Internet DRAFT - draft-briscoe-tsvwg-aqm-dualq-coupled
draft-briscoe-tsvwg-aqm-dualq-coupled
Active Queue Management (aqm) K. De Schepper
Internet-Draft Nokia Bell Labs
Intended status: Experimental B. Briscoe, Ed.
Expires: May 4, 2017 O. Bondarenko
Simula Research Lab
I. Tsang
Nokia Bell Labs
October 31, 2016
DualQ Coupled AQM for Low Latency, Low Loss and Scalable Throughput
draft-briscoe-tsvwg-aqm-dualq-coupled-00
Abstract
Data Centre TCP (DCTCP) was designed to provide predictably low
queuing latency, near-zero loss, and throughput scalability using
explicit congestion notification (ECN) and an extremely simple
marking behaviour on switches. However, DCTCP does not co-exist with
existing TCP traffic---throughput starves. So, until now, DCTCP
could only be deployed where a clean-slate environment could be
arranged, such as in private data centres. This specification
defines `DualQ Coupled Active Queue Management (AQM)' to allow
scalable congestion controls like DCTCP to safely co-exist with
classic Internet traffic. The Coupled AQM ensures that a flow runs
at about the same rate whether it uses DCTCP or TCP Reno/Cubic, but
without inspecting transport layer flow identifiers. When tested in
a residential broadband setting, DCTCP achieved sub-millisecond
average queuing delay and zero congestion loss under a wide range of
mixes of DCTCP and `Classic' broadband Internet traffic, without
compromising the performance of the Classic traffic. The solution
also reduces network complexity and eliminates network configuration.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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material or to cite them other than as "work in progress."
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This Internet-Draft will expire on May 4, 2017.
Copyright Notice
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Problem and Scope . . . . . . . . . . . . . . . . . . . . 2
1.2. Terminology . . . . . . . . . . . . . . . . . . . . . . . 5
1.3. Features . . . . . . . . . . . . . . . . . . . . . . . . 5
2. DualQ Coupled AQM Algorithm . . . . . . . . . . . . . . . . . 6
2.1. Coupled AQM . . . . . . . . . . . . . . . . . . . . . . . 7
2.2. Dual Queue . . . . . . . . . . . . . . . . . . . . . . . 8
2.3. Traffic Classification . . . . . . . . . . . . . . . . . 8
2.4. Normative Requirements . . . . . . . . . . . . . . . . . 8
3. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 9
4. Security Considerations . . . . . . . . . . . . . . . . . . . 10
4.1. Overload Handling . . . . . . . . . . . . . . . . . . . . 10
5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 11
6. References . . . . . . . . . . . . . . . . . . . . . . . . . 11
6.1. Normative References . . . . . . . . . . . . . . . . . . 11
6.2. Informative References . . . . . . . . . . . . . . . . . 12
Appendix A. Example DualQ Coupled PI2 Algorithm . . . . . . . . 14
Appendix B. Example DualQ Coupled Curvy RED Algorithm . . . . . 17
Appendix C. Guidance on Controlling Throughput Equivalence . . . 23
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 24
1. Introduction
1.1. Problem and Scope
Latency is becoming the critical performance factor for many (most?)
applications on the public Internet, e.g. Web, voice, conversational
video, gaming, finance apps, remote desktop and cloud-based
applications. In the developed world, further increases in access
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network bit-rate offer diminishing returns, whereas latency is still
a multi-faceted problem. In the last decade or so, much has been
done to reduce propagation time by placing caches or servers closer
to users. However, queuing remains a major component of latency.
The Diffserv architecture provides Expedited Forwarding [RFC3246], so
that low latency traffic can jump the queue of other traffic.
However, on access links dedicated to individual sites (homes, small
enterprises or mobile devices), often all traffic at any one time
will be latency-sensitive. Then Diffserv is of little use. Instead,
we need to remove the causes of any unnecessary delay.
The bufferbloat project has shown that excessively-large buffering
(`bufferbloat') has been introducing significantly more delay than
the underlying propagation time. These delays appear only
intermittently--only when a capacity-seeking (e.g. TCP) flow is long
enough for the queue to fill the buffer, making every packet in other
flows sharing the buffer sit through the queue.
Active queue management (AQM) was originally developed to solve this
problem (and others). Unlike Diffserv, which gives low latency to
some traffic at the expense of others, AQM controls latency for _all_
traffic in a class. In general, AQMs introduce an increasing level
of discard from the buffer the longer the queue persists above a
shallow threshold. This gives sufficient signals to capacity-seeking
(aka. greedy) flows to keep the buffer empty for its intended
purpose: absorbing bursts. However, RED [RFC2309] and other
algorithms from the 1990s were sensitive to their configuration and
hard to set correctly. So, AQM was not widely deployed.
More recent state-of-the-art AQMs, e.g.
fq_CoDel [I-D.ietf-aqm-fq-codel], PIE [I-D.ietf-aqm-pie], Adaptive
RED [ARED01], are easier to configure, because they define the
queuing threshold in time not bytes, so it is invariant for different
link rates. However, no matter how good the AQM, the sawtoothing
rate of TCP will either cause queuing delay to vary or cause the link
to be under-utilized. Even with a perfectly tuned AQM, the
additional queuing delay will be of the same order as the underlying
speed-of-light delay across the network. Flow-queuing can isolate
one flow from another, but it cannot isolate a TCP flow from the
delay variations it inflicts on itself, and it has other problems -
it overrides the flow rate decisions of variable rate video
applications, it does not recognise the flows within IPSec VPN
tunnels and it is relatively expensive to implement.
It seems that further changes to the network alone will now yield
diminishing returns. Data Centre TCP (DCTCP [I-D.ietf-tcpm-dctcp])
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teaches us that a small but radical change to TCP is needed to cut
two major outstanding causes of queuing delay variability:
1. the `sawtooth' varying rate of TCP itself;
2. the smoothing delay deliberately introduced into AQMs to permit
bursts without triggering losses.
The former causes a flow's round trip time (RTT) to vary from about 1
to 2 times the base RTT between the machines in question. The latter
delays the system's response to change by a worst-case
(transcontinental) RTT, which could be hundreds of times the actual
RTT of typical traffic from localized CDNs.
Latency is not our only concern:
3. It was known when TCP was first developed that it would not scale
to high bandwidth-delay products.
Given regular broadband bit-rates over WAN distances are
already [RFC3649] beyond the scaling range of `classic' TCP Reno,
`less unscalable' Cubic [I-D.ietf-tcpm-cubic] and
Compound [I-D.sridharan-tcpm-ctcp] variants of TCP have been
successfully deployed. However, these are now approaching their
scaling limits. Unfortunately, fully scalable TCPs such as DCTCP
cause `classic' TCP to starve itself, which is why they have been
confined to private data centres or research testbeds (until now).
This document specifies a `DualQ Coupled AQM' extension that solves
the problem of coexistence between scalable and classic flows,
without having to inspect flow identifiers. The AQM is not like
flow-queuing approaches [I-D.ietf-aqm-fq-codel] 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 flow. In contrast, the AQM exploits the behaviour of
scalable congestion controls like DCTCP so that every packet in every
flow sharing the queue for DCTCP-like traffic can be served with very
low latency.
This AQM extension can be combined with any single qeueu AQM that
generates a statistical or deterministic mark/drop probability driven
by the queue dynamics. In many cases it simplifies the basic control
algorithm, and requires little extra processing. Therefore it is
believed the Coupled AQM would be applicable and easy to deploy in
all types of buffers; buffers in cost-reduced mass-market residential
equipment; buffers in end-system stacks; buffers in carrier-scale
equipment including remote access servers, routers, firewalls and
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Ethernet switches; buffers in network interface cards, buffers in
virtualized network appliances, hypervisors, and so on.
The supporting papers [PI216] and [DCttH15] give the full rationale
for the AQM's design, both discursively and in more precise
mathematical form.
1.2. Terminology
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in [RFC2119]. In this
document, these words will appear with that interpretation only when
in ALL CAPS. Lower case uses of these words are not to be
interpreted as carrying RFC-2119 significance.
The DualQ Coupled AQM uses two queues for two services. Each of the
following terms identifies both the service and the queue that
provides the service:
Classic (denoted by subscript C): The `Classic' service is intended
for all the behaviours that currently co-exist with TCP Reno (TCP
Cubic, Compound, SCTP, etc).
Low-Latency, Low-Loss and Scalable (L4S, denoted by subscript L):
The `L4S' service is intended for a set of congestion controls
with scalable properties such as DCTCP (e.g.
Relentless [Mathis09]).
Either service can cope with a proportion of unresponsive or less-
responsive traffic as well (e.g. DNS, VoIP, etc), just as a single
queue AQM can. The DualQ Coupled AQM behaviour is similar to a
single FIFO queue with respect to unresponsive and overload traffic.
1.3. Features
The AQM couples marking and/or dropping across the two queues such
that a flow will get roughly the same throughput whichever it uses.
Therefore both queues can feed into the full capacity of a link and
no rates need to be configured for the queues. The L4S queue enables
scalable congestion controls like DCTCP to give stunningly low and
predictably low latency, without compromising the performance of
competing 'Classic' Internet traffic. Thousands of tests have been
conducted in a typical fixed residential broadband setting. Typical
experiments used base round trip delays up to 100ms between the data
centre and home network, and large amounts of background traffic in
both queues. For every L4S packet, the AQM kept the average queuing
delay below 1ms (or 2 packets if serialization delay is bigger for
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slow links), and no losses at all were introduced by the AQM.
Details of the extensive experiments will be made available [PI216]
[DCttH15].
Subjective testing was also conducted using a demanding panoramic
interactive video application run over a stack with DCTCP enabled and
deployed on the testbed. Each user could pan or zoom their own high
definition (HD) sub-window of a larger video scene from a football
match. Even though the user was also downloading large amounts of
L4S and Classic data, latency was so low that the picture appeared to
stick to their finger on the touchpad (all the L4S data achieved the
same ultra-low latency). With an alternative AQM, the video
noticeably lagged behind the finger gestures.
Unlike Diffserv Expedited Forwarding, the L4S queue does not have to
be limited to a small proportion of the link capacity in order to
achieve low delay. The L4S queue can be filled with a heavy load of
capacity-seeking flows like DCTCP and still achieve low delay. The
L4S queue does not rely on the presence of other traffic in the
Classic queue that can be 'overtaken'. It gives low latency to L4S
traffic whether or not there is Classic traffic, and the latency of
Classic traffic does not suffer when a proportion of the traffic is
L4S. The two queues are only necessary because DCTCP-like flows
cannot keep latency predictably low and keep utilization high if they
are mixed with legacy TCP flows,
The experiments used the Linux implementation of DCTCP that is
deployed in private data centres, without any modification despite
its known deficiencies. Nonetheless, certain modifications will be
necessary before DCTCP is safe to use on the Internet, which are
recorded for now in Appendix A of
[I-D.briscoe-tsvwg-aqm-tcpm-rmcat-l4s-problem]. 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.
2. DualQ Coupled AQM Algorithm
There are two main aspects to the algorithm:
o the Coupled AQM that addresses throughput equivalence between
Classic (e.g. Reno, Cubic) flows and L4S (e.g. DCTCP) flows
o the Dual Queue structure that provides latency separation for L4S
flows to isolate them from the typically large Classic queue.
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2.1. Coupled AQM
In the 1990s, the `TCP formula' was derived for the relationship
between TCP's congestion window, cwnd, and its drop probability, p.
To a first order approximation, cwnd of TCP Reno is inversely
proportional to the square root of p. TCP Cubic implements a Reno-
compatibility mode, which is the only relevant mode for typical RTTs
under 20ms, while the throughput of a single flow is less than about
500Mb/s. Therefore we can assume that Cubic traffic behaves similar
to Reno (but with a slightly different constant of proportionality),
and we shall use the term 'Classic' for the collection of Reno and
Cubic in Reno mode.
In our supporting paper [PI216], we derive 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 we use the term 'L4S' traffic for all similar
behaviour.
In order to make a DCTCP flow run at roughly the same rate as a Reno
TCP flow (all other factors being equal), we make the drop or marking
probability for Classic traffic, p_C distinct from the marking
probability for L4S traffic, p_L (in contrast to RFC3168 which
requires them to be the same). We make the Classic drop probability
p_C proportional to the square of the L4S marking probability p_L.
This is because we need to make the Reno flow rate equal the DCTCP
flow rate, so we have to square the square root of p_C in the Reno
rate equation to make it the same as the straight p_L in the DCTCP
rate equation.
There is a really simple way to implement the square of a probability
- by testing the queue against two random numbers not one. This is
the approach adopted in Appendix A and Appendix B.
Stating this as a formula, the relation between Classic drop
probability, p_C, and L4S marking probability, p_L needs to take the
form:
p_C = ( p_L / k )^2 (1)
where k is the constant of proportionality. Optionally, k can be
expressed as a power of 2, so k=2^k', where k' is another constant.
Then implementations can avoid costly division by shifting p_L by k'
bits to the right.
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2.2. Dual Queue
Classic traffic builds a large queue, so a separate queue is provided
for L4S traffic, and it is scheduled with strict priority.
Nonetheless, coupled marking ensures that giving priority to L4S
traffic still leaves the right amount of spare scheduling time for
Classic flows to each get equivalent throughput to DCTCP flows (all
other factors such as RTT being equal). The algorithm achieves this
without having to inspect flow identifiers.
2.3. Traffic Classification
Both the Coupled AQM and DualQ mechanisms need an identifier to
distinguish L4S and C packets. A separate draft
[I-D.briscoe-tsvwg-ecn-l4s-id] recommends using the ECT(1) codepoint
of the ECN field as this identifier, having assessed various
alternatives.
Given L4S work is currently on the experimental track, but the
definition of the ECN field is on the standards track [RFC3168],
another standards track document has proved necessary to make the
ECT(1) codepoint available for experimentation
[I-D.black-tsvwg-ecn-experimentation].
2.4. Normative Requirements
In the Dual Queue, L4S packets MUST be given priority over Classic,
although strict priority MAY not be appropriate.
All L4S traffic MUST be ECN-capable, although some Classic traffic
MAY also be ECN-capable.
Whatever identifier is used for L4S traffic, it will still be
necessary to agree on the meaning of an ECN marking on L4S traffic,
relative to a drop of Classic traffic. In order to prevent
starvation of Classic traffic by scalable L4S traffic (e.g. DCTCP)
the drop probability of Classic traffic MUST be proportional to the
square of the marking probability of L4S traffic, In other words, the
power to which p_L is raised in Eqn. (1) MUST be 2.
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). k does not have to
be standardized because differences do not prevent interoperability.
However, k has to take some value, and each operator can make that
choice.
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A value of k=2 is currently RECOMMENDED as the default for Internet
access networks. 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 requirements for scalable congestion controls on the Internet
(termed the TCP Prague requirements) are only in initial draft form
[I-D.briscoe-tsvwg-aqm-tcpm-rmcat-l4s-problem] and subject to change.
If the aggressiveness of DCTCP is not defined as the benchmark for
scalable controls on the Internet, the recommended value of k will
also be subject to change.
Whatever value is recommended, 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.
Typically, access network operators isolate customers from each other
with some form of layer-2 multiplexing (TDM in DOCSIS, CDMA in 3G) or
L3 scheduling (WRR in broadband), rather than relying on TCP to share
capacity between customers [RFC0970]. In such cases, the choice of k
will solely affect relative flow rates within each customer's access
capacity, not between customers. Also, k will not affect relative
flow rates at any times when all flows are Classic or all L4S, and it
will not affect small flows.
Example DualQ Coupled AQM algorithms called PI2 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. Curvy RED
requires less operations per packet than RED and can be used if the
range of RTTs is limited. PI2 is a simplification of PIE with stable
Proportional-Integral control for both Classic and L4S congestion
controls. Nonetheless, it would be possible to control the queues
with other alternative AQMs, as long as the above normative
requirements (those expressed in capitals) are observed, which are
intended to be independent of the specific AQM.
{ToDo: Add management and monitoring requirements}
3. IANA Considerations
This specification contains no IANA considerations.
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4. Security Considerations
4.1. Overload Handling
Where the interests of users or flows might conflict, it could be
necessary to police traffic to isolate any harm to performance. This
is a policy issue that needs to be separable from a basic AQM, but an
AQM does need to handle overload. A trade-off needs to be made
between complexity and the risk of either class harming the other.
It is an operator policy to define what must happen if the service
time of the classic queue becomes too great. In the following
subsections three optional non-exclusive overload protections are
defined. Their objective is for the overload behaviour of the DualQ
AQM to be similar to a single queue AQM. The example implementation
in Appendix A implements the 'delay on overload' policy. Other
overload protections can be envisaged:
Minimum throughput service: By replacing the priority scheduler
with a weighted round robin scheduler, a minimum throughput
service can be guaranteed for Classic traffic. Typically the
scheduling weight of the Classic queue will be small (e.g. 5%) to
avoid interference with the coupling but big enough to avoid
complete starvation of Classic traffic.
Delay on overload: To control milder overload of responsive traffic,
particularly when close to the maximum congestion signal, delay
can be used as an alternative congestion control mechanism. The
Dual Queue Coupled AQM can be made to behave like a single First-
In First-Out (FIFO) queue with different service times by
replacing the priority scheduler with a very simple scheduler that
could be called a "time-shifted FIFO", which is the same as the
Modifier Earliest Deadline First (MEDF) scheduler of [MEDF]. The
scheduler adds T_m 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 defines the maximum extra queuing delay
(T_m) of Classic packets relative to L4S.
Drop on overload: On severe overload, e.g. due to non responsive
traffic, queues will typically overflow and packet drop will be
unavoidable. It is important to avoid unresponsive ECN traffic
(either Classic or L4S) driving the AQM to 100% drop and mark
probability. Congestion controls that have a minimum congestion
window will become unresponsive to ECN marking when the marking
probability is high. This situation can be avoided by applying
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the drop probability to all packets of all traffic types when it
exceeds a certain threshold or by limiting the drop and marking
probabilities to a lower maximum value (up to where fairnes
between the different traffic types is still guaranteed) and rely
on delay to control temporary high congestion and eventually queue
overflow. If the classic drop probability is applied to all types
of traffic when it is higher than a threshold probability the
queueing delay can be controlled up to any overload situation, and
no further measures are required. If a maximum classic and
coupled L4S probability of less than 100% is used, both queues
need scheduling opportunities and should eventually experience
drop. This can be achieved with a scheduler that guarantees a
minimum throughput for each queue, such as a weighted round robin
or time-shifted FIFO scheduler. In that case a common queue limit
can be configured that will drop packets of both types of traffic.
To keep the throughput of both L4S and Classic flows equal over the
full load range, a different control strategy needs to be defined
above the point where one congestion control first saturates to a
probability of 100% (if k>1, L4S will saturate first). Possible
strategies include: also dropping L4S; increasing the queueing delay
for both; or ensuring that L4S traffic still responds to marking
below a window of 2 segments (see Appendix A of
[I-D.briscoe-tsvwg-aqm-tcpm-rmcat-l4s-problem]).
5. Acknowledgements
Thanks to Anil Agarwal for detailed review comments and suggestions
on how to make our explanation clearer.
The authors' contributions are part-funded by the European Community
under its Seventh Framework Programme through the Reducing Internet
Transport Latency (RITE) project (ICT-317700). The views expressed
here are solely those of the authors.
6. References
6.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<http://www.rfc-editor.org/info/rfc2119>.
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6.2. Informative References
[ARED01] Floyd, S., Gummadi, R., and S. Shenker, "Adaptive RED: An
Algorithm for Increasing the Robustness of RED's Active
Queue Management", ACIRI Technical Report , August 2001,
<http://www.icir.org/floyd/red.html>.
[CoDel] Nichols, K. and V. Jacobson, "Controlling Queue Delay",
ACM Queue 10(5), May 2012,
<http://queue.acm.org/issuedetail.cfm?issue=2208917>.
[CRED_Insights]
Briscoe, B., "Insights from Curvy RED (Random Early
Detection)", BT Technical Report TR-TUB8-2015-003, July
2015,
<http://www.bobbriscoe.net/projects/latency/credi_tr.pdf>.
[DCttH15] De Schepper, K., Bondarenko, O., Briscoe, B., and I.
Tsang, "`Data Centre to the Home': Ultra-Low Latency for
All", 2015, <http://www.bobbriscoe.net/projects/latency/
dctth_preprint.pdf>.
(Under submission)
[I-D.black-tsvwg-ecn-experimentation]
Black, D., "Explicit Congestion Notification (ECN)
Experimentation", draft-black-tsvwg-ecn-experimentation-02
(work in progress), October 2016.
[I-D.briscoe-tsvwg-aqm-tcpm-rmcat-l4s-problem]
Briscoe, B., Schepper, K., and M. Bagnulo, "Low Latency,
Low Loss, Scalable Throughput (L4S) Internet Service:
Problem Statement", draft-briscoe-tsvwg-aqm-tcpm-rmcat-
l4s-problem-02 (work in progress), July 2016.
[I-D.briscoe-tsvwg-ecn-l4s-id]
Schepper, K., Briscoe, B., and I. Tsang, "Identifying
Modified Explicit Congestion Notification (ECN) Semantics
for Ultra-Low Queuing Delay", draft-briscoe-tsvwg-ecn-l4s-
id-02 (work in progress), October 2016.
[I-D.ietf-aqm-fq-codel]
Hoeiland-Joergensen, T., McKenney, P.,
dave.taht@gmail.com, d., Gettys, J., and E. Dumazet, "The
FlowQueue-CoDel Packet Scheduler and Active Queue
Management Algorithm", draft-ietf-aqm-fq-codel-06 (work in
progress), March 2016.
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[I-D.ietf-aqm-pie]
Pan, R., Natarajan, P., Baker, F., and G. White, "PIE: A
Lightweight Control Scheme To Address the Bufferbloat
Problem", draft-ietf-aqm-pie-10 (work in progress),
September 2016.
[I-D.ietf-tcpm-cubic]
Rhee, I., Xu, L., Ha, S., Zimmermann, A., Eggert, L., and
R. Scheffenegger, "CUBIC for Fast Long-Distance Networks",
draft-ietf-tcpm-cubic-02 (work in progress), August 2016.
[I-D.ietf-tcpm-dctcp]
Bensley, S., Eggert, L., Thaler, D., Balasubramanian, P.,
and G. Judd, "Datacenter TCP (DCTCP): TCP Congestion
Control for Datacenters", draft-ietf-tcpm-dctcp-02 (work
in progress), July 2016.
[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.
[Mathis09]
Mathis, M., "Relentless Congestion Control", PFLDNeT'09 ,
May 2009, <http://www.hpcc.jp/pfldnet2009/
Program_files/1569198525.pdf>.
[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.
[PI216] De Schepper, K., Bondarenko, O., Briscoe, B., and I.
Tsang, "PI2: A Linearized AQM for both Classic and
Scalable TCP", ACM CoNEXT'16 , December 2016,
<https://riteproject.files.wordpress.com/2015/10/
pi2_conext.pdf>.
(To appear)
[RFC0970] Nagle, J., "On Packet Switches With Infinite Storage",
RFC 970, DOI 10.17487/RFC0970, December 1985,
<http://www.rfc-editor.org/info/rfc970>.
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[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,
<http://www.rfc-editor.org/info/rfc2309>.
[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,
<http://www.rfc-editor.org/info/rfc3168>.
[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,
<http://www.rfc-editor.org/info/rfc3246>.
[RFC3649] Floyd, S., "HighSpeed TCP for Large Congestion Windows",
RFC 3649, DOI 10.17487/RFC3649, December 2003,
<http://www.rfc-editor.org/info/rfc3649>.
[RFC5681] Allman, M., Paxson, V., and E. Blanton, "TCP Congestion
Control", RFC 5681, DOI 10.17487/RFC5681, September 2009,
<http://www.rfc-editor.org/info/rfc5681>.
Appendix A. Example DualQ Coupled PI2 Algorithm
As a first concrete example, the pseudocode below gives the DualQ
Coupled AQM algorithm based on the PI2 Classic AQM, we used and
tested. For this example only the pseudo code is given. An open
source implementation for Linux is available at:
https://github.com/olgabo/dualpi2.
1: dualpi2_enqueue(lq, cq, pkt) { % Test limit and classify lq or cq
2: stamp(pkt) % attach arrival time to packet
3: if ( lq.len() + cq.len() > limit )
4: drop(pkt) % drop packet if q is full
5: else {
6: if ( ecn(pkt) modulo 2 == 0 ) % ECN bits = not-ect or ect(0)
7: cq.enqueue(pkt)
8: else % ECN bits = ect(1) or ce
9: lq.enqueue(pkt)
10: }
11: }
Figure 1: Example Enqueue Pseudocode for DualQ Coupled PI2 AQM
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1: dualpi2_dequeue(lq, cq) { % Couples L4S & Classic queues, lq & cq
2: while ( lq.len() + cq.len() > 0 )
3: if ( lq.time() + tshift >= cq.time() ) {
4: lq.dequeue(pkt)
5: if ( (pkt.time() > T) or (p > rand()) )
6: mark(pkt)
7: return(pkt) % return the packet and stop here
8: } else {
9: cq.dequeue(pkt)
10: if ( p/k > max(rand(), rand()) ) % same as testing (p/k)^2
11: if ( ecn(pkt) == 0 ) % ECN field = not-ect
12: drop(pkt) % squared drop, redo loop
13: else {
14: mark(pkt) % squared mark
15: return(pkt) % return the packet and stop here
16: }
17: else
18: return(pkt) % return the packet and stop here
19: }
20: }
21: return(NULL) % no packet to dequeue
22: }
Figure 2: Example Dequeue Pseudocode for DualQ Coupled PI2 AQM
1: dualpi2_update(lq, cq) { % Update p every Tupdate
2: curq = cq.time() % use queuing time of first-in Classic packet
3: alpha_U = alpha * Tupdate % done once when parameters are set
4: beta_U = beta * Tupdate % done once when parameters are set
5: p = p + alpha_U * (curq - target) + beta_U * (curq - prevq)
6: prevq = curq
7: }
Figure 3: Example PI-Update Pseudocode for DualQ Coupled PI2 AQM
When packets arrive, first a common queue limit is checked as shown
in line 3 of the enqueuing pseudocode in Figure 1. Note that the
limit is deliberately tested before enqueue to avoid any bias against
larger packets (so the actual buffer has to be one packet larger than
limit). If limit is not exceeded, the packet will be classified and
enqueued to the Classic or L4S queue dependent on the least
significant bit of the ECN field in the IP header (line 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.
The pseudocode in Figure 2 summarises the per packet dequeue
implementation of the DualPI2 code. Line 3 implements the time-
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shifted FIFO scheduling. It takes the packet that waited the
longest, biased by a time-shift of tshift for the Classic traffic.
If an L4S packet is scheduled, lines 5 and 6 mark the packet if
either the L4S threshold T is exceeded, or if a random marking
decision is drawn according to the probability p (maintained by the
dualpi2_update() function discussed below). If a Classic packet is
scheduled, lines 10 to 16 drop or mark the packet based on 2 random
decisions resulting in the squared probability (p/k)^2 (hence the
name PI2 for Classic traffic). Note that p is reduced by the factor
k here. This has 2 effects; first the steady state probability is
halved as required to give Classic TCP and DCTCP traffic equal
throughput; secondly, the effect of the dynamic gain parameters alpha
and beta are halved as well, which is also needed give Classic TCP
and DCTCP control the same stability.
The probability p is kept up to date by the core PI algorithm in
Figure 3 which is executed every Tupdate ([I-D.ietf-aqm-pie] now
recommends 16ms, but in our testing so far we have used the earlier
recommendation of 32ms). Note that p solely depends on the queuing
time in the Classic queue. In line 2, the current queuing delay is
evaluated by inspecting the timestamp of the next packet to schedule
in the Classic queue. The function cq.time() subtracts the time
stamped at enqueue from the current time and implicitly takes the
current queuing delay as 0 if the queue is empty. Line 3 and 4 only
need to be executed when the configuration parameters are changed.
Alpha and beta in Hz are gain factors per 1 second. If a briefer
update time is configured, alpha_U and beta_U (_U = per Tupdate) also
have to be reduced, to ensure that the same response is given over
time. As such, a smaller Tupdate will only result in a response with
smaller and finer steps, not a more aggressive response. The new
probability is calculated in line 5, where target is the target
queuing delay, as defined in [I-D.ietf-aqm-pie]. In corner cases, p
can overflow the range [0,1] so the resulting value of p has to be
bounded (omitted from the pseudocode). Unlike PIE, alpha_U and
beta_U are not tuned dependent on p, every Tupdate. Instead, in PI2
alpha_U and beta_U can be constants because the squaring applied to
Classic traffic tunes them inherently, as explained in [PI216].
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, PI2 achieves good results with the following
parameters:
tshift = 40ms
T = max(1ms, serialization time of 2 MTU)
target = 20ms
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Tupdate = 32ms
k = 2
alpha = 20Hz (alpha/k = 10Hz for Classic)
beta = 200Hz (beta/k = 100Hz for Classic)
Appendix B. Example DualQ Coupled Curvy RED Algorithm
As another example, the pseudocode below gives the Curvy RED based
DualQ Coupled AQM 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.
1: dualq_dequeue(lq, cq) { % Couples L4S & Classic queues, lq & cq
2: if ( lq.dequeue(pkt) ) {
3a: p_L = cq.sec() / 2^S_L
3b: if ( lq.byt() > T )
3c: mark(pkt)
3d: elif ( p_L > maxrand(U) )
4: mark(pkt)
5: return(pkt) % return the packet and stop here
6: }
7: while ( cq.dequeue(pkt) ) {
8a: alpha = 2^(-f_C)
8b: Q_C = alpha * pkt.sec() + (1-alpha)* Q_C % Classic Q EWMA
9a: sqrt_p_C = Q_C / 2^S_C
9b: if ( sqrt_p_C > maxrand(2*U) )
10: drop(pkt) % Squared drop, redo loop
11: else
12: return(pkt) % return the packet and stop here
13: }
14: return(NULL) % no packet to dequeue
15: }
16: maxrand(u) { % return the max of u random numbers
17: maxr=0
18: while (u-- > 0)
19: maxr = max(maxr, rand()) % 0 <= rand() < 1
20: return(maxr)
21: }
Figure 4: Example Dequeue Pseudocode for DualQ Coupled Curvy RED AQM
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Packet classification code is not shown, as it is no different from
Figure 1. Potential classification schemes are discussed in
Section 2. Overload protection code will be included in a future
draft {ToDo}.
At the outer level, the structure of dualq_dequeue() implements
strict priority scheduling. The code is written assuming the AQM is
applied on dequeue (Note 1) . Every time dualq_dequeue() is called,
the if-block in lines 2-6 determines whether there is an L4S packet
to dequeue by calling lq.dequeue(pkt), and otherwise the while-block
in lines 7-13 determines whether there is a Classic packet to
dequeue, by calling cq.dequeue(pkt). (Note 2)
In the lower priority Classic queue, a while loop is used so that, if
the AQM determines that a classic packet should be dropped, it
continues to test for classic packets deciding whether to drop each
until it actually forwards one. Thus, every call to dualq_dequeue()
returns one packet if at least one is present in either queue,
otherwise it returns NULL at line 14. (Note 3)
Within each queue, the decision whether to drop or mark is taken as
follows (to simplify the explanation, it is assumed that U=1):
L4S: If the test at line 2 determines there is an L4S packet to
dequeue, the tests at lines 3a and 3c determine whether to mark
it. The first is a simple test of whether the L4S queue (lq.byt()
in bytes) is greater than a step threshold T in bytes (Note 4).
The second test is similar to the random ECN marking in RED, but
with the following differences: i) the marking function does not
start with a plateau of zero marking until a minimum threshold,
rather the marking probability starts to increase as soon as the
queue is positive; ii) marking depends on queuing time, not bytes,
in order to scale for any link rate without being reconfigured;
iii) marking of the L4S queue does not depend on itself, it
depends on the queuing time of the _other_ (Classic) queue, where
cq.sec() is the queuing time of the packet at the head of the
Classic queue (zero if empty); iv) marking depends on the
instantaneous queuing time (of the other Classic queue), not a
smoothed average; v) the queue is compared with the maximum of U
random numbers (but if U=1, this is the same as the single random
number used in RED).
Specifically, in line 3a the marking probability p_L is set to the
Classic queueing time qc.sec() in seconds divided by the L4S
scaling parameter 2^S_L, which represents the queuing time (in
seconds) at which marking probability would hit 100%. Then in line
3d (if U=1) the result is compared with a uniformly distributed
random number between 0 and 1, which ensures that marking
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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 5).
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).
Lines 9a and 9b implement the drop function. In line 9a the
averaged queuing time Q_C is divided by the Classic scaling
parameter 2^S_C, in the same way that queuing time was scaled for
L4S marking. This scaled queuing time is given the variable name
sqrt_p_C because it will be squared to compute Classic drop
probability, so before it is squared it is effectively the square
root of the drop probability. The squaring is done by comparing
it with the maximum out of two random numbers (assuming U=1).
Comparing it with the maximum out of two is the same as the
logical `AND' of two tests, which ensures drop probability rises
with the square of queuing time (Note 6). Again, the scaling
parameter is expressed as a power of 2 so that division can be
implemented as a right bit-shift in line 9 of the integer
pseudocode.
The marking/dropping functions in each queue (lines 3 & 9) are two
cases of a new generalization of RED called Curvy RED, motivated as
follows. When we compared the performance of our AQM with fq_CoDel
and PIE, we came to the conclusion that their goal of holding queuing
delay to a fixed target is misguided [CRED_Insights]. As the number
of flows increases, if the AQM does not allow TCP to increase queuing
delay, it has to introduce abnormally high levels of loss. Then loss
rather than queuing becomes the dominant cause of delay for short
flows, due to timeouts and tail losses.
Curvy RED constrains delay with a softened target that allows some
increase in delay as load increases. This is achieved by increasing
drop probability on a convex curve relative to queue growth (the
square curve in the Classic queue, if U=1). Like RED, the curve hugs
the zero axis while the queue is shallow. Then, as load increases,
it introduces a growing barrier to higher delay. But, unlike RED, it
requires only one parameter, the scaling, not three. The diadvantage
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of Curvy RED is that it is not adapted to a wide range of RTTs.
Curvy RED can be used as is when the RTT range to support is limited
otherwise an adaptation mechanism is required.
There follows a summary listing of the two parameters used for each
of the two queues:
Classic:
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;
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
2^(-f_C).
L4S :
S_L (and k): As for the Classic queue, the scaling factor of the
L4S marking function scales Classic queueing times in the range
[0, 2^(S_L)] seconds into a probability in the range [0,1].
Note that S_L = S_C + k, where k is the coupling between the
queues (Section 2.1). So S_L and k count as only one
parameter;
T : The queue size in bytes at which step threshold marking
starts in the L4S queue.
{ToDo: These are the raw parameters used within the algorithm. A
configuration front-end could accept more meaningful parameters and
convert them into these raw parameters.}
From our experiments so far, recommended values for these parameters
are: S_C = -1; f_C = 5; T = 5 * MTU for the range of base RTTs
typical on the public Internet. [CRED_Insights] explains why these
parameters are applicable whatever rate link this AQM implementation
is deployed on and how the parameters would need to be adjusted for a
scenario with a different range of RTTs (e.g. a data centre) {ToDo
incorporate a summary of that report into this draft}. The setting of
k depends on policy (see Section 2.4 and Appendix C respectively for
its recommended setting and guidance on alternatives).
There is also a cUrviness parameter, U, which is a small positive
integer. It is likely to take the same hard-coded value for all
implementations, once experiments have determined a good value. We
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have solely used U=1 in our experiments so far, but results might be
even better with U=2 or higher.
Note that the dropping function at line 9 calls maxrand(2*U), which
gives twice as much curviness as the call to maxrand(U) in the
marking function at line 3. This is the trick that implements the
square rule in equation (1) (Section 2.1). This is based on the fact
that, given a number X from 1 to 6, the probability that two dice
throws will both be less than X is the square of the probability that
one throw will be less than X. So, when U=1, the L4S marking
function is linear and the Classic dropping function is squared. If
U=2, L4S would be a square function and Classic would be quartic.
And so on.
The maxrand(u) function in lines 16-21 simply generates u random
numbers and returns the maximum (Note 7). Typically, maxrand(u)
could be run in parallel out of band. For instance, if U=1, the
Classic queue would require the maximum of two random numbers. So,
instead of calling maxrand(2*U) in-band, the maximum of every pair of
values from a pseudorandom number generator could be generated out-
of-band, and held in a buffer ready for the Classic queue to consume.
1: dualq_dequeue(lq, cq) { % Couples L4S & Classic queues, lq & cq
2: if ( lq.dequeue(pkt) ) {
3: if ((lq.byt() > T) || ((cq.ns() >> (S_L-2)) > maxrand(U)))
4: mark(pkt)
5: return(pkt) % return the packet and stop here
6: }
7: while ( cq.dequeue(pkt) ) {
8: Q_C += (pkt.ns() - Q_C) >> f_C % Classic Q EWMA
9: if ( (Q_C >> (S_C-2) ) > maxrand(2*U) )
10: drop(pkt) % Squared drop, redo loop
11: else
12: return(pkt) % return the packet and stop here
13: }
14: return(NULL) % no packet to dequeue
15: }
Figure 5: 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
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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.
5. An EWMA is only one possible way to filter bursts; other more
adaptive smoothing methods could be valid and it might be
appropriate to decrease the EWMA faster than it increases.
6. In practice at line 10 the Classic queue would probably test for
ECN capability on the packet to determine whether to drop or mark
the packet. However, for brevity such detail is omitted. All
packets classified into the L4S queue have to be ECN-capable, so
no dropping logic is necessary at line 3. Nonetheless, L4S
packets could be dropped by overload code (see Section 4.1).
7. In the integer variant of the pseudocode (Figure 5) 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.
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Appendix C. Guidance on Controlling Throughput Equivalence
+---------------+------+-------+
| RTT_C / RTT_L | Reno | Cubic |
+---------------+------+-------+
| 1 | k=1 | k=0 |
| 2 | k=2 | k=1 |
| 3 | k=2 | k=2 |
| 4 | k=3 | k=2 |
| 5 | k=3 | k=3 |
+---------------+------+-------+
Table 1: Value of k for which DCTCP throughput is roughly the same as
Reno or Cubic, for some example RTT ratios
To determine the appropriate policy, the operator first has to judge
whether it wants DCTCP flows to have roughly equal throughput with
Reno or with Cubic (because, even in its Reno-compatibility mode,
Cubic is about 1.4 times more aggressive than Reno). Then the
operator needs to decide at what ratio of RTTs it wants DCTCP and
Classic flows to have roughly equal throughput. For example choosing
the recommended value of k=0 will make DCTCP throughput roughly the
same as Cubic, _if their RTTs are the same_.
However, even if the base RTTs are the same, the actual RTTs are
unlikely to be the same, because Classic (Cubic or Reno) traffic
needs a large queue to avoid under-utilization and excess drop,
whereas L4S (DCTCP) does not. The operator might still choose this
policy if it judges that DCTCP throughput should be rewarded for
keeping its own queue short.
On the other hand, the operator will choose one of the higher values
for k, if it wants to slow DCTCP down to roughly the same throughput
as Classic flows, to compensate for Classic flows slowing themselves
down by causing themselves extra queuing delay.
The values for k in the table are derived from the formulae, which
was developed in [DCttH15]:
2^k = 1.64 (RTT_reno / RTT_dc) (2)
2^k = 1.19 (RTT_cubic / RTT_dc ) (3)
For localized traffic from a particular ISP's data centre, we used
the measured RTTs to calculate that a value of k=3 would achieve
throughput equivalence, and our experiments verified the formula very
closely.
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Authors' Addresses
Koen De Schepper
Nokia Bell Labs
Antwerp
Belgium
Email: koen.de_schepper@nokia.com
URI: https://www.bell-labs.com/usr/koen.de_schepper
Bob Briscoe (editor)
Simula Research Lab
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 Bell Labs
Antwerp
Belgium
Email: ing-jyh.tsang@nokia.com
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