Internet Engineering Task Force (IETF) B. Briscoe, Ed.
Request for Comments: 9330 Independent
Category: Informational K. De Schepper
ISSN: 2070-1721 Nokia Bell Labs
M. Bagnulo
Universidad Carlos III de Madrid
G. White
CableLabs
January 2023
Low Latency, Low Loss, and Scalable Throughput (L4S) Internet Service:
Architecture
Abstract
This document describes the L4S architecture, which enables Internet
applications to achieve low queuing latency, low congestion loss, and
scalable throughput control. L4S is based on the insight that the
root cause of queuing delay is in the capacity-seeking congestion
controllers of senders, not in the queue itself. With the L4S
architecture, all Internet applications could (but do not have to)
transition away from congestion control algorithms that cause
substantial queuing delay and instead adopt a new class of congestion
controls that can seek capacity with very little queuing. These are
aided by a modified form of Explicit Congestion Notification (ECN)
from the network. With this new architecture, applications can have
both low latency and high throughput.
The architecture primarily concerns incremental deployment. It
defines mechanisms that allow the new class of L4S congestion
controls to coexist with 'Classic' congestion controls in a shared
network. The aim is for L4S latency and throughput to be usually
much better (and rarely worse) while typically not impacting Classic
performance.
Status of This Memo
This document is not an Internet Standards Track specification; it is
published for informational purposes.
This document is a product of the Internet Engineering Task Force
(IETF). It represents the consensus of the IETF community. It has
received public review and has been approved for publication by the
Internet Engineering Steering Group (IESG). Not all documents
approved by the IESG are candidates for any level of Internet
Standard; see Section 2 of RFC 7841.
Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
https://www.rfc-editor.org/info/rfc9330.
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Copyright (c) 2023 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Table of Contents
1. Introduction
1.1. Document Roadmap
2. L4S Architecture Overview
3. Terminology
4. L4S Architecture Components
4.1. Protocol Mechanisms
4.2. Network Components
4.3. Host Mechanisms
5. Rationale
5.1. Why These Primary Components?
5.2. What L4S Adds to Existing Approaches
6. Applicability
6.1. Applications
6.2. Use Cases
6.3. Applicability with Specific Link Technologies
6.4. Deployment Considerations
6.4.1. Deployment Topology
6.4.2. Deployment Sequences
6.4.3. L4S Flow but Non-ECN Bottleneck
6.4.4. L4S Flow but Classic ECN Bottleneck
6.4.5. L4S AQM Deployment within Tunnels
7. IANA Considerations
8. Security Considerations
8.1. Traffic Rate (Non-)Policing
8.1.1. (Non-)Policing Rate per Flow
8.1.2. (Non-)Policing L4S Service Rate
8.2. 'Latency Friendliness'
8.3. Interaction between Rate Policing and L4S
8.4. ECN Integrity
8.5. Privacy Considerations
9. Informative References
Acknowledgements
Authors' Addresses
1. Introduction
At any one time, it is increasingly common for all of the traffic in
a bottleneck link (e.g., a household's Internet access or Wi-Fi) to
come from applications that prefer low delay: interactive web, web
services, voice, conversational video, interactive video, interactive
remote presence, instant messaging, online and cloud-rendered gaming,
remote desktop, cloud-based applications, cloud-rendered virtual
reality or augmented reality, and video-assisted remote control of
machinery and industrial processes. In the last decade or so, much
has been done to reduce propagation delay by placing caches or
servers closer to users. However, queuing remains a major, albeit
intermittent, component of latency. For instance, spikes of hundreds
of milliseconds are not uncommon, even with state-of-the-art Active
Queue Management (AQM) [COBALT] [DOCSIS3AQM]. A Classic AQM in an
access network bottleneck is typically configured to buffer the
sawteeth of lone flows, which can cause peak overall network delay to
roughly double during a long-running flow, relative to expected base
(unloaded) path delay [BufferSize]. Low loss is also important
because, for interactive applications, losses translate into even
longer retransmission delays.
It has been demonstrated that, once access network bit rates reach
levels now common in the developed world, increasing link capacity
offers diminishing returns if latency (delay) is not addressed
[Dukkipati06] [Rajiullah15]. Therefore, the goal is an Internet
service with very low queuing latency, very low loss, and scalable
throughput. Very low queuing latency means less than 1 millisecond
(ms) on average and less than about 2 ms at the 99th percentile.
End-to-end delay above 50 ms [Raaen14], or even above 20 ms [NASA04],
starts to feel unnatural for more demanding interactive applications.
Therefore, removing unnecessary delay variability increases the reach
of these applications (the distance over which they are comfortable
to use) and/or provides additional latency budget that can be used
for enhanced processing. This document describes the L4S
architecture for achieving these goals.
Differentiated services (Diffserv) offers Expedited Forwarding (EF)
[RFC3246] for some packets at the expense of others, but this makes
no difference when all (or most) of the traffic at a bottleneck at
any one time requires low latency. In contrast, L4S still works well
when all traffic is L4S -- a service that gives without taking needs
none of the configuration or management baggage (traffic policing or
traffic contracts) associated with favouring some traffic flows over
others.
Queuing delay degrades performance intermittently [Hohlfeld14]. It
occurs i) when a large enough capacity-seeking (e.g., TCP) flow is
running alongside the user's traffic in the bottleneck link, which is
typically in the access network, or ii) when the low latency
application is itself a large capacity-seeking or adaptive rate flow
(e.g., interactive video). At these times, the performance
improvement from L4S must be sufficient for network operators to be
motivated to deploy it.
Active Queue Management (AQM) is part of the solution to queuing
under load. AQM improves performance for all traffic, but there is a
limit to how much queuing delay can be reduced by solely changing the
network without addressing the root of the problem.
The root of the problem is the presence of standard congestion
control (Reno [RFC5681]) or compatible variants (e.g., CUBIC
[RFC8312]) that are used in TCP and in other transports, such as QUIC
[RFC9000]. We shall use the term 'Classic' for these Reno-friendly
congestion controls. Classic congestion controls induce relatively
large sawtooth-shaped excursions of queue occupancy. So if a network
operator naively attempts to reduce queuing delay by configuring an
AQM to operate at a shallower queue, a Classic congestion control
will significantly underutilize the link at the bottom of every
sawtooth. These sawteeth have also been growing in duration as flow
rate scales (see Section 5.1 and [RFC3649]).
It has been demonstrated that, if the sending host replaces a Classic
congestion control with a 'Scalable' alternative, the performance
under load of all the above interactive applications can be
significantly improved once a suitable AQM is deployed in the
network. Taking the example solution cited below that uses Data
Center TCP (DCTCP) [RFC8257] and a Dual-Queue Coupled AQM [RFC9332]
on a DSL or Ethernet link, queuing delay under heavy load is roughly
1-2 ms at the 99th percentile without losing link utilization
[L4Seval22] [DualPI2Linux] (for other link types, see Section 6.3).
This compares with 5-20 ms on _average_ with a Classic congestion
control and current state-of-the-art AQMs, such as Flow Queue CoDel
[RFC8290], Proportional Integral controller Enhanced (PIE) [RFC8033],
or DOCSIS PIE [RFC8034] and about 20-30 ms at the 99th percentile
[DualPI2Linux].
L4S is designed for incremental deployment. It is possible to deploy
the L4S service at a bottleneck link alongside the existing best
efforts service [DualPI2Linux] so that unmodified applications can
start using it as soon as the sender's stack is updated. Access
networks are typically designed with one link as the bottleneck for
each site (which might be a home, small enterprise, or mobile
device), so deployment at either or both ends of this link should
give nearly all the benefit in the respective direction. With some
transport protocols, namely TCP [ACCECN], the sender has to check
that the receiver has been suitably updated to give more accurate
feedback, whereas with more recent transport protocols, such as QUIC
[RFC9000] and Datagram Congestion Control Protocol (DCCP) [RFC4340],
all receivers have always been suitable.
This document presents the L4S architecture. It consists of three
components: network support to isolate L4S traffic from Classic
traffic; protocol features that allow network elements to identify
L4S traffic; and host support for L4S congestion controls. The
protocol is defined separately in [RFC9331] as an experimental change
to Explicit Congestion Notification (ECN). This document describes
and justifies the component parts and how they interact to provide
the low latency, low loss, and scalable Internet service. It also
details the approach to incremental deployment, as briefly summarized
above.
1.1. Document Roadmap
This document describes the L4S architecture in three passes. First,
the brief overview in Section 2 gives the very high-level idea and
states the main components with minimal rationale. This is only
intended to give some context for the terminology definitions that
follow in Section 3 and to explain the structure of the rest of the
document. Then, Section 4 goes into more detail on each component
with some rationale but still mostly stating what the architecture
is, rather than why. Finally, Section 5 justifies why each element
of the solution was chosen (Section 5.1) and why these choices were
different from other solutions (Section 5.2).
After the architecture has been described, Section 6 clarifies its
applicability by describing the applications and use cases that
motivated the design, the challenges applying the architecture to
various link technologies, and various incremental deployment models
(including the two main deployment topologies, different sequences
for incremental deployment, and various interactions with preexisting
approaches). The document ends with the usual tailpieces, including
extensive discussion of traffic policing and other security
considerations in Section 8.
2. L4S Architecture Overview
Below, we outline the three main components to the L4S architecture:
1) the Scalable congestion control on the sending host; 2) the AQM at
the network bottleneck; and 3) the protocol between them.
But first, the main point to grasp is that low latency is not
provided by the network; low latency results from the careful
behaviour of the Scalable congestion controllers used by L4S senders.
The network does have a role, primarily to isolate the low latency of
the carefully behaving L4S traffic from the higher queuing delay
needed by traffic with preexisting Classic behaviour. The network
also alters the way it signals queue growth to the transport. It
uses the Explicit Congestion Notification (ECN) protocol, but it
signals the very start of queue growth immediately, without the
smoothing delay typical of Classic AQMs. Because ECN support is
essential for L4S, senders use the ECN field as the protocol that
allows the network to identify which packets are L4S and which are
Classic.
1) Host:
Scalable congestion controls already exist. They solve the
scaling problem with Classic congestion controls, such as Reno or
CUBIC. Because flow rate has scaled since TCP congestion control
was first designed in 1988, assuming the flow lasts long enough,
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 and [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.
With a Scalable congestion control, the average time from one
congestion signal to the next (the recovery time) remains
invariant as flow rate scales, all other factors being equal.
This maintains the same degree of control over queuing and
utilization, whatever the flow rate, as well as ensuring that
high throughput is more robust to disturbances. The Scalable
control used most widely (in controlled environments) is DCTCP
[RFC8257], which has been implemented and deployed in Windows
Server Editions (since 2012), in Linux, and in FreeBSD. Although
DCTCP as-is functions well over wide-area round-trip times
(RTTs), most implementations lack certain safety features that
would be necessary for use outside controlled environments, like
data centres (see Section 6.4.3). Therefore, Scalable congestion
control needs to be implemented in TCP and other transport
protocols (QUIC, Stream Control Transmission Protocol (SCTP),
RTP/RTCP, RTP Media Congestion Avoidance Techniques (RMCAT),
etc.). Indeed, between the present document being drafted and
published, the following Scalable congestion controls were
implemented: Prague over TCP and QUIC [PRAGUE-CC] [PragueLinux],
an L4S variant of the RMCAT SCReAM controller [SCReAM-L4S], and
the L4S ECN part of Bottleneck Bandwidth and Round-trip
propagation time (BBRv2) [BBRv2] intended for TCP and QUIC
transports.
2) Network:
L4S traffic needs to be isolated from the queuing latency of
Classic traffic. One queue per application flow (FQ) is one way
to achieve this, e.g., FQ-CoDel [RFC8290]. However, using just
two queues is sufficient and does not require inspection of
transport layer headers in the network, which is not always
possible (see Section 5.2). With just two queues, it might seem
impossible to know how much capacity to schedule for each queue
without inspecting how many flows at any one time are using each.
And it would be undesirable to arbitrarily divide access network
capacity into two partitions. The Dual-Queue Coupled AQM was
developed as a minimal complexity solution to this problem. It
acts like a 'semi-permeable' membrane that partitions latency but
not bandwidth. As such, the two queues are for transitioning
from Classic to L4S behaviour, not bandwidth prioritization.
Section 4 gives a high-level explanation of how the per-flow
queue (FQ) and DualQ variants of L4S work, and [RFC9332] gives a
full explanation of the DualQ Coupled AQM framework. A specific
marking algorithm is not mandated for L4S AQMs. Appendices of
[RFC9332] give non-normative examples that have been implemented
and evaluated and give recommended default parameter settings.
It is expected that L4S experiments will improve knowledge of
parameter settings and whether the set of marking algorithms
needs to be limited.
3) Protocol:
A sending host needs to distinguish L4S and Classic packets with
an identifier so that the network can classify them into their
separate treatments. The L4S identifier spec [RFC9331] concludes
that all alternatives involve compromises, but the ECT(1) and
Congestion Experienced (CE) codepoints of the ECN field represent
a workable solution. As already explained, the network also uses
ECN to immediately signal the very start of queue growth to the
transport.
3. Terminology
Classic Congestion Control: A congestion control behaviour that can
coexist with standard Reno [RFC5681] without causing significantly
negative impact on its flow rate [RFC5033]. The scaling problem
with Classic congestion control is explained, with examples, in
Section 5.1 and in [RFC3649].
Scalable Congestion Control: A congestion control where the average
time from one congestion signal to the next (the recovery time)
remains invariant as flow rate scales, all other factors being
equal. 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 [RELENTLESS],
Prague for TCP and QUIC [PRAGUE-CC] [PragueLinux], BBRv2 [BBRv2]
[BBR-CC], and the L4S variant of SCReAM for real-time media
[SCReAM-L4S] [RFC8298]. See Section 4.3 of [RFC9331] for more
explanation.
Classic Service: The Classic service is intended for all the
congestion control behaviours that coexist with Reno [RFC5681]
(e.g., Reno itself, CUBIC [RFC8312], Compound [CTCP], and TFRC
[RFC5348]). The term 'Classic queue' means a queue providing the
Classic service.
Low Latency, Low Loss, and Scalable throughput (L4S) service: The
'L4S' service is intended for traffic from Scalable congestion
control algorithms, such as the Prague congestion control
[PRAGUE-CC], which was derived from DCTCP [RFC8257]. The L4S
service is for more general traffic than just Prague -- it allows
the set of congestion controls with similar scaling properties to
Prague to evolve, such as the examples listed above (Relentless,
SCReAM, etc.). The term 'L4S queue' means a queue providing the
L4S service.
The terms Classic or L4S can also qualify other nouns, such as
'queue', 'codepoint', 'identifier', 'classification', 'packet',
and '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 to not build
a queue (e.g., DNS, Voice over IP (VoIP), game sync datagrams,
etc.).
Reno-friendly: The subset of Classic traffic that is friendly to the
standard Reno congestion control defined for TCP in [RFC5681].
The TFRC spec [RFC5348] indirectly implies that 'friendly' is
defined as "generally within a factor of two of the sending rate
of a TCP flow under the same conditions". Reno-friendly is used
here 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 [RFC9000].
Classic ECN: The original Explicit Congestion Notification (ECN)
protocol [RFC3168] that requires ECN signals to be treated as
equivalent to 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 the ECN spec
[RFC3168], i.e., 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.
Site: A home, mobile device, small enterprise, or campus where the
network bottleneck is typically the access link to the site. Not
all network arrangements fit this model, but it is a useful,
widely applicable generalization.
Traffic Policing: Limiting traffic by dropping packets or shifting
them to a lower service class (as opposed to introducing delay,
which is termed 'traffic shaping'). Policing can involve limiting
the average rate and/or burst size. Policing focused on limiting
queuing but not the average flow rate is termed 'congestion
policing', 'latency policing', 'burst policing', or 'queue
protection' in this document. Otherwise, the term rate policing
is used.
4. L4S Architecture Components
The L4S architecture is composed of the elements in the following
three subsections.
4.1. Protocol Mechanisms
The L4S architecture involves: a) unassignment of the previous use of
the identifier; b) reassignment of the same identifier; and c)
optional further identifiers:
a. An essential aspect of a Scalable congestion control is the use
of explicit congestion signals. Classic ECN [RFC3168] requires
an ECN signal to be treated as equivalent to drop, both when it
is generated in the network and when it is responded to by hosts.
L4S needs networks and hosts to support a more fine-grained
meaning for each ECN signal that is less severe than a drop, so
that the L4S signals:
* can be much more frequent and
* can be signalled immediately, without the significant delay
required to smooth out fluctuations in the queue.
To enable L4S, the Standards Track Classic ECN spec [RFC3168] has
had to be updated to allow L4S packets to depart from the
'equivalent-to-drop' constraint. [RFC8311] is a Standards Track
update to relax specific requirements in [RFC3168] (and certain
other Standards Track RFCs), which clears the way for the
experimental changes proposed for L4S. Also, the ECT(1)
codepoint was previously assigned as the experimental ECN nonce
[RFC3540], which [RFC8311] recategorizes as historic to make the
codepoint available again.
b. [RFC9331] specifies that ECT(1) is used as the identifier to
classify L4S packets into a separate treatment from Classic
packets. This satisfies the requirement for identifying an
alternative ECN treatment in [RFC4774].
The CE codepoint is used to indicate Congestion Experienced by
both L4S and Classic treatments. This raises the concern that a
Classic AQM earlier on the path might have marked some ECT(0)
packets as CE. Then, these packets will be erroneously
classified into the L4S queue. Appendix B of [RFC9331] explains
why five unlikely eventualities all have to coincide for this to
have any detrimental effect, which even then would only involve a
vanishingly small likelihood of a spurious retransmission.
c. A network operator might wish to include certain unresponsive,
non-L4S traffic in the L4S queue if it is deemed to be paced
smoothly enough and at a low enough rate not to build a queue,
for instance, VoIP, low rate datagrams to sync online games,
relatively low rate application-limited traffic, DNS, Lightweight
Directory Access Protocol (LDAP), etc. This traffic would need
to be tagged with specific identifiers, e.g., a low-latency
Diffserv codepoint such as Expedited Forwarding (EF) [RFC3246],
Non-Queue-Building (NQB) [NQB-PHB], or operator-specific
identifiers.
4.2. Network Components
The L4S architecture aims to provide low latency without the _need_
for per-flow operations in network components. Nonetheless, the
architecture does not preclude per-flow solutions. The following
bullets describe the known arrangements: a) the DualQ Coupled AQM
with an L4S AQM in one queue coupled from a Classic AQM in the other;
b) per-flow queues with an instance of a Classic and an L4S AQM in
each queue; and c) Dual queues with per-flow AQMs but no per-flow
queues:
a. The Dual-Queue Coupled AQM (illustrated in Figure 1) achieves the
'semi-permeable' membrane property mentioned earlier as follows:
* Latency isolation: Two separate queues are used to isolate L4S
queuing delay from the larger queue that Classic traffic needs
to maintain full utilization.
* Bandwidth pooling: The two queues act as if they are a single
pool of bandwidth in which flows of either type get roughly
equal throughput without the scheduler needing to identify any
flows. This is achieved by having an AQM in each queue, but
the Classic AQM provides a congestion signal to both queues in
a manner that ensures a consistent response from the two
classes of congestion control. Specifically, the Classic AQM
generates a drop/mark probability based on congestion in its
own queue, which it uses both to drop/mark packets in its own
queue and to affect the marking probability in the L4S queue.
The strength of the coupling of the congestion signalling
between the two queues is enough to make the L4S flows slow
down to leave the right amount of capacity for the Classic
flows (as they would if they were the same type of traffic
sharing the same queue).
Then, the scheduler can serve the L4S queue with priority
(denoted by the '1' on the higher priority input), because the
L4S traffic isn't offering up enough traffic to use all the
priority that it is given. Therefore:
* for latency isolation on short timescales (sub-round-trip),
the prioritization of the L4S queue protects its low latency
by allowing bursts to dissipate quickly;
* but for bandwidth pooling on longer timescales (round-trip and
longer), the Classic queue creates an equal and opposite
pressure against the L4S traffic to ensure that neither has
priority when it comes to bandwidth -- the tension between
prioritizing L4S and coupling the marking from the Classic AQM
results in approximate per-flow fairness.
To protect against the prioritization of persistent L4S traffic
deadlocking the Classic queue for a while in some
implementations, it is advisable for the priority to be
conditional, not strict (see Appendix A of the DualQ spec
[RFC9332]).
When there is no Classic traffic, the L4S queue's own AQM comes
into play. It starts congestion marking with a very shallow
queue, so L4S traffic maintains very low queuing delay.
If either queue becomes persistently overloaded, drop of some
ECN-capable packets is introduced, as recommended in Section 7 of
the ECN spec [RFC3168] and Section 4.2.1 of the AQM
recommendations [RFC7567]. The trade-offs with different
approaches are discussed in Section 4.2.3 of the DualQ spec
[RFC9332] (not shown in the figure here).
The Dual-Queue Coupled AQM has been specified as generically as
possible [RFC9332] without specifying the particular AQMs to use
in the two queues so that designers are free to implement diverse
ideas. Informational appendices in that document give pseudocode
examples of two different specific AQM approaches: one called
DualPI2 (pronounced Dual PI Squared) [DualPI2Linux] that uses the
PI2 variant of PIE and a zero-config variant of Random Early
Detection (RED) called Curvy RED. A DualQ Coupled AQM based on
PIE has also been specified and implemented for Low Latency
DOCSIS [DOCSIS3.1].
(3) (2)
.-------^------..------------^------------------.
,-(1)-----. _____
; ________ : L4S -------. | |
:|Scalable| : _\ ||__\_|mark |
:| sender | : __________ / / || / |_____|\ _________
:|________|\; | |/ -------' ^ \1|condit'nl|
`---------'\_| IP-ECN | Coupling : \|priority |_\
________ / |Classifier| : /|scheduler| /
|Classic |/ |__________|\ -------. __:__ / |_________|
| sender | \_\ || | ||__\_|mark/|/
|________| / || | || / |drop |
Classic -------' |_____|
(1) Scalable sending host
(2) Isolation in separate network queues
(3) Packet identification protocol
Figure 1: Components of an L4S DualQ Coupled AQM Solution
b. Per-Flow Queues and AQMs: A scheduler with per-flow queues, such
as FQ-CoDel or FQ-PIE, can be used for L4S. For instance, within
each queue of an FQ-CoDel system, as well as a CoDel AQM, there
is typically also the option of ECN marking at an immediate
(unsmoothed) shallow threshold to support use in data centres
(see Section 5.2.7 of the FQ-CoDel spec [RFC8290]). In Linux,
this has been modified so that the shallow threshold can be
solely applied to ECT(1) packets [FQ_CoDel_Thresh]. Then, if
there is a flow of Not-ECT or ECT(0) packets in the per-flow
queue, the Classic AQM (e.g., CoDel) is applied; whereas, if
there is a flow of ECT(1) packets in the queue, the shallower
(typically sub-millisecond) threshold is applied. In addition,
ECT(0) and Not-ECT packets could potentially be classified into a
separate flow queue from ECT(1) and CE packets to avoid them
mixing if they share a common flow identifier (e.g., in a VPN).
c. Dual queues but per-flow AQMs: It should also be possible to use
dual queues for isolation but with per-flow marking to control
flow rates (instead of the coupled per-queue marking of the Dual-
Queue Coupled AQM). One of the two queues would be for isolating
L4S packets, which would be classified by the ECN codepoint.
Flow rates could be controlled by flow-specific marking. The
policy goal of the marking could be to differentiate flow rates
(e.g., [Nadas20], which requires additional signalling of a per-
flow 'value') or to equalize flow rates (perhaps in a similar way
to Approx Fair CoDel [AFCD] [CODEL-APPROX-FAIR] but with two
queues not one).
Note that, whenever the term 'DualQ' is used loosely without
saying whether marking is per queue or per flow, it means a dual-
queue AQM with per-queue marking.
4.3. Host Mechanisms
The L4S architecture includes two main mechanisms in the end host
that we enumerate next:
a. Scalable congestion control at the sender: Section 2 defines a
Scalable congestion control as one where the average time from
one congestion signal to the next (the recovery time) remains
invariant as flow rate scales, all other factors being equal.
DCTCP is the most widely used example. It has been documented as
an informational record of the protocol currently in use in
controlled environments [RFC8257]. A list of safety and
performance improvements for a Scalable congestion control to be
usable on the public Internet has been drawn up (see the so-
called 'Prague L4S requirements' in Appendix A of [RFC9331]).
The subset that involve risk of harm to others have been captured
as normative requirements in Section 4 of [RFC9331]. TCP Prague
[PRAGUE-CC] has been implemented in Linux as a reference
implementation to address these requirements [PragueLinux].
Transport protocols other than TCP use various congestion
controls that are designed to be friendly with Reno. Before they
can use the L4S service, they will need to be updated to
implement a Scalable congestion response, which they will have to
indicate by using the ECT(1) codepoint. Scalable variants are
under consideration for more recent transport protocols (e.g.,
QUIC), and the L4S ECN part of BBRv2 [BBRv2] [BBR-CC] is a
Scalable congestion control intended for the TCP and QUIC
transports, amongst others. Also, an L4S variant of the RMCAT
SCReAM controller [RFC8298] has been implemented [SCReAM-L4S] for
media transported over RTP.
Section 4.3 of the L4S ECN spec [RFC9331] defines Scalable
congestion control in more detail and specifies the requirements
that an L4S Scalable congestion control has to comply with.
b. The ECN feedback in some transport protocols is already
sufficiently fine-grained for L4S (specifically DCCP [RFC4340]
and QUIC [RFC9000]). But others either require updates or are in
the process of being updated:
* For the case of TCP, the feedback protocol for ECN embeds the
assumption from Classic ECN [RFC3168] that an ECN mark is
equivalent to a drop, making it unusable for a Scalable TCP.
Therefore, the implementation of TCP receivers will have to be
upgraded [RFC7560]. Work to standardize and implement more
accurate ECN feedback for TCP (AccECN) is in progress [ACCECN]
[PragueLinux].
* ECN feedback was only roughly sketched in the appendix of the
now obsoleted second specification of SCTP [RFC4960], while a
fuller specification was proposed in a long-expired document
[ECN-SCTP]. A new design would need to be implemented and
deployed before SCTP could support L4S.
* For RTP, sufficient ECN feedback was defined in [RFC6679], but
[RFC8888] defines the latest Standards Track improvements.
5. Rationale
5.1. Why These Primary Components?
Explicit congestion signalling (protocol): Explicit congestion
signalling is a key part of the L4S approach. In contrast, use of
drop as a congestion signal creates tension because drop is both
an impairment (less would be better) and a useful signal (more
would be better):
* Explicit congestion signals can be used many times per round
trip to keep tight control without any impairment. Under heavy
load, even more explicit signals can be applied so that the
queue can be kept short whatever the load. In contrast,
Classic AQMs have to introduce very high packet drop at high
load to keep the queue short. By using ECN, an L4S congestion
control's sawtooth reduction can be smaller and therefore
return to the operating point more often, without worrying that
more sawteeth will cause more signals. The consequent smaller
amplitude sawteeth fit between an empty queue and a very
shallow marking threshold (~1 ms in the public Internet), so
queue delay variation can be very low, without risk of
underutilization.
* Explicit congestion signals can be emitted immediately to track
fluctuations of the queue. L4S shifts smoothing from the
network to the host. The network doesn't know the round-trip
times (RTTs) of any of the flows. So if the network is
responsible for smoothing (as in the Classic approach), it has
to assume a worst case RTT, otherwise long RTT flows would
become unstable. This delays Classic congestion signals by
100-200 ms. In contrast, each host knows its own RTT. So, in
the L4S approach, the host can smooth each flow over its own
RTT, introducing no more smoothing delay than strictly
necessary (usually only a few milliseconds). A host can also
choose not to introduce any smoothing delay if appropriate,
e.g., during flow start-up.
Neither of the above are feasible if explicit congestion
signalling has to be considered 'equivalent to drop' (as was
required with Classic ECN [RFC3168]), because drop is an
impairment as well as a signal. So drop cannot be excessively
frequent, and drop cannot be immediate; otherwise, too many drops
would turn out to have been due to only a transient fluctuation in
the queue that would not have warranted dropping a packet in
hindsight. Therefore, in an L4S AQM, the L4S queue uses a new L4S
variant of ECN that is not equivalent to drop (see Section 5.2 of
the L4S ECN spec [RFC9331]), while the Classic queue uses either
Classic ECN [RFC3168] or drop, which are still equivalent to each
other.
Before Classic ECN was standardized, there were various proposals
to give an ECN mark a different meaning from drop. However, there
was no particular reason to agree on any one of the alternative
meanings, so 'equivalent to drop' was the only compromise that
could be reached. [RFC3168] contains a statement that:
An environment where all end nodes were ECN-Capable could
allow new criteria to be developed for setting the CE
codepoint, and new congestion control mechanisms for end-node
reaction to CE packets. However, this is a research issue,
and as such is not addressed in this document.
Latency isolation (network): L4S congestion controls keep queue
delay low, whereas Classic congestion controls need a queue of the
order of the RTT to avoid underutilization. One queue cannot have
two lengths; therefore, L4S traffic needs to be isolated in a
separate queue (e.g., DualQ) or queues (e.g., FQ).
Coupled congestion notification: Coupling the congestion
notification between two queues as in the DualQ Coupled AQM is not
necessarily essential, but it is a simple way to allow senders to
determine their rate packet by packet, rather than be overridden
by a network scheduler. An alternative is for a network scheduler
to control the rate of each application flow (see the discussion
in Section 5.2).
L4S packet identifier (protocol): Once there are at least two
treatments in the network, hosts need an identifier at the IP
layer to distinguish which treatment they intend to use.
Scalable congestion notification: A Scalable congestion control in
the host keeps the signalling frequency from the network high,
whatever the flow rate, so that queue delay variations can be
small when conditions are stable, and rate can track variations in
available capacity as rapidly as possible otherwise.
Low loss: Latency is not the only concern of L4S. The 'Low Loss'
part of the name denotes that L4S generally achieves zero
congestion loss due to its use of ECN. Otherwise, loss would
itself cause delay, particularly for short flows, due to
retransmission delay [RFC2884].
Scalable throughput: 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 Reno-friendly congestion control algorithms
[RFC3649]. It was known when TCP congestion avoidance was first
developed in 1988 that it would not scale to high bandwidth-delay
products (see footnote 6 in [TCP-CA]). Today, regular broadband
flow rates over WAN distances are already beyond the scaling range
of Classic Reno congestion control. So 'less unscalable' CUBIC
[RFC8312] and Compound [CTCP] variants of TCP have been
successfully deployed. However, these are now approaching their
scaling limits.
For instance, we will consider a scenario with a maximum RTT of 30
ms at the peak of each sawtooth. As Reno packet rate scales 8
times from 1,250 to 10,000 packet/s (from 15 to 120 Mb/s with 1500
B packets), the time to recover from a congestion event rises
proportionately by 8 times as well, from 422 ms to 3.38 s. It is
clearly problematic for a congestion control to take multiple
seconds to recover from each congestion event. CUBIC [RFC8312]
was developed to be less unscalable, but it is approaching its
scaling limit; with the same max RTT of 30 ms, at 120 Mb/s, CUBIC
is still fully in its Reno-friendly mode, so it takes about 4.3 s
to recover. However, once flow rate scales by 8 times again to
960 Mb/s it enters true CUBIC mode, with a recovery time of 12.2
s. From then on, each further scaling by 8 times doubles CUBIC's
recovery time (because the cube root of 8 is 2), e.g., at 7.68 Gb/
s, the recovery time is 24.3 s. In contrast, a Scalable
congestion control like DCTCP or Prague induces 2 congestion
signals per round trip on average, which remains invariant for any
flow rate, keeping dynamic control very tight.
For a feel of where the global average lone-flow download sits on
this scale at the time of writing (2021), according to [BDPdata],
the global average fixed access capacity was 103 Mb/s in 2020 and
the average base RTT to a CDN was 25 to 34 ms in 2019. Averaging
of per-country data was weighted by Internet user population (data
collected globally is necessarily of variable quality, but the
paper does double-check that the outcome compares well against a
second source). So a lone CUBIC flow would at best take about 200
round trips (5 s) to recover from each of its sawtooth reductions,
if the flow even lasted that long. This is described as 'at best'
because it assumes everyone uses an AQM, whereas in reality, most
users still have a (probably bloated) tail-drop buffer. In the
tail-drop case, the likely average recovery time would be at least
4 times 5 s, if not more, because RTT under load would be at least
double that of an AQM, and the recovery time of Reno-friendly
flows depends on the square of RTT.
Although work on scaling congestion controls tends to start with
TCP as the transport, the above is not intended to exclude other
transports (e.g., SCTP and QUIC) or less elastic algorithms (e.g.,
RMCAT), which all tend to adopt the same or similar developments.
5.2. What L4S Adds to Existing Approaches
All the following approaches address some part of the same problem
space as L4S. In each case, it is shown that L4S complements them or
improves on them, rather than being a mutually exclusive alternative:
Diffserv: Diffserv addresses the problem of bandwidth apportionment
for important traffic as well as queuing latency for delay-
sensitive traffic. Of these, L4S solely addresses the problem of
queuing latency. Diffserv will still be necessary where important
traffic requires priority (e.g., for commercial reasons or for
protection of critical infrastructure traffic) -- see
[L4S-DIFFSERV]. Nonetheless, the L4S approach can provide low
latency for all traffic within each Diffserv class (including the
case where there is only the one default Diffserv class).
Also, Diffserv can only provide a latency benefit if a small
subset of the traffic on a bottleneck link requests low latency.
As already explained, it has no effect when all the applications
in use at one time at a single site (e.g., a home, small business,
or mobile device) require low latency. In contrast, because L4S
works for all traffic, it needs none of the management baggage
(traffic policing or traffic contracts) associated with favouring
some packets over others. This lack of management baggage ought
to give L4S a better chance of end-to-end deployment.
In particular, if networks do not trust end systems to identify
which packets should be favoured, they assign packets to Diffserv
classes themselves. However, the techniques available to such
networks, like inspection of flow identifiers or deeper inspection
of application signatures, do not always sit well with encryption
of the layers above IP [RFC8404]. In these cases, users can have
either privacy or Quality of Service (QoS), but not both.
As with Diffserv, the L4S identifier is in the IP header. But, in
contrast to Diffserv, the L4S identifier does not convey a want or
a need for a certain level of quality. Rather, it promises a
certain behaviour (Scalable congestion response), which networks
can objectively verify if they need to. This is because low delay
depends on collective host behaviour, whereas bandwidth priority
depends on network behaviour.
State-of-the-art AQMs: AQMs for Classic traffic, such as PIE and FQ-
CoDel, give a significant reduction in queuing delay relative to
no AQM at all. L4S is intended to complement these AQMs and
should not distract from the need to deploy them as widely as
possible. Nonetheless, AQMs alone cannot reduce queuing delay too
far without significantly reducing link utilization, because the
root cause of the problem is on the host -- where Classic
congestion controls use large sawtoothing rate variations. The
L4S approach resolves this tension between delay and utilization
by enabling hosts to minimize the amplitude of their sawteeth. A
single-queue Classic AQM is not sufficient to allow hosts to use
small sawteeth for two reasons: i) smaller sawteeth would not get
lower delay in an AQM designed for larger amplitude Classic
sawteeth, because a queue can only have one length at a time and
ii) much smaller sawteeth implies much more frequent sawteeth, so
L4S flows would drive a Classic AQM into a high level of ECN-
marking, which would appear as heavy congestion to Classic flows,
which in turn would greatly reduce their rate as a result (see
Section 6.4.4).
Per-flow queuing or marking: Similarly, per-flow approaches, such as
FQ-CoDel or Approx Fair CoDel [AFCD], are not incompatible with
the L4S approach. However, per-flow queuing alone is not enough
-- it only isolates the queuing of one flow from others, not from
itself. Per-flow implementations need to have support for
Scalable congestion control added, which has already been done for
FQ-CoDel in Linux (see Section 5.2.7 of [RFC8290] and
[FQ_CoDel_Thresh]). Without this simple modification, per-flow
AQMs, like FQ-CoDel, would still not be able to support
applications that need both very low delay and high bandwidth,
e.g., video-based control of remote procedures or interactive
cloud-based video (see Note 1 below).
Although per-flow techniques are not incompatible with L4S, it is
important to have the DualQ alternative. This is because handling
end-to-end (layer 4) flows in the network (layer 3 or 2) precludes
some important end-to-end functions. For instance:
a. Per-flow forms of L4S, like FQ-CoDel, are incompatible with
full end-to-end encryption of transport layer identifiers for
privacy and confidentiality (e.g., IPsec or encrypted VPN
tunnels, as opposed to DTLS over UDP), because they require
packet inspection to access the end-to-end transport flow
identifiers.
In contrast, the DualQ form of L4S requires no deeper
inspection than the IP layer. So as long as operators take
the DualQ approach, their users can have both very low queuing
delay and full end-to-end encryption [RFC8404].
b. With per-flow forms of L4S, the network takes over control of
the relative rates of each application flow. Some see it as
an advantage that the network will prevent some flows running
faster than others. Others consider it an inherent part of
the Internet's appeal that applications can control their rate
while taking account of the needs of others via congestion
signals. They maintain that this has allowed applications
with interesting rate behaviours to evolve, for instance: i) a
variable bit-rate video that varies around an equal share,
rather than being forced to remain equal at every instant or
ii) end-to-end scavenger behaviours [RFC6817] that use less
than an equal share of capacity [LEDBAT_AQM].
The L4S architecture does not require the IETF to commit to
one approach over the other, because it supports both so that
the 'market' can decide. Nonetheless, in the spirit of 'Do
one thing and do it well' [McIlroy78], the DualQ option
provides low delay without prejudging the issue of flow-rate
control. Then, flow rate policing can be added separately if
desired. In contrast to scheduling, a policer would allow
application control up to a point, but the network would still
be able to set the point at which it intervened to prevent one
flow completely starving another.
Note:
1. It might seem that self-inflicted queuing delay within a per-
flow queue should not be counted, because if the delay wasn't
in the network, it would just shift to the sender. However,
modern adaptive applications, e.g., HTTP/2 [RFC9113] or some
interactive media applications (see Section 6.1), can keep low
latency objects at the front of their local send queue by
shuffling priorities of other objects dependent on the
progress of other transfers (for example, see [lowat]). They
cannot shuffle objects once they have released them into the
network.
Alternative Back-off ECN (ABE): Here again, L4S is not an
alternative to ABE but a complement that introduces much lower
queuing delay. ABE [RFC8511] alters the host behaviour in
response to ECN marking to utilize a link better and give ECN
flows faster throughput. It uses ECT(0) and assumes the network
still treats ECN and drop the same. Therefore, ABE exploits any
lower queuing delay that AQMs can provide. But, as explained
above, AQMs still cannot reduce queuing delay too much without
losing link utilization (to allow for other, non-ABE, flows).
BBR: Bottleneck Bandwidth and Round-trip propagation time (BBR)
[BBR-CC] controls queuing delay end-to-end without needing any
special logic in the network, such as an AQM. So it works pretty
much on any path. BBR keeps queuing delay reasonably low, but
perhaps not quite as low as with state-of-the-art AQMs, such as
PIE or FQ-CoDel, and certainly nowhere near as low as with L4S.
Queuing delay is also not consistently low, due to BBR's regular
bandwidth probing spikes and its aggressive flow start-up phase.
L4S complements BBR. Indeed, BBRv2 can use L4S ECN where
available and a Scalable L4S congestion control behaviour in
response to any ECN signalling from the path [BBRv2]. The L4S ECN
signal complements the delay-based congestion control aspects of
BBR with an explicit indication that hosts can use, both to
converge on a fair rate and to keep below a shallow queue target
set by the network. Without L4S ECN, both these aspects need to
be assumed or estimated.
6. Applicability
6.1. Applications
A transport layer that solves the current latency issues will provide
new service, product, and application opportunities.
With the L4S approach, the following existing applications also
experience significantly better quality of experience under load:
* gaming, including cloud-based gaming;
* VoIP;
* video conferencing;
* web browsing;
* (adaptive) video streaming; and
* instant messaging.
The significantly lower queuing latency also enables some interactive
application functions to be offloaded to the cloud that would hardly
even be usable today, including:
* cloud-based interactive video and
* cloud-based virtual and augmented reality.
The above two applications have been successfully demonstrated with
L4S, both running together over a 40 Mb/s broadband access link
loaded up with the numerous other latency-sensitive applications in
the previous list, as well as numerous downloads, with all sharing
the same bottleneck queue simultaneously [L4Sdemo16]
[L4Sdemo16-Video]. For the former, a panoramic video of a football
stadium could be swiped and pinched so that, on the fly, a proxy in
the cloud could generate a sub-window of the match video under the
finger-gesture control of each user. For the latter, a virtual
reality headset displayed a viewport taken from a 360-degree camera
in a racing car. The user's head movements controlled the viewport
extracted by a cloud-based proxy. In both cases, with a 7 ms end-to-
end base delay, the additional queuing delay of roughly 1 ms was so
low that it seemed the video was generated locally.
Using a swiping finger gesture or head movement to pan a video are
extremely latency-demanding actions -- far more demanding than VoIP
-- because human vision can detect extremely low delays of the order
of single milliseconds when delay is translated into a visual lag
between a video and a reference point (the finger or the orientation
of the head sensed by the balance system in the inner ear, i.e., the
vestibular system). With an alternative AQM, the video noticeably
lagged behind the finger gestures and head movements.
Without the low queuing delay of L4S, cloud-based applications like
these would not be credible without significantly more access-network
bandwidth (to deliver all possible areas of the video that might be
viewed) and more local processing, which would increase the weight
and power consumption of head-mounted displays. When all interactive
processing can be done in the cloud, only the data to be rendered for
the end user needs to be sent.
Other low latency high bandwidth applications, such as:
* interactive remote presence and
* video-assisted remote control of machinery or industrial processes
are not credible at all without very low queuing delay. No amount of
extra access bandwidth or local processing can make up for lost time.
6.2. Use Cases
The following use cases for L4S are being considered by various
interested parties:
* where the bottleneck is one of various types of access network,
e.g., DSL, Passive Optical Networks (PONs), DOCSIS cable, mobile,
satellite; or where it's a Wi-Fi link (see Section 6.3 for some
technology-specific details)
* private networks of heterogeneous data centres, where there is no
single administrator that can arrange for all the simultaneous
changes to senders, receivers, and networks needed to deploy
DCTCP:
- a set of private data centres interconnected over a wide area
with separate administrations but within the same company
- a set of data centres operated by separate companies
interconnected by a community of interest network (e.g., for
the finance sector)
- multi-tenant (cloud) data centres where tenants choose their
operating system stack (Infrastructure as a Service (IaaS))
* different types of transport (or application) congestion control:
- elastic (TCP/SCTP);
- real-time (RTP, RMCAT); and
- query-response (DNS/LDAP).
* where low delay QoS is required but without inspecting or
intervening above the IP layer [RFC8404]:
- Mobile and other networks have tended to inspect higher layers
in order to guess application QoS requirements. However, with
growing demand for support of privacy and encryption, L4S
offers an alternative. There is no need to select which
traffic to favour for queuing when L4S can give favourable
queuing to all traffic.
* If queuing delay is minimized, applications with a fixed delay
budget can communicate over longer distances or via more
circuitous paths, e.g., longer chains of service functions
[RFC7665] or of onion routers.
* If delay jitter is minimized, it is possible to reduce the
dejitter buffers on the receiving end of video streaming, which
should improve the interactive experience.
6.3. Applicability with Specific Link Technologies
Certain link technologies aggregate data from multiple packets into
bursts and buffer incoming packets while building each burst. Wi-Fi,
PON, and cable all involve such packet aggregation, whereas fixed
Ethernet and DSL do not. No sender, whether L4S or not, can do
anything to reduce the buffering needed for packet aggregation. So
an AQM should not count this buffering as part of the queue that it
controls, given no amount of congestion signals will reduce it.
Certain link technologies also add buffering for other reasons,
specifically:
* Radio links (cellular, Wi-Fi, or satellite) that are distant from
the source are particularly challenging. The radio link capacity
can vary rapidly by orders of magnitude, so it is considered
desirable to hold a standing queue that can utilize sudden
increases of capacity.
* Cellular networks are further complicated by a perceived need to
buffer in order to make hand-overs imperceptible.
L4S cannot remove the need for all these different forms of
buffering. However, by removing 'the longest pole in the tent'
(buffering for the large sawteeth of Classic congestion controls),
L4S exposes all these 'shorter poles' to greater scrutiny.
Until now, the buffering needed for these additional reasons tended
to be over-specified -- with the excuse that none were 'the longest
pole in the tent'. But having removed the 'longest pole', it becomes
worthwhile to minimize them, for instance, reducing packet
aggregation burst sizes and MAC scheduling intervals.
Also, certain link types, particularly radio-based links, are far
more prone to transmission losses. Section 6.4.3 explains how an L4S
response to loss has to be as drastic as a Classic response.
Nonetheless, research referred to in the same section has
demonstrated potential for considerably more effective loss repair at
the link layer, due to the relaxed ordering constraints of L4S
packets.
6.4. Deployment Considerations
L4S AQMs, whether DualQ [RFC9332] or FQ [RFC8290], are in themselves
an incremental deployment mechanism for L4S -- so that L4S traffic
can coexist with existing Classic (Reno-friendly) traffic.
Section 6.4.1 explains why only deploying an L4S AQM in one node at
each end of the access link will realize nearly all the benefit of
L4S.
L4S involves both the network and end systems, so Section 6.4.2
suggests some typical sequences to deploy each part and why there
will be an immediate and significant benefit after deploying just one
part.
Sections 6.4.3 and 6.4.4 describe the converse incremental deployment
case where there is no L4S AQM at the network bottleneck, so any L4S
flow traversing this bottleneck has to take care in case it is
competing with Classic traffic.
6.4.1. Deployment Topology
L4S AQMs will not have to be deployed throughout the Internet before
L4S can benefit anyone. Operators of public Internet access networks
typically design their networks so that the bottleneck will nearly
always occur at one known (logical) link. This confines the cost of
queue management technology to one place.
The case of mesh networks is different and will be discussed later in
this section. However, the known-bottleneck case is generally true
for Internet access to all sorts of different 'sites', where the word
'site' includes home networks, small- to medium-sized campus or
enterprise networks and even cellular devices (Figure 2). Also, this
known-bottleneck case tends to be applicable whatever the access link
technology, whether xDSL, cable, PON, cellular, line of sight
wireless, or satellite.
Therefore, the full benefit of the L4S service should be available in
the downstream direction when an L4S AQM is deployed at the ingress
to this bottleneck link. And similarly, the full upstream service
will typically be available once an L4S AQM is deployed at the
ingress into the upstream link. (Of course, multihomed sites would
only see the full benefit once all their access links were covered.)
______
( )
__ __ ( )
|DQ\________/DQ|( enterprise )
___ |__/ \__| ( /campus )
( ) (______)
( ) ___||_
+----+ ( ) __ __ / \
| DC |-----( Core )|DQ\_______________/DQ|| home |
+----+ ( ) |__/ \__||______|
(_____) __
|DQ\__/\ __ ,===.
|__/ \ ____/DQ||| ||mobile
\/ \__|||_||device
| o |
`---'
Figure 2: Likely Location of DualQ (DQ) Deployments in Common
Access Topologies
Deployment in mesh topologies depends on how overbooked the core is.
If the core is non-blocking, or at least generously provisioned so
that the edges are nearly always the bottlenecks, it would only be
necessary to deploy an L4S AQM at the edge bottlenecks. For example,
some data-centre networks are designed with the bottleneck in the
hypervisor or host Network Interface Controllers (NICs), while others
bottleneck at the top-of-rack switch (both the output ports facing
hosts and those facing the core).
An L4S AQM would often next be needed where the Wi-Fi links in a home
sometimes become the bottleneck. Also an L4S AQM would eventually
need to be deployed at any other persistent bottlenecks, such as
network interconnections, e.g., some public Internet exchange points
and the ingress and egress to WAN links interconnecting data centres.
6.4.2. Deployment Sequences
For any one L4S flow to provide benefit, it requires three (or
sometimes two) parts to have been deployed: i) the congestion control
at the sender; ii) the AQM at the bottleneck; and iii) older
transports (namely TCP) need upgraded receiver feedback too. This
was the same deployment problem that ECN faced [RFC8170], so we have
learned from that experience.
Firstly, L4S deployment exploits the fact that DCTCP already exists
on many Internet hosts (e.g., Windows, FreeBSD, and Linux), both
servers and clients. Therefore, an L4S AQM can be deployed at a
network bottleneck to immediately give a working deployment of all
the L4S parts for testing, as long as the ECT(0) codepoint is
switched to ECT(1). DCTCP needs some safety concerns to be fixed for
general use over the public Internet (see Section 4.3 of the L4S ECN
spec [RFC9331]), but DCTCP is not on by default, so these issues can
be managed within controlled deployments or controlled trials.
Secondly, the performance improvement with L4S is so significant that
it enables new interactive services and products that were not
previously possible. It is much easier for companies to initiate new
work on deployment if there is budget for a new product trial. In
contrast, if there were only an incremental performance improvement
(as with Classic ECN), spending on deployment tends to be much harder
to justify.
Thirdly, the L4S identifier is defined so that network operators can
initially enable L4S exclusively for certain customers or certain
applications. However, this is carefully defined so that it does not
compromise future evolution towards L4S as an Internet-wide service.
This is because the L4S identifier is defined not only as the end-to-
end ECN field, but it can also optionally be combined with any other
packet header or some status of a customer or their access link (see
Section 5.4 of [RFC9331]). Operators could do this anyway, even if
it were not blessed by the IETF. However, it is best for the IETF to
specify that, if they use their own local identifier, it must be in
combination with the IETF's identifier, ECT(1). Then, if an operator
has opted for an exclusive local-use approach, they only have to
remove this extra rule later to make the service work across the
Internet -- it will already traverse middleboxes, peerings, etc.
+-+--------------------+----------------------+---------------------+
| | Servers or proxies | Access link | Clients |
+-+--------------------+----------------------+---------------------+
|0| DCTCP (existing) | | DCTCP (existing) |
+-+--------------------+----------------------+---------------------+
|1| |Add L4S AQM downstream| |
| | WORKS DOWNSTREAM FOR CONTROLLED DEPLOYMENTS/TRIALS |
+-+--------------------+----------------------+---------------------+
|2| Upgrade DCTCP to | |Replace DCTCP feedb'k|
| | TCP Prague | | with AccECN |
| | FULLY WORKS DOWNSTREAM |
+-+--------------------+----------------------+---------------------+
| | | | Upgrade DCTCP to |
|3| | Add L4S AQM upstream | TCP Prague |
| | | | |
| | FULLY WORKS UPSTREAM AND DOWNSTREAM |
+-+--------------------+----------------------+---------------------+
Figure 3: Example L4S Deployment Sequence
Figure 3 illustrates some example sequences in which the parts of L4S
might be deployed. It consists of the following stages, preceded by
a presumption that DCTCP is already installed at both ends:
1. DCTCP is not applicable for use over the public Internet, so it
is emphasized here that any DCTCP flow has to be completely
contained within a controlled trial environment.
Within this trial environment, once an L4S AQM has been deployed,
the trial DCTCP flow will experience immediate benefit, without
any other deployment being needed. In this example, downstream
deployment is first, but in other scenarios, the upstream might
be deployed first. If no AQM at all was previously deployed for
the downstream access, an L4S AQM greatly improves the Classic
service (as well as adding the L4S service). If an AQM was
already deployed, the Classic service will be unchanged (and L4S
will add an improvement on top).
2. In this stage, the name 'TCP Prague' [PRAGUE-CC] is used to
represent a variant of DCTCP that is designed to be used in a
production Internet environment (that is, it has to comply with
all the requirements in Section 4 of the L4S ECN spec [RFC9331],
which then means it can be used over the public Internet). If
the application is primarily unidirectional, 'TCP Prague' at the
sending end will provide all the benefit needed, as long as the
receiving end supports Accurate ECN (AccECN) feedback [ACCECN].
For TCP transports, AccECN feedback is needed at the other end,
but it is a generic ECN feedback facility that is already planned
to be deployed for other purposes, e.g., DCTCP and BBR. The two
ends can be deployed in either order because, in TCP, an L4S
congestion control only enables itself if it has negotiated the
use of AccECN feedback with the other end during the connection
handshake. Thus, deployment of TCP Prague on a server enables
L4S trials to move to a production service in one direction,
wherever AccECN is deployed at the other end. This stage might
be further motivated by the performance improvements of TCP
Prague relative to DCTCP (see Appendix A.2 of the L4S ECN spec
[RFC9331]).
Unlike TCP, from the outset, QUIC ECN feedback [RFC9000] has
supported L4S. Therefore, if the transport is QUIC, one-ended
deployment of a Prague congestion control at this stage is simple
and sufficient.
For QUIC, if a proxy sits in the path between multiple origin
servers and the access bottlenecks to multiple clients, then
upgrading the proxy with a Scalable congestion control would
provide the benefits of L4S over all the clients' downstream
bottlenecks in one go -- whether or not all the origin servers
were upgraded. Conversely, where a proxy has not been upgraded,
the clients served by it will not benefit from L4S at all in the
downstream, even when any origin server behind the proxy has been
upgraded to support L4S.
For TCP, a proxy upgraded to support 'TCP Prague' would provide
the benefits of L4S downstream to all clients that support AccECN
(whether or not they support L4S as well). And in the upstream,
the proxy would also support AccECN as a receiver, so that any
client deploying its own L4S support would benefit in the
upstream direction, irrespective of whether any origin server
beyond the proxy supported AccECN.
3. This is a two-move stage to enable L4S upstream. An L4S AQM or
TCP Prague can be deployed in either order as already explained.
To motivate the first of two independent moves, the deferred
benefit of enabling new services after the second move has to be
worth it to cover the first mover's investment risk. As
explained already, the potential for new interactive services
provides this motivation. An L4S AQM also improves the upstream
Classic service significantly if no other AQM has already been
deployed.
Note that other deployment sequences might occur. For instance, the
upstream might be deployed first; a non-TCP protocol might be used
end to end, e.g., QUIC and RTP; a body, such as the 3GPP, might
require L4S to be implemented in 5G user equipment; or other random
acts of kindness might arise.
6.4.3. L4S Flow but Non-ECN Bottleneck
If L4S is enabled between two hosts, the L4S sender is required to
coexist safely with Reno in response to any drop (see Section 4.3 of
the L4S ECN spec [RFC9331]).
Unfortunately, as well as protecting Classic traffic, this rule
degrades the L4S service whenever there is any loss, even if the
cause is not persistent congestion at a bottleneck, for example:
* congestion loss at other transient bottlenecks, e.g., due to
bursts in shallower queues;
* transmission errors, e.g., due to electrical interference; and
* rate policing.
Three complementary approaches are in progress to address this issue,
but they are all currently research:
* In Prague congestion control, ignore certain losses deemed
unlikely to be due to congestion (using some ideas from BBR
[BBR-CC] regarding isolated losses). This could mask any of the
above types of loss while still coexisting with drop-based
congestion controls.
* A combination of Recent Acknowledgement (RACK) [RFC8985], L4S, and
link retransmission without resequencing could repair transmission
errors without the head of line blocking delay usually associated
with link-layer retransmission [UnorderedLTE] [RFC9331].
* Hybrid ECN/drop rate policers (see Section 8.3).
L4S deployment scenarios that minimize these issues (e.g., over
wireline networks) can proceed in parallel to this research, in the
expectation that research success could continually widen L4S
applicability.
6.4.4. L4S Flow but Classic ECN Bottleneck
Classic ECN support is starting to materialize on the Internet as an
increased level of CE marking. It is hard to detect whether this is
all due to the addition of support for ECN in implementations of FQ-
CoDel and/or FQ-COBALT, which is not generally problematic, because
flow queue (FQ) scheduling inherently prevents a flow from exceeding
the 'fair' rate irrespective of its aggressiveness. However, some of
this Classic ECN marking might be due to single-queue ECN deployment.
This case is discussed in Section 4.3 of the L4S ECN spec [RFC9331].
6.4.5. L4S AQM Deployment within Tunnels
An L4S AQM uses the ECN field to signal congestion. So in common
with Classic ECN, if the AQM is within a tunnel or at a lower layer,
correct functioning of ECN signalling requires standards-compliant
propagation of the ECN field up the layers [RFC6040] [ECN-SHIM]
[ECN-ENCAP].
7. IANA Considerations
This document has no IANA actions.
8. Security Considerations
8.1. Traffic Rate (Non-)Policing
8.1.1. (Non-)Policing Rate per Flow
In the current Internet, ISPs usually enforce separation between the
capacity of shared links assigned to different 'sites' (e.g.,
households, businesses, or mobile users -- see terminology in
Section 3) using some form of scheduler [RFC0970]. And they use
various techniques, like redirection to traffic scrubbing facilities,
to deal with flooding attacks. However, there has never been a
universal need to police the rate of individual application flows --
the Internet has generally always relied on self-restraint of
congestion controls at senders for sharing intra-'site' capacity.
L4S has been designed not to upset this status quo. If a DualQ is
used to provide L4S service, Section 4.2 of [RFC9332] explains how it
is designed to give no more rate advantage to unresponsive flows than
a single-queue AQM would, whether or not there is traffic overload.
Also, in case per-flow rate policing is ever required, it can be
added because it is orthogonal to the distinction between L4S and
Classic. As explained in Section 5.2, the DualQ variant of L4S
provides low delay without prejudging the issue of flow-rate control.
So if flow-rate control is needed, per-flow queuing (FQ) with L4S
support can be used instead, or flow rate policing can be added as a
modular addition to a DualQ. However, per-flow rate control is not
usually deployed as a security mechanism, because an active attacker
can just shard its traffic over more flow identifiers if the rate of
each is restricted.
8.1.2. (Non-)Policing L4S Service Rate
Section 5.2 explains how Diffserv only makes a difference if some
packets get less favourable treatment than others, which typically
requires traffic rate policing for a low latency class. In contrast,
it should not be necessary to rate-police access to the L4S service
to protect the Classic service, because L4S is designed to reduce
delay without harming the delay or rate of any Classic traffic.
During early deployment (and perhaps always), some networks will not
offer the L4S service. In general, these networks should not need to
police L4S traffic. They are required (by both the ECN spec
[RFC3168] and the L4S ECN spec [RFC9331]) not to change the L4S
identifier, which would interfere with end-to-end congestion control.
If they already treat ECN traffic as Not-ECT, they can merely treat
L4S traffic as Not-ECT too. At a bottleneck, such networks will
introduce some queuing and dropping. When a Scalable congestion
control detects a drop, it will have to respond safely with respect
to Classic congestion controls (as required in Section 4.3 of
[RFC9331]). This will degrade the L4S service to be no better (but
never worse) than Classic best efforts whenever a non-ECN bottleneck
is encountered on a path (see Section 6.4.3).
In cases that are expected to be rare, networks that solely support
Classic ECN [RFC3168] in a single queue bottleneck might opt to
police L4S traffic so as to protect competing Classic ECN traffic
(for instance, see Section 6.1.3 of the L4S operational guidance
[L4SOPS]). However, Section 4.3 of the L4S ECN spec [RFC9331]
recommends that the sender adapts its congestion response to properly
coexist with Classic ECN flows, i.e., reverting to the self-restraint
approach.
Certain network operators might choose to restrict access to the L4S
service, perhaps only to selected premium customers as a value-added
service. Their packet classifier (item 2 in Figure 1) could identify
such customers against some other field (e.g., source address range),
as well as classifying on the ECN field. If only the ECN L4S
identifier matched, but not (say) the source address, the classifier
could direct these packets (from non-premium customers) into the
Classic queue. Explaining clearly how operators can use additional
local classifiers (see Section 5.4 of [RFC9331]) is intended to
remove any motivation to clear the L4S identifier. Then at least the
L4S ECN identifier will be more likely to survive end to end, even
though the service may not be supported at every hop. Such local
arrangements would only require simple registered/not-registered
packet classification, rather than the managed, application-specific
traffic policing against customer-specific traffic contracts that
Diffserv uses.
8.2. 'Latency Friendliness'
Like the Classic service, the L4S service relies on self-restraint to
limit the rate in response to congestion. In addition, the L4S
service requires self-restraint in terms of limiting latency
(burstiness). It is hoped that self-interest and guidance on dynamic
behaviour (especially flow start-up, which might need to be
standardized) will be sufficient to prevent transports from sending
excessive bursts of L4S traffic, given the application's own latency
will suffer most from such behaviour.
Because the L4S service can reduce delay without discernibly
increasing the delay of any Classic traffic, it should not be
necessary to police L4S traffic to protect the delay of Classic
traffic. However, whether burst policing becomes necessary to
protect other L4S traffic remains to be seen. Without it, there will
be potential for attacks on the low latency of the L4S service.
If needed, various arrangements could be used to address this
concern:
Local bottleneck queue protection: A per-flow (5-tuple) queue
protection function [DOCSIS-Q-PROT] has been developed for the low
latency queue in DOCSIS, which has adopted the DualQ L4S
architecture. It protects the low latency service from any queue-
building flows that accidentally or maliciously classify
themselves into the low latency queue. It is designed to score
flows based solely on their contribution to queuing (not flow rate
in itself). Then, if the shared low latency queue is at risk of
exceeding a threshold, the function redirects enough packets of
the highest scoring flow(s) into the Classic queue to preserve low
latency.
Distributed traffic scrubbing: Rather than policing locally at each
bottleneck, it may only be necessary to address problems
reactively, e.g., punitively target any deployments of new bursty
malware, in a similar way to how traffic from flooding attack
sources is rerouted via scrubbing facilities.
Local bottleneck per-flow scheduling: Per-flow scheduling should
inherently isolate non-bursty flows from bursty flows (see
Section 5.2 for discussion of the merits of per-flow scheduling
relative to per-flow policing).
Distributed access subnet queue protection: Per-flow queue
protection could be arranged for a queue structure distributed
across a subnet intercommunicating using lower layer control
messages (see Section 2.1.4 of [QDyn]). For instance, in a radio
access network, user equipment already sends regular buffer status
reports to a radio network controller, which could use this
information to remotely police individual flows.
Distributed Congestion Exposure to ingress policers: The Congestion
Exposure (ConEx) architecture [RFC7713] uses an egress audit to
motivate senders to truthfully signal path congestion in-band,
where it can be used by ingress policers. An edge-to-edge variant
of this architecture is also possible.
Distributed domain-edge traffic conditioning: An architecture
similar to Diffserv [RFC2475] may be preferred, where traffic is
proactively conditioned on entry to a domain, rather than
reactively policed only if it leads to queuing once combined with
other traffic at a bottleneck.
Distributed core network queue protection: The policing function
could be divided between per-flow mechanisms at the network
ingress that characterize the burstiness of each flow into a
signal carried with the traffic and per-class mechanisms at
bottlenecks that act on these signals if queuing actually occurs
once the traffic converges. This would be somewhat similar to
[Nadas20], which is in turn similar to the idea behind core
stateless fair queuing.
No single one of these possible queue protection capabilities is
considered an essential part of the L4S architecture, which works
without any of them under non-attack conditions (much as the Internet
normally works without per-flow rate policing). Indeed, even where
latency policers are deployed, under normal circumstances, they would
not intervene, and if operators found they were not necessary, they
could disable them. Part of the L4S experiment will be to see
whether such a function is necessary and which arrangements are most
appropriate to the size of the problem.
8.3. Interaction between Rate Policing and L4S
As mentioned in Section 5.2, L4S should remove the need for low
latency Diffserv classes. However, those Diffserv classes that give
certain applications or users priority over capacity would still be
applicable in certain scenarios (e.g., corporate networks). Then,
within such Diffserv classes, L4S would often be applicable to give
traffic low latency and low loss as well. Within such a Diffserv
class, the bandwidth available to a user or application is often
limited by a rate policer. Similarly, in the default Diffserv class,
rate policers are sometimes used to partition shared capacity.
A Classic rate policer drops any packets exceeding a set rate,
usually also giving a burst allowance (variants exist where the
policer re-marks noncompliant traffic to a discard-eligible Diffserv
codepoint, so they can be dropped elsewhere during contention).
Whenever L4S traffic encounters one of these rate policers, it will
experience drops and the source will have to fall back to a Classic
congestion control, thus losing the benefits of L4S (Section 6.4.3).
So in networks that already use rate policers and plan to deploy L4S,
it will be preferable to redesign these rate policers to be more
friendly to the L4S service.
L4S-friendly rate policing is currently a research area (note that
this is not the same as latency policing). It might be achieved by
setting a threshold where ECN marking is introduced, such that it is
just under the policed rate or just under the burst allowance where
drop is introduced. For instance, the two-rate, three-colour marker
[RFC2698] or a PCN threshold and excess-rate marker [RFC5670] could
mark ECN at the lower rate and drop at the higher. Or an existing
rate policer could have congestion-rate policing added, e.g., using
the 'local' (non-ConEx) variant of the ConEx aggregate congestion
policer [CONG-POLICING]. It might also be possible to design
Scalable congestion controls to respond less catastrophically to loss
that has not been preceded by a period of increasing delay.
The design of L4S-friendly rate policers will require a separate,
dedicated document. For further discussion of the interaction
between L4S and Diffserv, see [L4S-DIFFSERV].
8.4. ECN Integrity
Various ways have been developed to protect the integrity of the
congestion feedback loop (whether signalled by loss, Classic ECN, or
L4S ECN) against misbehaviour by the receiver, sender, or network (or
all three). Brief details of each, including applicability, pros,
and cons, are given in Appendix C.1 of the L4S ECN spec [RFC9331].
8.5. Privacy Considerations
As discussed in Section 5.2, the L4S architecture does not preclude
approaches that inspect end-to-end transport layer identifiers. For
instance, L4S support has been added to FQ-CoDel, which classifies by
application flow identifier in the network. However, the main
innovation of L4S is the DualQ AQM framework that does not need to
inspect any deeper than the outermost IP header, because the L4S
identifier is in the IP-ECN field.
Thus, the L4S architecture enables very low queuing delay without
_requiring_ inspection of information above the IP layer. This means
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].
Because L4S can provide low delay for a broad set of applications
that choose to use it, there is no need for individual applications
or classes within that broad set to be distinguishable in any way
while traversing networks. This removes much of the ability to
correlate between the delay requirements of traffic and other
identifying features [RFC6973]. There may be some types of traffic
that prefer not to use L4S, but the coarse binary categorization of
traffic reveals very little that could be exploited to compromise
privacy.
9. Informative References
[ACCECN] Briscoe, B., Kühlewind, M., and R. Scheffenegger, "More
Accurate ECN Feedback in TCP", Work in Progress, Internet-
Draft, draft-ietf-tcpm-accurate-ecn-22, 9 November 2022,
<
https://datatracker.ietf.org/doc/html/draft-ietf-tcpm-
accurate-ecn-22>.
[AFCD] Xue, L., Kumar, S., Cui, C., Kondikoppa, P., Chiu, C-H.,
and S-J. Park, "Towards fair and low latency next
generation high speed networks: AFCD queuing", Journal of
Network and Computer Applications, Volume 70, pp. 183-193,
DOI 10.1016/j.jnca.2016.03.021, July 2016,
<
https://doi.org/10.1016/j.jnca.2016.03.021>.
[BBR-CC] Cardwell, N., Cheng, Y., Hassas Yeganeh, S., 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/doc/html/draft-cardwell-
iccrg-bbr-congestion-control-02>.
[BBRv2] "TCP BBR v2 Alpha/Preview Release", commit 17700ca, June
2022, <
https://github.com/google/bbr>.
[BDPdata] Briscoe, B., "PI2 Parameters", TR-BB-2021-001,
arXiv:2107.01003 [cs.NI], DOI 10.48550/arXiv.2107.01003,
October 2021, <
https://arxiv.org/abs/2107.01003>.
[BufferSize]
Appenzeller, G., Keslassy, I., and N. McKeown, "Sizing
Router Buffers", SIGCOMM '04: Proceedings of the 2004
conference on Applications, technologies, architectures,
and protocols for computer communications, pp. 281-292,
DOI 10.1145/1015467.1015499, October 2004,
<
https://doi.org/10.1145/1015467.1015499>.
[COBALT] Palmei, J., Gupta, S., Imputato, P., Morton, J.,
Tahiliani, M. P., Avallone, S., and D. Täht, "Design and
Evaluation of COBALT Queue Discipline", IEEE International
Symposium on Local and Metropolitan Area Networks
(LANMAN), DOI 10.1109/LANMAN.2019.8847054, July 2019,
<
https://ieeexplore.ieee.org/abstract/document/8847054>.
[CODEL-APPROX-FAIR]
Morton, J. and P. Heist, "Controlled Delay Approximate
Fairness AQM", Work in Progress, Internet-Draft, draft-
morton-tsvwg-codel-approx-fair-01, 9 March 2020,
<
https://datatracker.ietf.org/doc/html/draft-morton-tsvwg-
codel-approx-fair-01>.
[CONG-POLICING]
Briscoe, B., "Network Performance Isolation using
Congestion Policing", Work in Progress, Internet-Draft,
draft-briscoe-conex-policing-01, 14 February 2014,
<
https://datatracker.ietf.org/doc/html/draft-briscoe-
conex-policing-01>.
[CTCP] Sridharan, M., Tan, K., Bansal, D., and D. Thaler,
"Compound TCP: A New TCP Congestion Control for High-Speed
and Long Distance Networks", Work in Progress, Internet-
Draft, draft-sridharan-tcpm-ctcp-02, 11 November 2008,
<
https://datatracker.ietf.org/doc/html/draft-sridharan-
tcpm-ctcp-02>.
[DOCSIS-Q-PROT]
Briscoe, B., Ed. and G. White, "The DOCSIS® 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/doc/html/draft-briscoe-
docsis-q-protection-06>.
[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>.
[DOCSIS3AQM]
White, G., "Active Queue Management Algorithms for DOCSIS
3.0: A Simulation Study of CoDel, SFQ-CoDel and PIE in
DOCSIS 3.0 Networks", CableLabs Technical Report, April
2013, <
https://www.cablelabs.com/wp-
content/uploads/2013/11/
Active_Queue_Management_Algorithms_DOCSIS_3_0.pdf>.
[DualPI2Linux]
Albisser, O., De Schepper, K., Briscoe, B., Tilmans, O.,
and H. Steen, "DUALPI2 - Low Latency, Low Loss and
Scalable (L4S) AQM", Proceedings of Linux Netdev 0x13,
March 2019, <
https://www.netdevconf.org/0x13/
session.html?talk-DUALPI2-AQM>.
[Dukkipati06]
Dukkipati, N. and N. McKeown, "Why Flow-Completion Time is
the Right Metric for Congestion Control", ACM SIGCOMM
Computer Communication Review, Volume 36, Issue 1, pp.
59-62, DOI 10.1145/1111322.1111336, January 2006,
<
https://dl.acm.org/doi/10.1145/1111322.1111336>.
[ECN-ENCAP]
Briscoe, B. and J. Kaippallimalil, "Guidelines for Adding
Congestion Notification to Protocols that Encapsulate IP",
Work in Progress, Internet-Draft, draft-ietf-tsvwg-ecn-
encap-guidelines-17, 11 July 2022,
<
https://datatracker.ietf.org/doc/html/draft-ietf-tsvwg-
ecn-encap-guidelines-17>.
[ECN-SCTP] Stewart, R., Tuexen, M., and X. Dong, "ECN for Stream
Control Transmission Protocol (SCTP)", Work in Progress,
Internet-Draft, draft-stewart-tsvwg-sctpecn-05, 15 January
2014, <
https://datatracker.ietf.org/doc/html/draft-
stewart-tsvwg-sctpecn-05>.
[ECN-SHIM] Briscoe, B., "Propagating Explicit Congestion Notification
Across IP Tunnel Headers Separated by a Shim", Work in
Progress, Internet-Draft, draft-ietf-tsvwg-rfc6040update-
shim-15, 11 July 2022,
<
https://datatracker.ietf.org/doc/html/draft-ietf-tsvwg-
rfc6040update-shim-15>.
[FQ_CoDel_Thresh]
"fq_codel: generalise ce_threshold marking for subset of
traffic", commit dfcb63ce1de6b10b, October 2021,
<
https://git.kernel.org/pub/scm/linux/kernel/git/netdev/
net-next.git/commit/?id=dfcb63ce1de6b10b>.
[Hohlfeld14]
Hohlfeld, O., Pujol, E., Ciucu, F., Feldmann, A., and P.
Barford, "A QoE Perspective on Sizing Network Buffers",
IMC '14: Proceedings of the 2014 Conference on Internet
Measurement, pp. 333-346, DOI 10.1145/2663716.2663730,
November 2014,
<
https://doi.acm.org/10.1145/2663716.2663730>.
[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, 4 November 2018,
<
https://datatracker.ietf.org/doc/html/draft-briscoe-
tsvwg-l4s-diffserv-02>.
[L4Sdemo16]
Bondarenko, O., De Schepper, K., Tsang, I., Briscoe, B.,
Petlund, A., and C. Griwodz, "Ultra-Low Delay for All:
Live Experience, Live Analysis", Proceedings of the 7th
International Conference on Multimedia Systems, Article
No. 33, pp. 1-4, DOI 10.1145/2910017.2910633, May 2016,
<
https://dl.acm.org/citation.cfm?doid=2910017.2910633>.
[L4Sdemo16-Video]
"Videos used in IETF dispatch WG 'Ultra-Low Queuing Delay
for All Apps' slot",
<
https://riteproject.eu/dctth/#1511dispatchwg>.
[L4Seval22]
De Schepper, K., Albisser, O., Tilmans, O., and B.
Briscoe, "Dual Queue Coupled AQM: Deployable Very Low
Queuing Delay for All", TR-BB-2022-001, arXiv:2209.01078
[cs.NI], DOI 10.48550/arXiv.2209.01078, September 2022,
<
https://arxiv.org/abs/2209.01078>.
[L4SOPS] White, G., Ed., "Operational Guidance for Deployment of
L4S in the Internet", Work in Progress, Internet-Draft,
draft-ietf-tsvwg-l4sops-03, 28 April 2022,
<
https://datatracker.ietf.org/doc/html/draft-ietf-tsvwg-
l4sops-03>.
[LEDBAT_AQM]
Al-Saadi, R., Armitage, G., and J. But, "Characterising
LEDBAT Performance Through Bottlenecks Using PIE, FQ-CoDel
and FQ-PIE Active Queue Management", IEEE 42nd Conference
on Local Computer Networks (LCN), DOI 10.1109/LCN.2017.22,
October 2017,
<
https://ieeexplore.ieee.org/document/8109367>.
[lowat] Meenan, P., "Optimizing HTTP/2 prioritization with BBR and
tcp_notsent_lowat", Cloudflare Blog, October 2018,
<
https://blog.cloudflare.com/http-2-prioritization-with-
nginx/>.
[McIlroy78]
McIlroy, M.D., Pinson, E. N., and B. A. Tague, "UNIX Time-
Sharing System: Foreword", The Bell System Technical
Journal 57: 6, pp. 1899-1904,
DOI 10.1002/j.1538-7305.1978.tb02135.x, July 1978,
<
https://archive.org/details/bstj57-6-1899>.
[Nadas20] Nádas, S., Gombos, G., Fejes, F., and S. Laki, "A
Congestion Control Independent L4S Scheduler", ANRW '20:
Proceedings of the Applied Networking Research Workshop,
pp. 45-51, DOI 10.1145/3404868.3406669, July 2020,
<
https://doi.org/10.1145/3404868.3406669>.
[NASA04] Bailey, R., Trey Arthur III, J., and S. Williams, "Latency
Requirements for Head-Worn Display S/EVS Applications",
Proceedings of SPIE 5424, DOI 10.1117/12.554462, April
2004, <
https://ntrs.nasa.gov/api/citations/20120009198/
downloads/20120009198.pdf?attachment=true>.
[NQB-PHB] White, G. and T. Fossati, "A Non-Queue-Building Per-Hop
Behavior (NQB PHB) for Differentiated Services", Work in
Progress, Internet-Draft, draft-ietf-tsvwg-nqb-15, 11
January 2023, <
https://datatracker.ietf.org/doc/html/
draft-ietf-tsvwg-nqb-15>.
[PRAGUE-CC]
De Schepper, K., Tilmans, O., and B. Briscoe, Ed., "Prague
Congestion Control", Work in Progress, Internet-Draft,
draft-briscoe-iccrg-prague-congestion-control-01, 11 July
2022, <
https://datatracker.ietf.org/doc/html/draft-
briscoe-iccrg-prague-congestion-control-01>.
[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)", Proceedings Linux Netdev 0x13,
March 2019, <
https://www.netdevconf.org/0x13/
session.html?talk-tcp-prague-l4s>.
[QDyn] Briscoe, B., "Rapid Signalling of Queue Dynamics", TR-BB-
2017-001, arXiv:1904.07044 [cs.NI],
DOI 10.48550/arXiv.1904.07044, April 2019,
<
https://arxiv.org/abs/1904.07044>.
[Raaen14] Raaen, K. and T-M. Grønli, "Latency Thresholds for
Usability in Games: A Survey", Norsk IKT-konferanse for
forskning og utdanning (Norwegian ICT conference for
research and education), 2014,
<
http://ojs.bibsys.no/index.php/NIK/article/view/9/6>.
[Rajiullah15]
Rajiullah, M., "Towards a Low Latency Internet:
Understanding and Solutions", Dissertation, Karlstad
University, 2015, <
https://www.diva-
portal.org/smash/get/diva2:846109/FULLTEXT01.pdf>.
[RELENTLESS]
Mathis, M., "Relentless Congestion Control", Work in
Progress, Internet-Draft, draft-mathis-iccrg-relentless-
tcp-00, 4 March 2009,
<
https://datatracker.ietf.org/doc/html/draft-mathis-iccrg-
relentless-tcp-00>.
[RFC0970] Nagle, J., "On Packet Switches With Infinite Storage",
RFC 970, DOI 10.17487/RFC0970, December 1985,
<
https://www.rfc-editor.org/info/rfc970>.
[RFC2475] Blake, S., Black, D., Carlson, M., Davies, E., Wang, Z.,
and W. Weiss, "An Architecture for Differentiated
Services", RFC 2475, DOI 10.17487/RFC2475, December 1998,
<
https://www.rfc-editor.org/info/rfc2475>.
[RFC2698] Heinanen, J. and R. Guerin, "A Two Rate Three Color
Marker", RFC 2698, DOI 10.17487/RFC2698, September 1999,
<
https://www.rfc-editor.org/info/rfc2698>.
[RFC2884] Hadi Salim, J. and U. Ahmed, "Performance Evaluation of
Explicit Congestion Notification (ECN) in IP Networks",
RFC 2884, DOI 10.17487/RFC2884, July 2000,
<
https://www.rfc-editor.org/info/rfc2884>.
[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>.
[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>.
[RFC3540] Spring, N., Wetherall, D., and D. Ely, "Robust Explicit
Congestion Notification (ECN) Signaling with Nonces",
RFC 3540, DOI 10.17487/RFC3540, June 2003,
<
https://www.rfc-editor.org/info/rfc3540>.
[RFC3649] Floyd, S., "HighSpeed TCP for Large Congestion Windows",
RFC 3649, DOI 10.17487/RFC3649, December 2003,
<
https://www.rfc-editor.org/info/rfc3649>.
[RFC4340] Kohler, E., Handley, M., and S. Floyd, "Datagram
Congestion Control Protocol (DCCP)", RFC 4340,
DOI 10.17487/RFC4340, March 2006,
<
https://www.rfc-editor.org/info/rfc4340>.
[RFC4774] Floyd, S., "Specifying Alternate Semantics for the
Explicit Congestion Notification (ECN) Field", BCP 124,
RFC 4774, DOI 10.17487/RFC4774, November 2006,
<
https://www.rfc-editor.org/info/rfc4774>.
[RFC4960] Stewart, R., Ed., "Stream Control Transmission Protocol",
RFC 4960, DOI 10.17487/RFC4960, September 2007,
<
https://www.rfc-editor.org/info/rfc4960>.
[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>.
[RFC5670] Eardley, P., Ed., "Metering and Marking Behaviour of PCN-
Nodes", RFC 5670, DOI 10.17487/RFC5670, November 2009,
<
https://www.rfc-editor.org/info/rfc5670>.
[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>.
[RFC6040] Briscoe, B., "Tunnelling of Explicit Congestion
Notification", RFC 6040, DOI 10.17487/RFC6040, November
2010, <
https://www.rfc-editor.org/info/rfc6040>.
[RFC6679] Westerlund, M., Johansson, I., Perkins, C., O'Hanlon, P.,
and K. Carlberg, "Explicit Congestion Notification (ECN)
for RTP over UDP", RFC 6679, DOI 10.17487/RFC6679, August
2012, <
https://www.rfc-editor.org/info/rfc6679>.
[RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
"Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
DOI 10.17487/RFC6817, December 2012,
<
https://www.rfc-editor.org/info/rfc6817>.
[RFC6973] Cooper, A., Tschofenig, H., Aboba, B., Peterson, J.,
Morris, J., Hansen, M., and R. Smith, "Privacy
Considerations for Internet Protocols", RFC 6973,
DOI 10.17487/RFC6973, July 2013,
<
https://www.rfc-editor.org/info/rfc6973>.
[RFC7560] Kuehlewind, M., Ed., Scheffenegger, R., and B. Briscoe,
"Problem Statement and Requirements for Increased Accuracy
in Explicit Congestion Notification (ECN) Feedback",
RFC 7560, DOI 10.17487/RFC7560, August 2015,
<
https://www.rfc-editor.org/info/rfc7560>.
[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>.
[RFC7665] Halpern, J., Ed. and C. Pignataro, Ed., "Service Function
Chaining (SFC) Architecture", RFC 7665,
DOI 10.17487/RFC7665, October 2015,
<
https://www.rfc-editor.org/info/rfc7665>.
[RFC7713] Mathis, M. and B. Briscoe, "Congestion Exposure (ConEx)
Concepts, Abstract Mechanism, and Requirements", RFC 7713,
DOI 10.17487/RFC7713, December 2015,
<
https://www.rfc-editor.org/info/rfc7713>.
[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>.
[RFC8170] Thaler, D., Ed., "Planning for Protocol Adoption and
Subsequent Transitions", RFC 8170, DOI 10.17487/RFC8170,
May 2017, <
https://www.rfc-editor.org/info/rfc8170>.
[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>.
[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>.
[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>.
[RFC8511] Khademi, N., Welzl, M., Armitage, G., and G. Fairhurst,
"TCP Alternative Backoff with ECN (ABE)", RFC 8511,
DOI 10.17487/RFC8511, December 2018,
<
https://www.rfc-editor.org/info/rfc8511>.
[RFC8888] Sarker, Z., Perkins, C., Singh, V., and M. Ramalho, "RTP
Control Protocol (RTCP) Feedback for Congestion Control",
RFC 8888, DOI 10.17487/RFC8888, January 2021,
<
https://www.rfc-editor.org/info/rfc8888>.
[RFC8985] Cheng, Y., Cardwell, N., Dukkipati, N., and P. Jha, "The
RACK-TLP Loss Detection Algorithm for TCP", RFC 8985,
DOI 10.17487/RFC8985, February 2021,
<
https://www.rfc-editor.org/info/rfc8985>.
[RFC9000] Iyengar, J., Ed. and M. Thomson, Ed., "QUIC: A UDP-Based
Multiplexed and Secure Transport", RFC 9000,
DOI 10.17487/RFC9000, May 2021,
<
https://www.rfc-editor.org/info/rfc9000>.
[RFC9113] Thomson, M., Ed. and C. Benfield, Ed., "HTTP/2", RFC 9113,
DOI 10.17487/RFC9113, June 2022,
<
https://www.rfc-editor.org/info/rfc9113>.
[RFC9331] De Schepper, K. and B. Briscoe, Ed., "The Explicit
Congestion Notification (ECN) Protocol for Low Latency,
Low Loss, and Scalable Throughput (L4S)", RFC 9331,
DOI 10.17487/RFC9331, January 2023,
<
https://www.rfc-editor.org/info/rfc9331>.
[RFC9332] De Schepper, K., Briscoe, B., Ed., and G. White, "Dual-
Queue Coupled Active Queue Management (AQM) for Low
Latency, Low Loss, and Scalable Throughput (L4S)",
RFC 9332, DOI 10.17487/RFC9332, January 2023,
<
https://www.rfc-editor.org/info/rfc9332>.
[SCReAM-L4S]
"SCReAM", commit fda6c53, June 2022,
<
https://github.com/EricssonResearch/scream>.
[TCP-CA] Jacobson, V. and M. Karels, "Congestion Avoidance and
Control", Laurence Berkeley Labs Technical Report ,
November 1988, <
https://ee.lbl.gov/papers/congavoid.pdf>.
[UnorderedLTE]
Austrheim, M., "Implementing immediate forwarding for 4G
in a network simulator", Master's Thesis, University of
Oslo, 2018.
Acknowledgements
Thanks to Richard Scheffenegger, Wes Eddy, Karen Nielsen, David
Black, Jake Holland, Vidhi Goel, Ermin Sakic, Praveen
Balasubramanian, Gorry Fairhurst, Mirja Kuehlewind, Philip Eardley,
Neal Cardwell, Pete Heist, and Martin Duke for their useful review
comments. Thanks also to the area reviewers: Marco Tiloca, Lars
Eggert, Roman Danyliw, and Éric Vyncke.
Bob Briscoe and Koen De Schepper were partly funded by the European
Community under its Seventh Framework Programme through the Reducing
Internet Transport Latency (RITE) project (ICT-317700). The
contribution of Koen De Schepper was also partly funded by the
5Growth and DAEMON EU H2020 projects. Bob Briscoe was also partly
funded by the Research Council of Norway through the TimeIn project,
partly by CableLabs, and partly by the Comcast Innovation Fund. The
views expressed here are solely those of the authors.
Authors' Addresses
Bob Briscoe (editor)
Independent
United Kingdom
Email:
[email protected]
URI:
https://bobbriscoe.net/
Koen De Schepper
Nokia Bell Labs
Antwerp
Belgium
Email:
[email protected]
URI:
https://www.bell-labs.com/about/researcher-profiles/
koende_schepper/
Marcelo Bagnulo
Universidad Carlos III de Madrid
Av. Universidad 30
28911 Madrid
Spain
Phone: 34 91 6249500
Email:
[email protected]
URI:
https://www.it.uc3m.es
Greg White
CableLabs
United States of America
Email:
[email protected]
