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
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This Internet-Draft will expire on 2 March 2023.
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Copyright Notice
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document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
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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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