--- 1/draft-ietf-tsvwg-aqm-dualq-coupled-08.txt 2019-07-03 16:13:24.368257685 -0700 +++ 2/draft-ietf-tsvwg-aqm-dualq-coupled-09.txt 2019-07-03 16:13:24.508261246 -0700 @@ -1,289 +1,362 @@ Transport Area working group (tsvwg) K. De Schepper Internet-Draft Nokia Bell Labs Intended status: Experimental B. Briscoe, Ed. -Expires: May 8, 2019 CableLabs - O. Bondarenko - Simula Research Lab - I. Tsang - Nokia - November 04, 2018 +Expires: January 6, 2020 G. White + CableLabs + July 05, 2019 DualQ Coupled AQMs for Low Latency, Low Loss and Scalable Throughput (L4S) - draft-ietf-tsvwg-aqm-dualq-coupled-08 + draft-ietf-tsvwg-aqm-dualq-coupled-09 Abstract The Low Latency Low Loss Scalable Throughput (L4S) architecture allows data flows over the public Internet to predictably achieve ultra-low queuing latency, generally zero congestion loss and scaling of per-flow throughput without the problems of traditional TCP. To - achieve this, L4S data flows use a 'scalable' congestion control - similar to Data Centre TCP (DCTCP) and a form of Explicit Congestion - Notification (ECN) with modified behaviour. However, until now, - scalable congestion controls did not co-exist with existing TCP Reno/ - Cubic traffic---scalable controls are so aggressive that 'Classic' - TCP algorithms drive themselves to starvation. Therefore, until now, - L4S controls could only be deployed where a clean-slate environment - could be arranged, such as in private data centres (hence the name - DCTCP). This specification defines `DualQ Coupled Active Queue - Management (AQM)', which enables these scalable congestion controls - to safely co-exist with Classic Internet traffic. + achieve this, L4S data flows have to use one of the family of + 'Scalable' congestion controls (Data Centre TCP and TCP Prague are + examples) and a form of Explicit Congestion Notification (ECN) with + modified behaviour. However, until now, Scalable congestion controls + did not co-exist with existing TCP Reno/Cubic traffic --- Scalable + controls are so aggressive that 'Classic' TCP algorithms drive + themselves to a small capacity share. Therefore, until now, L4S + controls could only be deployed where a clean-slate environment could + be arranged, such as in private data centres (hence the name DCTCP). + This specification defines `DualQ Coupled Active Queue Management + (AQM)', which enables these Scalable congestion controls to safely + co-exist with Classic Internet traffic. - The Coupled AQM ensures that a flow runs at about the same rate - whether it uses DCTCP or TCP Reno/Cubic. It achieves this - indirectly, without having to inspect transport layer flow - identifiers, When tested in a residential broadband setting, DCTCP - also achieves sub-millisecond average queuing delay and zero - congestion loss under a wide range of mixes of DCTCP and `Classic' - broadband Internet traffic, without compromising the performance of - the Classic traffic. The solution also reduces network complexity - and eliminates network configuration. + The Coupled AQM ensures that competing Scalable and Classic flows run + at about the same rate. It achieves this indirectly, without having + to inspect transport layer flow identifiers, When tested in a + residential broadband setting, DCTCP also achieves sub-millisecond + average queuing delay and zero congestion loss under a wide range of + mixes of DCTCP and `Classic' broadband Internet traffic, without + compromising the performance of the Classic traffic. The solution + also reduces network complexity and requires no configuration for the + public Internet. Status of This Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet- Drafts is at https://datatracker.ietf.org/drafts/current/. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." - This Internet-Draft will expire on May 8, 2019. + This Internet-Draft will expire on January 6, 2020. Copyright Notice - Copyright (c) 2018 IETF Trust and the persons identified as the + Copyright (c) 2019 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, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License. Table of Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3 - 1.1. Problem and Scope . . . . . . . . . . . . . . . . . . . . 3 - 1.2. Terminology . . . . . . . . . . . . . . . . . . . . . . . 5 - 1.3. Features . . . . . . . . . . . . . . . . . . . . . . . . 6 - 2. DualQ Coupled AQM . . . . . . . . . . . . . . . . . . . . . . 7 - 2.1. Coupled AQM . . . . . . . . . . . . . . . . . . . . . . . 8 - 2.2. Dual Queue . . . . . . . . . . . . . . . . . . . . . . . 9 - 2.3. Traffic Classification . . . . . . . . . . . . . . . . . 9 - 2.4. Overall DualQ Coupled AQM Structure . . . . . . . . . . . 10 - 2.5. Normative Requirements for a DualQ Coupled AQM . . . . . 12 - 2.5.1. Functional Requirements . . . . . . . . . . . . . . . 12 - 2.5.1.1. Requirements in Unexpected Cases . . . . . . . . 13 - 2.5.2. Management Requirements . . . . . . . . . . . . . . . 15 - 3. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 16 - 4. Security Considerations . . . . . . . . . . . . . . . . . . . 16 - 4.1. Overload Handling . . . . . . . . . . . . . . . . . . . . 16 + 1.1. Outline of the Problem . . . . . . . . . . . . . . . . . 3 + 1.2. Scope . . . . . . . . . . . . . . . . . . . . . . . . . . 5 + 1.3. Terminology . . . . . . . . . . . . . . . . . . . . . . . 7 + 1.4. Features . . . . . . . . . . . . . . . . . . . . . . . . 8 + 2. DualQ Coupled AQM . . . . . . . . . . . . . . . . . . . . . . 9 + 2.1. Coupled AQM . . . . . . . . . . . . . . . . . . . . . . . 9 + 2.2. Dual Queue . . . . . . . . . . . . . . . . . . . . . . . 10 + 2.3. Traffic Classification . . . . . . . . . . . . . . . . . 11 + 2.4. Overall DualQ Coupled AQM Structure . . . . . . . . . . . 11 + 2.5. Normative Requirements for a DualQ Coupled AQM . . . . . 14 + 2.5.1. Functional Requirements . . . . . . . . . . . . . . . 14 + 2.5.1.1. Requirements in Unexpected Cases . . . . . . . . 15 + 2.5.2. Management Requirements . . . . . . . . . . . . . . . 16 + 2.5.2.1. Configuration . . . . . . . . . . . . . . . . . . 16 + 2.5.2.2. Monitoring . . . . . . . . . . . . . . . . . . . 18 + 2.5.2.3. Anomaly Detection . . . . . . . . . . . . . . . . 18 + 2.5.2.4. Deployment, Coexistence and Scaling . . . . . . . 19 + 3. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 19 + 4. Security Considerations . . . . . . . . . . . . . . . . . . . 19 + 4.1. Overload Handling . . . . . . . . . . . . . . . . . . . . 19 4.1.1. Avoiding Classic Starvation: Sacrifice L4S Throughput - or Delay? . . . . . . . . . . . . . . . . . . . . . . 17 + or Delay? . . . . . . . . . . . . . . . . . . . . . . 20 4.1.2. Congestion Signal Saturation: Introduce L4S Drop or - Delay? . . . . . . . . . . . . . . . . . . . . . . . 18 - 4.1.3. Protecting against Unresponsive ECN-Capable Traffic . 19 - 5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 19 - 6. References . . . . . . . . . . . . . . . . . . . . . . . . . 20 - 6.1. Normative References . . . . . . . . . . . . . . . . . . 20 - 6.2. Informative References . . . . . . . . . . . . . . . . . 20 - Appendix A. Example DualQ Coupled PI2 Algorithm . . . . . . . . 23 - A.1. Pass #1: Core Concepts . . . . . . . . . . . . . . . . . 23 - A.2. Pass #2: Overload Details . . . . . . . . . . . . . . . . 30 - Appendix B. Example DualQ Coupled Curvy RED Algorithm . . . . . 33 - Appendix C. Guidance on Controlling Throughput Equivalence . . . 39 - Appendix D. Open Issues . . . . . . . . . . . . . . . . . . . . 40 - Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 41 + Delay? . . . . . . . . . . . . . . . . . . . . . . . 21 + 4.1.3. Protecting against Unresponsive ECN-Capable Traffic . 22 + 5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 22 + 6. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 23 + 7. References . . . . . . . . . . . . . . . . . . . . . . . . . 23 + 7.1. Normative References . . . . . . . . . . . . . . . . . . 23 + 7.2. Informative References . . . . . . . . . . . . . . . . . 24 + Appendix A. Example DualQ Coupled PI2 Algorithm . . . . . . . . 27 + A.1. Pass #1: Core Concepts . . . . . . . . . . . . . . . . . 28 + A.2. Pass #2: Overload Details . . . . . . . . . . . . . . . . 36 + Appendix B. Example DualQ Coupled Curvy RED Algorithm . . . . . 40 + B.1. Curvy RED in Pseudocode . . . . . . . . . . . . . . . . . 40 + B.2. Efficient Implementation of Curvy RED . . . . . . . . . . 46 + Appendix C. Guidance on Controlling Throughput Equivalence . . . 48 + Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 49 1. Introduction -1.1. Problem and Scope + This document specifies a framework for DualQ Coupled AQMs, which is + the network part of the L4S architecture [I-D.ietf-tsvwg-l4s-arch]. + L4S enables both ultra-low queuing latency and high throughput at the + same time, for ad hoc numbers of capacity-seeking applications all + sharing the same capacity. + +1.1. Outline of the Problem Latency is becoming the critical performance factor for many (most?) applications on the public Internet, e.g. interactive Web, Web services, voice, conversational video, interactive video, interactive remote presence, instant messaging, online gaming, remote desktop, cloud-based applications, and video-assisted remote control of machinery and industrial processes. In the developed world, further increases in access network bit-rate offer diminishing returns, whereas latency is still a multi-faceted problem. In the last decade or so, much has been done to reduce propagation time by placing caches or servers closer to users. However, queuing remains a major intermittent component of latency. - The Diffserv architecture provides Expedited Forwarding [RFC3246], so - that low latency traffic can jump the queue of other traffic. - However, on access links dedicated to individual sites (homes, small - enterprises or mobile devices), often all traffic at any one time - will be latency-sensitive and, if all the traffic on a link is marked - as EF, Diffserv cannot reduce the delay of any of it. In contrast, - the Low Latency Low Loss Scalable throughput (L4S) approach removes - the causes of any unnecessary queuing delay. + Traditionally ultra-low latency has only been available for a few + selected low rate applications, that confine their sending rate + within a specially carved-off portion of capacity, which is + prioritized over other traffic, e.g. Diffserv EF [RFC3246]. Up to + now it has not been possible to allow any number of low latency, high + throughput applications to seek to fully utilize available capacity, + because the capacity-seeking process itself causes too much queuing + delay. - The bufferbloat project has shown that excessively-large buffering - (`bufferbloat') has been introducing significantly more delay than - the underlying propagation time. These delays appear only - intermittently--only when a capacity-seeking (e.g. TCP) flow is long - enough for the queue to fill the buffer, making every packet in other - flows sharing the buffer sit through the queue. + To reduce this queuing delay caused by the capacity seeking process, + changes either to the network alone or to end-systems alone are in + progress. L4S involves a recognition that both approaches are + yielding diminishing returns: - Active queue management (AQM) was originally developed to solve this - problem (and others). Unlike Diffserv, which gives low latency to - some traffic at the expense of others, AQM controls latency for _all_ - traffic in a class. In general, AQMs introduce an increasing level - of discard from the buffer the longer the queue persists above a - shallow threshold. This gives sufficient signals to capacity-seeking - (aka. greedy) flows to keep the buffer empty for its intended - purpose: absorbing bursts. However, RED [RFC2309] and other - algorithms from the 1990s were sensitive to their configuration and - hard to set correctly. So, AQM was not widely deployed in the 1990s. + o Recent state-of-the-art active queue management (AQM) in the + network, e.g. fq_CoDel [RFC8290], PIE [RFC8033], Adaptive + RED [ARED01] ) has reduced queuing delay for all traffic, not just + a select few applications. However, no matter how good the AQM, + the capacity-seeking (sawtoothing) rate of TCP-like congestion + controls represents a lower limit that will either cause queuing + delay to vary or cause the link to be under-utilized. These AQMs + are tuned to allow a typical capacity-seeking TCP-Friendly flow to + induce an average queue that roughly doubles the base RTT, adding + 5-15 ms of queuing on average (cf. 500 microseconds with L4S for + the same mix of long-running and web traffic). However, for many + applications low delay is not useful unless it is consistently + low. With these AQMs, 99th percentile queuing delay is 20-30 ms + (cf. 2 ms with the same traffic over L4S). - More recent state-of-the-art AQMs, e.g. fq_CoDel [RFC8290], - PIE [RFC8033], Adaptive RED [ARED01], are easier to configure, - because they define the queuing threshold in time not bytes, so it is - invariant for different link rates. However, no matter how good the - AQM, the sawtoothing rate of TCP will either cause queuing delay to - vary or cause the link to be under-utilized. Even with a perfectly - tuned AQM, the additional queuing delay will be of the same order as - the underlying speed-of-light delay across the network. Flow-queuing - can isolate one flow from another, but it cannot isolate a TCP flow - from the delay variations it inflicts on itself, and it has other - problems - it overrides the flow rate decisions of variable rate - video applications, it does not recognise the flows within IPSec VPN - tunnels and it is relatively expensive to implement. + o Similarly, recent research into using e2e congestion control + without needing an AQM in the network (e.g.BBRv1 [BBRv1]) seems to + have hit a similar lower limit to queuing delay of about 20ms on + average (and any additional BBRv1 flow adds another 20ms of + queuing) but there are also regular 25ms delay spikes due to + bandwidth probes and 60ms spikes due to flow-starts. - It seems that further changes to the network alone will now yield - diminishing returns. Data Centre TCP (DCTCP [RFC8257]) teaches us - that a small but radical change to TCP is needed to cut two major - outstanding causes of queuing delay variability: + L4S learns from the experience of Data Center TCP [RFC8257], which + shows the power of complementary changes both in the network and on + end-systems. DCTCP teaches us that two small but radical changes to + congestion control are needed to cut the two major outstanding causes + of queuing delay variability: - 1. the `sawtooth' varying rate of TCP itself; + 1. Far smaller rate variations (sawteeth) than TCP-Friendly + congestion controls; - 2. the smoothing delay deliberately introduced into AQMs to permit - bursts without triggering losses. + 2. A shift of smoothing and hence smoothing delay from network to + sender. - The former causes a flow's round trip time (RTT) to vary from about 1 - to 2 times the base RTT between the machines in question. The latter - delays the system's response to change by a worst-case - (transcontinental) RTT, which could be hundreds of times the actual - RTT of typical traffic from localized CDNs. + Without the former, a 'Classic' flow's round trip time (RTT) varies + between roughly 1 and 2 times the base RTT between the machines in + question. Without the latter a 'Classic' flow's response to changing + events is delayed by a worst-case (transcontinental) RTT, which could + be hundreds of times the actual smoothing delay needed for the RTT of + typical traffic from localized CDNs. - Latency is not our only concern: + These changes are the two main features of the family of so-called + 'Scalable' congestion controls (which includes DCTCP). Both these + changes only reduce delay in combination with a complementary change + in the network and they are both only feasible with ECN, not drop, + for the signalling: - 3. It was known when TCP was first developed that it would not scale - to high bandwidth-delay products [TCP-CA]. + 1. The smaller sawteeth need an extremely shallow ECN packet-marking + threshold in the queue. - Given regular broadband bit-rates over WAN distances are - already [RFC3649] beyond the scaling range of `classic' TCP Reno, - `less unscalable' Cubic [RFC8312] and - Compound [I-D.sridharan-tcpm-ctcp] variants of TCP have been - successfully deployed. However, these are now approaching their - scaling limits. Unfortunately, fully scalable TCPs such as DCTCP - cause `classic' TCP to starve itself, which is why they have been - confined to private data centres or research testbeds (until now). + 2. And no smoothing in the network means that every fluctuation of + the queue is signalled immediately. + + Without ECN, either of these would lead to very high loss levels. + But, with ECN, the resulting high marking levels are fine. + + However, until now, Scalable congestion controls (like DCTCP) did not + co-exist with existing ECN-capable TCP Reno [RFC5681] or Cubic + [RFC8312] traffic --- Scalable controls are so aggressive that these + 'Classic' TCP algorithms drive themselves to a small capacity share. + Therefore, until now, L4S controls could only be deployed where a + clean-slate environment could be arranged, such as in private data + centres (hence the name DCTCP). This document specifies a `DualQ Coupled AQM' extension that solves - the problem of coexistence between scalable and classic flows, - without having to inspect flow identifiers. The AQM is not like - flow-queuing approaches [RFC8290] that classify packets by flow - identifier into numerous separate queues in order to isolate sparse - flows from the higher latency in the queues assigned to heavier - flows. In contrast, the AQM exploits the behaviour of scalable - congestion controls like DCTCP so that every packet in every flow - sharing the queue for DCTCP-like traffic can be served with very low - latency. + the problem of coexistence between Scalable and Classic flows, + without having to inspect flow identifiers. It is not like flow- + queuing approaches [RFC8290] that classify packets by flow identifier + into separate queues in order to isolate sparse flows from the higher + latency in the queues assigned to heavier flows. If a flow needs + both low delay and high throughput, having a queue to itself does not + isolate it from the harm it causes to itself. In contrast, L4S + addresses the root cause of the latency problem --- it is an enabler + for the smooth low latency scalable behaviour of Scalable congestion + controls, so that every packet in every flow can enjoy very low + latency, then there is no need to isolate each flow into a separate + queue. - This AQM extension can be combined with any AQM designed for a single - queue that generates a statistical or deterministic mark/drop - probability driven by the queue dynamics. In many cases it - simplifies the basic control algorithm, and requires little extra - processing. Therefore it is believed the Coupled AQM would be - applicable and easy to deploy in all types of buffers; buffers in - cost-reduced mass-market residential equipment; buffers in end-system - stacks; buffers in carrier-scale equipment including remote access - servers, routers, firewalls and Ethernet switches; buffers in network - interface cards, buffers in virtualized network appliances, - hypervisors, and so on. +1.2. Scope + + L4S involves complementary changes in the network and on end-systems: + + Network: A DualQ Coupled AQM (defined in the present document); + + End-system: A Scalable congestion control (defined in Section 2.1. + + Packet identifier: The network and end-system parts of L4S can be + deployed incrementally, because they both identify L4S packets + using the experimentally assigned explicit congestion notification + (ECN) codepoints in the IP header: ECT(1) and CE [RFC8311] + [I-D.ietf-tsvwg-ecn-l4s-id]. + + Data Center TCP (DCTCP [RFC8257]) is an example of a Scalable + congestion control that has been deployed for some time in Linux, + Windows and FreeBSD operating systems and Relentless TCP [Mathis09] + is another example. During the progress of this document through the + IETF a number of other Scalable congestion controls were implemented, + e.g. TCP Prague [PragueLinux], QUIC Prague and the L4S variant of + SCREAM for real-time media [RFC8298]. (Note: after the v3.19 Linux + kernel, bugs were introduced into DCTCP's scalable behaviour and not + all the patches applied for L4S evaluation had been applied to the + mainline Linux kernel, which was at v5.2 at the time of writing). + + The focus of this specification is to get the network part of the L4S + service in place. Then, without any management intervention, + applications can exploit this new network capability as their + operating systems migrate to Scalable congestion controls, which can + then evolve _while_ their benefits are being enjoyed by everyone on + the Internet. + + The DualQ Coupled AQM framework can incorporate any AQM designed for + a single queue that generates a statistical or deterministic mark/ + drop probability driven by the queue dynamics. Pseudocode examples + of two different DualQ Coupled AQMs are given the appendices. In + many cases the framework simplifies the basic control algorithm, and + requires little extra processing. Therefore it is believed the + Coupled AQM would be applicable and easy to deploy in all types of + buffers; buffers in cost-reduced mass-market residential equipment; + buffers in end-system stacks; buffers in carrier-scale equipment + including remote access servers, routers, firewalls and Ethernet + switches; buffers in network interface cards, buffers in virtualized + network appliances, hypervisors, and so on. For the public Internet, nearly all the benefit will typically be achieved by deploying the Coupled AQM into either end of the access link between a 'site' and the Internet, which is invariably the bottleneck. Here, the term 'site' is used loosely to mean a home, an office, a campus or mobile user equipment. + Latency is not the only concern of L4S: + + o The 'Low Loss" part of the name denotes that L4S generally + achieves zero congestion loss (which would otherwise cause + retransmission delays), due to its use of ECN. + + o The "Scalable throughput" part of the name denotes that the per- + flow throughput of Scalable congestion controls should scale + indefinitely, avoiding the imminent scaling problems with TCP- + Friendly congestion control algorithms [RFC3649]. + + The former is clearly in scope of this AQM document. However, the + latter is an outcome of the end-system behaviour, and therefore + outside the scope of this AQM document, even though the AQM is an + enabler. + The overall L4S architecture [I-D.ietf-tsvwg-l4s-arch] gives more - detail, including on wider deployment aspects such as coexistence in - bottlenecks where a DualQ Coupled AQM has not been deployed. The - supporting papers [PI2] and [DCttH15] give the full rationale for the - AQM's design, both discursively and in more precise mathematical - form. + detail, including on wider deployment aspects such as backwards + compatibility of Scalable congestion controls in bottlenecks where a + DualQ Coupled AQM has not been deployed. The supporting papers [PI2] + and [DCttH15] give the full rationale for the AQM's design, both + discursively and in more precise mathematical form. -1.2. Terminology +1.3. Terminology The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in [RFC2119] when, and only when, they appear in all capitals, as shown here. The DualQ Coupled AQM uses two queues for two services. Each of the following terms identifies both the service and the queue that provides the service: Classic (denoted by subscript C): The `Classic' service is intended for all the behaviours that currently co-exist with TCP Reno (TCP Cubic, Compound, SCTP, etc). Low-Latency, Low-Loss and Scalable (L4S, denoted by subscript L): The `L4S' service is intended for a set of congestion controls - with scalable properties (e.g. DCTCP [RFC8257], Relentless - TCP [Mathis09], the L4S variant of SCREAM for real-time - media {ToDo: ref}). For the public Internet a scalable control - has to comply with the requirements in [I-D.ietf-tsvwg-ecn-l4s-id] - (aka. the 'TCP Prague requirements'). + with scalable properties, such as TCP Prague and DCTCP. For the + public Internet an L4S transport has to comply with the + requirements in Section 4 of [I-D.ietf-tsvwg-ecn-l4s-id] (aka. + the 'Prague L4S requirements'). Either service can cope with a proportion of unresponsive or less- responsive traffic as well, as long (e.g. DNS, VoIP, game sync datagrams, etc), just as a single queue AQM can if this traffic makes minimal contribution to queuing. The DualQ Coupled AQM behaviour below is defined to be similar to a single FIFO queue with respect to unresponsive and overload traffic. -1.3. Features +1.4. Features - The AQM couples marking and/or dropping across the two queues such - that a flow will get roughly the same throughput whichever it uses. - Therefore both queues can feed into the full capacity of a link and - no rates need to be configured for the queues. The L4S queue enables - scalable congestion controls like DCTCP to give stunningly low and - predictably low latency, without compromising the performance of - competing 'Classic' Internet traffic. Thousands of tests have been - conducted in a typical fixed residential broadband setting. Typical - experiments used base round trip delays up to 100ms between the data - centre and home network, and large amounts of background traffic in - both queues. For every L4S packet, the AQM kept the average queuing - delay below 1ms (or 2 packets if serialization delay is bigger for - slow links), and no losses at all were introduced by the AQM. - Details of the extensive experiments are available [PI2] [DCttH15]. + The AQM couples marking and/or dropping from the Classic queue to the + L4S queue in such a way that a flow will get roughly the same + throughput whichever it uses. Therefore both queues can feed into + the full capacity of a link and no rates need to be configured for + the queues. The L4S queue enables Scalable congestion controls like + DCTCP or TCP Prague to give stunningly low and predictably low + latency, without compromising the performance of competing 'Classic' + Internet traffic. + + Thousands of tests have been conducted in a typical fixed residential + broadband setting. Experiments used a range of base round trip + delays up to 100ms and link rates up to 200 Mb/s between the data + centre and home network, with varying amounts of background traffic + in both queues. For every L4S packet, the AQM kept the average + queuing delay below 1ms (or 2 packets where serialization delay + exceeded 1ms on slower links), with 99th percentile no worse than + 2ms. No losses at all were introduced by the L4S AQM. Details of + the extensive experiments are available [PI2] [DCttH15]. Subjective testing was also conducted by multiple people all simultaneously using very demanding high bandwidth low latency applications over a single shared access link [L4Sdemo16]. In one application, each user could use finger gestures to pan or zoom their own high definition (HD) sub-window of a larger video scene generated on the fly in 'the cloud' from a football match. Another user wearing VR goggles was remotely receiving a feed from a 360-degree camera in a racing car, again with the sub-window in their field of vision generated on the fly in 'the cloud' dependent on their head @@ -293,153 +366,156 @@ latency was so low that the football picture appeared to stick to the user's finger on the touchpad and the experience fed from the remote camera did not noticeably lag head movements. All the L4S data (even including the downloads) achieved the same ultra-low latency. With an alternative AQM, the video noticeably lagged behind the finger gestures and head movements. Unlike Diffserv Expedited Forwarding, the L4S queue does not have to be limited to a small proportion of the link capacity in order to achieve low delay. The L4S queue can be filled with a heavy load of - capacity-seeking flows like DCTCP and still achieve low delay. The - L4S queue does not rely on the presence of other traffic in the + capacity-seeking flows (TCP Prague etc.) and still achieve low delay. + The L4S queue does not rely on the presence of other traffic in the Classic queue that can be 'overtaken'. It gives low latency to L4S traffic whether or not there is Classic traffic, and the latency of Classic traffic does not suffer when a proportion of the traffic is - L4S. The two queues are only necessary because DCTCP-like flows - cannot keep latency predictably low and keep utilization high if they - are mixed with legacy TCP flows, + L4S. - The experiments used the Linux implementation of DCTCP that is - deployed in private data centres, without any modification despite - its known deficiencies. Nonetheless, certain modifications will be - necessary before DCTCP is safe to use on the Internet, which are - recorded in Appendix A of [I-D.ietf-tsvwg-ecn-l4s-id]. However, the - focus of this specification is to get the network service in place. - Then, without any management intervention, applications can exploit - it by migrating to scalable controls like DCTCP, which can then - evolve _while_ their benefits are being enjoyed by everyone on the - Internet. + The two queues are only necessary because: + + o the large variations (sawteeth) of Classic flows need roughly a + base RTT of queuing delay to ensure full utilization + + o while Scalable flows do not need a queue to keep utilization high, + but they cannot keep latency predictably low if they are mixed + with legacy TCP flows, + + The L4S queue has latency priority, but the coupling from the Classic + to the L4S AQM (explained below) ensures that it does not have + bandwidth priority over the Classic queue. 2. DualQ Coupled AQM There are two main aspects to the approach: o the Coupled AQM that addresses throughput equivalence between Classic (e.g. Reno, Cubic) flows and L4S flows (that satisfy the - TCP Prague requirements). + Prague L4S requirements). o the Dual Queue structure that provides latency separation for L4S flows to isolate them from the typically large Classic queue. 2.1. Coupled AQM In the 1990s, the `TCP formula' was derived for the relationship between TCP's congestion window, cwnd, and its drop probability, p. To a first order approximation, cwnd of TCP Reno is inversely proportional to the square root of p. - We focus on Reno as the worst case, because if we do not harm Reno, - we will not harm Cubic. Nonetheless, TCP Cubic implements a Reno- - compatibility mode, which is the only relevant mode for typical RTTs - under 20ms as long as the throughput of a single flow is less than - about 500Mb/s. Therefore it can be assumed that Cubic traffic - behaves similarly to Reno (but with a slightly different constant of - proportionality). The term 'Classic' will be used for the collection - of Reno-friendly traffic including Cubic in Reno mode. + The design focuses on Reno as the worst case, because if it does no + harm to Reno, it will not harm Cubic or any traffic designed to be + friendly to Reno. TCP Cubic implements a Reno-compatibility mode, + which is relevant for typical RTTs under 20ms as long as the + throughput of a single flow is less than about 700Mb/s. In such + cases it can be assumed that Cubic traffic behaves similarly to Reno + (but with a slightly different constant of proportionality). The + term 'Classic' will be used for the collection of Reno-friendly + traffic including Cubic in Reno mode. The supporting paper [PI2] includes the derivation of the equivalent rate equation for DCTCP, for which cwnd is inversely proportional to p (not the square root), where in this case p is the ECN marking probability. DCTCP is not the only congestion control that behaves - like this, so the term 'L4S' traffic will be used for all similar - behaviour. + like this, so the term 'Scalable' will be used for all similar + congestion control behaviours (see examples in Section 1.2). The + term 'L4S' is also used for traffic driven by a Scalable congestion + control that also complies with the additional 'Prague L4S' + requirements [I-D.ietf-tsvwg-ecn-l4s-id]. - For safe co-existence, under stationary conditions, a DCTCP flow has - to run at roughly the same rate as a Reno TCP flow (all other factors - being equal). So the drop or marking probability for Classic + For safe co-existence, under stationary conditions, a Scalable flow + has to run at roughly the same rate as a Reno TCP flow (all other + factors being equal). So the drop or marking probability for Classic traffic, p_C has to be distinct from the marking probability for L4S traffic, p_L. [RFC8311] updates the original ECN specification [RFC3168] to allow these probabilities to be distinct, because RFC 3168 required them to be the same. Also, to remain stable, Classic sources need the network to smooth - p_C so it changes relatively slowly. In contrast, L4S avoids - smoothing in the network, because it delays all signals for a worst- - case RTT. So instead, L4S sources smooth the ECN marking probability - themselves, so they expect the network to generate ECN marks with a - probability p_L that tracks the instantaneous unsmoothed queue. + p_C so it changes relatively slowly. It is hard for a network node + to know the RTTs of all the flows, so a Classic AQM adds a _worst- + case_ RTT of smoothing delay (about 100-200 ms). In contrast, L4S + shifts responsibility for smoothing ECN feedback to the sender, which + only delays its response by its _own_ RTT, and allows a more + immediate response if necessary. The Coupled AQM achieves safe coexistence by making the Classic drop probability p_C proportional to the square of the coupled L4S probability p_CL. p_CL is an input to the instantaneous L4S marking probability p_L but it changes as slowly as p_C. This makes the Reno flow rate roughly equal the DCTCP flow rate, because the squaring of p_CL counterbalances the square root of p_C in the Classic 'TCP formula'. Stating this as a formula, the relation between Classic drop probability, p_C, and the coupled L4S probability p_CL needs to take the form: p_C = ( p_CL / k )^2 (1) - where k is the constant of proportionality, which we shall call the + where k is the constant of proportionality, which is termed the coupling factor. 2.2. Dual Queue - Classic traffic typically builds a large queue to prevent under- + Classic traffic needs to build a large queue to prevent under- utilization. Therefore a separate queue is provided for L4S traffic, - and it is scheduled with priority over Classic. Priority is - conditional to prevent starvation of Classic traffic. + and it is scheduled with priority over the Classic queue. Priority + is conditional to prevent starvation of Classic traffic. Nonetheless, coupled marking ensures that giving priority to L4S traffic still leaves the right amount of spare scheduling time for Classic flows to each get equivalent throughput to DCTCP flows (all other factors such as RTT being equal). 2.3. Traffic Classification Both the Coupled AQM and DualQ mechanisms need an identifier to distinguish L and C packets. Then the coupling algorithm can achieve coexistence without having to inspect flow identifiers, because it can apply the appropriate marking or dropping probability to all flows of each type. A separate specification [I-D.ietf-tsvwg-ecn-l4s-id] requires the sender to use - the ECT(1) codepoint of the ECN field as this identifier, having - assessed various alternatives. An additional process document has - proved necessary to make the ECT(1) codepoint available for + the ECT(1) and CE codepoints of the ECN field as this identifier, + having assessed various alternatives. An additional process document + has proved necessary to make the ECT(1) codepoint available for experimentation [RFC8311]. For policy reasons, an operator might choose to steer certain packets (e.g. from certain flows or with certain addresses) out of the L queue, even though they identify themselves as L4S by their ECN - codepoints. In such cases, the device MUST NOT alter the ECN field, - so that it is preserved end-to-end. The aim is that each operator - can choose how it treats L4S traffic locally, but an individual - operator does not alter the identification of L4S packets, which - would prevent other operators downstream from making their own - choices on how to treat L4S traffic. + codepoints. In such cases, [I-D.ietf-tsvwg-ecn-l4s-id] says that the + device "MUST NOT alter the end-to-end L4S ECN identifier", so that it + is preserved end-to-end. The aim is that each operator can choose + how it treats L4S traffic locally, but an individual operator does + not alter the identification of L4S packets, which would prevent + other operators downstream from making their own choices on how to + treat L4S traffic. - In addition, other identifiers could be used to classify certain - additional packet types into the L queue, that are deemed not to risk - harming the L4S service. For instance addresses of specific + In addition, an operator could use other identifiers to classify + certain additional packet types into the L queue that it deems will + not risk harm to the L4S service. For instance addresses of specific applications or hosts (see [I-D.ietf-tsvwg-ecn-l4s-id]), specific Diffserv codepoints such as EF (Expedited Forwarding) and Voice-Admit service classes (see [I-D.briscoe-tsvwg-l4s-diffserv]) or certain - protocols (e.g. ARP, DNS). - - Note that the mechanism only reads these classifiers, it MUST NOT re- - mark or alter these identifiers (except for marking the ECN field - with the CE codepoint - with increasing frequency to indicate - increasing congestion). + protocols (e.g. ARP, DNS). Note that the mechanism only reads these + identifiers. [I-D.ietf-tsvwg-ecn-l4s-id] says it "MUST NOT alter + these non-ECN identifiers". 2.4. Overall DualQ Coupled AQM Structure Figure 1 shows the overall structure that any DualQ Coupled AQM is likely to have. This schematic is intended to aid understanding of the current designs of DualQ Coupled AQMs. However, it is not intended to preclude other innovative ways of satisfying the normative requirements in Section 2.5 that minimally define a DualQ Coupled AQM. @@ -463,26 +539,27 @@ So the slow-moving input to ECN marking in the L queue (the coupled L4S probability) is: p_CL = k*p', (3) where k is the constant coupling factor (see Appendix C). It can be seen that these two transformations of p' implement the required coupling given in equation (1) earlier. - The actual probability p_L that we apply to the L queue needs to - track the immediate L queue delay, as well as track p_CL under - stationary conditions. So we use a native AQM in the L queue that - calculates a probability p'_L as a function of the instantaneous L - queue. And, given the L queue has conditional strict priority over - the C queue, whenever the L queue grows, we should apply marking + The actual ECN marking probability p_L that is applied to the L queue + needs to track the immediate L queue delay under L-only congestion + conditions, as well as track p_CL under coupled congestion + conditions. So the L queue uses a native AQM that calculates a + probability p'_L as a function of the instantaneous L queue delay. + And, given the L queue has conditional strict priority over the C + queue, whenever the L queue grows, the AQM should apply marking probability p'_L, but p_L should not fall below p_CL. This suggests: p_L = max(p'_L, p_CL), (4) which has also been found to work very well in practice. _________ | | ,------. L4S queue | |===>| ECN | ,'| _______|_| |marker|\ @@ -517,33 +594,34 @@ where a continually busy L4S queue blocks a DNS request in the Classic queue, arbitrarily delaying the start of a new Classic flow. Example DualQ Coupled AQM algorithms called DualPI2 and Curvy RED are given in Appendix A and Appendix B. Either example AQM can be used to couple packet marking and dropping across a dual Q. DualPI2 uses a Proportional-Integral (PI) controller as the Base AQM. Indeed, this Base AQM with just the squared output and no L4S queue can be used as a drop-in replacement for PIE [RFC8033], in which case - we call it just PI2 [PI2]. PI2 is a principled simplification of PIE - that is both more responsive and more stable in the face of + it is just called PI2 [PI2]. PI2 is a principled simplification of + PIE that is both more responsive and more stable in the face of dynamically varying load. Curvy RED is derived from RED [RFC2309], but its configuration parameters are insensitive to link rate and it requires less operations per packet. However, DualPI2 is more responsive and stable over a wider range of RTTs than Curvy RED. As a consequence, - DualPI2 has attracted more development attention than Curvy RED, - leaving the Curvy RED design incomplete and not so fully evaluated. + DualPI2 has attracted more development and evaluation attention than + Curvy RED, leaving the Curvy RED design incomplete and not so fully + evaluated. - Both AQMs regulate their queue in units of time not bytes. As - already explained, this ensures configuration can be invariant for + Both AQMs regulate their queue in units of time rather than bytes. + As already explained, this ensures configuration can be invariant for different drain rates. With AQMs in a dualQ structure this is particularly important because the drain rate of each queue can vary rapidly as flows for the two queues arrive and depart, even if the combined link rate is constant. It would be possible to control the queues with other alternative AQMs, as long as the normative requirements (those expressed in capitals) in Section 2.5 are observed. 2.5. Normative Requirements for a DualQ Coupled AQM @@ -551,70 +629,71 @@ The following requirements are intended to capture only the essential aspects of a DualQ Coupled AQM. They are intended to be independent of the particular AQMs used for each queue. 2.5.1. Functional Requirements A Dual Queue Coupled AQM implementation MUST utilize two queues, each with an AQM algorithm. The two queues can be part of a larger queuing hierarchy [I-D.briscoe-tsvwg-l4s-diffserv]. - The AQM algorithm for the low latency (L) queue MUST apply ECN - marking. + The AQM algorithm for the low latency (L) queue MUST be able to apply + ECN marking to ECN-capable packets. The scheduler draining the two queues MUST give L4S packets priority over Classic, although priority MUST be bounded in order not to starve Classic traffic. [I-D.ietf-tsvwg-ecn-l4s-id] defines the meaning of an ECN marking on - L4S traffic, relative to drop of Classic traffic. In order to - prevent starvation of Classic traffic by scalable L4S traffic, it - says, "The likelihood that an AQM drops a Not-ECT Classic packet - (p_C) MUST be roughly proportional to the square of the likelihood - that it would have marked it if it had been an L4S packet (p_L)." - The term 'likelihood' is used to allow for marking and dropping to be - either probabilistic or deterministic. + L4S traffic, relative to drop of Classic traffic. In order to ensure + coexistence of Classic and Scalable L4S traffic, it says, "The + likelihood that an AQM drops a Not-ECT Classic packet (p_C) MUST be + roughly proportional to the square of the likelihood that it would + have marked it if it had been an L4S packet (p_L)." The term + 'likelihood' is used to allow for marking and dropping to be either + probabilistic or deterministic. For the current specification, this translates into the following requirement. A DualQ Coupled AQM MUST apply ECN marking to traffic in the L queue that is no lower than that derived from the likelihood of drop (or ECN marking) in the Classic queue using Eqn. (1). The constant of proportionality, k, in Eqn (1) determines the relative flow rates of Classic and L4S flows when the AQM concerned is the bottleneck (all other factors being equal). [I-D.ietf-tsvwg-ecn-l4s-id] says, "The constant of proportionality (k) does not have to be standardised for interoperability, but a value of 2 is RECOMMENDED." - Assuming scalable congestion controls for the Internet will be as + Assuming Scalable congestion controls for the Internet will be as aggressive as DCTCP, this will ensure their congestion window will be roughly the same as that of a standards track TCP congestion control (Reno) [RFC5681] and other so-called TCP-friendly controls, such as TCP Cubic in its TCP-friendly mode. The choice of k is a matter of operator policy, and operators MAY choose a different value using Table 1 and the guidelines in Appendix C. - If multiple users share capacity at a bottleneck (e.g. in the - Internet access link of a campus network), the operator's choice of k - will determine capacity sharing between the flows of different users. - However, on the public Internet, access network operators typically - isolate customers from each other with some form of layer-2 - multiplexing (OFDM(A) in DOCSIS3.1, CDMA in 3G, SC-FDMA in LTE) or L3 - scheduling (WRR in DSL), rather than relying on TCP to share capacity - between customers [RFC0970]. In such cases, the choice of k will - solely affect relative flow rates within each customer's access - capacity, not between customers. Also, k will not affect relative - flow rates at any times when all flows are Classic or all L4S, and it - will not affect the relative throughput of small flows. + If multiple customers or users share capacity at a bottleneck (e.g. + in the Internet access link of a campus network), the operator's + choice of k will determine capacity sharing between the flows of + different customers. However, on the public Internet, access network + operators typically isolate customers from each other with some form + of layer-2 multiplexing (OFDM(A) in DOCSIS3.1, CDMA in 3G, SC-FDMA in + LTE) or L3 scheduling (WRR in DSL), rather than relying on TCP to + share capacity between customers [RFC0970]. In such cases, the + choice of k will solely affect relative flow rates within each + customer's access capacity, not between customers. Also, k will not + affect relative flow rates at any times when all flows are Classic or + all flows are L4S, and it will not affect the relative throughput of + small flows. 2.5.1.1. Requirements in Unexpected Cases The flexibility to allow operator-specific classifiers (Section 2.3) leads to the need to specify what the AQM in each queue ought to do with packets that do not carry the ECN field expected for that queue. It is recommended that the AQM in each queue inspects the ECN field to determine what sort of congestion notification to signal, then decides whether to apply congestion notification to this particular packet, as follows: @@ -643,109 +722,160 @@ SHOULD apply drop using a drop probability appropriate to Classic congestion control and appropriate to the target delay in the L queue o If a packet that carries an ECT(1) codepoint is classified into the C queue: * the C AQM SHOULD apply CE-marking using the coupled AQM probability p_CL (= k*p'). - If the DualQ Coupled AQM has detected overload, it will signal - congestion solely using drop, irrespective of the ECN field. - The above requirements are worded as "SHOULDs", because operator- specific classifiers are for flexibility, by definition. Therefore, alternative actions might be appropriate in the operator's specific circumstances. An example would be where the operator knows that certain legacy traffic marked with one codepoint actually has a congestion response associated with another codepoint. + If the DualQ Coupled AQM has detected overload, it MUST signal + congestion solely using drop, irrespective of the ECN field. + Switching to drop if ECN marking is persistently high is required by + Section 7 of [RFC3168] and Section 4.2.1 of [RFC7567]. + 2.5.2. Management Requirements +2.5.2.1. Configuration + By default, a DualQ Coupled AQM SHOULD NOT need any configuration for use at a bottleneck on the public Internet [RFC7567]. The following parameters MAY be operator-configurable, e.g. to tune for non- Internet settings: o Optional packet classifier(s) to use in addition to the ECN field (see Section 2.3); - o Expected typical RTT (a parameter for typical or target queuing - delay in each queue might be configurable instead; if so it MUST - be expressed in units of time); + o Expected typical RTT, which can be used to determine the queuing + delay of the Classic AQM at its operating point, in order to + prevent typical lone TCP flows from under-utilizing capacity. For + example: - o Expected maximum RTT (a stability parameter that depends on - maximum RTT might be configurable instead); + * for the PI2 algorithm (Appendix A) the queuing delay target is + set to the typical RTT; + + * for the Curvy RED algorithm (Appendix B) the queuing delay at + the desired operating point of the curvy ramp is configured to + encompass a typical RTT; + + * if another Classic AQM was used, it would be likely to need an + operating point for the queue based on the typical RTT, and if + so it SHOULD be expressed in units of time. + + An operating point that is manually calculated might be directly + configurable instead, e.g. for links with large numbers of flows + where under-utilization by a single TCP flow would be unlikely. + + o Expected maximum RTT, which can be used to set the stability + parameter(s) of the Classic AQM. For example: + + * for the PI2 algorithm (Appendix A), the gain parameters of the + PI algorithm depend on the maximum RTT. + + * for the Curvy RED algorithm (Appendix B) the smoothing + parameter is chosen to filter out transients in the queue + within a maximum RTT. + + Stability parameter(s) that are manually calculated assuming a + maximum RTT might be directly configurable instead. o Coupling factor, k; - o The limit to the conditional priority of L4S (scheduler-dependent, - e.g. the scheduler weight for WRR, or the time-shift for time- - shifted FIFO); + o A limit to the conditional priority of L4S. This is scheduler- + dependent, but it SHOULD be expressed as a relation between the + max delay of a C packet and an L packet. For example: + + * for a WRR scheduler a weight ratio between L and C of w:1 means + that the maximum delay to a C packet is w times that of an L + packet. + + * for a time-shifted FIFO (TS-FIFO) scheduler (see Section 4.1.1) + a time-shift of tshift means that the maximum delay to a C + packet is tshift greater than that of an L packet. tshift could + be expressed as a multiple of the typical RTT rather than as an + absolute delay. o The maximum Classic ECN marking probability, p_Cmax, before switching over to drop. +2.5.2.2. Monitoring + An experimental DualQ Coupled AQM SHOULD allow the operator to monitor each of the following operational statistics on demand, per queue and per configurable sample interval, for performance monitoring and perhaps also for accounting in some cases: o Bits forwarded, from which utilization can be calculated; - o Total packets arriving, enqueued and dequeued to distinguish tail - discard from proactive AQM discard; + o Total packets in the three categories: arrived, presented to the + AQM, and forwarded. The difference between the first two will + measure any non-AQM tail discard. The difference between the last + two will measure proactive AQM discard; o ECN packets marked, non-ECN packets dropped, ECN packets dropped, - from which marking and dropping probabilities can be calculated; + which can be combined with the three total packet counts above to + calculate marking and dropping probabilities; o Queue delay (not including serialization delay of the head packet or medium acquisition delay) - see further notes below. Unlike the other statistics, queue delay cannot be captured in a simple accumulating counter. Therefore the type of queue delay statistics produced (mean, percentiles, etc.) will depend on implementation constraints. To facilitate comparative evaluation of different implementations and approaches, an implementation SHOULD allow mean and 99th percentile queue delay to be derived (per queue per sample interval). A relatively simple way to do this would be to store a coarse-grained histogram of queue delay. This could be done with a small number of bins with configurable edges that represent contiguous ranges of queue delay. Then, over a sample interval, each bin would accumulate a count of the number of packets that had fallen within each range. The maximum queue delay per queue per interval MAY also be recorded. +2.5.2.3. Anomaly Detection + An experimental DualQ Coupled AQM SHOULD asynchronously report the following data about anomalous conditions: o Start-time and duration of overload state. A hysteresis mechanism SHOULD be used to prevent flapping in and out of overload causing an event storm. For instance, exit from overload state could trigger one report, but also latch a timer. Then, during that time, if the AQM enters and exits overload state any number of times, the duration in overload state is accumulated but no new report is generated until the first time the AQM is out of overload once the timer has expired. +2.5.2.4. Deployment, Coexistence and Scaling + [RFC5706] suggests that deployment, coexistence and scaling should also be covered as management requirements. The raison d'etre of the - DualQ Couple AQM is to enable deployment and coexistence of scalable + DualQ Coupled AQM is to enable deployment and coexistence of Scalable congestion controls - as incremental replacements for today's TCP- friendly controls that do not scale with bandwidth-delay product. - Therefore, these motivating issues are explained in the Introduction - and detailed in the L4S architecture [I-D.ietf-tsvwg-l4s-arch]. - Also, the descriptions of specific DualQ Coupled AQM algorithms in - the appendices cover scaling of their configuration parameters, e.g. - with respect to RTT and sampling frequency. + Therefore there is no need to repeat these motivating issues here + given they are already explained in the Introduction and detailed in + the L4S architecture [I-D.ietf-tsvwg-l4s-arch]. + + The descriptions of specific DualQ Coupled AQM algorithms in the + appendices cover scaling of their configuration parameters, e.g. with + respect to RTT and sampling frequency. 3. IANA Considerations This specification contains no IANA considerations. 4. Security Considerations 4.1. Overload Handling Where the interests of users or flows might conflict, it could be @@ -765,66 +895,78 @@ Under overload the higher priority L4S service will have to sacrifice some aspect of its performance. Alternative solutions are provided below that each relax a different factor: e.g. throughput, delay, drop. These choices need to be made either by the developer or by operator policy, rather than by the IETF. 4.1.1. Avoiding Classic Starvation: Sacrifice L4S Throughput or Delay? Priority of L4S is required to be conditional to avoid total - throughput starvation of Classic by heavy L4S traffic. This raises - the question of whether to sacrifice L4S throughput or L4S delay (or - some other policy) to mitigate starvation of Classic: + starvation of Classic by heavy L4S traffic. This raises the question + of whether to sacrifice L4S throughput or L4S delay (or some other + policy) to mitigate starvation of Classic: Sacrifice L4S throughput: By using weighted round robin as the conditional priority scheduler, the L4S service can sacrifice some - throughput during overload to guarantee a minimum throughput - service for Classic traffic. The scheduling weight of the Classic - queue should be small (e.g. 1/16). Then, in most traffic - scenarios the scheduler will not interfere and it will not need to - - the coupling mechanism and the end-systems will share out the - capacity across both queues as if it were a single pool. However, - because the congestion coupling only applies in one direction - (from C to L), if L4S traffic is over-aggressive or unresponsive, - the scheduler weight for Classic traffic will at least be large - enough to ensure it does not starve. + throughput during overload. This can either be thought of as + guaranteeing a minimum throughput service for Classic traffic, or + as guaranteeing a maximum delay for a packet at the head of the + Classic queue. + + The scheduling weight of the Classic queue should be small (e.g. + 1/16). Then, in most traffic scenarios the scheduler will not + interfere and it will not need to - the coupling mechanism and the + end-systems will share out the capacity across both queues as if + it were a single pool. However, because the congestion coupling + only applies in one direction (from C to L), if L4S traffic is + over-aggressive or unresponsive, the scheduler weight for Classic + traffic will at least be large enough to ensure it does not + starve. In cases where the ratio of L4S to Classic flows (e.g. 19:1) is greater than the ratio of their scheduler weights (e.g. 15:1), the L4S flows will get less than an equal share of the capacity, but only slightly. For instance, with the example numbers given, each L4S flow will get (15/16)/19 = 4.9% when ideally each would get 1/20=5%. In the rather specific case of an unresponsive flow - taking up a large part of the capacity set aside for L4S, using - WRR could significantly reduce the capacity left for any - responsive L4S flows. + taking up just less than the capacity set aside for L4S (e.g. + 14/16 in the above example), using WRR could significantly reduce + the capacity left for any responsive L4S flows. + + The scheduling weight of the Classic queue should not be too + small, otherwise a C packet at the head of the queue could be + excessively delayed by a continually busy L queue. For instance + if the Classic weight is 1/16, the maximum that a Classic packet + at the head of the queue can be delayed by L traffic is the + serialization delay of 15 MTU-sized packets. Sacrifice L4S Delay: To control milder overload of responsive traffic, particularly when close to the maximum congestion signal, the operator could choose to control overload of the Classic queue by allowing some delay to 'leak' across to the L4S queue. The scheduler can be made to behave like a single First-In First-Out (FIFO) queue with different service times by implementing a very simple conditional priority scheduler that could be called a "time-shifted FIFO" (see the Modifier Earliest Deadline First (MEDF) scheduler of [MEDF]). This scheduler adds tshift to the queue delay of the next L4S packet, before comparing it with the queue delay of the next Classic packet, then it selects the packet with the greater adjusted queue delay. Under regular conditions, this time-shifted FIFO scheduler behaves just like a strict priority scheduler. But under moderate or high overload it prevents starvation of the Classic queue, because the time-shift (tshift) defines the maximum extra queuing delay of Classic packets relative to L4S. - The example implementation in Appendix A can implement either policy. + The example implementations in Appendix A and Appendix B could both + be implemented with either policy. 4.1.2. Congestion Signal Saturation: Introduce L4S Drop or Delay? To keep the throughput of both L4S and Classic flows roughly equal over the full load range, a different control strategy needs to be defined above the point where one AQM first saturates to a probability of 100% leaving no room to push back the load any harder. If k>1, L4S will saturate first, even though saturation could be caused by unresponsive traffic in either queue. @@ -876,140 +1018,205 @@ Experiments with the DualPI2 AQM (Appendix A) have shown that introducing 'drop on saturation' at 100% L4S marking addresses this problem with unresponsive ECN as well as addressing the saturation problem. It leaves only a small range of congestion levels where unresponsive traffic gains any advantage from using the ECN capability, and the advantage is hardly detectable [DualQ-Test]. 5. Acknowledgements - Thanks to Anil Agarwal, Sowmini Varadhan's and Gabi Bracha for - detailed review comments particularly of the appendices and - suggestions on how to make our explanation clearer. Thanks also to - Greg White for improving the normative requirements and both Greg and - Tom Henderson for insights on the choice of schedulers, queue delay - measurement techniques. + Thanks to Anil Agarwal, Sowmini Varadhan's, Gabi Bracha, Nicolas + Kuhn, Tom Henderson and David Pullen for detailed review comments + particularly of the appendices and suggestions on how to make the + explanations clearer. Thanks also to Tom Henderson for insights on + the choice of schedulers and queue delay measurement techniques. - The authors' contributions were originally part-funded by the - European Community under its Seventh Framework Programme through the - Reducing Internet Transport Latency (RITE) project (ICT-317700). Bob - Briscoe's contribution was also part-funded by the Research Council - of Norway through the TimeIn project. The views expressed here are - solely those of the authors. + The early contributions of Koen De Schepper, Bob Briscoe, Olga + Bondarenko and Inton Tsang were part-funded by the European Community + under its Seventh Framework Programme through the Reducing Internet + Transport Latency (RITE) project (ICT-317700). Bob Briscoe's + contribution was also part-funded by the Research Council of Norway + through the TimeIn project. The views expressed here are solely + those of the authors. -6. References +6. Contributors -6.1. Normative References + The following contributed implementations and evaluations that + validated and helped to improve this specification: + + Olga Albisser of Simula Research Lab, Norway + (Olga Bondarenko during early drafts) implemented the prototype + DualPI2 AQM for Linux with Koen De Schepper and conducted + extensive evaluations as well as implementing the live performance + visualization GUI [L4Sdemo16]. + + Olivier Tilmans of Nokia + Bell Labs, Belgium prepared and maintains the Linux implementation + of DualPI2 for upstreaming. + + Tom Henderson of the University of Washington, WA, + US implemented various Coupled DualQ AQMs for ns3, including + DualPI2 and DualPIE over point to point and DOCSIS 3.1 link models + and conducted extensive evaluations. + + Ing Jyh (Inton) Tsang of Nokia, Belgium built the End-to-End Data + Centre to the Home broadband testbed on which Coupled DualQ + implementations were tested. + +7. References + +7.1. Normative References + + [I-D.ietf-tsvwg-ecn-l4s-id] + Schepper, K. and B. Briscoe, "Identifying Modified + Explicit Congestion Notification (ECN) Semantics for + Ultra-Low Queuing Delay (L4S)", draft-ietf-tsvwg-ecn-l4s- + id-06 (work in progress), March 2019. [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, March 1997, . -6.2. Informative References + [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, + . + + [RFC8311] Black, D., "Relaxing Restrictions on Explicit Congestion + Notification (ECN) Experimentation", RFC 8311, + DOI 10.17487/RFC8311, January 2018, + . + +7.2. Informative References + + [Alizadeh-stability] + Alizadeh, M., Javanmard, A., and B. Prabhakar, "Analysis + of DCTCP: Stability, Convergence, and Fairness", ACM + SIGMETRICS 2011 , June 2011, + . + + [AQMmetrics] + Kwon, M. and S. Fahmy, "A Comparison of Load-based and + Queue- based Active Queue Management Algorithms", Proc. + Int'l Soc. for Optical Engineering (SPIE) 4866:35--46 DOI: + 10.1117/12.473021, 2002, + . [ARED01] Floyd, S., Gummadi, R., and S. Shenker, "Adaptive RED: An Algorithm for Increasing the Robustness of RED's Active Queue Management", ACIRI Technical Report , August 2001, . + [BBRv1] Cardwell, N., Cheng, Y., Hassas Yeganeh, S., and V. + Jacobson, "BBR Congestion Control", Internet Draft draft- + cardwell-iccrg-bbr-congestion-control-00, July 2017, + . + [CoDel] Nichols, K. and V. Jacobson, "Controlling Queue Delay", ACM Queue 10(5), May 2012, . [CRED_Insights] Briscoe, B., "Insights from Curvy RED (Random Early - Detection)", BT Technical Report TR-TUB8-2015-003, July - 2015, - . + Detection)", BT Technical Report TR-TUB8-2015-003 + arXiv:1904.07339 [cs.NI], July 2015, + . [DCttH15] De Schepper, K., Bondarenko, O., Briscoe, B., and I. Tsang, "`Data Centre to the Home': Ultra-Low Latency for - All", 2015, . + All", RITE project Technical Report , 2015, + . - (Under submission) + [DOCSIS3.1] + CableLabs, "MAC and Upper Layer Protocols Interface + (MULPI) Specification, CM-SP-MULPIv3.1", Data-Over-Cable + Service Interface Specifications DOCSIS(R) 3.1 Version i17 + or later, January 2019, . + + [DualPI2Linux] + Albisser, O., De Schepper, K., Briscoe, B., Tilmans, O., + and H. Steen, "DUALPI2 - Low Latency, Low Loss and + Scalable (L4S) AQM", Proc. Linux Netdev 0x13 , March 2019, + . [DualQ-Test] Steen, H., "Destruction Testing: Ultra-Low Delay using Dual Queue Coupled Active Queue Management", Masters Thesis, Dept of Informatics, Uni Oslo , May 2017. [I-D.briscoe-tsvwg-l4s-diffserv] Briscoe, B., "Interactions between Low Latency, Low Loss, Scalable Throughput (L4S) and Differentiated Services", - draft-briscoe-tsvwg-l4s-diffserv-00 (work in progress), - March 2018. - - [I-D.ietf-tsvwg-ecn-l4s-id] - Schepper, K., Briscoe, B., and I. Tsang, "Identifying - Modified Explicit Congestion Notification (ECN) Semantics - for Ultra-Low Queuing Delay", draft-ietf-tsvwg-ecn-l4s- - id-02 (work in progress), March 2018. + draft-briscoe-tsvwg-l4s-diffserv-02 (work in progress), + November 2018. [I-D.ietf-tsvwg-l4s-arch] Briscoe, B., Schepper, K., and M. Bagnulo, "Low Latency, Low Loss, Scalable Throughput (L4S) Internet Service: - Architecture", draft-ietf-tsvwg-l4s-arch-02 (work in - progress), March 2018. - - [I-D.sridharan-tcpm-ctcp] - Sridharan, M., Tan, K., Bansal, D., and D. Thaler, - "Compound TCP: A New TCP Congestion Control for High-Speed - and Long Distance Networks", draft-sridharan-tcpm-ctcp-02 - (work in progress), November 2008. + Architecture", draft-ietf-tsvwg-l4s-arch-03 (work in + progress), October 2018. [L4Sdemo16] Bondarenko, O., De Schepper, K., Tsang, I., and B. Briscoe, "Ultra-Low Delay for All: Live Experience, Live Analysis", Proc. MMSYS'16 pp33:1--33:4, May 2016, . + [LLD] White, G., Sundaresan, K., and B. Briscoe, "Low Latency + DOCSIS: Technology Overview", CableLabs White Paper , + February 2019, . + [Mathis09] Mathis, M., "Relentless Congestion Control", PFLDNeT'09 , May 2009, . [MEDF] Menth, M., Schmid, M., Heiss, H., and T. Reim, "MEDF - a simple scheduling algorithm for two real-time transport service classes with application in the UTRAN", Proc. IEEE Conference on Computer Communications (INFOCOM'03) Vol.2 pp.1116-1122, March 2003. [PI2] De Schepper, K., Bondarenko, O., Briscoe, B., and I. Tsang, "PI2: A Linearized AQM for both Classic and Scalable TCP", ACM CoNEXT'16 , December 2016, . - (To appear) + [PragueLinux] + Briscoe, B., De Schepper, K., Albisser, O., Misund, J., + Tilmans, O., Kuehlewind, M., and A. Ahmed, "Implementing + the `TCP Prague' Requirements for Low Latency Low Loss + Scalable Throughput (L4S)", Proc. Linux Netdev 0x13 , + March 2019, . [RFC0970] Nagle, J., "On Packet Switches With Infinite Storage", RFC 970, DOI 10.17487/RFC0970, December 1985, . [RFC2309] Braden, B., Clark, D., Crowcroft, J., Davie, B., Deering, S., Estrin, D., Floyd, S., Jacobson, V., Minshall, G., Partridge, C., Peterson, L., Ramakrishnan, K., Shenker, S., Wroclawski, J., and L. Zhang, "Recommendations on Queue Management and Congestion Avoidance in the Internet", RFC 2309, DOI 10.17487/RFC2309, April 1998, . - [RFC3168] Ramakrishnan, K., Floyd, S., and D. Black, "The Addition - of Explicit Congestion Notification (ECN) to IP", - RFC 3168, DOI 10.17487/RFC3168, September 2001, - . - [RFC3246] Davie, B., Charny, A., Bennet, J., Benson, K., Le Boudec, J., Courtney, W., Davari, S., Firoiu, V., and D. Stiliadis, "An Expedited Forwarding PHB (Per-Hop Behavior)", RFC 3246, DOI 10.17487/RFC3246, March 2002, . [RFC3649] Floyd, S., "HighSpeed TCP for Large Congestion Windows", RFC 3649, DOI 10.17487/RFC3649, December 2003, . @@ -1043,781 +1250,944 @@ and G. Judd, "Data Center TCP (DCTCP): TCP Congestion Control for Data Centers", RFC 8257, DOI 10.17487/RFC8257, October 2017, . [RFC8290] Hoeiland-Joergensen, T., McKenney, P., Taht, D., Gettys, J., and E. Dumazet, "The Flow Queue CoDel Packet Scheduler and Active Queue Management Algorithm", RFC 8290, DOI 10.17487/RFC8290, January 2018, . - [RFC8311] Black, D., "Relaxing Restrictions on Explicit Congestion - Notification (ECN) Experimentation", RFC 8311, - DOI 10.17487/RFC8311, January 2018, - . + [RFC8298] Johansson, I. and Z. Sarker, "Self-Clocked Rate Adaptation + for Multimedia", RFC 8298, DOI 10.17487/RFC8298, December + 2017, . [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, . - [TCP-CA] Jacobson, V. and M. Karels, "Congestion Avoidance and - Control", Laurence Berkeley Labs Technical Report , - November 1988, . + [SigQ-Dyn] + Briscoe, B., "Rapid Signalling of Queue Dynamics", + Technical Report TR-BB-2017-001 arXiv:1904.07044 [cs.NI], + September 2017, . Appendix A. Example DualQ Coupled PI2 Algorithm As a first concrete example, the pseudocode below gives the DualPI2 algorithm. DualPI2 follows the structure of the DualQ Coupled AQM - framework in Figure 1. A simple step threshold (in units of queuing - time) is used for the Native L4S AQM, but a ramp is also described as - an alternative. And the PI2 algorithm [PI2] is used for the Classic - AQM. PI2 is an improved variant of the PIE AQM [RFC8033]. + framework in Figure 1. A simple ramp function (configured in units + of queuing time) with unsmoothed ECN marking is used for the Native + L4S AQM. The ramp can also be configured as a step function. The + PI2 algorithm [PI2] is used for the Classic AQM. PI2 is an improved + variant of the PIE AQM [RFC8033]. - We will introduce the pseudocode in two passes. The first pass + The pseudocode will be introduced in two passes. The first pass explains the core concepts, deferring handling of overload to the second pass. To aid comparison, line numbers are kept in step between the two passes by using letter suffixes where the longer code needs extra lines. + All variables are assumed to be floating point in their basic units + (size in bytes, time in seconds, rates in bytes/second, alpha and + beta in Hz, and probabilities from 0 to 1. Constants expressed in k, + M, G, u, m, %, ... are assumed to be converted to their appropriate + multiple or fraction. A real implementation that wants to use + integer values needs to handle appropriate scaling factors and allow + accordingly appropriate resolution of its integer types (including + temporary internal values during calculations). + A full open source implementation for Linux is available at: - https://github.com/olgabo/dualpi2. + https://github.com/L4STeam/sch_dualpi2_upstream and explained in + [DualPI2Linux]. The specification of the DualQ Coupled AQM for + DOCSIS cable modems and CMTSs is available in [DOCSIS3.1] and + explained in [LLD]. A.1. Pass #1: Core Concepts The pseudocode manipulates three main structures of variables: the packet (pkt), the L4S queue (lq) and the Classic queue (cq). The - pseudocode consists of the following five functions: + pseudocode consists of the following six functions: - o initialization code (Figure 2) that sets parameter defaults (the - API for setting non-default values is omitted for brevity) + o the initialization function dualpi2_params_init(...) (Figure 2) + that sets parameter defaults (the API for setting non-default + values is omitted for brevity) - o enqueue code (Figure 3) + o the enqueue function dualpi2_enqueue(lq, cq, pkt) (Figure 3) - o dequeue code (Figure 4) + o the dequeue function dualpi2_dequeue(lq, cq, pkt) (Figure 4) - o a ramp function (Figure 5) used to calculate the ECN-marking - probability for the L4S queue + o recur(likelihood) for de-randomized ECN marking (shown at the end + of Figure 4). - o code to regularly update the base probability (p) used in the - dequeue code (Figure 6). + o the L4S AQM function laqm(qdelay) (Figure 5) used to calculate the + ECN-marking probability for the L4S queue + + o the base AQM function that implements the PI algorithm + dualpi2_update(lq, cq) (Figure 6) used to regularly update the + base probability (p'), which is squared for the Classic AQM as + well as being coupled across to the L4S queue. It also uses the following functions that are not shown in full here: o scheduler(), which selects between the head packets of the two queues; the choice of scheduler technology is discussed later; o cq.len() or lq.len() returns the current length (aka. backlog) of the relevant queue in bytes; o cq.time() or lq.time() returns the current queuing delay (aka. sojourn time or service time) of the relevant queue in units of - time; + time (see Note a); - Queuing delay could be measured directly by storing a per-packet - time-stamp as each packet is enqueued, and subtracting this from the - system time when the packet is dequeued. If time-stamping is not - easy to introduce with certain hardware, queuing delay could be - predicted indirectly by dividing the size of the queue by the - predicted departure rate, which might be known precisely for some - link technologies (see for example [RFC8034]). + o mark(pkt) and drop(pkt) for ECN-marking and dropping a packet; - In our experiments so far (building on experiments with PIE) on - broadband access links ranging from 4 Mb/s to 200 Mb/s with base RTTs - from 5 ms to 100 ms, DualPI2 achieves good results with the default - parameters in Figure 2. The parameters are categorised by whether - they relate to the Base PI2 AQM, the L4S AQM or the framework - coupling them together. Variables derived from these parameters are + In experiments so far (building on experiments with PIE) on broadband + access links ranging from 4 Mb/s to 200 Mb/s with base RTTs from 5 ms + to 100 ms, DualPI2 achieves good results with the default parameters + in Figure 2. The parameters are categorised by whether they relate + to the Base PI2 AQM, the L4S AQM or the framework coupling them + together. Constants and variables derived from these parameters are also included at the end of each category. Each parameter is explained as it is encountered in the walk-through of the pseudocode below. 1: dualpi2_params_init(...) { % Set input parameter defaults - 2: % PI2 AQM parameters - 3: target = 15 ms % PI AQM Classic queue delay target - 4: Tupdate = 16 ms % PI Classic queue sampling interval - 5: alpha = 10 Hz^2 % PI integral gain - 6: beta = 100 Hz^2 % PI proportional gain - 7: p_Cmax = 1/4 % Max Classic drop/mark prob - 8: % Constants derived from PI2 AQM parameters - 9: alpha_U = alpha *Tupdate % PI integral gain per update interval - 10: beta_U = beta * Tupdate % PI prop'nal gain per update interval - 11: - 12: % DualQ Coupled framework parameters - 13: k = 2 % Coupling factor - 14: % scheduler weight or equival't parameter (scheduler-dependent) - 15: limit = MAX_LINK_RATE * 250 ms % Dual buffer size + 2: % DualQ Coupled framework parameters + 5: limit = MAX_LINK_RATE * 250 ms % Dual buffer size + 3: k = 2 % Coupling factor + 4: % NOT SHOWN % scheduler-dependent weight or equival't parameter + 6: + 7: % PI2 AQM parameters + 8: RTT_max = 100 ms % Worst case RTT expected + 9: RTT_typ = 15 ms % Typical RTT + 11: % PI2 constants derived from above PI2 parameters + 10: p_Cmax = min(1/k^2, 1) % Max Classic drop/mark prob + 12: target = RTT_typ % PI AQM Classic queue delay target + 13: Tupdate = min(RTT_typ, RTT_max/3) % PI sampling interval + 14: alpha = 0.1 * Tupdate / RTT_max^2 % PI integral gain in Hz + 15: beta = 0.3 / RTT_max % PI proportional gain in Hz 16: 17: % L4S ramp AQM parameters 18: minTh = 475 us % L4S min marking threshold in time units 19: range = 525 us % Range of L4S ramp in time units 20: Th_len = 2 * MTU % Min L4S marking threshold in bytes - 21: % Constants derived from L4S AQM parameters - 22: p_Lmax = min(k*sqrt(p_Cmax), 1) % Max L4S marking prob - 23: floor = Th_len * 8 / MIN_LINK_RATE % MIN_LINK_RATE is in Mb/s + 21: % L4S constants incl. those derived from other parameters + 22: p_Lmax = 1 % Max L4S marking prob + 23: floor = Th_len / MIN_LINK_RATE 24: if (minTh < floor) { - 25: % Adjust ramp to exceed serialization time of 2 MTU - 26: range = max(range - (floor-minTh), 1) % 1us avoids /0 error - 27: minTh = floor - 28: } - 29: maxTh = minTh+range % L4S min marking threshold in time units - 30: } + 25: % Shift ramp so minTh >= serialization time of 2 MTU + 26: minTh = floor + 27: } + 28: maxTh = minTh+range % L4S max marking threshold in time units + 29: } Figure 2: Example Header Pseudocode for DualQ Coupled PI2 AQM - For brevity the pseudocode shows some parameters in units of - microseconds (us), but a real implementation would probably use - nanoseconds. - - The overall goal of the code is to maintain the base probability (p), - which is an internal variable from which the marking and dropping - probabilities for L4S and Classic traffic (p_L and p_C) are derived. - The variable named p in the pseudocode and in this walk-through is - the same as p' (p-prime) in Section 2.4. The probabilities p_L and - p_C are derived in lines 3, 4 and 5 of the dualpi2_update() function - (Figure 6) then used in the dualpi2_dequeue() function (Figure 4). - The code walk-through below builds up to explaining that part of the - code eventually, but it starts from packet arrival. + The overall goal of the code is to maintain the base probability (p', + p-prime as in Section 2.4), which is an internal variable from which + the marking and dropping probabilities for L4S and Classic traffic + (p_L and p_C) are derived, with p_L in turn being derived from p_CL. + The probabilities p_CL and p_C are derived in lines 4 and 5 of the + dualpi2_update() function (Figure 6) then used in the + dualpi2_dequeue() function where p_L is also derived from p_CL at + line 6 (Figure 4). The code walk-through below builds up to + explaining that part of the code eventually, but it starts from + packet arrival. 1: dualpi2_enqueue(lq, cq, pkt) { % Test limit and classify lq or cq - 2: if ( lq.len() + cq.len() > limit ) + 2: if ( lq.len() + cq.len() + MTU > limit) 3: drop(pkt) % drop packet if buffer is full - 4: else { % Packet classifier - 5: if ( ecn(pkt) modulo 2 == 1 ) % ECN bits = ECT(1) or CE - 6: lq.enqueue(pkt) - 7: else % ECN bits = not-ECT or ECT(0) - 8: cq.enqueue(pkt) - 9: } + 4: timestamp(pkt) % attach arrival time to packet + 5: % Packet classifier + 6: if ( ecn(pkt) modulo 2 == 1 ) % ECN bits = ECT(1) or CE + 7: lq.enqueue(pkt) + 8: else % ECN bits = not-ECT or ECT(0) + 9: cq.enqueue(pkt) 10: } Figure 3: Example Enqueue Pseudocode for DualQ Coupled PI2 AQM 1: dualpi2_dequeue(lq, cq, pkt) { % Couples L4S & Classic queues 2: while ( lq.len() + cq.len() > 0 ) 3: if ( scheduler() == lq ) { 4: lq.dequeue(pkt) % Scheduler chooses lq 5: p'_L = laqm(lq.time()) % Native L4S AQM 6: p_L = max(p'_L, p_CL) % Combining function - 7: if ( p_L > rand() ) % Linear marking + 7: if ( recur(p_L) ) % Linear marking 8: mark(pkt) 9: } else { 10: cq.dequeue(pkt) % Scheduler chooses cq - 11: if ( p_C > rand() ) { % probability p_C = p^2 + 11: if ( p_C > rand() ) { % probability p_C = p'^2 12: if ( ecn(pkt) == 0 ) { % if ECN field = not-ECT 13: drop(pkt) % squared drop 14: continue % continue to the top of the while loop 15: } 16: mark(pkt) % squared mark 17: } 18: } 19: return(pkt) % return the packet and stop 20: } 21: return(NULL) % no packet to dequeue 22: } + 23: recur(likelihood) { % Returns TRUE with a certain likelihood + 24: count += likelihood + 25: if (count > 1) { + 26: count -= 1 + 27: return TRUE + 28: } + 29: return FALSE + 30: } + Figure 4: Example Dequeue Pseudocode for DualQ Coupled PI2 AQM When packets arrive, first a common queue limit is checked as shown - in line 2 of the enqueuing pseudocode in Figure 3. Note that the - limit is deliberately tested before enqueue to avoid any bias against - larger packets (so depending whether the implementation stores a - packet while testing whether to drop it from the tail, it might be - necessary for the actual buffer memory to be one MTU larger than - limit). + in line 2 of the enqueuing pseudocode in Figure 3. This assumes a + shared buffer for the two queues (Note b discusses the merits of + separate buffers). In order to avoid any bias against larger + packets, 1 MTU of space is always allowed and the limit is + deliberately tested before enqueue. - Line 2 assumes an implementation where lq and cq share common buffer - memory. An alternative implementation could use separate buffers for - each queue, in which case the arriving packet would have to be - classified first to determine which buffer to check for available - space. The choice is a trade off; a shared buffer can use less - memory whereas separate buffers isolate the L4S queue from tail-drop - due to large bursts of Classic traffic (e.g. a Classic TCP during - slow-start over a long RTT). + If limit is not exceeded, the packet is timestamped in line 4. This + assumes that queue delay is measured using the sojourn time technique + (see Note a for alternatives). - Returning to the shared buffer case, if limit is not exceeded, the - packet will be classified and enqueued to the Classic or L4S queue - dependent on the least significant bit of the ECN field in the IP - header (line 5). Packets with a codepoint having an LSB of 0 (Not- - ECT and ECT(0)) will be enqueued in the Classic queue. Otherwise, - ECT(1) and CE packets will be enqueued in the L4S queue. Optional - additional packet classification flexibility is omitted for brevity - (see [I-D.ietf-tsvwg-ecn-l4s-id]). + At lines 5-9, the packet is classified and enqueued to the Classic or + L4S queue dependent on the least significant bit of the ECN field in + the IP header (line 6). Packets with a codepoint having an LSB of 0 + (Not-ECT and ECT(0)) will be enqueued in the Classic queue. + Otherwise, ECT(1) and CE packets will be enqueued in the L4S queue. + Optional additional packet classification flexibility is omitted for + brevity (see [I-D.ietf-tsvwg-ecn-l4s-id]). The dequeue pseudocode (Figure 4) is repeatedly called whenever the lower layer is ready to forward a packet. It schedules one packet for dequeuing (or zero if the queue is empty) then returns control to the caller, so that it does not block while that packet is being forwarded. While making this dequeue decision, it also makes the necessary AQM decisions on dropping or marking. The alternative of applying the AQMs at enqueue would shift some processing from the critical time when each packet is dequeued. However, it would also add a whole queue of delay to the control signals, making the control - loop very sloppy. + loop sloppier (for a typical RTT it would double the Classic queue's + feedback delay). All the dequeue code is contained within a large while loop so that if it decides to drop a packet, it will continue until it selects a packet to schedule. Line 3 of the dequeue pseudocode is where the scheduler chooses between the L4S queue (lq) and the Classic queue (cq). Detailed implementation of the scheduler is not shown (see discussion later). - o If an L4S packet is scheduled, lines 7 and 8 ECN-mark the packet - if a random marking decision is drawn according to p_L. Line 6 - calculates p_L as the maximum of the coupled L4S probability p_CL - and the probability from the native L4S AQM p'_L. This implements - the max() function shown in Figure 1 to couple the outputs of the - two AQMs together. Of the two probabilities input to p_L in line - 6: + o If an L4S packet is scheduled, in lines 7 and 8 the packet is ECN- + marked with likelihood p_L. The recur() function at the end of + Figure 4 is used, which is preferred over random marking because + it avoids delay due to randomization when interpreting congestion + signals, but it still desynchronizes the saw-teeth of the flows. + Line 6 calculates p_L as the maximum of the coupled L4S + probability p_CL and the probability from the native L4S AQM p'_L. + This implements the max() function shown in Figure 1 to couple the + outputs of the two AQMs together. Of the two probabilities input + to p_L in line 6: * p'_L is calculated per packet in line 5 by the laqm() function (see Figure 5), * whereas p_CL is maintained by the dualpi2_update() function - which runs every Tupdate (default 16ms) (see Figure 2). + which runs every Tupdate (Tupdate is set in line 13 of + Figure 2. It defaults to 16 ms in the reference Linux + implementation because it has to be rounded to a multiple of 4 + ms). o If a Classic packet is scheduled, lines 10 to 17 drop or mark the - packet based on the squared probability p_C. + packet with probability p_C. The Native L4S AQM algorithm (Figure 5) is a ramp function, similar - to the RED algorithm, but simpler due to the following differences: + to the RED algorithm, but simplified as follows: - o The min and max of the ramp are defined in units of queuing delay, - not bytes, so that configuration remains invariant as the queue + o The extent of the ramp is defined in units of queuing delay, not + bytes, so that configuration remains invariant as the queue departure rate varies. - o It uses instantaneous queueing delay to remove smoothing delay - (L4S senders smooth incoming ECN feedback when necessary). + o It uses instantaneous queueing delay, which avoids the complexity + of smoothing, but also avoids embedding a worst-case RTT of + smoothing delay in the network (see Section 2.1). o The ramp rises linearly directly from 0 to 1, not to a an intermediate value of p'_L as RED would, because there is no need to keep ECN marking probability low. - o Marking does not have to be randomized. Determinism is being - experimented with instead of randomness; to reduce the delay - necessary to smooth out the noise of randomness from the signal. - In this case, for each packet, the algorithm would accumulate p_L - in a counter and mark the packet that took the counter over 1, - then subtract 1 from the counter and continue. + o Marking does not have to be randomized. Determinism is used + instead of randomness; to reduce the delay necessary to smooth out + the noise of randomness from the signal. - This ramp function requires two configuration parameters, the minimum + The ramp function requires two configuration parameters, the minimum threshold (minTh) and the width of the ramp (range), both in units of - queuing time), as shown in the parameter initialization code in - Figure 2. A minimum marking threshold parameter (Th_len) in - transmission units (default 2 MTU) is also necessary to ensure that - the ramp does not trigger excessive marking on slow links. The code - in lines 23-28 of Figure 2 converts 2 MTU into time units and adjusts - the ramp thresholds to be no shallower than this floor. + queuing time), as shown in lines 18 & 19 of the initialization + function in Figure 2. The ramp function can be configured as a step + (see Note c). - An operator can effectively turn the ramp into a step function, as - used by DCTCP, by setting the range to its minimum value (e.g. 1 ns). - Then the condition for the ramp calculation will hardly ever arise. - There is some concern that using the step function of DCTCP for the - Native L4S AQM requires end-systems to smooth the signal for an - unnecessarily large number of round trips to ensure sufficient - fidelity. A ramp seems to be no worse than a step in initial - experiments with existing DCTCP. Therefore, it is recommended that a - ramp is configured in place of a step, which will allow congestion - control algorithms to investigate faster smoothing algorithms. + Although the DCTCP paper [Alizadeh-stability] recommends an ECN + marking threshold of 0.17*RTT_typ, it also shows that the threshold + can be much shallower with hardly any worse under-utilization of the + link (because the amplitude of DCTCP's sawteeth is so small). Based + on extensive experiments, for the public Internet a default minimum + ECN marking threshold of about RTT_typ/30 is recommended. + + A minimum marking threshold parameter (Th_len) in transmission units + (default 2 MTU) is also necessary to ensure that the ramp does not + trigger excessive marking on slow links. The code in lines 24-27 of + the initialization function (Figure 2) converts 2 MTU into time units + and shifts the ramp so that the min threshold is no shallower than + this floor. 1: laqm(qdelay) { % Returns native L4S AQM probability 2: if (qdelay >= maxTh) 3: return 1 4: else if (qdelay > minTh) - 5: return (qdelay - minTh)/range % Divide would use a bit-shift + 5: return (qdelay - minTh)/range % Divide could use a bit-shift 6: else 7: return 0 8: } Figure 5: Example Pseudocode for the Native L4S AQM - 1: dualpi2_update(lq, cq, target) { % Update p every Tupdate + 1: dualpi2_update(lq, cq) { % Update p' every Tupdate 2: curq = cq.time() % use queuing time of first-in Classic packet - 3: p = p + alpha_U * (curq - target) + beta_U * (curq - prevq) - 4: p_CL = p * k % Coupled L4S prob = base prob * coupling factor - 5: p_C = p^2 % Classic prob = (base prob)^2 + 3: p' = p' + alpha * (curq - target) + beta * (curq - prevq) + 4: p_CL = k * p' % Coupled L4S prob = base prob * coupling factor + 5: p_C = p'^2 % Classic prob = (base prob)^2 6: prevq = curq 7: } Figure 6: Example PI-Update Pseudocode for DualQ Coupled PI2 AQM - p_CL depends on the base probability (p), which is kept up to date by - the core PI algorithm in Figure 6 executed every Tupdate. + The coupled marking probability, p_CL depends on the base probability + (p'), which is kept up to date by the core PI algorithm in Figure 6 + executed every Tupdate. - Note that p solely depends on the queuing time in the Classic queue. + Note that p' solely depends on the queuing time in the Classic queue. In line 2, the current queuing delay (curq) is evaluated from how long the head packet was in the Classic queue (cq). The function cq.time() (not shown) subtracts the time stamped at enqueue from the - current time and implicitly takes the current queuing delay as 0 if - the queue is empty. + current time (see Note a) and implicitly takes the current queuing + delay as 0 if the queue is empty. The algorithm centres on line 3, which is a classical Proportional- - Integral (PI) controller that alters p dependent on: a) the error + Integral (PI) controller that alters p' dependent on: a) the error between the current queuing delay (curq) and the target queuing delay ('target' - see [RFC8033]); and b) the change in queuing delay since the last sample. The name 'PI' represents the fact that the second factor (how fast the queue is growing) is _P_roportional to load while the first is the _I_ntegral of the load (so it removes any standing queue in excess of the target). - The two 'gain factors' in line 3, alpha_U and beta_U, respectively - weight how strongly each of these elements ((a) and (b)) alters p. - They are in units of 'per second of delay' or Hz, because they - transform differences in queueing delay into changes in probability. + The two 'gain factors' in line 3, alpha and beta, respectively weight + how strongly each of these elements ((a) and (b)) alters p'. They + are in units of 'per second of delay' or Hz, because they transform + differences in queueing delay into changes in probability (assuming + probability has a value from 0 to 1). - alpha_U and beta_U are derived from the input parameters alpha and - beta (see lines 5 and 6 of Figure 2). These recommended values of - alpha and beta come from the stability analysis in [PI2] so that the - AQM can change p as fast as possible in response to changes in load - without over-compensating and therefore causing oscillations in the - queue. + alpha and beta determine how much p' ought to change after each + update interval (Tupdate). For smaller Tupdate, p' should change by + the same amount per second, but in finer more frequent steps. So + alpha depends on Tupdate (see line 14 of the initialization function + in Figure 2). It is best to update p' as frequently as possible, but + Tupdate will probably be constrained by hardware performance. As + shown in line 13, the update interval should be at least as frequent + as once per the RTT of a typical flow (RTT_typ) as long as it does + not exceed roughly RTT_max/3. For link rates from 4 - 200 Mb/s, a + target RTT of 15ms and a maximum RTT of 100ms, it has been verified + through extensive testing that Tupdate=16ms (as recommended in + [RFC8033]) is sufficient. - alpha and beta determine how much p ought to change if it was updated - every second. It is best to update p as frequently as possible, but - the update interval (Tupdate) will probably be constrained by - hardware performance. For link rates from 4 - 200 Mb/s, we found - Tupdate=16ms (as recommended in [RFC8033]) is sufficient. However - small the chosen value of Tupdate, p should change by the same amount - per second, but in finer more frequent steps. So the gain factors - used for updating p in Figure 6 need to be scaled by (Tupdate/1s), - which is done in lines 9 and 10 of Figure 2). The suffix '_U' - represents 'per update time' (Tupdate). + The choice of alpha and beta also determines the AQM's stable + operating range. The AQM ought to change p' as fast as possible in + response to changes in load without over-compensating and therefore + causing oscillations in the queue. Therefore, the values of alpha + and beta also depend on the RTT of the expected worst-case flow + (RTT_max). - In corner cases, p can overflow the range [0,1] so the resulting - value of p has to be bounded (omitted from the pseudocode). Then, as - already explained, the coupled and Classic probabilities are derived - from the new p in lines 4 and 5 as p_CL = k*p and p_C = p^2. + Recommended derivations of the gain constants alpha and beta can be + approximated for Reno over a PI2 AQM as: alpha = 0.1 * Tupdate / + RTT_max^2; beta = 0.3 / RTT_max, as shown in lines 14 & 15 of + Figure 2. These are derived from the stability analysis in [PI2]. + For the default values of Tupdate=16 ms and RTT_max = 100 ms, they + result in alpha = 0.16; beta = 3.2 (discrepancies are due to + rounding). These defaults have been verified with a wide range of + link rates, target delays and a range of traffic models with mixed + and similar RTTs, short and long flows, etc. + + In corner cases, p' can overflow the range [0,1] so the resulting + value of p' has to be bounded (omitted from the pseudocode). Then, + as already explained, the coupled and Classic probabilities are + derived from the new p' in lines 4 and 5 of Figure 6 as p_CL = k*p' + and p_C = p'^2. Because the coupled L4S marking probability (p_CL) is factored up by k, the dynamic gain parameters alpha and beta are also inherently - factored up by k for the L4S queue, which is necessary to ensure that - Classic TCP and DCTCP controls have the same stability. So, if alpha - is 10 Hz^2, the effective gain factor for the L4S queue is k*alpha, - which is 20 Hz^2 with the default coupling factor of k=2. + factored up by k for the L4S queue. So, the effective gain factor + for the L4S queue is k*alpha (with defaults alpha = 0.16 Hz and k=2, + effective L4S alpha = 0.32 Hz). - Unlike in PIE [RFC8033], alpha_U and beta_U do not need to be tuned - every Tupdate dependent on p. Instead, in PI2, alpha_U and beta_U - are independent of p because the squaring applied to Classic traffic + Unlike in PIE [RFC8033], alpha and beta do not need to be tuned every + Tupdate dependent on p'. Instead, in PI2, alpha and beta are + independent of p' because the squaring applied to Classic traffic tunes them inherently. This is explained in [PI2], which also explains why this more principled approach removes the need for most of the heuristics that had to be added to PIE. - {ToDo: Scaling beta with Tupdate and scaling both alpha & beta with - RTT} + Notes: + + a. The drain rate of the queue can vary if it is scheduled relative + to other queues, or to cater for fluctuations in a wireless + medium. To auto-adjust to changes in drain rate, the queue must + be measured in time, not bytes or packets [AQMmetrics] [CoDel]. + Queuing delay could be measured directly by storing a per-packet + time-stamp as each packet is enqueued, and subtracting this from + the system time when the packet is dequeued. If time-stamping is + not easy to introduce with certain hardware, queuing delay could + be predicted indirectly by dividing the size of the queue by the + predicted departure rate, which might be known precisely for some + link technologies (see for example [RFC8034]). + + b. Line 2 of the dualpi2_enqueue() function (Figure 3) assumes an + implementation where lq and cq share common buffer memory. An + alternative implementation could use separate buffers for each + queue, in which case the arriving packet would have to be + classified first to determine which buffer to check for available + space. The choice is a trade off; a shared buffer can use less + memory whereas separate buffers isolate the L4S queue from tail- + drop due to large bursts of Classic traffic (e.g. a Classic TCP + during slow-start over a long RTT). + + c. There has been some concern that using the step function of DCTCP + for the Native L4S AQM requires end-systems to smooth the signal + for an unnecessarily large number of round trips to ensure + sufficient fidelity. A ramp is no worse than a step in initial + experiments with existing DCTCP. Therefore, it is recommended + that a ramp is configured in place of a step, which will allow + congestion control algorithms to investigate faster smoothing + algorithms. + + A ramp is more general that a step, because an operator can + effectively turn the ramp into a step function, as used by DCTCP, + by setting the range to zero. There will not be a divide by zero + problem at line 4 of Figure 5 because, if minTh is equal to + maxTh, the condition for this ramp calculation cannot arise. A.2. Pass #2: Overload Details Figure 7 repeats the dequeue function of Figure 4, but with overload details added. Similarly Figure 8 repeats the core PI algorithm of - Figure 6 with overload details added. The initialization, enqueue - and L4S AQM functions are unchanged. + Figure 6 with overload details added. The initialization, enqueue, + L4S AQM and recur functions are unchanged. - In line 7 of the initialization function (Figure 2), the default - maximum Classic drop probability p_Cmax = 1/4 or 25%. This is the - point at which it is deemed that the Classic queue has become - persistently overloaded, so it switches to using solely drop, even - for ECN-capable packets. This protects the queue against any - unresponsive traffic that falsely claims that it is responsive to ECN - marking, as required by [RFC3168] and [RFC7567]. + In line 10 of the initialization function (Figure 2), the maximum + Classic drop probability p_Cmax = min(1/k^2, 1) or 1/4 for the + default coupling factor k=2. p_Cmax is the point at which it is + deemed that the Classic queue has become persistently overloaded, so + it switches to using drop, even for ECN-capable packets. ECT packets + that are not dropped can still be ECN-marked. - Line 22 of the initialization function translates this into a maximum - L4S marking probability (p_Lmax) by rearranging Equation (1). With a - coupling factor of k=2 (the default) or greater, this translates to a - maximum L4S marking probability of 1 (or 100%). This is intended to - ensure that the L4S queue starts to introduce dropping once marking - saturates and can rise no further. The 'TCP Prague' requirements + In practice, 25% has been found to be a good threshold to preserve + fairness between ECN capable and non ECN capable traffic. This + protects the queues against both temporary overload from responsive + flows and more persistent overload from any unresponsive traffic that + falsely claims to be responsive to ECN. + + When the Classic ECN marking probability reaches the p_Cmax threshold + (1/k^2), the marking probability coupled to the L4S queue, p_CL will + always be 100% for any k (by equation (1) in Section 2). So, for + readability, the constant p_Lmax is defined as 1 in line 22 of the + initialization function Figure 2. This is intended to ensure that + the L4S queue starts to introduce dropping once ECN-marking saturates + at 100% and can rise no further. The 'Prague L4S' requirements [I-D.ietf-tsvwg-ecn-l4s-id] state that, when an L4S congestion control detects a drop, it falls back to a response that coexists - with 'Classic' TCP. So it is correct that the L4S queue drops - packets proportional to p^2, as if they are Classic packets. + with 'Classic' TCP. So it is correct that, when the L4S queue drops + packets, it drops them proportional to p'^2, as if they are Classic + packets. Both these switch-overs are triggered by the tests for overload introduced in lines 4b and 12b of the dequeue function (Figure 7). - Lines 8c to 8g drop L4S packets with probability p^2. Lines 8h to 8i - mark the remaining packets with probability p_CL. If p_Lmax = 1, - which is the suggested default configuration, all remaining packets - will be marked because, to have reached the else block at line 8b, - p_CL >= 1. + Lines 8c to 8g drop L4S packets with probability p'^2. Lines 8h to + 8i mark the remaining packets with probability p_CL. Given p_Lmax = + 1, all remaining packets will be marked because, to have reached the + else block at line 8b, p_CL >= 1. Lines 2c to 2d in the core PI algorithm (Figure 8) deal with overload of the L4S queue when there is no Classic traffic. This is necessary, because the core PI algorithm maintains the appropriate drop probability to regulate overload, but it depends on the length - of the Classic queue. If there is no Classic queue the naive - algorithm in Figure 6 drops nothing, even if the L4S queue is - overloaded - so tail drop would have to take over (lines 3 and 4 of - Figure 3). + of the Classic queue. If there is no Classic queue the naive PI + update function in Figure 6 would drop nothing, even if the L4S queue + were overloaded - so tail drop would have to take over (lines 2 and 3 + of Figure 3). - If the test at line 2a finds that the Classic queue is empty, line 2d - measures the current queue delay using the L4S queue instead. While - the L4S queue is not overloaded, its delay will always be tiny - compared to the target Classic queue delay. So p_L will be driven to - zero, and the L4S queue will naturally be governed solely by - threshold marking (lines 5 and 6 of the dequeue algorithm in - Figure 7). But, if unresponsive L4S source(s) cause overload, the - DualQ transitions smoothly to L4S marking based on the PI algorithm. - And as overload increases, it naturally transitions from marking to - dropping by the switch-over mechanism already described. + Instead, the test at line 2a of the full PI update function in + Figure 8 keeps delay on target using drop. If the test at line 2a of + finds that the Classic queue is empty, line 2d measures the current + queue delay using the L4S queue instead. While the L4S queue is not + overloaded, its delay will always be tiny compared to the target + Classic queue delay. So p_CL will be driven to zero, and the L4S + queue will naturally be governed solely by p'_L from the native L4S + AQM (lines 5 and 6 of the dequeue algorithm in Figure 7). But, if + unresponsive L4S source(s) cause overload, the DualQ transitions + smoothly to L4S marking based on the PI algorithm. If overload + increases further, it naturally transitions from marking to dropping + by the switch-over mechanism already described. - 1: dualpi2_dequeue(lq, cq) { % Couples L4S & Classic queues, lq & cq - 2: while ( lq.len() + cq.len() > 0 ) + 1: dualpi2_dequeue(lq, cq, pkt) { % Couples L4S & Classic queues + 2: while ( lq.len() + cq.len() > 0 ) { 3: if ( scheduler() == lq ) { - 4a: lq.dequeue(pkt) + 4a: lq.dequeue(pkt) % L4S scheduled 4b: if ( p_CL < p_Lmax ) { % Check for overload saturation 5: p'_L = laqm(lq.time()) % Native L4S AQM 6: p_L = max(p'_L, p_CL) % Combining function - 7: if ( p_L > rand() ) % Linear marking + 7: if ( recur(p_L) ) % Linear marking 8a: mark(pkt) 8b: } else { % overload saturation - 8c: if ( p_C > rand() ) { % probability p_C = p^2 + 8c: if ( p_C > rand() ) { % probability p_C = p'^2 8e: drop(pkt) % revert to Classic drop due to overload 8f: continue % continue to the top of the while loop 8g: } - 8h: if ( p_CL > rand() ) % probability p_CL = k * p + 8h: if ( p_CL > rand() ) % probability p_CL = k * p' 8i: mark(pkt) % linear marking of remaining packets 8j: } 9: } else { - 10: cq.dequeue(pkt) - 11: if ( p_C > rand() ) { % probability p_C = p^2 + 10: cq.dequeue(pkt) % Classic scheduled + 11: if ( p_C > rand() ) { % probability p_C = p'^2 12a: if ( (ecn(pkt) == 0) % ECN field = not-ECT 12b: OR (p_C >= p_Cmax) ) { % Overload disables ECN 13: drop(pkt) % squared drop, redo loop 14: continue % continue to the top of the while loop 15: } 16: mark(pkt) % squared mark 17: } 18: } 19: return(pkt) % return the packet and stop 20: } 21: return(NULL) % no packet to dequeue 22: } Figure 7: Example Dequeue Pseudocode for DualQ Coupled PI2 AQM - (Including Integer Arithmetic and Overload Code) + (Including Overload Code) - 1: dualpi2_update(lq, cq, target) { % Update p every Tupdate + 1: dualpi2_update(lq, cq) { % Update p' every Tupdate 2a: if ( cq.len() > 0 ) 2b: curq = cq.time() %use queuing time of first-in Classic packet 2c: else % Classic queue empty 2d: curq = lq.time() % use queuing time of first-in L4S packet - 3: p = p + alpha_U * (curq - target) + beta_U * (curq - prevq) - 4: p_CL = p * k % Coupled L4S prob = base prob * coupling factor - 5: p_C = p^2 % Classic prob = (base prob)^2 + 3: p' = p' + alpha * (curq - target) + beta * (curq - prevq) + 4: p_CL = p' * k % Coupled L4S prob = base prob * coupling factor + 5: p_C = p'^2 % Classic prob = (base prob)^2 6: prevq = curq 7: } Figure 8: Example PI-Update Pseudocode for DualQ Coupled PI2 AQM (Including Overload Code) The choice of scheduler technology is critical to overload protection (see Section 4.1). o A well-understood weighted scheduler such as weighted round robin - (WRR) is recommended. The scheduler weight for Classic should be - low, e.g. 1/16. + (WRR) is recommended. As long as the scheduler weight for Classic + is small (e.g. 1/16), its exact value is unimportant because it + does not normally determine capacity shares. The weight is only + important to prevent unresponsive L4S traffic starving Classic + traffic. This is because capacity sharing between the queues is + normally determined by the coupled congestion signal, which + overrides the scheduler, by making L4S sources leave roughly equal + per-flow capacity available for Classic flows. - o Alternatively, a time-shifted FIFO could be used. This is a very - simple scheduler, but it does not fully isolate latency in the L4S - queue from uncontrolled bursts in the Classic queue. It works by - selecting the head packet that has waited the longest, biased - against the Classic traffic by a time-shift of tshift. To - implement time-shifted FIFO, the "if (scheduler() == lq )" test in - line 3 of the dequeue code would simply be replaced by "if ( - lq.time() + tshift >= cq.time() )". For the public Internet a - good value for tshift is 50ms. For private networks with smaller - diameter, about 4*target would be reasonable. + o Alternatively, a time-shifted FIFO (TS-FIFO) could be used. It + works by selecting the head packet that has waited the longest, + biased against the Classic traffic by a time-shift of tshift. To + implement time-shifted FIFO, the scheduler() function in line 3 of + the dequeue code would simply be implemented as the scheduler() + function at the bottom of Figure 10 in Appendix B. For the public + Internet a good value for tshift is 50ms. For private networks + with smaller diameter, about 4*target would be reasonable. TS- + FIFO is a very simple scheduler, but complexity might need to be + added to address some deficiencies (which is why it is not + recommended over WRR): + + * TS-FIFO does not fully isolate latency in the L4S queue from + uncontrolled bursts in the Classic queue; + + * TS-FIFO is only appropriate if time-stamping of packets is + feasible; + + * Even if time-stamping is supported, the sojourn time of the + head packet is always stale. For instance, if a burst arrives + at an empty queue, the sojourn time will only measure the delay + of the burst once the burst is over, even though the queue knew + about it from the start. At the cost of more operations and + more storage, a 'scaled sojourn time' metric of queue delay can + be used, which is the sojourn time of a packet scaled by the + ratio of the queue sizes when the packet departed and arrived + [SigQ-Dyn]. o A strict priority scheduler would be inappropriate, because it would starve Classic if L4S was overloaded. Appendix B. Example DualQ Coupled Curvy RED Algorithm As another example of a DualQ Coupled AQM algorithm, the pseudocode - below gives the Curvy RED based algorithm we used and tested. - Although we designed the AQM to be efficient in integer arithmetic, - to aid understanding it is first given using real-number arithmetic. - Then, one possible optimization for integer arithmetic is given, also - in pseudocode. To aid comparison, the line numbers are kept in step - between the two by using letter suffixes where the longer code needs - extra lines. + below gives the Curvy RED based algorithm. Although the AQM was + designed to be efficient in integer arithmetic, to aid understanding + it is first given using floating point arithmetic (Figure 10). Then, + one possible optimization for integer arithmetic is given, also in + pseudocode (Figure 11). To aid comparison, the line numbers are kept + in step between the two by using letter suffixes where the longer + code needs extra lines. - 1: dualq_dequeue(lq, cq) { % Couples L4S & Classic queues, lq & cq - 2: if ( lq.dequeue(pkt) ) { - 3a: p_L = cq.sec() / 2^S_L - 3b: if ( lq.byt() > T ) - 3c: mark(pkt) - 3d: elif ( p_L > maxrand(U) ) - 4: mark(pkt) - 5: return(pkt) % return the packet and stop here - 6: } - 7: while ( cq.dequeue(pkt) ) { - 8a: alpha = 2^(-f_C) - 8b: Q_C = alpha * pkt.sec() + (1-alpha)* Q_C % Classic Q EWMA - 9a: sqrt_p_C = Q_C / 2^S_C - 9b: if ( sqrt_p_C > maxrand(2*U) ) - 10: drop(pkt) % Squared drop, redo loop - 11: else - 12: return(pkt) % return the packet and stop here - 13: } - 14: return(NULL) % no packet to dequeue - 15: } +B.1. Curvy RED in Pseudocode - 16: maxrand(u) { % return the max of u random numbers - 17: maxr=0 - 18: while (u-- > 0) - 19: maxr = max(maxr, rand()) % 0 <= rand() < 1 - 20: return(maxr) + The pseudocode manipulates three main structures of variables: the + packet (pkt), the L4S queue (lq) and the Classic queue (cq) and + consists of the following five functions: + + o the initialization function cred_params_init(...) (Figure 2) that + sets parameter defaults (the API for setting non-default values is + omitted for brevity); + + o the dequeue function cred_dequeue(lq, cq, pkt) (Figure 4); + + o the scheduling function scheduler(), which selects between the + head packets of the two queues. + + It also uses the following functions that are either shown elsewhere, + or not shown in full here: + + o the enqueue function, which is identical to that used for DualPI2, + dualpi2_enqueue(lq, cq, pkt) in Figure 3; + + o mark(pkt) and drop(pkt) for ECN-marking and dropping a packet; + + o cq.len() or lq.len() returns the current length (aka. backlog) of + the relevant queue in bytes; + + o cq.time() or lq.time() returns the current queuing delay (aka. + sojourn time or service time) of the relevant queue in units of + time (see Note a in Appendix A.1). + + Because Curvy RED was evaluated before DualPI2, certain improvements + introduced for DualPI2 were not evaluated for Curvy RED. In the + pseudocode below, the straightforward improvements have been added on + the assumption they will provide similar benefits, but that has not + been proven experimentally. They are: i) a conditional priority + scheduler instead of strict priority ii) a time-based threshold for + the native L4S AQM; iii) ECN support for the Classic AQM. A recent + evaluation has proved that a minimum ECN-marking threshold (minTh) + greatly improves performance, so this is also included in the + pseudocode. + + Overload protection has not been added to the Curvy RED pseudocode + below so as not to detract from the main features. It would be added + in exactly the same way as in Appendix A.2 for the DualPI2 + pseudocode. The native L4S AQM uses a step threshold, but a ramp + like that described for DualPI2 could be used instead. The scheduler + uses the simple TS-FIFO algorithm, but it could be replaced with WRR. + + The Curvy RED algorithm has not been maintained or evaluated to the + same degree as the DualPI2 algorithm. In initial experiments on + broadband access links ranging from 4 Mb/s to 200 Mb/s with base RTTs + from 5 ms to 100 ms, Curvy RED achieved good results with the default + parameters in Figure 9. + + The parameters are categorised by whether they relate to the Classic + AQM, the L4S AQM or the framework coupling them together. Constants + and variables derived from these parameters are also included at the + end of each category. These are the raw input parameters for the + algorithm. A configuration front-end could accept more meaningful + parameters (e.g. RTT_max and RTT_typ) and convert them into these + raw parameters, as has been done for DualPI2 in Appendix A. Where + necessary, parameters are explained further in the walk-through of + the pseudocode below. + + 1: cred_params_init(...) { % Set input parameter defaults + 2: % DualQ Coupled framework parameters + 3: limit = MAX_LINK_RATE * 250 ms % Dual buffer size + 4: k' = 1 % Coupling factor as a power of 2 + 5: tshift = 50 ms % Time shift of TS-FIFO scheduler + 6: % Constants derived from Classic AQM parameters + 7: k = 2^k' % Coupling factor from Equation (1) + 6: + 7: % Classic AQM parameters + 8: g_C = 5 % EWMA smoothing parameter as a power of 1/2 + 9: S_C = -1 % Classic ramp scaling factor as a power of 2 + 10: minTh = 500 ms % No Classic drop/mark below this queue delay + 11: % Constants derived from Classic AQM parameters + 12: gamma = 2^(-g_C) % EWMA smoothing parameter + 13: range_C = 2^S_C % Range of Classic ramp + 14: + 15: % L4S AQM parameters + 16: T = 1 ms % Queue delay threshold for native L4S AQM + 17: % Constants derived from above parameters + 18: S_L = S_C - k' % L4S ramp scaling factor as a power of 2 + 19: range_L = 2^S_L % Range of L4S ramp + 20: } + + Figure 9: Example Header Pseudocode for DualQ Coupled Curvy RED AQM + + 1: cred_dequeue(lq, cq, pkt) { % Couples L4S & Classic queues + 2: while ( lq.len() + cq.len() > 0 ) { + 3: if ( scheduler() == lq ) { + 4: lq.dequeue(pkt) % L4S scheduled + 5a: p_CL = (cq.time() - minTh) / range_L + 5b: if ( ( lq.time() > T ) + 5c: OR ( p_CL > maxrand(U) ) ) + 6: mark(pkt) + 7: } else { + 8: cq.dequeue(pkt) % Classic scheduled + 9a: Q_C = gamma * qc.time() + (1-gamma) * Q_C % Classic Q EWMA + 10a: sqrt_p_C = (Q_C - minTh) / range_C + 10b: if ( sqrt_p_C > maxrand(2*U) ) { + 11: if ( (ecn(pkt) == 0) { % ECN field = not-ECT + 12: drop(pkt) % Squared drop, redo loop + 13: continue % continue to the top of the while loop + 14: } + 15: mark(pkt) + 16: } + 17: } + 18: return(pkt) % return the packet and stop here + 19: } + 20: return(NULL) % no packet to dequeue 21: } - Figure 9: Example Dequeue Pseudocode for DualQ Coupled Curvy RED AQM + 22: maxrand(u) { % return the max of u random numbers + 23: maxr=0 + 24: while (u-- > 0) + 25: maxr = max(maxr, rand()) % 0 <= rand() < 1 + 26: return(maxr) + 27: } - Packet classification code is not shown, as it is no different from - Figure 3. Potential classification schemes are discussed in - Section 2.3. The Curvy RED algorithm has not been maintained to the - same degree as the DualPI2 algorithm. Some ideas used in DualPI2 - would need to be translated into Curvy RED, such as i) the - conditional priority scheduler instead of strict priority ii) the - time-based L4S threshold; iii) turning off ECN as overload - protection; iv) Classic ECN support. These are not shown in the - Curvy RED pseudocode, but would need to be implemented for - production. {ToDo} + 28: scheduler() { + 29: if ( lq.time() + tshift >= cq.time() ) + 30: return lq; + 31: else + 32: return cq; + 33: } - At the outer level, the structure of dualq_dequeue() implements - strict priority scheduling. The code is written assuming the AQM is - applied on dequeue (Note 1) . Every time dualq_dequeue() is called, - the if-block in lines 2-6 determines whether there is an L4S packet - to dequeue by calling lq.dequeue(pkt), and otherwise the while-block - in lines 7-13 determines whether there is a Classic packet to - dequeue, by calling cq.dequeue(pkt). (Note 2) - In the lower priority Classic queue, a while loop is used so that, if - the AQM determines that a classic packet should be dropped, it - continues to test for classic packets deciding whether to drop each - until it actually forwards one. Thus, every call to dualq_dequeue() - returns one packet if at least one is present in either queue, - otherwise it returns NULL at line 14. (Note 3) + Figure 10: Example Dequeue Pseudocode for DualQ Coupled Curvy RED AQM - Within each queue, the decision whether to drop or mark is taken as - follows (to simplify the explanation, it is assumed that U=1): + The dequeue pseudocode (Figure 10) is repeatedly called whenever the + lower layer is ready to forward a packet. It schedules one packet + for dequeuing (or zero if the queue is empty) then returns control to + the caller, so that it does not block while that packet is being + forwarded. While making this dequeue decision, it also makes the + necessary AQM decisions on dropping or marking. The alternative of + applying the AQMs at enqueue would shift some processing from the + critical time when each packet is dequeued. However, it would also + add a whole queue of delay to the control signals, making the control + loop very sloppy. - L4S: If the test at line 2 determines there is an L4S packet to - dequeue, the tests at lines 3a and 3c determine whether to mark - it. The first is a simple test of whether the L4S queue (lq.byt() - in bytes) is greater than a step threshold T in bytes (Note 4). - The second test is similar to the random ECN marking in RED, but - with the following differences: i) the marking function does not - start with a plateau of zero marking until a minimum threshold, - rather the marking probability starts to increase as soon as the - queue is positive; ii) marking depends on queuing time, not bytes, - in order to scale for any link rate without being reconfigured; - iii) marking of the L4S queue does not depend on itself, it - depends on the queuing time of the _other_ (Classic) queue, where - cq.sec() is the queuing time of the packet at the head of the - Classic queue (zero if empty); iv) marking depends on the - instantaneous queuing time (of the other Classic queue), not a - smoothed average; v) the queue is compared with the maximum of U + The code is written assuming the AQMs are applied on dequeue (Note + 1). All the dequeue code is contained within a large while loop so + that if it decides to drop a packet, it will continue until it + selects a packet to schedule. If both queues are empty, the routine + returns NULL at line 20. Line 3 of the dequeue pseudocode is where + the conditional priority scheduler chooses between the L4S queue (lq) + and the Classic queue (cq). The time-shifted FIFO scheduler is shown + at lines 28-33, which would be suitable if simplicity is paramount + (see Note 2). + + Within each queue, the decision whether to forward, drop or mark is + taken as follows (to simplify the explanation, it is assumed that + U=1): + + L4S: If the test at line 3 determines there is an L4S packet to + dequeue, the tests at lines 5b and 5c determine whether to mark + it. The first is a simple test of whether the L4S queue delay + (lq.time()) is greater than a step threshold T (Note 3). The + second test is similar to the random ECN marking in RED, but with + the following differences: ii) marking depends on queuing time, + not bytes, in order to scale for any link rate without being + reconfigured; ii) marking of the L4S queue does not depend on + itself, it depends on the queuing time of the _other_ (Classic) + queue, where cq.time() is the queuing time of the packet at the + head of the Classic queue (zero if empty); iii) marking depends on + the instantaneous queuing time (of the other Classic queue), not a + smoothed average; iv) the queue is compared with the maximum of U random numbers (but if U=1, this is the same as the single random number used in RED). - Specifically, in line 3a the marking probability p_L is set to the - Classic queueing time qc.sec() in seconds divided by the L4S - scaling parameter 2^S_L, which represents the queuing time (in - seconds) at which marking probability would hit 100%. Then in line - 3d (if U=1) the result is compared with a uniformly distributed - random number between 0 and 1, which ensures that marking - probability will linearly increase with queueing time. The - scaling parameter is expressed as a power of 2 so that division - can be implemented as a right bit-shift (>>) in line 3 of the - integer variant of the pseudocode (Figure 10). + Specifically, in line 5a the coupled marking probability p_CL is + set to the excess of the Classic queueing delay qc.time() above + the minimum queuing delay threshold (minTh) all divided by the L4S + scaling parameter range_L. range_L represents the queuing delay + (in seconds) added to minTh at which marking probability would hit + 100%. Then in line 5c (if U=1) the result is compared with a + uniformly distributed random number between 0 and 1, which ensures + that marking probability will linearly increase with queueing + time. - Classic: If the test at line 7 determines that there is at least one - Classic packet to dequeue, the test at line 9b determines whether - to drop it. But before that, line 8b updates Q_C, which is an - exponentially weighted moving average (Note 5) of the queuing time - in the Classic queue, where pkt.sec() is the instantaneous - queueing time of the current Classic packet and alpha is the EWMA - constant for the classic queue. In line 8a, alpha is represented - as an integer power of 2, so that in line 8 of the integer code - the division needed to weight the moving average can be - implemented by a right bit-shift (>> f_C). + Classic: If the scheduler at line 3 chooses to dequeue a Classic + packet and jumps to line 7, the test at line 10b determines + whether to drop or mark it. But before that, line 9a updates Q_C, + which is an exponentially weighted moving average (Note 4) of the + queuing time in the Classic queue, where qc.time() is the current + instantaneous queueing time of the Classic queue and gamma is the + EWMA constant (default 1/32, see line 12 of the initialization + function). - Lines 9a and 9b implement the drop function. In line 9a the + Lines 10a and 10b implement the Classic AQM. In line 10a the averaged queuing time Q_C is divided by the Classic scaling - parameter 2^S_C, in the same way that queuing time was scaled for - L4S marking. This scaled queuing time is given the variable name - sqrt_p_C because it will be squared to compute Classic drop - probability, so before it is squared it is effectively the square - root of the drop probability. The squaring is done by comparing - it with the maximum out of two random numbers (assuming U=1). - Comparing it with the maximum out of two is the same as the + parameter range_C, in the same way that queuing time was scaled + for L4S marking. This scaled queuing time will be squared to + compute Classic drop probability so, before it is squared, it is + effectively the square root of the drop probability, hence it is + given the variable name sqrt_p_C. The squaring is done by + comparing it with the maximum out of two random numbers (assuming + U=1). Comparing it with the maximum out of two is the same as the logical `AND' of two tests, which ensures drop probability rises - with the square of queuing time (Note 6). Again, the scaling - parameter is expressed as a power of 2 so that division can be - implemented as a right bit-shift in line 9 of the integer - pseudocode. + with the square of queuing time. - The marking/dropping functions in each queue (lines 3 & 9) are two - cases of a new generalization of RED called Curvy RED, motivated as - follows. When we compared the performance of our AQM with fq_CoDel - and PIE, we came to the conclusion that their goal of holding queuing - delay to a fixed target is misguided [CRED_Insights]. As the number - of flows increases, if the AQM does not allow TCP to increase queuing - delay, it has to introduce abnormally high levels of loss. Then loss - rather than queuing becomes the dominant cause of delay for short - flows, due to timeouts and tail losses. + The AQM functions in each queue (lines 5c & 10b) are two cases of a + new generalization of RED called Curvy RED, motivated as follows. + When the performance of this AQM was compared with fq_CoDel and PIE, + their goal of holding queuing delay to a fixed target seemed + misguided [CRED_Insights]. As the number of flows increases, if the + AQM does not allow TCP to increase queuing delay, it has to introduce + abnormally high levels of loss. Then loss rather than queuing + becomes the dominant cause of delay for short flows, due to timeouts + and tail losses. Curvy RED constrains delay with a softened target that allows some increase in delay as load increases. This is achieved by increasing drop probability on a convex curve relative to queue growth (the square curve in the Classic queue, if U=1). Like RED, the curve hugs the zero axis while the queue is shallow. Then, as load increases, it introduces a growing barrier to higher delay. But, unlike RED, it - requires only one parameter, the scaling, not three. The diadvantage - of Curvy RED is that it is not adapted to a wide range of RTTs. - Curvy RED can be used as is when the RTT range to support is limited + requires only two parameters, not three. The disadvantage of Curvy + RED is that it is not adapted to a wide range of RTTs. Curvy RED can + be used as is when the RTT range to be supported is limited, otherwise an adaptation mechanism is required. - There follows a summary listing of the two parameters used for each - of the two queues: - - Classic: + From our limited experiments with Curvy RED so far, recommended + values of these parameters are: S_C = -1; g_C = 5; T = 5 * MTU at the + link rate (about 1ms at 60Mb/s) for the range of base RTTs typical on + the public Internet. [CRED_Insights] explains why these parameters + are applicable whatever rate link this AQM implementation is deployed + on and how the parameters would need to be adjusted for a scenario + with a different range of RTTs (e.g. a data centre). The setting of + k depends on policy (see Section 2.5 and Appendix C respectively for + its recommended setting and guidance on alternatives). - S_C : The scaling factor of the dropping function scales Classic - queuing times in the range [0, 2^(S_C)] seconds into a dropping - probability in the range [0,1]. To make division efficient, it - is constrained to be an integer power of two; + There is also a cUrviness parameter, U, which is a small positive + integer. It is likely to take the same hard-coded value for all + implementations, once experiments have determined a good value. Only + U=1 has been used in experiments so far, but results might be even + better with U=2 or higher. - f_C : To smooth the queuing time of the Classic queue and make - multiplication efficient, we use a negative integer power of - two for the dimensionless EWMA constant, which we define as - alpha = 2^(-f_C). + Notes: - L4S : + 1. The alternative of applying the AQMs at enqueue would shift some + processing from the critical time when each packet is dequeued. + However, it would also add a whole queue of delay to the control + signals, making the control loop sloppier (for a typical RTT it + would double the Classic queue's feedback delay). On a platform + where packet timestamping is feasible, e.g. Linux, it is also + easiest to apply the AQMs at dequeue because that is where + queuing time is also measured. - S_L (and k'): As for the Classic queue, the scaling factor of - the L4S marking function scales Classic queueing times in the - range [0, 2^(S_L)] seconds into a probability in the range - [0,1]. Note that S_L = S_C + k', where k' is the coupling - between the queues. So S_L and k' count as only one parameter; - k' is related to k in Equation (1) (Section 2.1) by k=2^k', - where both k and k' are constants. Then implementations can - avoid costly division by shifting p_L by k' bits to the right. + 2. WRR better isolates the L4S queue from large delay bursts in the + Classic queue, but it is slightly less simple than TS-FIFO. If + WRR were used, a low default Classic weight (e.g. 1/16) would + need to be configured in place of the time shift in line 5 of the + initialization function (Figure 9). - T : The queue size in bytes at which step threshold marking - starts in the L4S queue. + 3. A step function is shown for simplicity. A ramp function (see + Figure 5 and the discussion around it in Appendix A.1) is + recommended, because it is more general than a step and has the + potential to enable L4S congestion controls to converge more + rapidly. - {ToDo: These are the raw parameters used within the algorithm. A - configuration front-end could accept more meaningful parameters and - convert them into these raw parameters.} + 4. An EWMA is only one possible way to filter bursts; other more + adaptive smoothing methods could be valid and it might be + appropriate to decrease the EWMA faster than it increases, e.g. + by using the minimum of the smoothed and instantaneous queue + delays, min(Q_C, qc.time()). - From our experiments so far, recommended values for these parameters - are: S_C = -1; f_C = 5; T = 5 * MTU for the range of base RTTs - typical on the public Internet. [CRED_Insights] explains why these - parameters are applicable whatever rate link this AQM implementation - is deployed on and how the parameters would need to be adjusted for a - scenario with a different range of RTTs (e.g. a data centre) {ToDo - incorporate a summary of that report into this draft}. The setting of - k depends on policy (see Section 2.5 and Appendix C respectively for - its recommended setting and guidance on alternatives). +B.2. Efficient Implementation of Curvy RED - There is also a cUrviness parameter, U, which is a small positive - integer. It is likely to take the same hard-coded value for all - implementations, once experiments have determined a good value. We - have solely used U=1 in our experiments so far, but results might be - even better with U=2 or higher. + Although code optimization depends on the platform, the following + notes explain where the design of Curvy RED was particularly + motivated by efficient implementation. - Note that the dropping function at line 9 calls maxrand(2*U), which - gives twice as much curviness as the call to maxrand(U) in the - marking function at line 3. This is the trick that implements the - square rule in equation (1) (Section 2.1). This is based on the fact - that, given a number X from 1 to 6, the probability that two dice - throws will both be less than X is the square of the probability that - one throw will be less than X. So, when U=1, the L4S marking - function is linear and the Classic dropping function is squared. If - U=2, L4S would be a square function and Classic would be quartic. - And so on. + The Classic AQM at line 10b calls maxrand(2*U), which gives twice as + much curviness as the call to maxrand(U) in the marking function at + line 5c. This is the trick that implements the square rule in + equation (1) (Section 2.1). This is based on the fact that, given a + number X from 1 to 6, the probability that two dice throws will both + be less than X is the square of the probability that one throw will + be less than X. So, when U=1, the L4S marking function is linear and + the Classic dropping function is squared. If U=2, L4S would be a + square function and Classic would be quartic. And so on. The maxrand(u) function in lines 16-21 simply generates u random - numbers and returns the maximum (Note 7). Typically, maxrand(u) - could be run in parallel out of band. For instance, if U=1, the - Classic queue would require the maximum of two random numbers. So, - instead of calling maxrand(2*U) in-band, the maximum of every pair of - values from a pseudorandom number generator could be generated out- - of-band, and held in a buffer ready for the Classic queue to consume. + numbers and returns the maximum. Typically, maxrand(u) could be run + in parallel out of band. For instance, if U=1, the Classic queue + would require the maximum of two random numbers. So, instead of + calling maxrand(2*U) in-band, the maximum of every pair of values + from a pseudorandom number generator could be generated out-of-band, + and held in a buffer ready for the Classic queue to consume. - 1: dualq_dequeue(lq, cq) { % Couples L4S & Classic queues, lq & cq - 2: if ( lq.dequeue(pkt) ) { - 3: if ((lq.byt() > T) || ((cq.ns() >> (S_L-2)) > maxrand(U))) - 4: mark(pkt) - 5: return(pkt) % return the packet and stop here - 6: } - 7: while ( cq.dequeue(pkt) ) { - 8: Q_C += (pkt.ns() - Q_C) >> f_C % Classic Q EWMA - 9: if ( (Q_C >> (S_C-2) ) > maxrand(2*U) ) - 10: drop(pkt) % Squared drop, redo loop - 11: else - 12: return(pkt) % return the packet and stop here - 13: } - 14: return(NULL) % no packet to dequeue - 15: } + 1: cred_dequeue(lq, cq, pkt) { % Couples L4S & Classic queues + 2: while ( lq.len() + cq.len() > 0 ) { + 3: if ( scheduler() == lq ) { + 4: lq.dequeue(pkt) % L4S scheduled + 5: if ((lq.time() > T) OR (cq.ns() >> (S_L-2) > maxrand(U))) + 6: mark(pkt) + 7: } else { + 8: cq.dequeue(pkt) % Classic scheduled + 9: Q_C += (cq.ns() - Q_C) >> g_C % Classic Q EWMA + 10: if ( (Q_C >> (S_C-2) ) > maxrand(2*U) ) { + 11: if ( (ecn(pkt) == 0) { % ECN field = not-ECT + 12: drop(pkt) % Squared drop, redo loop + 13: continue % continue to the top of the while loop + 14: } + 15: mark(pkt) + 16: } + 17: } + 18: return(pkt) % return the packet and stop here + 19: } + 20: return(NULL) % no packet to dequeue + 21: } - Figure 10: Optimised Example Dequeue Pseudocode for Coupled DualQ AQM + Figure 11: Optimised Example Dequeue Pseudocode for Coupled DualQ AQM using Integer Arithmetic - Notes: - - 1. The drain rate of the queue can vary if it is scheduled relative - to other queues, or to cater for fluctuations in a wireless - medium. To auto-adjust to changes in drain rate, the queue must - be measured in time, not bytes or packets [CoDel]. In our Linux - implementation, it was easiest to measure queuing time at - dequeue. Queuing time can be estimated when a packet is enqueued - by measuring the queue length in bytes and dividing by the recent - drain rate. - - 2. An implementation has to use priority queueing, but it need not - implement strict priority. - - 3. If packets can be enqueued while processing dequeue code, an - implementer might prefer to place the while loop around both - queues so that it goes back to test again whether any L4S packets - arrived while it was dropping a Classic packet. - - 4. In order not to change too many factors at once, for now, we keep - the marking function for DCTCP-only traffic as similar as - possible to DCTCP. However, unlike DCTCP, all processing is at - dequeue, so we determine whether to mark a packet at the head of - the queue by the byte-length of the queue _behind_ it. We plan - to test whether using queuing time will work in all - circumstances, and if we find that the step can cause - oscillations, we will investigate replacing it with a steep - random marking curve. + The two ranges, range_L and range_C are expressed as powers of 2 so + that division can be implemented as a right bit-shift (>>) in lines 5 + and 10 of the integer variant of the pseudocode (Figure 11). - 5. An EWMA is only one possible way to filter bursts; other more - adaptive smoothing methods could be valid and it might be - appropriate to decrease the EWMA faster than it increases. + For the integer variant of the pseudocode, an integer version of the + rand() function used at line 25 of the maxrand(function) in Figure 10 + would be arranged to return an integer in the range 0 <= maxrand() < + 2^32 (not shown). This would scale up all the floating point + probabilities in the range [0,1] by 2^32. - 6. In practice at line 10 the Classic queue would probably test for - ECN capability on the packet to determine whether to drop or mark - the packet. However, for brevity such detail is omitted. All - packets classified into the L4S queue have to be ECN-capable, so - no dropping logic is necessary at line 3. Nonetheless, L4S - packets could be dropped by overload code (see Section 4.1). + Queuing delays are also scaled up by 2^32, but in two stages: i) In + lines 5 and 10 queuing times cq.ns() and pkt.ns() are returned in + integer nanoseconds, making the values about 2^30 times larger than + when the units were seconds, ii) then in lines 3 and 9 an adjustment + of -2 to the right bit-shift multiplies the result by 2^2, to + complete the scaling by 2^32. - 7. In the integer variant of the pseudocode (Figure 10) real numbers - are all represented as integers scaled up by 2^32. In lines 3 & - 9 the function maxrand() is arranged to return an integer in the - range 0 <= maxrand() < 2^32. Queuing times are also scaled up by - 2^32, but in two stages: i) In lines 3 and 8 queuing times - cq.ns() and pkt.ns() are returned in integer nanoseconds, making - the values about 2^30 times larger than when the units were - seconds, ii) then in lines 3 and 9 an adjustment of -2 to the - right bit-shift multiplies the result by 2^2, to complete the - scaling by 2^32. + In line 8 of the initialization function, the EWMA constant gamma is + represented as an integer power of 2, g_C, so that in line 9 of the + integer code the division needed to weight the moving average can be + implemented by a right bit-shift (>> g_C). Appendix C. Guidance on Controlling Throughput Equivalence +---------------+------+-------+ | RTT_C / RTT_L | Reno | Cubic | +---------------+------+-------+ | 1 | k'=1 | k'=0 | | 2 | k'=2 | k'=1 | | 3 | k'=2 | k'=2 | | 4 | k'=3 | k'=2 | @@ -1849,60 +2219,42 @@ for k', if it wants to slow DCTCP down to roughly the same throughput as Classic flows, to compensate for Classic flows slowing themselves down by causing themselves extra queuing delay. The values for k' in the table are derived from the formulae, which was developed in [DCttH15]: 2^k' = 1.64 (RTT_reno / RTT_dc) (2) 2^k' = 1.19 (RTT_cubic / RTT_dc ) (3) - For localized traffic from a particular ISP's data centre, we used - the measured RTTs to calculate that a value of k'=3 (equivalant to - k=8) would achieve throughput equivalence, and our experiments - verified the formula very closely. + For localized traffic from a particular ISP's data centre, using the + measured RTTs, it was calculated that a value of k'=3 (equivalant to + k=8) would achieve throughput equivalence, and experiments verified + the formula very closely. For a typical mix of RTTs from local data centres and across the general Internet, a value of k'=1 (equivalent to k=2) is recommended as a good workable compromise. -Appendix D. Open Issues - - Most of the following open issues are also tagged '{ToDo}' at the - appropriate point in the document: - - PI2 appendix: scaling of alpha & beta, esp. dependence of beta_U - on Tupdate - - Curvy RED appendix: complete the unfinished parts - Authors' Addresses Koen De Schepper Nokia Bell Labs Antwerp Belgium Email: koen.de_schepper@nokia.com URI: https://www.bell-labs.com/usr/koen.de_schepper Bob Briscoe (editor) CableLabs UK Email: ietf@bobbriscoe.net URI: http://bobbriscoe.net/ - Olga Bondarenko - Simula Research Lab - Lysaker - Norway - - Email: olgabnd@gmail.com - URI: https://www.simula.no/people/olgabo - - Ing-jyh Tsang - Nokia - Antwerp - Belgium + Greg White + CableLabs + Louisville, CO + US - Email: ing-jyh.tsang@nokia.com + Email: G.White@CableLabs.com