of proportionality

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   (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 congestion control
   (Reno) [RFC5681] and other so-called TCP-friendly controls, such as
   TCP Cubic in its TCP-friendly mode.

   {ToDo: The requirements for scalable congestion controls on the
   Internet (termed the TCP Prague requirements)
   [I-D.ietf-tsvwg-ecn-l4s-id] are not necessarily final.  If the
   aggressiveness of DCTCP is not defined as the benchmark for scalable
   controls on the Internet, the recommended value of k will also be
   subject to change.}

   The choice of k is a matter of operator policy, and operators MAY
   choose a different value using Table 1 and the guidelines in
   Appendix C.

   If multiple 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 users.
   However, on the public Internet, access network operators typically
   isolate customers from each other with some form of layer-2
   multiplexing (TDM in DOCSIS, CDMA in 3G) or L3 scheduling (WRR in
   DSL), rather than relying on TCP 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 L4S, and it will not
   affect small flows.

2.5.2.  Management Requirements

   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:

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

   o  Expected typical RTT (a parameter for typical or target queuing
      delay in each queue might be configurable instead);

   o  Expected maximum RTT (a stability parameter that depends on
      maximum RTT might be configurable instead);

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   o  Coupling factor, k;

   o  The limit to the conditional priority of L4S (scheduler-dependent,
      e.g. the scheduler weight for WRR, or the time-shift for time-
      shifted FIFO);

   o  The maximum Classic ECN marking probability, p_Cmax, before
      switching over to drop.

   An experimental DualQ Coupled AQM SHOULD allow the operator to
   monitor the following operational statistics:

   o  Bits forwarded (total and per queue per sample interval), from
      which utilization can be calculated

   o  Q delay (per queue over sample interval)

   o  Total packets arriving, enqueued and dequeued (per queue per
      sample interval)

   o  ECN packets marked, non-ECN packets dropped, ECN packets dropped
      (per queue per sample interval), from which marking and dropping
      probabilities can be calculated

   o  Time and duration of each overload event.

   The type of statistics produced for variables like Q delay (mean,
   percentiles, etc.) will depend on implementation constraints.

3.  IANA Considerations

   This specification contains no IANA considerations.

4.  Security Considerations

4.1.  Overload Handling

   Where the interests of users or flows might conflict, it could be
   necessary to police traffic to isolate any harm to the performance of
   individual flows.  However it is hard to avoid unintended side-
   effects with policing, and in a trusted environment policing is not
   necessary.  Therefore per-flow policing needs to be separable from a
   basic AQM, as an option under policy control.

   However, a basic DualQ AQM does at least need to handle overload.  A
   useful objective would be for the overload behaviour of the DualQ AQM
   to be at least no worse than a single queue AQM.  However, a trade-
   off needs to be made between complexity and the risk of either

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   traffic class harming the other.  In each of the following three
   subsections, an overload issue specific to the DualQ is described,
   followed by proposed solution(s).

   Under overload the higher priority L4S service will have to sacrifice
   some aspect of its performance.  Alternative solutions are provided
   below that each relax a different factor: e.g. throughput, delay,
   drop.  Some of these choices might need to be determined by operator
   policy or by the developer, rather than by the IETF. {ToDo: Reach
   consensus on which it is to be in each case.}

4.1.1.  Avoiding Classic Starvation: Sacrifice L4S Throughput or Delay?

   Priority of L4S is required to be conditional to avoid total
   throughput starvation of Classic by heavy L4S traffic.  This raises
   the question of whether to sacrifice L4S throughput or L4S delay (or
   some other policy) to mitigate starvation of Classic:

   Sacrifice L4S throughput:   By using weighted round robin as the
      conditional priority scheduler, the L4S service can sacrifice some
      throughput during overload to guarantee a minimum throughput
      service for Classic traffic.  The scheduling weight of the Classic
      queue should be small (e.g. 1/16).  Then, in most traffic
      scenarios the scheduler will not interfere and it will not need to
      - the coupling mechanism and the end-systems will share out the
      capacity across both queues as if it were a single pool.  However,
      because the congestion coupling only applies in one direction
      (from C to L), 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 cases where the ratio of L4S to Classic flows (e.g. 19:1) is
      greater 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 a large part of the capacity set aside for L4S, using
      WRR could significantly reduce the capacity left for any
      responsive L4S flows.

   Sacrifice L4S Delay:  To control milder overload of responsive
      traffic, particularly when close to the maximum congestion signal,
      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

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      "time-shifted FIFO" (see the Modifier Earliest Deadline First
      (MEDF) scheduler of [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.

   The example implementation in Appendix A can implement either policy.

4.1.2.  Congestion Signal Saturation: Introduce L4S Drop or Delay?

   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 one AQM first saturates to a
   probability of 100% leaving no room to push back the load any harder.
   If k>1, L4S will saturate first, but saturation can be caused by
   unresponsive traffic in either queue.

   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 TCP-like flow
   that is normally responsive, but above a certain congestion level it
   will not be able to reduce its congestion window below the minimum of
   2 segments, effectively becoming unresponsive.  (Note that L4S
   traffic ought to remain responsive below a window of 2 segments (see

   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 tail drop too):

   Drop on Saturation:  Saturation can be avoided by setting a maximum
      threshold for 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.

   Delay on Saturation:  When L4S marking saturates, instead of
      switching to drop, the drop and marking probabilities 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

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      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 applies only the "drop on
   saturation" policy.

4.1.3.  Protecting against Unresponsive ECN-Capable Traffic

   Unresponsive traffic has a greater advantage if it is also ECN-
   capable.  The advantage is undetectable at normal low levels of drop/
   marking, but it becomes significant with the higher levels of drop/
   marking typical during overload.  This is an issue whether the ECN-
   capable traffic is L4S or Classic.

   This raises the question of whether and when to switch off ECN
   marking and use solely drop instead, as required by both Section 7 of
   [RFC3168] and Section 4.2.1 of [RFC7567].

   Experiments with the DualPI2 AQM (Appendix A) have shown that
   introducing 'drop on saturation' at 100% L4S marking addresses this
   problem with unresponsive ECN as well as addressing the saturation
   problem.  It leaves only a small range of congestion levels where
   unresponsive traffic gains any advantage from using the ECN
   capability, and the advantage is hardly detectable [DualQ-Test].

5.  Acknowledgements

   Thanks to Anil Agarwal, Sowmini Varadhan's and Gabi Bracha for
   detailed review comments particularly of the appendices and
   suggestions on how to make our explanation clearer.  Thanks also to
   Greg White and Tom Henderson for insights on the choice of schedulers
   and queue delay measurement techniques.

   The authors' contributions were originally part-funded by the
   European Community under its Seventh Framework Programme through the
   Reducing Internet Transport Latency (RITE) project (ICT-317700).  Bob
   Briscoe's contribution was also part-funded by the Research Council
   of Norway through the TimeIn project.  The views expressed here are
   solely those of the authors.

6.  References

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6.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,

6.2.  Informative References

   [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,

   [CoDel]    Nichols, K. and V. Jacobson, "Controlling Queue Delay",
              ACM Queue 10(5), May 2012,

              Briscoe, B., "Insights from Curvy RED (Random Early
              Detection)", BT Technical Report TR-TUB8-2015-003, July

   [DCttH15]  De Schepper, K., Bondarenko, O., Briscoe, B., and I.
              Tsang, "`Data Centre to the Home': Ultra-Low Latency for
              All", 2015, <

              (Under submission)

              Steen, H., "Destruction Testing: Ultra-Low Delay using
              Dual Queue Coupled Active Queue Management", Masters
              Thesis, Dept of Informatics, Uni Oslo , May 2017.

              Briscoe, B., "Interactions between Low Latency, Low Loss,
              Scalable Throughput (L4S) and Differentiated Services",
              draft-briscoe-tsvwg-l4s-diffserv-00 (work in progress),
              March 2018.

              Rhee, I., Xu, L., Ha, S., Zimmermann, A., Eggert, L., and
              R. Scheffenegger, "CUBIC for Fast Long-Distance Networks",
              draft-ietf-tcpm-cubic-07 (work in progress), November

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              Schepper, K., Briscoe, B., and I. Tsang, "Identifying
              Modified Explicit Congestion Notification (ECN) Semantics
              for Ultra-Low Queuing Delay", draft-ietf-tsvwg-ecn-l4s-
              id-02 (work in progress), March 2018.

              Briscoe, B., Schepper, K., and M. Bagnulo, "Low Latency,
              Low Loss, Scalable Throughput (L4S) Internet Service:
              Architecture", draft-ietf-tsvwg-l4s-arch-02 (work in
              progress), March 2018.

              Sridharan, M., Tan, K., Bansal, D., and D. Thaler,
              "Compound TCP: A New TCP Congestion Control for High-Speed
              and Long Distance Networks", draft-sridharan-tcpm-ctcp-02
              (work in progress), November 2008.

              Mathis, M., "Relentless Congestion Control", PFLDNeT'09 ,
              May 2009, <

   [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.

   [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,

              (To appear)

   [RFC0970]  Nagle, J., "On Packet Switches With Infinite Storage",
              RFC 970, DOI 10.17487/RFC0970, December 1985,

   [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,

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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,

   [RFC3246]  Davie, B., Charny, A., Bennet, J., Benson, K., Le Boudec,
              J., Courtney, W., Davari, S., Firoiu, V., and D.
              Stiliadis, "An Expedited Forwarding PHB (Per-Hop
              Behavior)", RFC 3246, DOI 10.17487/RFC3246, March 2002,

   [RFC3649]  Floyd, S., "HighSpeed TCP for Large Congestion Windows",
              RFC 3649, DOI 10.17487/RFC3649, December 2003,

   [RFC5681]  Allman, M., Paxson, V., and E. Blanton, "TCP Congestion
              Control", RFC 5681, DOI 10.17487/RFC5681, September 2009,

   [RFC7567]  Baker, F., Ed. and G. Fairhurst, Ed., "IETF
              Recommendations Regarding Active Queue Management",
              BCP 197, RFC 7567, DOI 10.17487/RFC7567, July 2015,

   [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,

   [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, <>.

   [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, <>.

   [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,

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   [RFC8311]  Black, D., "Relaxing Restrictions on Explicit Congestion
              Notification (ECN) Experimentation", RFC 8311,
              DOI 10.17487/RFC8311, January 2018,

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 step threshold (in units of queuing
   time) is used for the Native L4S AQM, but a ramp is also described as
   an alternative.  And the PI2 algorithm [PI2] is used for the Classic
   AQM.  PI2 is an improved variant of the PIE AQM [RFC8033].

   We will introduce the pseudocode in two passes.  The first pass
   explains the core concepts, deferring handling of 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.

   A full open source implementation for Linux is available at:

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 four functions:

   o  initialization code (Figure 2) that sets parameter defaults (the
      API for setting non-default values is omitted for brevity)

   o  enqueue code (Figure 3)

   o  dequeue code (Figure 4)

   o  code to regularly update the base probability (p) used in the
      dequeue code (Figure 5).

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

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

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

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   o  cq.time() or lq.time() returns the current queuing delay (aka.
      sojourn time or service time) of the relevant queue in units of

   Queuing delay could be measured directly 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 [RFC8034]).

   In our 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.  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

   1:  dualpi2_params_init(...) {         % Set input parameter defaults
   2:    % PI2 AQM parameters
   3:    target = 15 ms              % PI AQM Classic queue delay target
   4:    Tupdate = 16 ms            % PI Classic queue sampling interval
   5:    alpha = 10 Hz^2                              % PI integral gain
   6:    beta = 100 Hz^2                          % PI proportional gain
   7:    p_Cmax = 1/4                       % Max Classic drop/mark prob
   8:    % Derived PI2 AQM variables
   9:    alpha_U = alpha *Tupdate % PI integral gain per update interval
   10:   beta_U = beta * Tupdate  % PI prop'nal gain per update interval
   12:   % DualQ Coupled framework parameters
   13:   k = 2                                         % Coupling factor
   14:   % scheduler weight or equival't parameter (scheduler-dependent)
   15:   limit = MAX_LINK_RATE * 250 ms               % Dual buffer size
   17:   % L4S AQM parameters
   18:   T_time = 1 ms                   % L4S marking threshold in time
   19:   T_len = 2 * MTU            % Min L4S marking threshold in bytes
   20:   % Derived L4S AQM variables
   21:   p_Lmax = min(k*sqrt(p_Cmax), 1)          % Max L4S marking prob
   22: }

       Figure 2: Example Header Pseudocode for DualQ Coupled PI2 AQM

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   The overall goal of the code is to maintain the base probability (p),
   which is an internal variable from which the marking and dropping
   probabilities for L4S and Classic traffic (p_L and p_C) are derived.
   The variable named p in the pseudocode and in this walk-through is
   the same as p' (p-prime) in Section 2.4.  The probabilities p_L and
   p_C are derived in lines 3, 4 and 5 of the dualpi2_update() function
   (Figure 5) then used in the dualpi2_dequeue() function (Figure 4).
   The code walk-through below builds up to explaining that part of the
   code eventually, but it starts from packet arrival.

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

      Figure 3: Example Enqueue Pseudocode for DualQ Coupled PI2 AQM

   1:  dualpi2_dequeue(lq, cq, pkt) {     % Couples L4S & Classic queues
   2:    while ( lq.len() + cq.len() > 0 )
   3:      if ( scheduler() == lq ) {
   4:        lq.dequeue(pkt)                      % Scheduler chooses lq
   5:        if ( ((lq.time() > T_time)              % step marking ...
   6:              AND (lq.len() > T_len))
   7:            OR (p_CL > rand()) )             % ...or linear marking
   8:          mark(pkt)
   9:      } else {
   10:       cq.dequeue(pkt)                      % Scheduler chooses cq
   11:       if ( p_C > rand() ) {               % 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: }

      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.  Note that the
   limit is deliberately tested before enqueue to avoid any bias against
   larger packets (so the actual buffer has to be one MTU larger than
   limit).  If limit is not exceeded, the packet will be classified and
   enqueued to the Classic or L4S queue dependent on the least
   significant bit of the ECN field in the IP header (line 5).  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 dequeue pseudocode (Figure 4) schedules one packet for dequeuing
   (or zero if the queue is empty).  It also makes all the AQM decisions
   on dropping and 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 very sloppy.

   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).

   o  If an L4S packet is scheduled, lines 5 to 8 mark the packet if
      either the L4S threshold (T_time) is exceeded, or if a random
      marking decision is drawn according to p_CL (maintained by the
      dualpi2_update() function discussed below).  This logical 'OR' on
      a per-packet basis implements the max() function shown in Figure 1
      to couple the outputs of the two AQMs together.  The L4S threshold
      is usually in units of time (default T_time = 1 ms).  However, on
      slow links the packet serialization time can approach the
      threshold T_time, so line 6 sets a floor of T_len (=2 MTU) to the
      threshold, otherwise marking is always too frequent on slow links.

   o  If a Classic packet is scheduled, lines 10 to 17 drop or mark the
      packet based on the squared probability p_C.

   There is some concern that using a step function for the Native L4S
   AQM requires end-systems to smooth the signal for a lot longer -
   until its fidelity is sufficient.  The latency benefits of a ramp are
   being investigated as a simple alternative to the step.  This ramp
   would be similar to the RED algorithm, with the following

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   o  The min and max of the ramp are defined in units of queuing delay,
      not bytes, so that configuration remains invariant as the queue
      departure rate varies.

   o  It uses instantaneous queueing delay without smoothing (smoothing
      is done in the end-systems).

   o  Determinism is being experimented with instead of randomness; to
      reduce the delay necessary to smooth out the noise of randomness
      from the signal.  For each packet, the algorithm would accumulate
      p'_L in a counter and mark the packet that took the counter over
      1, then subtract 1 from the counter and continue.

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

   This ramp algorithm would require two configuration parameters (min
   and max threshold in units of queuing time), in contrast to the
   single parameter of a step.

   1:  dualpi2_update(lq, cq, target) {         % Update p every Tupdate
   2:    curq = cq.time()  % use queuing time of first-in Classic packet
   3:    p = p + alpha_U * (curq - target) + beta_U * (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:  }

     Figure 5: Example PI-Update Pseudocode for DualQ Coupled PI2 AQM

   The base probability (p) is kept up to date by the core PI algorithm
   in Figure 5, which is 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 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' - see [RFC8033]); 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

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   while the first is the _I_ntegral of the load (so it removes any
   standing queue in excess of the target).

   The two 'gain factors' in line 3, alpha_U and beta_U, respectively
   weight how strongly each of these elements ((a) and (b)) alters p.
   They are in units of 'per second of delay' or Hz, because they
   transform differences in queueing delay into changes in probability.

   alpha_U and beta_U are derived from the input parameters alpha and
   beta (see lines 5 and 6 of Figure 2).  These recommended values of
   alpha and beta come from the stability analysis in [PI2] so that the
   AQM can change p as fast as possible in response to changes in load
   without over-compensating and therefore causing oscillations in the

   alpha and beta determine how much p ought to change if it was updated
   every second.  It is best to update p as frequently as possible, but
   the update interval (Tupdate) will probably be constrained by
   hardware performance.  For link rates from 4 - 200 Mb/s, we found
   Tupdate=16ms (as recommended in [RFC8033]) is sufficient.  However
   small the chosen value of Tupdate, p should change by the same amount
   per second, but in finer more frequent steps.  So the gain factors
   used for updating p in Figure 5 need to be scaled by (Tupdate/1s),
   which is done in lines 9 and 10 of Figure 2).  The suffix '_U'
   represents 'per update time' (Tupdate).

   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 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, which is necessary to ensure that
   Classic TCP and DCTCP controls have the same stability.  So, if alpha
   is 10 Hz^2, the effective gain factor for the L4S queue is k*alpha,
   which is 20 Hz^2 with the default coupling factor of k=2.

   Unlike in PIE [RFC8033], alpha_U and beta_U do not need to be tuned
   every Tupdate dependent on p.  Instead, in PI2, alpha_U and beta_U
   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.

   {ToDo: Scaling beta with Tupdate and scaling both alpha & beta with

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A.2.  Pass #2: Overload Details

   Figure 6 repeats the dequeue function of Figure 4, but with overload
   details added.  Similarly Figure 7 repeats the core PI algorithm of
   Figure 5 with overload details added.  The initialization and enqueue
   functions are unchanged.

   In line 7 of the initialization function (Figure 2), the default
   maximum Classic drop probability p_Cmax = 1/4 or 25%. This is the
   point at which it is deemed that the Classic queue has become
   persistently overloaded, so it switches to using solely drop, even
   for ECN-capable packets.  This protects the queue against any
   unresponsive traffic that falsely claims that it is responsive to ECN
   marking, as required by [RFC3168] and [RFC7567].

   Line 21 of the initialization function translates this into a maximum
   L4S marking probability (p_Lmax) by rearranging Equation (1).  With a
   coupling factor of k=2 (the default) or greater, this translates to a
   maximum L4S marking probability of 1 (or 100%).  This is intended to
   ensure that the L4S queue starts to introduce dropping once marking
   saturates and can rise no further.  The 'TCP Prague' 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 coexists
   with 'Classic' TCP.  So it is correct that the L4S queue drops
   packets proportional to p^2, as if they are Classic packets.

   Both these switch-overs are triggered by the tests for overload
   introduced in lines 4b and 12b of the dequeue function (Figure 6).
   Lines 8c to 8g drop L4S packets with probability p^2.  Lines 8h to 8i
   mark the remaining packets with probability p_CL.

   Lines 2c to 2d in the core PI algorithm (Figure 7) deal with overload
   of the L4S queue when there is 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 no Classic queue the naive
   algorithm in Figure 5 drops nothing, even if the L4S queue is
   overloaded - so tail drop would have to take over (lines 3 and 4 of
   Figure 3).

   If the test at line 2a finds that the Classic queue is empty, line 2d
   measures the current queue delay using the L4S queue instead.  While
   the L4S queue is not overloaded, its delay will always be tiny
   compared to the target Classic queue delay.  So p_L will be driven to
   zero, and the L4S queue will naturally be governed solely by
   threshold marking (lines 5 and 6 of the dequeue algorithm in
   Figure 6).  But, if unresponsive L4S source(s) cause overload, the
   DualQ transitions smoothly to L4S marking based on the PI algorithm.

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   And as overload increases, it naturally transitions from marking to
   dropping by the switch-over mechanism already described.

   1:  dualpi2_dequeue(lq, cq) { % Couples L4S & Classic queues, lq & cq
   2:    while ( lq.len() + cq.len() > 0 )
   3:      if ( scheduler() == lq ) {
   4a:       lq.dequeue(pkt)
   4b:       if ( p_CL < p_Lmax ) {      % Check for overload saturation
   5:          if ( ((lq.time() > T_time)             % step marking ...
   6:                AND (lq.len > T_len))
   7:              OR (p_CL > rand()) )           % ...or linear marking
   8a:            mark(pkt)
   8b:       } else {                              % overload saturation
   8c:         if ( p_C > rand() ) {             % 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 ( p_CL > rand() )           % probability p_CL = k * p
   8i:           mark(pkt)         % linear marking of remaining packets
   8j:       }
   9:      } else {
   10:       cq.dequeue(pkt)
   11:       if ( p_C > rand() ) {               % 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 6: Example Dequeue Pseudocode for DualQ Coupled PI2 AQM
             (Including Integer Arithmetic and Overload Code)

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   1:  dualpi2_update(lq, cq, target) {         % Update p every Tupdate
   2a:   if ( cq.len() > 0 )
   2b:     curq = cq.time() %use queuing time of first-in Classic packet
   2c:   else                                      % Classic queue empty
   2d:     curq = lq.time()    % use queuing time of first-in L4S packet
   3:    p = p + alpha_U * (curq - target) + beta_U * (curq - prevq)
   4:    p_CL = p * k           % L4S prob = base prob * coupling factor
   5:    p_C = p^2                        % Classic prob = (base prob)^2
   6:    prevq = curq
   7:  }

     Figure 7: Example PI-Update Pseudocode for DualQ Coupled PI2 AQM
                         (Including Overload Code)

   The choice of scheduler technology is critical to overload protection
   (see Section 4.1).

   o  A well-understood weighted scheduler such as weighted round robin
      (WRR) is recommended.  The scheduler weight for Classic should be
      low, e.g. 1/16.

   o  Alternatively, a time-shifted FIFO could be used.  This is a very
      simple scheduler, but it does not fully isolate latency in the L4S
      queue from uncontrolled bursts in the Classic queue.  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 "if (scheduler() == lq )" test in
      line 3 of the dequeue code would simply be replaced by "if (
      lq.time() + tshift >= cq.time() )".  For the public Internet a
      good value for tshift is 50ms.  For private networks with smaller
      diameter, about 4*target would be reasonable.

   o  A strict priority scheduler would be inappropriate, because it
      would starve Classic if L4S was overloaded.

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 we used and tested.
   Although we designed the AQM to be efficient in integer arithmetic,
   to aid understanding it is first given using real-number arithmetic.
   Then, one possible optimization for integer arithmetic is given, also
   in pseudocode.  To aid comparison, the line numbers are kept in step
   between the two by using letter suffixes where the longer code needs
   extra lines.

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   1:  dualq_dequeue(lq, cq) {  % Couples L4S & Classic queues, lq & cq
   2:    if ( lq.dequeue(pkt) ) {
   3a:     p_L = cq.sec() / 2^S_L
   3b:     if ( lq.byt() > T )
   3c:       mark(pkt)
   3d:     elif ( p_L > maxrand(U) )
   4:        mark(pkt)
   5:      return(pkt)                % return the packet and stop here
   6:    }
   7:    while ( cq.dequeue(pkt) ) {
   8a:     alpha = 2^(-f_C)
   8b:     Q_C = alpha * pkt.sec() + (1-alpha)* Q_C    % Classic Q EWMA
   9a:     sqrt_p_C = Q_C / 2^S_C
   9b:     if ( sqrt_p_C > maxrand(2*U) )
   10:       drop(pkt)                        % Squared drop, redo loop
   11:     else
   12:       return(pkt)              % return the packet and stop here
   13:   }
   14:   return(NULL)                           % no packet to dequeue
   15: }

   16: maxrand(u) {                % return the max of u random numbers
   17:     maxr=0
   18:     while (u-- > 0)
   19:         maxr = max(maxr, rand())               % 0 <= rand() < 1
   20:     return(maxr)
   21: }

   Figure 8: Example Dequeue Pseudocode for DualQ Coupled Curvy RED AQM

   Packet classification code is not shown, as it is no different from
   Figure 3.  Potential classification schemes are discussed in
   Section 2.3.  The Curvy RED algorithm has not been maintained to the
   same degree as the DualPI2 algorithm.  Some ideas used in DualPI2
   would need to be translated into Curvy RED, such as i) the
   conditional priority scheduler instead of strict priority ii) the
   time-based L4S threshold; iii) turning off ECN as overload
   protection; iv) Classic ECN support.  These are not shown in the
   Curvy RED pseudocode, but would need to be implemented for
   production. {ToDo}

   At the outer level, the structure of dualq_dequeue() implements
   strict priority scheduling.  The code is written assuming the AQM is
   applied on dequeue (Note 1) . Every time dualq_dequeue() is called,
   the if-block in lines 2-6 determines whether there is an L4S packet
   to dequeue by calling lq.dequeue(pkt), and otherwise the while-block
   in lines 7-13 determines whether there is a Classic packet to
   dequeue, by calling cq.dequeue(pkt).  (Note 2)

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   In the lower priority Classic queue, a while loop is used so that, if
   the AQM determines that a classic packet should be dropped, it
   continues to test for classic packets deciding whether to drop each
   until it actually forwards one.  Thus, every call to dualq_dequeue()
   returns one packet if at least one is present in either queue,
   otherwise it returns NULL at line 14.  (Note 3)

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

   L4S:  If the test at line 2 determines there is an L4S packet to
      dequeue, the tests at lines 3a and 3c determine whether to mark
      it.  The first is a simple test of whether the L4S queue (lq.byt()
      in bytes) is greater than a step threshold T in bytes (Note 4).
      The second test is similar to the random ECN marking in RED, but
      with the following differences: i) the marking function does not
      start with a plateau of zero marking until a minimum threshold,
      rather the marking probability starts to increase as soon as the
      queue is positive; ii) marking depends on queuing time, not bytes,
      in order to scale for any link rate without being reconfigured;
      iii) marking of the L4S queue does not depend on itself, it
      depends on the queuing time of the _other_ (Classic) queue, where
      cq.sec() is the queuing time of the packet at the head of the
      Classic queue (zero if empty); iv) marking depends on the
      instantaneous queuing time (of the other Classic queue), not a
      smoothed average; v) 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 3a the marking probability p_L is set to the
      Classic queueing time qc.sec() in seconds divided by the L4S
      scaling parameter 2^S_L, which represents the queuing time (in
      seconds) at which marking probability would hit 100%. Then in line
      3d (if U=1) the result is compared with a uniformly distributed
      random number between 0 and 1, which ensures that marking
      probability will linearly increase with queueing time.  The
      scaling parameter is expressed as a power of 2 so that division
      can be implemented as a right bit-shift (>>) in line 3 of the
      integer variant of the pseudocode (Figure 9).

   Classic:  If the test at line 7 determines that there is at least one
      Classic packet to dequeue, the test at line 9b determines whether
      to drop it.  But before that, line 8b updates Q_C, which is an
      exponentially weighted moving average (Note 5) of the queuing time
      in the Classic queue, where pkt.sec() is the instantaneous
      queueing time of the current Classic packet and alpha is the EWMA
      constant for the classic queue.  In line 8a, alpha is represented
      as an integer power of 2, so that in line 8 of the integer code

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      the division needed to weight the moving average can be
      implemented by a right bit-shift (>> f_C).

      Lines 9a and 9b implement the drop function.  In line 9a the
      averaged queuing time Q_C is divided by the Classic scaling
      parameter 2^S_C, in the same way that queuing time was scaled for
      L4S marking.  This scaled queuing time is given the variable name
      sqrt_p_C because it will be squared to compute Classic drop
      probability, so before it is squared it is effectively the square
      root of the drop probability.  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 (Note 6).  Again, the scaling
      parameter is expressed as a power of 2 so that division can be
      implemented as a right bit-shift in line 9 of the integer

   The marking/dropping functions in each queue (lines 3 & 9) are two
   cases of a new generalization of RED called Curvy RED, motivated as
   follows.  When we compared the performance of our AQM with fq_CoDel
   and PIE, we came to the conclusion that their goal of holding queuing
   delay to a fixed target is misguided [CRED_Insights].  As the number
   of flows increases, if the AQM does not allow TCP 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 one parameter, the scaling, not three.  The diadvantage
   of Curvy RED is that it is not adapted to a wide range of RTTs.
   Curvy RED can be used as is when the RTT range to support is limited
   otherwise an adaptation mechanism is required.

   There follows a summary listing of the two parameters used for each
   of the two queues:


      S_C :   The scaling factor of the dropping function scales Classic
         queuing times in the range [0, 2^(S_C)] seconds into a dropping
         probability in the range [0,1].  To make division efficient, it
         is constrained to be an integer power of two;

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      f_C :  To smooth the queuing time of the Classic queue and make
         multiplication efficient, we use a negative integer power of
         two for the dimensionless EWMA constant, which we define as
         alpha = 2^(-f_C).

   L4S :

      S_L (and k'):   As for the Classic queue, the scaling factor of
         the L4S marking function scales Classic queueing times in the
         range [0, 2^(S_L)] seconds into a probability in the range
         [0,1].  Note that S_L = S_C + k', where k' is the coupling
         between the queues.  So S_L and k' count as only one parameter;
         k' is related to k in Equation (1) (Section 2.1) by k=2^k',
         where both k and k' are constants.  Then implementations can
         avoid costly division by shifting p_L by k' bits to the right.

      T :  The queue size in bytes at which step threshold marking
         starts in the L4S queue.

   {ToDo: These are the raw parameters used within the algorithm.  A
   configuration front-end could accept more meaningful parameters and
   convert them into these raw parameters.}

   From our experiments so far, recommended values for these parameters
   are: S_C = -1; f_C = 5; T = 5 * MTU 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) {ToDo
   incorporate a summary of that report into this draft}. The setting of
   k depends on policy (see Section 2.5 and Appendix C respectively for
   its recommended setting and guidance on alternatives).

   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.  We
   have solely used U=1 in our experiments so far, but results might be
   even better with U=2 or higher.

   Note that the dropping function at line 9 calls maxrand(2*U), which
   gives twice as much curviness as the call to maxrand(U) in the
   marking function at line 3.  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

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   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 (Note 7).  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:  dualq_dequeue(lq, cq) {  % Couples L4S & Classic queues, lq & cq
   2:     if ( lq.dequeue(pkt) ) {
   3:        if ((lq.byt() > T) || ((cq.ns() >> (S_L-2)) > maxrand(U)))
   4:           mark(pkt)
   5:        return(pkt)              % return the packet and stop here
   6:     }
   7:     while ( cq.dequeue(pkt) ) {
   8:         Q_C += (pkt.ns() - Q_C) >> f_C           % Classic Q EWMA
   9:        if ( (Q_C >> (S_C-2) ) > maxrand(2*U) )
   10:          drop(pkt)                     % Squared drop, redo loop
   11:       else
   12:          return(pkt)           % return the packet and stop here
   13:    }
   14:    return(NULL)                           % no packet to dequeue
   15: }

   Figure 9: Optimised Example Dequeue Pseudocode for Coupled DualQ AQM
                         using Integer Arithmetic


   1.  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 must
       be measured in time, not bytes or packets [CoDel].  In our Linux
       implementation, it was easiest to measure queuing time at
       dequeue.  Queuing time can be estimated when a packet is enqueued
       by measuring the queue length in bytes and dividing by the recent
       drain rate.

   2.  An implementation has to use priority queueing, but it need not
       implement strict priority.

   3.  If packets can be enqueued while processing dequeue code, an
       implementer might prefer to place the while loop around both
       queues so that it goes back to test again whether any L4S packets
       arrived while it was dropping a Classic packet.

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   4.  In order not to change too many factors at once, for now, we keep
       the marking function for DCTCP-only traffic as similar as
       possible to DCTCP.  However, unlike DCTCP, all processing is at
       dequeue, so we determine whether to mark a packet at the head of
       the queue by the byte-length of the queue _behind_ it.  We plan
       to test whether using queuing time will work in all
       circumstances, and if we find that the step can cause
       oscillations, we will investigate replacing it with a steep
       random marking curve.

   5.  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.

   6.  In practice at line 10 the Classic queue would probably test for
       ECN capability on the packet to determine whether to drop or mark
       the packet.  However, for brevity such detail is omitted.  All
       packets classified into the L4S queue have to be ECN-capable, so
       no dropping logic is necessary at line 3.  Nonetheless, L4S
       packets could be dropped by overload code (see Section 4.1).

   7.  In the integer variant of the pseudocode (Figure 9) real numbers
       are all represented as integers scaled up by 2^32.  In lines 3 &
       9 the function maxrand() is arranged to return an integer in the
       range 0 <= maxrand() < 2^32.  Queuing times are also scaled up by
       2^32, but in two stages: i) In lines 3 and 8 queuing times
       cq.ns() and pkt.ns() are returned in integer nanoseconds, making
       the values about 2^30 times larger than when the units were
       seconds, ii) then in lines 3 and 9 an adjustment of -2 to the
       right bit-shift multiplies the result by 2^2, to complete the
       scaling by 2^32.

Appendix C.  Guidance on Controlling Throughput Equivalence

                     | RTT_C / RTT_L | Reno | Cubic |
                     |             1 | k'=1 | k'=0  |
                     |             2 | k'=2 | k'=1  |
                     |             3 | k'=2 | k'=2  |
                     |             4 | k'=3 | k'=2  |
                     |             5 | k'=3 | k'=3  |

    Table 1: Value of k' for which DCTCP throughput is roughly the same
               as Reno or Cubic, for some example RTT ratios

   k' is related to k in Equation (1) (Section 2.1) by k=2^k'.

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   To determine the appropriate policy, the operator first has to judge
   whether it wants DCTCP flows to have roughly equal throughput with
   Reno or with Cubic (because, even in its Reno-compatibility mode,
   Cubic is about 1.4 times more aggressive than Reno).  Then the
   operator needs to decide at what ratio of RTTs it wants DCTCP and
   Classic flows to have roughly equal throughput.  For example choosing
   k'=0 (equivalent to k=1) will make DCTCP throughput roughly the same
   as Cubic, _if their RTTs are the same_.

   However, even if the base RTTs are the same, the actual RTTs are
   unlikely to be the same, because Classic (Cubic or Reno) traffic
   needs a large queue to avoid under-utilization and excess drop,
   whereas L4S (DCTCP) does not.  The operator might still choose this
   policy if it judges that DCTCP throughput should be rewarded for
   keeping its own queue short.

   On the other hand, the operator will choose one of the higher values
   for k', if it wants to slow DCTCP down to roughly the same throughput
   as Classic flows, to compensate for Classic flows slowing themselves
   down by causing themselves extra queuing delay.

   The values for k' in the table are derived from the formulae, which
   was developed in [DCttH15]:

       2^k' = 1.64 (RTT_reno / RTT_dc)                  (2)
       2^k' = 1.19 (RTT_cubic / RTT_dc )                (3)

   For localized traffic from a particular ISP's data centre, we used
   the measured RTTs to calculate that a value of k'=3 (equivalant to
   k=8) would achieve throughput equivalence, and our experiments
   verified the formula very closely.

   For a typical mix of RTTs from local data centres and across the
   general Internet, a value of k'=1 (equivalent to k=2) is recommended
   as a good workable compromise.

Appendix D.  Open Issues

   Most of the following open issues are also tagged '{ToDo}' at the
   appropriate point in the document:

      Operational guidance to monitor L4S experiment

      Define additional classifier flexibility more clearly

      PI2 appendix: scaling of alpha & beta, esp. dependence of beta_U
      on Tupdate

De Schepper, et al.     Expires September 6, 2018              [Page 35]

Internet-Draft             DualQ Coupled AQMs                 March 2018

      Curvy RED appendix: complete the unfinished parts

Authors' Addresses

   Koen De Schepper
   Nokia Bell Labs


   Bob Briscoe (editor)


   Olga Bondarenko
   Simula Research Lab


   Ing-jyh Tsang


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