RFC 9330

Low Latency, Low Loss, and Scalable Throughput (L4S) Internet Service: Architecture 🎀

Part 1 of 11 💖 more parts coming!

okay bestie, this is RFC 9330 🦋 It's an IETF document, and it's a Informational one, so it's sharing knowledge, not setting a rule. It came out in January 2023 and it's part of the ISSN 2070-1721 series. The main authors are B. Briscoe, Ed. (listed as Independent), K. De Schepper (Nokia Bell Labs), M. Bagnulo (Universidad Carlos III de Madrid), and G. White (CableLabs). The title block doesn't list anything it obsoletes or updates, so this one stands on its own ✨

Abstract 🌸

okay so this doc describes the L4S architecture, which lets internet apps get low queuing latency, low congestion loss, and scalable throughput control 🔥 The big idea is that the real villain of queuing delay is the capacity-seeking congestion controllers in senders, not the queue itself 💅 With L4S, every internet app could (but doesn't have to) switch away from congestion control algorithms that create a lot of queuing delay, and instead use a new class of congestion controls that can seek capacity with very little queuing. These get help from a modified form of Explicit Congestion Notification (ECN) coming from the network ✨ So apps can have both low latency and high throughput. Iconic 👑

The architecture is mostly about incremental deployment. It defines mechanisms so the new L4S congestion controls can live alongside 'Classic' congestion controls on a shared network 🫶 The goal is that L4S latency and throughput are usually much better (and rarely worse), while typically not hurting Classic performance 🌈

Status of This Memo 🧚

this is not an Internet Standards Track specification. It's 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 went through public review and got approved for publication by the Internet Engineering Steering Group (IESG). Not every document the IESG approves is a candidate for any level of Internet Standard, so check Section 2 of RFC 7841 if you're curious 🙈

Info about the current status of this document, any errata, and how to send feedback is at https://www.rfc-editor.org/info/rfc9330 🍓

Copyright Notice 💜

Copyright (c) 2023 IETF Trust and the persons identified as the document authors. All rights reserved.

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

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's getting more and more common for all the traffic on a bottleneck link (like a household's internet access or Wi-Fi) to come from apps that want low delay 😭 We're talking interactive web, web services, voice, conversational video, interactive video, interactive remote presence, instant messaging, online and cloud-rendered gaming, remote desktop, cloud-based apps, cloud-rendered virtual reality or augmented reality, and video-assisted remote control of machinery and industrial processes. The last decade or so, a lot of work went into cutting propagation delay by putting caches or servers closer to users 🌸 But queuing is still a major, if intermittent, part of latency. For example, spikes of hundreds of milliseconds aren't unusual, even with state-of-the-art Active Queue Management (AQM) [COBALT] [DOCSIS3AQM]. A Classic AQM at an access network bottleneck is typically set up to buffer the sawteeth of lone flows, which can make peak overall network delay roughly double during a long-running flow, compared to the expected base (unloaded) path delay [BufferSize]. Low loss matters too, because for interactive apps, losses turn into even longer retransmission delays 💀

It's been shown that once access network bit rates hit the levels that are now common in the developed world, adding more link capacity gives diminishing returns if latency (delay) isn't addressed [Dukkipati06] [Rajiullah15]. So 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 apps 🙈 So removing unnecessary delay variability extends the reach of these apps (the distance over which they're comfy to use), and/or frees up latency budget for enhanced processing 🧚 This document describes the L4S architecture for hitting these goals.

Differentiated services (Diffserv) offers Expedited Forwarding (EF) [RFC3246] for some packets at the expense of others, but that doesn't help when all (or most) of the traffic at a bottleneck at any one time needs low latency 😤 L4S, on the other hand, still works well when all traffic is L4S. It's a service that gives without taking, so it skips the configuration and management baggage (traffic policing or traffic contracts) that comes with favouring some flows over others 💅

Queuing delay degrades performance on and off [Hohlfeld14]. It happens i) when a large enough capacity-seeking (e.g., TCP) flow is running alongside the user's traffic at the bottleneck link, which is typically in the access network, or ii) when the low latency app 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. (Lowercase "must" there, it's just a plain word, but it's giving pressure, ngl 😭)

AQM is part of the solution to queuing under load. It improves performance for all traffic, but there's a limit to how much queuing delay you can cut by changing only the network, without addressing the root of the problem 🎀

And the root of the problem? It's standard congestion control (Reno [RFC5681]) or compatible variants (e.g., CUBIC [RFC8312]) used in TCP and in other transports, such as QUIC [RFC9000]. We'll use the term 'Classic' for these Reno-friendly congestion controls. Classic congestion controls cause relatively large sawtooth-shaped swings in queue occupancy 📈 So if a network operator naively tries to cut queuing delay by setting an AQM to run at a shallower queue, a Classic congestion control will significantly underutilize the link at the bottom of every sawtooth 😬 These sawteeth have also been getting longer as flow rate scales (see Section 5.1 and [RFC3649]).

It's been shown that if the sending host swaps a Classic congestion control for a 'Scalable' alternative, the performance under load of all those interactive apps can improve a lot once a suitable AQM is deployed in the network 🦋 Take the example solution cited below, which 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). Compare that with 5-20 ms on _average_ using a Classic congestion control and current state-of-the-art AQMs like 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 💖 You can deploy the L4S service at a bottleneck link alongside the existing best efforts service [DualPI2Linux], so unmodified apps can start using it as soon as the sender's stack gets updated. Access networks are typically designed with one link as the bottleneck for each site (which might be a home, a small enterprise, or a mobile device), so deploying at either or both ends of that 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. With newer transport protocols like QUIC [RFC9000] and Datagram Congestion Control Protocol (DCCP) [RFC4340], all receivers have always been suitable ✨

This document presents the L4S architecture, and it has three components 👑 First, network support to isolate L4S traffic from Classic traffic. Second, protocol features that let network elements identify L4S traffic. Third, host support for L4S congestion controls. The protocol itself 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 work together 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 ✨

okay bestie, this doc walks through the L4S architecture in three passes 🦋. First up, the quick overview in Section 2 gives the super high-level vibe and names the main pieces with barely any reasoning. It's just there for context, so the terminology definitions in Section 3 make sense and you know how the rest of the doc is laid out 💅. Then Section 4 digs into each component a bit more, with some rationale, but it's still mostly about what the architecture is rather than why 🤓. Finally, Section 5 justifies why each piece of the solution got picked (Section 5.1) and why those picks are different from other solutions (Section 5.2) 💖.

After the architecture is described, Section 6 checks where it actually applies 🌈. It covers the applications and use cases that motivated the design, the challenges of applying the architecture to different link technologies, and a bunch of incremental deployment models. That includes the two main deployment topologies, different sequences for incremental deployment, and how it interacts with preexisting approaches. The doc ends with the usual tailpieces, including a lot of discussion about traffic policing and other security considerations in Section 8 🔐.

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2. L4S Architecture Overview 🧚

Okay so there are 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 thing to get is that low latency is not provided by the network. Low latency comes from the careful behaviour of the Scalable congestion controllers used by L4S senders 💅. The network does have a role, mainly to isolate the low latency of the carefully behaving L4S traffic from the higher queuing delay needed by traffic with preexisting Classic behaviour. It also changes 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 lets the network figure out 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. Flow rate has scaled since TCP congestion control was first designed in 1988, so, 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). The examples in Section 5.1 and [RFC3649] show this. Therefore, control of queuing and utilization becomes very slack, and the slightest disturbances (e.g., from new flows starting) stop a high rate from being attained 😭.

With a Scalable congestion control, the average time from one congestion signal to the next (the recovery time) stays invariant as flow rate scales, all other factors being equal. This keeps the same degree of control over queuing and utilization, whatever the flow rate, and makes sure 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 🍓. DCTCP as-is functions well over wide-area round-trip times (RTTs), but most implementations lack certain safety features that would be necessary for use outside controlled environments, like data centres (see Section 6.4.3). So 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.). Honestly, between this document being drafted and published, the following Scalable congestion controls got 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 do this, e.g., FQ-CoDel [RFC8290]. However, using just two queues is sufficient, and it does not require inspecting 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. It would also 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 they give recommended default parameter settings. It is expected that L4S experiments will improve knowledge of parameter settings and of 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.

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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. In the L4S case, though, 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 🚦.

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4. L4S Architecture Components 🧩

The L4S architecture is composed of the elements in the following three subsections 💖.

4.1. Protocol Mechanisms 🎀

okay so the L4S architecture is basically doing three things: a) un-assigning what the identifier used to mean, b) re-assigning that same identifier to something new, and c) optionally adding more identifiers on top 💅

a. The big deal with a Scalable congestion control is that it relies on explicit congestion signals 🔥 Classic ECN [RFC3168] is strict: an ECN signal counts as equivalent to a drop, both when the network generates it and when hosts respond to it. L4S needs networks and hosts to treat each ECN signal in a more fine-grained way, one that's less harsh than a drop, so that L4S signals:

• can be way more frequent, and
• can be signalled right away, without waiting around for the queue to be smoothed out 🦋

To make this work, the Standards Track Classic ECN spec [RFC3168] had to be updated so L4S packets can escape the 'equivalent-to-drop' rule 🙈 [RFC8311] is the Standards Track update that relaxes specific requirements in [RFC3168] (and some other Standards Track RFCs), which clears the path for the experimental L4S changes ✨ The ECT(1) codepoint was previously used for the experimental ECN nonce [RFC3540]. [RFC8311] recategorizes that as historic, which frees the codepoint up again 🌸

b. [RFC9331] says ECT(1) is the identifier used to sort L4S packets into their own treatment, separate from Classic packets 👑 This covers the need to identify an alternative ECN treatment that was called out in [RFC4774].

The CE codepoint means Congestion Experienced for both L4S and Classic treatments, and that raises a worry 😭 A Classic AQM earlier on the path might have already marked some ECT(0) packets as CE. Those packets could then get sorted into the L4S queue by mistake. Appendix B of [RFC9331] explains why five unlikely things would all have to line up for this to actually cause harm, and even then it would only mean a vanishingly small chance of a spurious retransmission 💀 bestie, the odds are tiny.

c. A network operator might want to put certain unresponsive, non-L4S traffic into the L4S queue, as long as it's paced smoothly enough and at a low enough rate that it doesn't build a queue 🧚 Examples include VoIP, low rate datagrams used to sync online games, relatively low rate application-limited traffic, DNS, Lightweight Directory Access Protocol (LDAP), and so on 🍓 This traffic would need to be tagged with specific identifiers, such as a low-latency Diffserv codepoint like Expedited Forwarding (EF) [RFC3246], Non-Queue-Building (NQB) [NQB-PHB], or operator-specific identifiers 💜

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4.2. Network Components ✨

The L4S architecture is aiming for low latency without needing per-flow operations in network components, and that's the whole vibe 🌈 It doesn't rule out per-flow solutions though, so no need to panic 🫶 The known arrangements are: 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 a Classic and an L4S AQM instance in each queue; and c) dual queues with per-flow AQMs but no per-flow queues 👑

a. The Dual-Queue Coupled AQM (shown in Figure 1) gets the 'semi-permeable' membrane property we talked about earlier like this 🦋

• Latency isolation: two separate queues keep L4S queuing delay apart from the bigger queue that Classic traffic needs to keep the link fully utilized 🌸
• Bandwidth pooling: the two queues act like a single pool of bandwidth, where flows of either type get roughly equal throughput without the scheduler having to identify any flows 🔥 Each queue has its own AQM, but the Classic AQM sends a congestion signal to both queues so the two classes of congestion control respond consistently. The Classic AQM works out a drop/mark probability from congestion in its own queue. It uses that probability to drop or mark packets in its own queue, and also to influence the marking probability in the L4S queue. The coupling is strong enough to make the L4S flows slow down and leave the right amount of capacity for Classic flows, the same as if they were all the same type sharing one queue 💅

Then the scheduler can serve the L4S queue with priority (the '1' on the higher priority input in the figure), because L4S traffic doesn't offer enough load to use all the priority it's given 🧚 So:

• for latency isolation on short timescales (sub-round-trip), prioritizing the L4S queue protects its low latency by letting bursts drain quickly ⭐
• for bandwidth pooling on longer timescales (round-trip and longer), the Classic queue creates an equal and opposite pressure on the L4S traffic, so neither one gets priority over the other when it comes to bandwidth. The tension between prioritizing L4S and coupling the marking from the Classic AQM ends up giving approximately per-flow fairness 🎀

To guard against persistent L4S traffic deadlocking the Classic queue for a while in some implementations, it is advisable for the priority to be conditional rather than strict (see Appendix A of the DualQ spec [RFC9332]). 🙈

When there's no Classic traffic, the L4S queue's own AQM takes over. It starts congestion marking with a very shallow queue, so L4S traffic keeps its queuing delay super low ✨

If either queue becomes persistently overloaded, some ECN-capable packets get dropped, as recommended in Section 7 of the ECN spec [RFC3168] and Section 4.2.1 of the AQM recommendations [RFC7567] 😭 The trade-offs between the different approaches are discussed in Section 4.2.3 of the DualQ spec [RFC9332] (not shown in the figure) 💜

The Dual-Queue Coupled AQM is specified as generically as possible in [RFC9332], without naming the particular AQMs used in the two queues, so designers are free to try out diverse ideas 🌈 The informational appendices in that document give pseudocode for two specific AQM approaches. One is DualPI2 (pronounced Dual PI Squared) [DualPI2Linux], which uses the PI2 variant of PIE, plus 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, like FQ-CoDel or FQ-PIE, can be used for L4S 🧚 For example, inside each queue of an FQ-CoDel system you typically also get the option of ECN marking at an immediate (unsmoothed) shallow threshold, which is handy for data centres (see Section 5.2.7 of the FQ-CoDel spec [RFC8290]) ✨ On Linux this has been modified so the shallow threshold can apply only to ECT(1) packets [FQ_CoDel_Thresh]. Then, if a per-flow queue has Not-ECT or ECT(0) packets, the Classic AQM (e.g., CoDel) is applied. If it has ECT(1) packets, the shallower threshold (typically sub-millisecond) is applied instead 🦋 ECT(0) and Not-ECT packets could also be sorted into a separate flow queue from ECT(1) and CE packets, so they don't mix when 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, rather than the coupled per-queue marking of the Dual-Queue Coupled AQM 👑 One queue would isolate L4S packets, which are classified by their ECN codepoint. Flow rates could then be controlled by flow-specific marking. The policy goal of that marking could be to differentiate flow rates (e.g., [Nadas20], which needs extra 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 instead of one 🌸

Heads up: whenever 'DualQ' gets used loosely without saying whether the marking is per queue or per flow, it means a dual-queue AQM with per-queue marking 🫶

4.3. Host Mechanisms 💻

okay so the L4S architecture has two main mechanisms living in the end host, and I'm listing them for you bestie 🫶

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]. There's also a list of safety and performance improvements a Scalable congestion control needs before it's usable on the public Internet. It's known as the so-called 'Prague L4S requirements' and it lives in Appendix A of [RFC9331]. The subset that involves risk of harm to others has 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]. Slay 💅

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. They'll have to indicate that by using the ECT(1) codepoint 🦋. Scalable variants are under consideration for more recent transport protocols (e.g., QUIC). 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. ECN feedback in transport protocols 🌸: 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? 🧠

okay bestie, this is the part where we explain WHY L4S is built the way it is. Let's go 💜

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, so 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. That way 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). For example, 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 💅✨

Okay bestie, this is where we see what L4S brings to the table next to everything else already out there 👑 All of these approaches tackle some piece of the same problem space as L4S, and the big plot twist is that L4S doesn't replace them. It complements them or makes them better, so they're not mutually exclusive 🫶

Diffserv: Diffserv handles bandwidth apportionment for important traffic AND queuing latency for delay-sensitive traffic 🎀 Of those two, L4S ONLY goes after queuing latency 🦋 Diffserv is still needed where important traffic has to get priority (like for commercial reasons or protecting critical infrastructure traffic) -- see [L4S-DIFFSERV] 💼 Still, L4S can give low latency to ALL traffic inside each Diffserv class, including when there's only the one default Diffserv class 🌈

Also, Diffserv can only give a latency benefit if a small slice of the traffic on a bottleneck link asks for low latency 🙈 As we already covered, it does nothing when every app in use at one time at a single site (like a home, small business, or mobile device) needs low latency. L4S works for all traffic, so it doesn't need any of the management baggage (traffic policing or traffic contracts) that comes with favouring some packets over others 🧳 That lack of baggage ought to give L4S a better shot at end-to-end deployment 🚀

Now, if networks don't trust end systems to say which packets should be favoured, they sort packets into Diffserv classes themselves 🕵️‍♀️ But the tools those networks have, like inspecting flow identifiers or digging deeper into application signatures, don't always vibe with encrypting the layers above IP [RFC8404] 😭 In these cases, users can get either privacy or Quality of Service (QoS), but not both. Rude.

Like Diffserv, the L4S identifier lives in the IP header 📮 But unlike Diffserv, the L4S identifier doesn't say "I want or need a certain level of quality" 💅 Instead it promises a certain behaviour (Scalable congestion response), and networks can objectively verify that if they need to ✅ That's because low delay depends on collective host behaviour, whereas bandwidth priority depends on network behaviour 🧠

State-of-the-art AQMs: AQMs for Classic traffic, like PIE and FQ-CoDel, give a significant cut in queuing delay compared to no AQM at all 🙌 L4S is meant to complement these AQMs, and it should NOT distract from deploying them as widely as possible 📣 Hmm, wait, let me say it properly: it should not distract from the need to deploy them as widely as possible 📣 Still, AQMs alone can't push queuing delay too far down without seriously cutting link utilization, because the root cause is on the host 💻 That's where Classic congestion controls use big sawtoothing rate variations 📉 L4S settles this delay-versus-utilization tension by letting hosts minimize the amplitude of their sawteeth 🦷✨ A single-queue Classic AQM isn't enough to let hosts use small sawteeth, and there are two reasons why 😤 i) Smaller sawteeth wouldn't get lower delay in an AQM designed for larger amplitude Classic sawteeth, because a queue can only have one length at a time. ii) Much smaller sawteeth means way more frequent sawteeth, so L4S flows would drive a Classic AQM into a high level of ECN-marking. That would look like heavy congestion to Classic flows, which would then greatly reduce their rate as a result (see Section 6.4.4) 💀

Per-flow queuing or marking: Similarly, per-flow approaches like FQ-CoDel or Approx Fair CoDel [AFCD] are NOT incompatible with L4S 🤝 However, per-flow queuing alone isn't enough, because it only isolates one flow's queuing from others, not from itself 🫠 Per-flow implementations need Scalable congestion control support 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 still couldn't support apps that need both very low delay AND high bandwidth, like video-based control of remote procedures or interactive cloud-based video (see Note 1 below) 🎥

Even though per-flow techniques aren't incompatible with L4S, it's important to have the DualQ alternative 💜 That's because handling end-to-end (layer 4) flows inside the network (layer 3 or 2) rules out 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 need packet inspection to get at the end-to-end transport flow identifiers 🔐 By contrast, the DualQ form of L4S needs 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 folks think it's a perk that the network stops some flows from running faster than others 🙋‍♀️ Others say it's an inherent part of the Internet's appeal that apps can control their own rate while considering others' needs via congestion signals 🌸 They argue this has let apps with interesting rate behaviours evolve. For instance: i) a variable bit-rate video that varies around an equal share, rather than being forced to stay 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 doesn't make the IETF commit to one approach over the other, because it supports both so the 'market' can decide 🛍️ Still, in the spirit of 'Do one thing and do it well' [McIlroy78] 🧠, the DualQ option gives low delay without prejudging the flow-rate control question. Then flow rate policing can be added separately if wanted 🧁 Compared to scheduling, a policer would let apps control things up to a point, but the network would still get to set the point where it steps in to stop one flow from completely starving another ⭐

Note: 1. It might seem like self-inflicted queuing delay within a per-flow queue shouldn't count, because if the delay wasn't in the network, it would just shift to the sender 🤔 But modern adaptive apps, e.g., HTTP/2 [RFC9113] or some interactive media apps (see Section 6.1), can keep low latency objects at the front of their local send queue by shuffling the priorities of other objects that depend on the progress of other transfers (for example, see [lowat]) 🏃‍♀️ They can't shuffle objects once they've been released into the network 💅

Alternative Back-off ECN (ABE): Here again, L4S isn't an alternative to ABE, it's a complement that brings much lower queuing delay 🦋 ABE [RFC8511] changes host behaviour in response to ECN marking so it uses a link better and gives ECN flows faster throughput 🚄 It uses ECT(0) and assumes the network still treats ECN and drop the same way 🧾 So ABE exploits any lower queuing delay that AQMs can provide ✨ But, as explained above, AQMs still can't cut queuing delay too much without losing link utilization (to leave room 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, like an AQM 🌐 So it works pretty much on any path 🛤️ BBR keeps queuing delay reasonably low, but maybe not as low as state-of-the-art AQMs like PIE or FQ-CoDel, and definitely nowhere near as low as L4S 📉 Queuing delay also isn't consistently low, thanks to BBR's regular bandwidth probing spikes and its aggressive flow start-up phase 🎢

L4S complements BBR 💖 Indeed, BBRv2 can use L4S ECN where it's 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 parts of BBR with an explicit indication that hosts can use, both to converge on a fair rate and to stay below a shallow queue target set by the network 🎯 Without L4S ECN, both of these aspects need to be assumed or estimated 🔍

6. Applicability ✨

6. Applicability (continued) ✨

6.1. Applications 💖

Okay bestie, a transport layer that actually fixes the current latency issues opens up brand new service, product, and application possibilities. Literally so many. 🦋

With the L4S approach, these existing apps also get a seriously better quality of experience under load 🔥:

• gaming, including cloud-based gaming 🎮
• VoIP 📞
• video conferencing 🎀
• web browsing 🌐
• (adaptive) video streaming 📺
• instant messaging 💬

The super low queuing latency also makes some interactive features possible to offload to the cloud, stuff that would hardly even be usable right now 😭:

• cloud-based interactive video and
• cloud-based virtual and augmented reality 🥽

Both of those two have been successfully demoed with L4S, running together over a 40 Mb/s broadband access link 🌸. That link was packed with the other latency-sensitive apps from the list above, plus a bunch of downloads, all sharing the same bottleneck queue at the same time [L4Sdemo16] [L4Sdemo16-Video]. For the first one, a panoramic video of a football stadium could be swiped and pinched, and on the fly a proxy in the cloud would generate a sub-window of the match video under each user's finger-gesture control 🏈. For the second, a VR headset showed a viewport taken from a 360-degree camera in a racing car 🏎️. The user's head movements controlled the viewport that a cloud-based proxy pulled out. In both cases, with a 7 ms end-to-end base delay, the extra queuing delay of roughly 1 ms was so low that the video honestly seemed to be generated locally 🤯.

Using a swiping finger gesture or a head movement to pan a video is extremely latency-demanding, like, way more demanding than VoIP 👀. Human vision can pick up delays of just single milliseconds when the delay shows up as 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, aka the vestibular system) 🧠. With an alternative AQM, the video noticeably lagged behind the finger gestures and head movements. Major ick 🙈.

Without L4S's low queuing delay, cloud-based apps like these wouldn't be credible without a lot more access-network bandwidth (to deliver every possible area of the video that might be viewed) and more local processing. That would add weight and power consumption to head-mounted displays 😩. When all the interactive processing can happen in the cloud, only the data to be rendered for the end user needs to be sent ☁️.

Other low-latency, high-bandwidth apps, 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 🧩

These are the L4S use cases that various interested parties are currently looking at 👑:

• Where the bottleneck is one of several 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's no single administrator who can arrange 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 privacy and encryption, L4S offers an alternative. There's no need to pick 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's 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 📡

Some link technologies gather data from multiple packets into bursts and buffer incoming packets while building each burst. Wi-Fi, PON, and cable all do this packet aggregation, whereas fixed Ethernet and DSL don't. No sender, 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 far from the source are particularly challenging. The radio link capacity can swing rapidly by orders of magnitude, so it's considered desirable to hold a standing queue that can use sudden increases of capacity 🎢.
• Cellular networks get extra complicated by a perceived need to buffer so that hand-overs are imperceptible 📱.

L4S can't 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 of them were "the longest pole in the tent". But now that the "longest pole" is gone, it becomes worthwhile to minimize them, for instance by reducing packet aggregation burst sizes and MAC scheduling intervals 💅.

Also, certain link types, particularly radio-based links, are way 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 😬. Still, research referred to in the same section has shown potential for considerably more effective loss repair at the link layer, thanks 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 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 for deploying 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's competing with Classic traffic 👀.

6.4.1. Deployment Topology 🗺️

L4S AQMs won't 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 apply 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

okay so bestie, for any one L4S flow to actually give you a benefit, three parts (or sometimes just two) need to be deployed 🫶: i) the congestion control at the sender, ii) the AQM at the bottleneck, and iii) older transports (aka TCP) need upgraded receiver feedback too. This is the SAME deployment problem ECN faced [RFC8170], so we learned from that glow-up 💅

Firstly, L4S deployment gets to cheat a little ✨ because DCTCP already exists on loads of Internet hosts (e.g., Windows, FreeBSD, and Linux), both servers and clients. So an L4S AQM can be deployed at a network bottleneck to instantly give you a working deployment of all the L4S parts for testing, as long as the ECT(0) codepoint is switched to ECT(1). DCTCP does need some safety concerns fixed before it's ready 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 unlocks new interactive services and products that weren't possible before 🔥. It is way easier for companies to kick off new deployment work when there's budget for a new product trial. Meanwhile, if you only got an incremental performance improvement (like with Classic ECN), spending on deployment is much harder to justify 😭

Thirdly, the L4S identifier is defined so that network operators can start by enabling L4S exclusively for certain customers or certain applications 🎀. The clever bit is that this is carefully defined so it does NOT get in the way of future evolution towards L4S as an Internet-wide service. 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 the IETF never blessed it. 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). That way, 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, and it will already traverse middleboxes, peerings, etc. 🦋

+-+--------------------+----------------------+---------------------+

+-+--------------------+----------------------+---------------------+

+-+--------------------+----------------------+---------------------+

+-+--------------------+----------------------+---------------------+

+-+--------------------+----------------------+---------------------+

+-+--------------------+----------------------+---------------------+

Figure 3: Example L4S Deployment Sequence

Figure 3 shows some example sequences in which the parts of L4S might get deployed 🍓. It has the following stages, with a presumption that DCTCP is already installed at both ends:

1. DCTCP is not applicable for use over the public Internet, so it's being 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 gets immediate benefit, without any other deployment being needed ✨. In this example, downstream deployment goes 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. It's 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. So, 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. So 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 show up on the Internet as an increased level of CE marking 👀. It's hard to detect whether this is all due to the addition of ECN support in implementations of FQ-CoDel and/or FQ-COBALT. That's generally not problematic, because flow queue (FQ) scheduling inherently stops a flow from exceeding the 'fair' rate, no matter how aggressive it is 💖. 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 🎀

Okay so, let's talk about whether anyone polices how fast each individual flow goes 👀. 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]. They also 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. It's giving trust the senders 💅

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. No sneaky advantage for the flows that don't play fair 🙅‍♀️

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. Bestie, that's a sneaky one 💀

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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. Less policing, more vibes ✨

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). So the worst case is just... normal internet 🫠

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. Simple sorting, no drama 🧚

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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. 😭 Basically, nobody wants their own app to lag, so everyone's incentive lines up 🙌

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. 🔬✨

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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). Yikes, that's a whole vibe kill 😩 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]. 📚

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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]. Guarding the feedback loop, no cap 🌈🔒

8.5. Privacy Considerations 🦋

Okay so, as we talked about in Section 5.2, L4S doesn't block approaches that peek at end-to-end transport layer identifiers 👀. For example, L4S support has been added to FQ-CoDel, which sorts traffic by application flow identifier inside the network. BUT the main glow-up of L4S is the DualQ AQM framework, which doesn't need to dig any deeper than the outermost IP header, because the L4S identifier lives in the IP-ECN field 💅.

So the L4S architecture gets you super low queuing delay without requiring any inspection of stuff above the IP layer ✨. This means that people who want to encrypt their application flow identifiers, e.g., with IPsec or other encrypted VPN tunnels, don't have to sacrifice low delay 🔒🫶 [RFC8404].

Because L4S can give low delay to a broad set of applications that choose to use it, there's no need for individual applications or classes inside that big group to be distinguishable in any way while they're crossing the network 🌈. This takes away a lot of the ability to link the delay needs of traffic with other identifying features [RFC6973]. Some types of traffic may prefer not to use L4S, sure, but the coarse binary sorting of traffic reveals very little that could be exploited to compromise privacy 🙈💜.

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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>.

Okay, we're at the tail end of the bibliography, and these two entries are a whole vibe 📚✨

[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 🫶

Huge thank-you energy to everyone who gave useful review comments, bestie 💖 The people below took the time to look at this work and make it better, so they get all the flowers 🌸

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. 👑

Funding is the part that keeps the lights on, so here it is 💜 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 🦋

Meet the people behind the spec, the squad that made this whole thing happen 🍓

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


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


Marcelo Bagnulo
Universidad Carlos III de Madrid
Av. Universidad 30
28911 Madrid
Spain
Phone: 34 91 6249500
Email: marcelo@it.uc3m.es
URI:   https://www.it.uc3m.es


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