Internet DRAFT - draft-ietf-aqm-eval-guidelines
draft-ietf-aqm-eval-guidelines
Internet Engineering Task Force N. Kuhn, Ed.
Internet-Draft CNES, Telecom Bretagne
Intended status: Informational P. Natarajan, Ed.
Expires: August 18, 2016 Cisco Systems
N. Khademi, Ed.
University of Oslo
D. Ros
Simula Research Laboratory AS
February 15, 2016
AQM Characterization Guidelines
draft-ietf-aqm-eval-guidelines-11
Abstract
Unmanaged large buffers in today's networks have given rise to a slew
of performance issues. These performance issues can be addressed by
some form of Active Queue Management (AQM) mechanism, optionally in
combination with a packet scheduling scheme such as fair queuing.
This document describes various criteria for performing precautionary
characterizations of AQM schemes.
Status of This Memo
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This Internet-Draft will expire on August 18, 2016.
Copyright Notice
Copyright (c) 2016 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
(http://trustee.ietf.org/license-info) in effect on the date of
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Goals of this document . . . . . . . . . . . . . . . . . 5
1.2. Requirements Language . . . . . . . . . . . . . . . . . . 6
1.3. Glossary . . . . . . . . . . . . . . . . . . . . . . . . 6
2. End-to-end metrics . . . . . . . . . . . . . . . . . . . . . 6
2.1. Flow completion time . . . . . . . . . . . . . . . . . . 7
2.2. Flow start up time . . . . . . . . . . . . . . . . . . . 7
2.3. Packet loss . . . . . . . . . . . . . . . . . . . . . . . 7
2.4. Packet loss synchronization . . . . . . . . . . . . . . . 8
2.5. Goodput . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.6. Latency and jitter . . . . . . . . . . . . . . . . . . . 9
2.7. Discussion on the trade-off between latency and goodput . 10
3. Generic setup for evaluations . . . . . . . . . . . . . . . . 10
3.1. Topology and notations . . . . . . . . . . . . . . . . . 11
3.2. Buffer size . . . . . . . . . . . . . . . . . . . . . . . 12
3.3. Congestion controls . . . . . . . . . . . . . . . . . . . 12
4. Methodology, Metrics, AQM Comparisons, Packet Sizes,
Scheduling and ECN . . . . . . . . . . . . . . . . . . . . . 13
4.1. Methodology . . . . . . . . . . . . . . . . . . . . . . . 13
4.2. Comments on metrics measurement . . . . . . . . . . . . . 13
4.3. Comparing AQM schemes . . . . . . . . . . . . . . . . . . 14
4.3.1. Performance comparison . . . . . . . . . . . . . . . 14
4.3.2. Deployment comparison . . . . . . . . . . . . . . . . 15
4.4. Packet sizes and congestion notification . . . . . . . . 15
4.5. Interaction with ECN . . . . . . . . . . . . . . . . . . 15
4.6. Interaction with Scheduling . . . . . . . . . . . . . . . 16
5. Transport Protocols . . . . . . . . . . . . . . . . . . . . . 16
5.1. TCP-friendly sender . . . . . . . . . . . . . . . . . . . 17
5.1.1. TCP-friendly sender with the same initial congestion
window . . . . . . . . . . . . . . . . . . . . . . . 17
5.1.2. TCP-friendly sender with different initial congestion
windows . . . . . . . . . . . . . . . . . . . . . . . 17
5.2. Aggressive transport sender . . . . . . . . . . . . . . . 18
5.3. Unresponsive transport sender . . . . . . . . . . . . . . 18
5.4. Less-than Best Effort transport sender . . . . . . . . . 19
6. Round Trip Time Fairness . . . . . . . . . . . . . . . . . . 19
6.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . 19
6.2. Recommended tests . . . . . . . . . . . . . . . . . . . . 20
6.3. Metrics to evaluate the RTT fairness . . . . . . . . . . 20
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7. Burst Absorption . . . . . . . . . . . . . . . . . . . . . . 20
7.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . 20
7.2. Recommended tests . . . . . . . . . . . . . . . . . . . . 21
8. Stability . . . . . . . . . . . . . . . . . . . . . . . . . . 22
8.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . 22
8.2. Recommended tests . . . . . . . . . . . . . . . . . . . . 23
8.2.1. Definition of the congestion Level . . . . . . . . . 23
8.2.2. Mild congestion . . . . . . . . . . . . . . . . . . . 23
8.2.3. Medium congestion . . . . . . . . . . . . . . . . . . 23
8.2.4. Heavy congestion . . . . . . . . . . . . . . . . . . 24
8.2.5. Varying the congestion level . . . . . . . . . . . . 24
8.2.6. Varying available capacity . . . . . . . . . . . . . 24
8.3. Parameter sensitivity and stability analysis . . . . . . 25
9. Various Traffic Profiles . . . . . . . . . . . . . . . . . . 26
9.1. Traffic mix . . . . . . . . . . . . . . . . . . . . . . . 26
9.2. Bi-directional traffic . . . . . . . . . . . . . . . . . 26
10. Multi-AQM Scenario . . . . . . . . . . . . . . . . . . . . . 27
10.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . 27
10.2. Details on the evaluation scenario . . . . . . . . . . . 27
11. Implementation cost . . . . . . . . . . . . . . . . . . . . . 27
11.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . 27
11.2. Recommended discussion . . . . . . . . . . . . . . . . . 28
12. Operator Control and Auto-tuning . . . . . . . . . . . . . . 28
12.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . 28
12.2. Recommended discussion . . . . . . . . . . . . . . . . . 29
13. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 29
14. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 30
15. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 30
16. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 31
17. Security Considerations . . . . . . . . . . . . . . . . . . . 31
18. References . . . . . . . . . . . . . . . . . . . . . . . . . 31
18.1. Normative References . . . . . . . . . . . . . . . . . . 31
18.2. Informative References . . . . . . . . . . . . . . . . . 33
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 34
1. Introduction
Active Queue Management (AQM) [RFC7567] addresses the concerns
arising from using unnecessarily large and unmanaged buffers to
improve network and application performance. Several AQM algorithms
have been proposed in the past years, most notably Random Early
Detection (RED), BLUE, and Proportional Integral controller (PI), and
more recently CoDel [NICH2012] and PIE [PAN2013]. In general, these
algorithms actively interact with the Transmission Control Protocol
(TCP) and any other transport protocol that deploys a congestion
control scheme to manage the amount of data they keep in the network.
The available buffer space in the routers and switches should be
large enough to accommodate the short-term buffering requirements.
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AQM schemes aim at reducing buffer occupancy, and therefore the end-
to-end delay. Some of these algorithms, notably RED, have also been
widely implemented in some network devices. However, the potential
benefits of the RED scheme have not been realized since RED is
reported to be usually turned off. The main reason of this
reluctance to use RED in today's deployments comes from its
sensitivity to the operating conditions in the network and the
difficulty of tuning its parameters.
A buffer is a physical volume of memory in which a queue or set of
queues are stored. When speaking of a specific queue in this
document, "buffer occupancy" refers to the amount of data (measured
in bytes or packets) that are in the queue, and the "maximum buffer
size" refers to the maximum buffer occupancy. In real
implementations of switches, a global memory is often shared between
the available devices, and thus, the maximum buffer size may vary
over the time.
Bufferbloat [BB2011] is the consequence of deploying large unmanaged
buffers on the Internet -- the buffering has often been measured to
be ten times or hundred times larger than needed. Large buffer sizes
in combination with TCP and/or unresponsive flows increases end-to-
end delay. This results in poor performance for latency-sensitive
applications such as real-time multimedia (e.g., voice, video,
gaming, etc). The degree to which this affects modern networking
equipment, especially consumer-grade equipment's, produces problems
even with commonly used web services. Active queue management is
thus essential to control queuing delay and decrease network latency.
The Active Queue Management and Packet Scheduling Working Group (AQM
WG) was chartered to address the problems with large unmanaged
buffers in the Internet. Specifically, the AQM WG is tasked with
standardizing AQM schemes that not only address concerns with such
buffers, but also are robust under a wide variety of operating
conditions. This document provides characterization guidelines that
can be used to assess the deployability of an AQM, whether it is
candidate for standardization at IETF or not.
[RFC7567] separately describes the AQM algorithm implemented in a
router from the scheduling of packets sent by the router. The rest
of this memo refers to the AQM as a dropping/marking policy as a
separate feature to any interface scheduling scheme. This document
may be complemented with another one on guidelines for assessing
combination of packet scheduling and AQM. We note that such a
document will inherit all the guidelines from this document plus any
additional scenarios relevant for packet scheduling such as flow
starvation evaluation or impact of the number of hash buckets.
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1.1. Goals of this document
The trade-off between reducing the latency and maximizing the goodput
is intrinsically linked to each AQM scheme and is key to evaluating
its performance. Whenever possible, solutions ought to aim at both
maximizing goodput and minimizing latency. Moreover, to ensure the
safety deployment of an AQM, its behaviour should be assessed in a
variety of scenarios.
This document recommends a generic list of scenarios against which an
AQM proposal should be evaluated, considering both potential
performance gain and safety of deployment. The guidelines help to
quantify performance of AQM schemes in terms of latency reduction,
goodput maximization and the trade-off between these two. The
document presents central aspects of an AQM algorithm that should be
considered whatever the context, such as burst absorption capacity,
RTT fairness or resilience to fluctuating network conditions. The
guidelines also discuss methods to understand the various aspects
associated with safely deploying and operating the AQM scheme. Thus,
one of the key objectives behind formulating the guidelines is to
help ascertain whether a specific AQM is not only better than drop-
tail (i.e. without AQM and with a BDP-sized buffer) but also safe to
deploy: the guidelines can be used to compare several AQM proposals
with each other, and should be used to compare a proposal with drop-
tail.
These guidelines do not define and are not bound to a particular
environment or evaluation toolset. Instead the guidelines can be
used to assert the potential gain of introducing an AQM for the
particular environment, which is of interest to the testers. These
guidelines do not cover every possible aspect of a particular
algorithm. These guidelines do not present context-dependent
scenarios (such as 802.11 WLANs, data-centers or rural broadband
networks). To keep the guidelines generic, a number of potential
router components and algorithms (such as DiffServ) are omitted.
The goals of this document can thus be summarized as follows:
o The present characterization guidelines provide a non-exhaustive
list of scenarios to help ascertain whether an AQM is not only
better than drop-tail (with a BDP-sized buffer), but also safe to
deploy; the guidelines can also be used to compare several AQM
proposals with each other.
o The present characterization guidelines (1) are not bound to a
particular evaluation toolset and (2) can be used for various
deployment contexts; testers are free to select a toolset that is
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best suited for the environment in which their proposal will be
deployed.
o The present characterization guidelines are intended to provide
guidance for better selecting an AQM for a specific environment;
it is not required that an AQM proposal is evaluated following
these guidelines for its standardization.
1.2. Requirements Language
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 RFC 2119 [RFC2119].
1.3. Glossary
o AQM: [RFC7567] separately describes the Active Queue Managment
(AQM) algorithm implemented in a router from the scheduling of
packets sent by the router. The rest of this memo refers to the
AQM as a dropping/marking policy as a separate feature to any
interface scheduling scheme.
o buffer: a physical volume of memory in which a queue or set of
queues are stored.
o buffer occupancy: amount of data that are stored in a buffer,
measured in bytes or packets.
o buffer size: maximum buffer occupancy, that is the maximum amount
of data that may be stored in a buffer, measured in bytes or
packets.
o goodput: goodput is defined as the number of bits per unit of time
forwarded to the correct destination minus any bits lost or
retransmitted [RFC2647].
o SQRT: the square root function.
o ROUND: the round function.
2. End-to-end metrics
End-to-end delay is the result of propagation delay, serialization
delay, service delay in a switch, medium-access delay and queuing
delay, summed over the network elements along the path. AQM schemes
may reduce the queuing delay by providing signals to the sender on
the emergence of congestion, but any impact on the goodput must be
carefully considered. This section presents the metrics that could
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be used to better quantify (1) the reduction of latency, (2)
maximization of goodput and (3) the trade-off between these two.
This section provides normative requirements for metrics that can be
used to assess the performance of an AQM scheme.
Some metrics listed in this section are not suited to every type of
traffic detailed in the rest of this document. It is therefore not
necessary to measure all of the following metrics: the chosen metric
may not be relevant to the context of the evaluation scenario (e.g.,
latency vs. goodput trade-off in application-limited traffic
scenarios). Guidance is provided for each metric.
2.1. Flow completion time
The flow completion time is an important performance metric for the
end-user when the flow size is finite. Considering the fact that an
AQM scheme may drop/mark packets, the flow completion time is
directly linked to the dropping/marking policy of the AQM scheme.
This metric helps to better assess the performance of an AQM
depending on the flow size. The Flow Completion Time (FCT) is
related to the flow size (Fs) and the goodput for the flow (G) as
follows:
FCT [s] = Fs [Byte] / ( G [Bit/s] / 8 [Bit/Byte] )
If this metric is used to evaluate the performance of web transfers,
it is suggested to rather consider the time needed to download all
the objects that compose the web page, as this makes more sense in
terms of user experience than assessing the time needed to download
each object.
2.2. Flow start up time
The flow start up time is the time between the request has been sent
from the client and the server starts to transmit data. The amount
of packets dropped by an AQM may seriously affect the waiting period
during which the data transfer has not started. This metric would
specifically focus on the operations such as DNS lookups, TCP opens
of SSL handshakes.
2.3. Packet loss
Packet loss can occur en-route, this can impact the end-to-end
performance measured at receiver.
The tester SHOULD evaluate loss experienced at the receiver using one
of the two metrics:
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o the packet loss ratio: this metric is to be frequently measured
during the experiment. The long-term loss ratio is of interest
for steady-state scenarios only;
o the interval between consecutive losses: the time between two
losses is to be measured.
The packet loss ratio can be assessed by simply evaluating the loss
ratio as a function of the number of lost packets and the total
number of packets sent. This might not be easily done in laboratory
testing, for which these guidelines advice the tester:
o to check that for every packet, a corresponding packet was
received within a reasonable time, as explained in [RFC2680].
o to keep a count of all packets sent, and a count of the non-
duplicate packets received, as explained in the section 10 of
[RFC2544].
The interval between consecutive losses, which is also called a gap,
is a metric of interest for VoIP traffic and, as a result, has been
further specified in [RFC3611].
2.4. Packet loss synchronization
One goal of an AQM algorithm is to help to avoid global
synchronization of flows sharing a bottleneck buffer on which the AQM
operates ([RFC2309],[RFC7567]). The "degree" of packet-loss
synchronization between flows SHOULD be assessed, with and without
the AQM under consideration.
As discussed e.g., in [HASS2008], loss synchronization among flows
may be quantified by several slightly different metrics that capture
different aspects of the same issue. However, in real-world
measurements the choice of metric could be imposed by practical
considerations -- e.g., whether fine-grained information on packet
losses in the bottleneck available or not. For the purpose of AQM
characterization, a good candidate metric is the global
synchronization ratio, measuring the proportion of flows losing
packets during a loss event. [JAY2006] used this metric in real-
world experiments to characterize synchronization along arbitrary
Internet paths; the full methodology is described in [JAY2006].
If an AQM scheme is evaluated using real-life network environments,
it is worth pointing out that some network events, such as failed
link restoration may cause synchronized losses between active flows
and thus confuse the meaning of this metric.
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2.5. Goodput
The goodput has been defined in section 3.17 of [RFC2647] as the
number of bits per unit of time forwarded to the correct destination
interface, minus any bits lost or retransmitted. This definition
induces that the test setup needs to be qualified to assure that it
is not generating losses on its own.
Measuring the end-to-end goodput provides an appreciation of how well
an AQM scheme improves transport and application performance. The
measured end-to-end goodput is linked to the dropping/marking policy
of the AQM scheme -- e.g., the fewer the number of packet drops, the
fewer packets need retransmission, minimizing the impact of AQM on
transport and application performance. Additionally, an AQM scheme
may resort to Explicit Congestion Notification (ECN) marking as an
initial means to control delay. Again, marking packets instead of
dropping them reduces the number of packet retransmissions and
increases goodput. End-to-end goodput values help to evaluate the
AQM scheme's effectiveness of an AQM scheme in minimizing packet
drops that impact application performance and to estimate how well
the AQM scheme works with ECN.
The measurement of the goodput allows the tester evaluate to which
extent an AQM is able to maintain a high bottleneck utilization.
This metric should be also obtained frequently during an experiment
as the long-term goodput is relevant for steady-state scenarios only
and may not necessarily reflect how the introduction of an AQM
actually impacts the link utilization during at a certain period of
time. Fluctuations in the values obtained from these measurements
may depend on other factors than the introduction of an AQM, such as
link layer losses due to external noise or corruption, fluctuating
bandwidths (802.11 WLANs), heavy congestion levels or transport
layer's rate reduction by congestion control mechanism.
2.6. Latency and jitter
The latency, or the one-way delay metric, is discussed in [RFC2679].
There is a consensus on an adequate metric for the jitter, that
represents the one-way delay variations for packets from the same
flow: the Packet Delay Variation (PDV), detailed in [RFC5481], serves
well all use cases.
The end-to-end latency includes components other than just the
queuing delay, such as the signal processing delay, transmission
delay and the processing delay. Moreover, the jitter is caused by
variations in queuing and processing delay (e.g., scheduling
effects). The introduction of an AQM scheme would impact these
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metrics (end-to-end latency and jitter) and therefore they should be
considered in the end-to-end evaluation of performance.
2.7. Discussion on the trade-off between latency and goodput
The metrics presented in this section may be considered as explained
in the rest of this document, in order to discuss and quantify the
trade-off between latency and goodput.
With regards to the goodput, and in addition to the long-term
stationary goodput value, it is RECOMMENDED to take measurements
every multiple of the minimum RTT (minRTT) between A and B. It is
suggested to take measurements at least every K x minRTT (to smooth
out the fluctuations), with K=10. Higher values for K are encouraged
whenever it is more appropriate for the presentation of the results.
The value for K may depend on the network's path characteristics.
The measurement period MUST be disclosed for each experiment and when
results/values are compared across different AQM schemes, the
comparisons SHOULD use exactly the same measurement periods. With
regards to latency, it is RECOMMENDED to take the samples on per-
packet basis whenever possible depending on the features provided by
hardware/software and the impact of sampling itself on the hardware
performance. It is generally RECOMMENDED to provide at least 10
samples per RTT.
From each of these sets of measurements, the cumulative density
function (CDF) of the considered metrics SHOULD be computed. If the
considered scenario introduces dynamically varying parameters,
temporal evolution of the metrics could also be generated. For each
scenario, the following graph may be generated: the x-axis shows
queuing delay (that is the average per-packet delay in excess of
minimum RTT), the y-axis the goodput. Ellipses are computed such as
detailed in [WINS2014]: "We take each individual [...] run [...] as
one point, and then compute the 1-epsilon elliptic contour of the
maximum-likelihood 2D Gaussian distribution that explains the points.
[...] we plot the median per-sender throughput and queueing delay as
a circle. [...] The orientation of an ellipse represents the
covariance between the throughput and delay measured for the
protocol." This graph provides part of a better understanding of (1)
the delay/goodput trade-off for a given congestion control mechanism
Section 5, and (2) how the goodput and average queue delay vary as a
function of the traffic load Section 8.2.
3. Generic setup for evaluations
This section presents the topology that can be used for each of the
following scenarios, the corresponding notations and discusses
various assumptions that have been made in the document.
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3.1. Topology and notations
+---------+ +-----------+
|senders A| |receivers B|
+---------+ +-----------+
+--------------+ +--------------+
|traffic class1| |traffic class1|
|--------------| |--------------|
| SEN.Flow1.1 +---------+ +-----------+ REC.Flow1.1 |
| + | | | | + |
| | | | | | | |
| + | | | | + |
| SEN.Flow1.X +-----+ | | +--------+ REC.Flow1.X |
+--------------+ | | | | +--------------+
+ +-+---+---+ +--+--+---+ +
| |Router L | |Router R | |
| |---------| |---------| |
| | AQM | | | |
| | BuffSize| | BuffSize| |
| | (Bsize) +-----+ (Bsize) | |
| +-----+--++ ++-+------+ |
+ | | | | +
+--------------+ | | | | +--------------+
|traffic classN| | | | | |traffic classN|
|--------------| | | | | |--------------|
| SEN.FlowN.1 +---------+ | | +-----------+ REC.FlowN.1 |
| + | | | | + |
| | | | | | | |
| + | | | | + |
| SEN.FlowN.Y +------------+ +-------------+ REC.FlowN.Y |
+--------------+ +--------------+
Figure 1: Topology and notations
Figure 1 is a generic topology where:
o sender with different traffic characteristics (i.e., traffic
profiles) can be introduced;
o the timing of each flow could be different (i.e., when does each
flow start and stop);
o each traffic profile can comprise various number of flows;
o each link is characterized by a couple (one-way delay, capacity);
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o flows are generated at A and sent to B, sharing a bottleneck (the
link between routers L and R);
o the tester SHOULD consider both scenarios of asymmetric and
symmetric bottleneck links in terms of bandwidth. In case of
asymmetric link, the capacity from senders to receivers is higher
than the one from receivers to senders; the symmetric link
scenario provides a basic understanding of the operation of the
AQM mechanism whereas the asymmetric link scenario evaluates an
AQM mechanism in a more realistic setup;
o in asymmetric link scenarios, the tester SHOULD study the bi-
directional traffic between A and B (downlink and uplink) with the
AQM mechanism deployed on one direction only. The tester MAY
additionally consider a scenario with AQM mechanism being deployed
on both directions. In each scenario, the tester SHOULD
investigate the impact of drop policy of the AQM on TCP ACK
packets and its impact on the performance.
Although this topology may not perfectly reflect actual topologies,
the simple topology is commonly used in the world of simulations and
small testbeds. It can be considered as adequate to evaluate AQM
proposals, similarly to the topology proposed in
[I-D.irtf-iccrg-tcpeval]. Testers ought to pay attention to the
topology that has been used to evaluate an AQM scheme when comparing
this scheme with a newly proposed AQM scheme.
3.2. Buffer size
The size of the buffers should be carefully chosen, and MAY be set to
the bandwidth-delay product; the bandwidth being the bottleneck
capacity and the delay the largest RTT in the considered network.
The size of the buffer can impact the AQM performance and is a
dimensioning parameter that will be considered when comparing AQM
proposals.
If a specific buffer size is required, the tester MUST justify and
detail the way the maximum queue size is set. Indeed, the maximum
size of the buffer may affect the AQM's performance and its choice
SHOULD be elaborated for a fair comparison between AQM proposals.
While comparing AQM schemes the buffer size SHOULD remain the same
across the tests.
3.3. Congestion controls
This document considers running three different congestion control
algorithms between A and B
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o Standard TCP congestion control: the base-line congestion control
is TCP NewReno with SACK, as explained in [RFC5681].
o Aggressive congestion controls: a base-line congestion control for
this category is TCP Cubic [I-D.ietf-tcpm-cubic].
o Less-than Best Effort (LBE) congestion controls: an LBE congestion
control 'results in smaller bandwidth and/or delay impact on
standard TCP than standard TCP itself, when sharing a bottleneck
with it.' [RFC6297]
Other transport congestion controls can OPTIONALLY be evaluated in
addition. Recent transport layer protocols are not mentioned in the
following sections, for the sake of simplicity.
4. Methodology, Metrics, AQM Comparisons, Packet Sizes, Scheduling and
ECN
4.1. Methodology
A description of each test setup SHOULD be detailed to allow this
test to be compared with other tests. This also allows others to
replicate the tests if needed. This test setup SHOULD detail
software and hardware versions. The tester could make its data
available.
The proposals SHOULD be evaluated on real-life systems, or they MAY
be evaluated with event-driven simulations (such as ns-2, ns-3,
OMNET, etc). The proposed scenarios are not bound to a particular
evaluation toolset.
The tester is encouraged to make the detailed test setup and the
results publicly available.
4.2. Comments on metrics measurement
The document presents the end-to-end metrics that ought to be used to
evaluate the trade-off between latency and goodput in Section 2. In
addition to the end-to-end metrics, the queue-level metrics (normally
collected at the device operating the AQM) provide a better
understanding of the AQM behavior under study and the impact of its
internal parameters. Whenever it is possible (e.g., depending on the
features provided by the hardware/software), these guidelines advice
to consider queue-level metrics, such as link utilization, queuing
delay, queue size or packet drop/mark statistics in addition to the
AQM-specific parameters. However, the evaluation MUST be primarily
based on externally observed end-to-end metrics.
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These guidelines do not aim to detail on the way these metrics can be
measured, since the way these metrics are measured is expected to
depend on the evaluation toolset.
4.3. Comparing AQM schemes
This document recognizes that these guidelines may be used for
comparing AQM schemes.
AQM schemes need to be compared against both performance and
deployment categories. In addition, this section details how best to
achieve a fair comparison of AQM schemes by avoiding certain
pitfalls.
4.3.1. Performance comparison
AQM schemes should be compared against the generic scenarios that are
summarized in Section 13. AQM schemes MAY be compared for specific
network environments such as data centers, home networks, etc. If an
AQM scheme has parameter(s) that were externally tuned for
optimization or other purposes, these values MUST be disclosed.
AQM schemes belong to different varieties such as queue-length based
schemes (ex. RED) or queueing-delay based scheme (ex. CoDel, PIE).
AQM schemes expose different control knobs associated with different
semantics. For example, while both PIE and CoDel are queueing-delay
based schemes and each expose a knob to control the queueing delay --
PIE's "queueing delay reference" vs. CoDel's "queueing delay target",
the two tuning parameters of the two schemes have different
semantics, resulting in different control points. Such differences
in AQM schemes can be easily overlooked while making comparisons.
This document RECOMMENDS the following procedures for a fair
performance comparison between the AQM schemes:
1. comparable control parameters and comparable input values:
carefully identify the set of parameters that control similar
behavior between the two AQM schemes and ensure these parameters
have comparable input values. For example, to compare how well a
queue-length based AQM scheme controls queueing delay vs. a
queueing-delay based AQM scheme, a tester can identify the
parameters of the schemes that control queue delay and ensure
that their input values are comparable. Similarly, to compare
how well two AQM schemes accommodate packet bursts, the tester
can identify burst-related control parameters and ensure they are
configured with similar values. Additionally, it would be
preferable if an AQM proposal listed such parameters and
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discussed how each relates to network characteristics such as
capacity, average RTT etc.
2. compare over a range of input configurations: there could be
situations when the set of control parameters that affect a
specific behavior have different semantics between the two AQM
schemes. As mentioned above, PIE has tuning parameters to
control queue delay that has a different semantics from those
used in CoDel. In such situations, these schemes need to be
compared over a range of input configurations. For example,
compare PIE vs. CoDel over the range of target delay input
configurations.
4.3.2. Deployment comparison
AQM schemes MUST be compared against deployment criteria such as the
parameter sensitivity (Section 8.3), auto-tuning (Section 12) or
implementation cost (Section 11).
4.4. Packet sizes and congestion notification
An AQM scheme may be considering packet sizes while generating
congestion signals. [RFC7141] discusses the motivations behind this.
For example, control packets such as DNS requests/responses, TCP
SYNs/ACKs are small, but their loss can severely impact the
application performance. An AQM scheme may therefore be biased
towards small packets by dropping them with smaller probability
compared to larger packets. However, such an AQM scheme is unfair to
data senders generating larger packets. Data senders, malicious or
otherwise, are motivated to take advantage of such AQM scheme by
transmitting smaller packets, and could result in unsafe deployments
and unhealthy transport and/or application designs.
An AQM scheme SHOULD adhere to the recommendations outlined in
[RFC7141], and SHOULD NOT provide undue advantage to flows with
smaller packets [RFC7567].
4.5. Interaction with ECN
Deployed AQM algorithms SHOULD implement Explicit Congestion
Notification (ECN) as well as loss to signal congestion to endpoints
[RFC7567]. ECN [RFC3168] is an alternative that allows AQM schemes
to signal receivers about network congestion that does not use packet
drop. The benefits of providing ECN support for an AQM scheme are
described in [WELZ2015]. Section 3 of [WELZ2015] describes expected
operation of routers enabling ECN. AQM schemes SHOULD NOT drop or
remark packets solely because the ECT(0) or ECT(1) codepoints are
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used, and when ECN-capable SHOULD set a CE-mark on ECN-capable
packets in the presence of incipient congestion.
If the tested AQM scheme can support ECN [RFC7567], the testers MUST
discuss and describe the support of ECN. Since these guidelines can
be used to evaluate the performance of the tested AQM with and
without ECN markings, they could also be used to quantify the
interest of enabling ECN.
4.6. Interaction with Scheduling
A network device may use per-flow or per-class queuing with a
scheduling algorithm to either prioritize certain applications or
classes of traffic, limit the rate of transmission, or to provide
isolation between different traffic flows within a common class
[RFC7567].
The scheduling and the AQM conjointly impact on the end-to-end
performance. Therefore, the AQM proposal MUST discuss the
feasibility to add scheduling combined with the AQM algorithm. This
discussion as an instance, MAY explain whether the dropping policy is
applied when packets are being enqueued or dequeued.
These guidelines do not propose guidelines to assess the performance
of scheduling algorithms. Indeed, as opposed to characterizing AQM
schemes that is related to their capacity to control the queuing
delay in a queue, characterizing scheduling schemes is related to the
scheduling itself and its interaction with the AQM scheme. As one
example, the scheduler may create sub-queues and the AQM scheme may
be applied on each of the sub-queues, and/or the AQM could be applied
on the whole queue. Also, schedulers might, such as FQ-CoDel
[HOEI2015] or FavorQueue [ANEL2014], introduce flow prioritization.
In these cases, specific scenarios should be proposed to ascertain
that these scheduler schemes not only helps in tackling the
bufferbloat, but also are robust under a wide variety of operating
conditions. This is out of the scope of this document that focus on
dropping and/or marking AQM schemes.
5. Transport Protocols
Network and end-devices need to be configured with a reasonable
amount of buffer space to absorb transient bursts. In some
situations, network providers tend to configure devices with large
buffers to avoid packet drops triggered by a full buffer and to
maximize the link utilization for standard loss-based TCP traffic.
AQM algorithms are often evaluated by considering Transmission
Control Protocol (TCP) [RFC0793] with a limited number of
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applications. TCP is a widely deployed transport. It fills up
available buffers until a sender transfering a bulk flow with TCP
receives a signal (packet drop) that reduces the sending rate. The
larger the buffer, the higher the buffer occupancy, and therefore the
queuing delay. An efficient AQM scheme sends out early congestion
signals to TCP to bring the queuing delay under control.
Not all endpoints (or applications) using TCP use the same flavor of
TCP. Variety of senders generate different classes of traffic which
may not react to congestion signals (aka non-responsive flows
[RFC7567]) or may not reduce their sending rate as expected (aka
Transport Flows that are less responsive than TCP[RFC7567], also
called "aggressive flows"). In these cases, AQM schemes seek to
control the queuing delay.
This section provides guidelines to assess the performance of an AQM
proposal for various traffic profiles -- different types of senders
(with different TCP congestion control variants, unresponsive,
aggressive).
5.1. TCP-friendly sender
5.1.1. TCP-friendly sender with the same initial congestion window
This scenario helps to evaluate how an AQM scheme reacts to a TCP-
friendly transport sender. A single long-lived, non application-
limited, TCP NewReno flow, with an Initial congestion Window (IW) set
to 3 packets, transfers data between sender A and receiver B. Other
TCP friendly congestion control schemes such as TCP-friendly rate
control [RFC5348] etc MAY also be considered.
For each TCP-friendly transport considered, the graph described in
Section 2.7 could be generated.
5.1.2. TCP-friendly sender with different initial congestion windows
This scenario can be used to evaluate how an AQM scheme adapts to a
traffic mix consisting of TCP flows with different values of the IW.
For this scenario, two types of flows MUST be generated between
sender A and receiver B:
o A single long-lived non application-limited TCP NewReno flow;
o A single application-limited TCP NewReno flow, with an IW set to 3
or 10 packets. The size of the data transferred must be strictly
higher than 10 packets and should be lower than 100 packets.
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The transmission of the non application-limited flow must start
before the transmission of the application-limited flow and only
after the steady state has been reached by non application-limited
flow.
For each of these scenarios, the graph described in Section 2.7 could
be generated for each class of traffic (application-limited and non
application-limited). The completion time of the application-limited
TCP flow could be measured.
5.2. Aggressive transport sender
This scenario helps testers to evaluate how an AQM scheme reacts to a
transport sender that is more aggressive than a single TCP-friendly
sender. We define 'aggressiveness' as a higher increase factor than
standard upon a successful transmission and/or a lower than standard
decrease factor upon a unsuccessful transmission (e.g., in case of
congestion controls with Additive-Increase Multiplicative-Decrease
(AIMD) principle, a larger AI and/or MD factors). A single long-
lived, non application-limited, TCP Cubic flow transfers data between
sender A and receiver B. Other aggressive congestion control schemes
MAY also be considered.
For each flavor of aggressive transports, the graph described in
Section 2.7 could be generated.
5.3. Unresponsive transport sender
This scenario helps testers to evaluate how an AQM scheme reacts to a
transport sender that is less responsive than TCP. Note that faulty
transport implementations on an end host and/or faulty network
elements en-route that "hide" congestion signals in packet headers
[RFC7567] may also lead to a similar situation, such that the AQM
scheme needs to adapt to unresponsive traffic. To this end, these
guidelines propose the two following scenarios.
The first scenario can be used to evaluate queue build up. It
considers unresponsive flow(s) whose sending rate is greater than the
bottleneck link capacity between routers L and R. This scenario
consists of a long-lived non application limited UDP flow transmits
data between sender A and receiver B. Graphs described in
Section 2.7 could be generated.
The second scenario can be used to evaluate if the AQM scheme is able
to keep the responsive fraction under control. This scenario
considers a mixture of TCP-friendly and unresponsive traffics. It
consists of a long-lived UDP flow from unresponsive application and a
single long-lived, non application-limited (unlimited data available
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to the transport sender from application layer), TCP New Reno flow
that transmit data between sender A and receiver B. As opposed to
the first scenario, the rate of the UDP traffic should not be greater
than the bottleneck capacity, and should be higher than half of the
bottleneck capacity. For each type of traffic, the graph described
in Section 2.7 could be generated.
5.4. Less-than Best Effort transport sender
This scenario helps to evaluate how an AQM scheme reacts to LBE
congestion controls that 'results in smaller bandwidth and/or delay
impact on standard TCP than standard TCP itself, when sharing a
bottleneck with it.' [RFC6297]. The potential fateful interaction
when AQM and LBE techniques are combined has been shown in
[GONG2014]; this scenario helps to evaluate whether the coexistence
of the proposed AQM and LBE techniques may be possible.
A single long-lived non application-limited TCP NewReno flow
transfers data between sender A and receiver B. Other TCP-friendly
congestion control schemes MAY also be considered. Single long-lived
non application-limited LEDBAT [RFC6817] flows transfer data between
sender A and receiver B. We recommend to set the target delay and
gain values of LEDBAT respectively to 5 ms and 10 [TRAN2014]. Other
LBE congestion control schemes, any of those listed in [RFC6297], MAY
also be considered.
For each of the TCP-friendly and LBE transports, the graph described
in Section 2.7 could be generated.
6. Round Trip Time Fairness
6.1. Motivation
An AQM scheme's congestion signals (via drops or ECN marks) must
reach the transport sender so that a responsive sender can initiate
its congestion control mechanism and adjust the sending rate. This
procedure is thus dependent on the end-to-end path RTT. When the RTT
varies, the onset of congestion control is impacted, and in turn
impacts the ability of an AQM scheme to control the queue. It is
therefore important to assess the AQM schemes for a set of RTTs
between A and B (e.g., from 5 ms to 200 ms).
The asymmetry in terms of difference in intrinsic RTT between various
paths sharing the same bottleneck SHOULD be considered so that the
fairness between the flows can be discussed since in this scenario, a
flow traversing on shorter RTT path may react faster to congestion
and recover faster from it compared to another flow on a longer RTT
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path. The introduction of AQM schemes may potentially improve this
type of fairness.
Introducing an AQM scheme may cause the unfairness between the flows,
even if the RTTs are identical. This potential unfairness SHOULD be
investigated as well.
6.2. Recommended tests
The RECOMMENDED topology is detailed in Figure 1.
To evaluate the RTT fairness, for each run, two flows divided into
two categories. Category I whose RTT between sender A and receiver B
SHOULD be 100ms. Category II which RTT between sender A and receiver
B should be in the range [5ms;560ms] inclusive. The maximum value
for the RTT represents the RTT of a satellite link that, according to
section 2 of [RFC2488] should be at least 558ms.
A set of evaluated flows MUST use the same congestion control
algorithm: all the generated flows could be single long-lived non
application-limited TCP NewReno flows.
6.3. Metrics to evaluate the RTT fairness
The outputs that MUST be measured are: (1) the cumulative average
goodput of the flow from Category I, goodput_Cat_I (Section 2.5); (2)
the cumulative average goodput of the flow from Category II,
goodput_Cat_II (Section 2.5); (3) the ratio goodput_Cat_II/
goodput_Cat_I; (4) the average packet drop rate for each category
(Section 2.3).
7. Burst Absorption
"AQM mechanisms need to control the overall queue sizes, to ensure
that arriving bursts can be accommodated without dropping packets"
[RFC7567].
7.1. Motivation
An AQM scheme can face bursts of packet arrivals due to various
reasons. Dropping one or more packets from a burst can result in
performance penalties for the corresponding flows, since dropped
packets have to be retransmitted. Performance penalties can result
in failing to meet SLAs and be a disincentive to AQM adoption.
The ability to accommodate bursts translates to larger queue length
and hence more queuing delay. On the one hand, it is important that
an AQM scheme quickly brings bursty traffic under control. On the
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other hand, a peak in the packet drop rates to bring a packet burst
quickly under control could result in multiple drops per flow and
severely impact transport and application performance. Therefore, an
AQM scheme ought to bring bursts under control by balancing both
aspects -- (1) queuing delay spikes are minimized and (2) performance
penalties for ongoing flows in terms of packet drops are minimized.
An AQM scheme that maintains short queues allows some remaining space
in the buffer for bursts of arriving packets. The tolerance to
bursts of packets depends upon the number of packets in the queue,
which is directly linked to the AQM algorithm. Moreover, an AQM
scheme may implement a feature controlling the maximum size of
accepted bursts, that can depend on the buffer occupancy or the
currently estimated queuing delay. The impact of the buffer size on
the burst allowance may be evaluated.
7.2. Recommended tests
For this scenario, tester MUST evaluate how the AQM performs with the
following traffic generated from sender A to receiver B:
o Web traffic with IW10;
o Bursty video frames;
o Constant Bit Rate (CBR) UDP traffic.
o A single non application-limited bulk TCP flow as background
traffic.
Figure 2 presents the various cases for the traffic that MUST be
generated between sender A and receiver B.
+-------------------------------------------------+
|Case| Traffic Type |
| +-----+------------+----+--------------------+
| |Video|Web (IW 10)| CBR| Bulk TCP Traffic |
+----|-----|------------|----|--------------------|
|I | 0 | 1 | 1 | 0 |
+----|-----|------------|----|--------------------|
|II | 0 | 1 | 1 | 1 |
|----|-----|------------|----|--------------------|
|III | 1 | 1 | 1 | 0 |
+----|-----|------------|----|--------------------|
|IV | 1 | 1 | 1 | 1 |
+----+-----+------------+----+--------------------+
Figure 2: Bursty traffic scenarios
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A new web page download could start after the previous web page
download is finished. Each web page could be composed by at least 50
objects and the size of each object should be at least 1kB. 6 TCP
parallel connections SHOULD be generated to download the objects,
each parallel connections having an initial congestion window set to
10 packets.
For each of these scenarios, the graph described in Section 2.7 could
be generated for each application. Metrics such as end-to-end
latency, jitter, flow completion time MAY be generated. For the
cases of frame generation of bursty video traffic as well as the
choice of web traffic pattern, these details and their presentation
are left to the testers.
8. Stability
8.1. Motivation
The safety of an AQM scheme is directly related to its stability
under varying operating conditions such as varying traffic profiles
and fluctuating network conditions. Since operating conditions can
vary often the AQM needs to remain stable under these conditions
without the need for additional external tuning.
Network devices can experience varying operating conditions depending
on factors such as time of the day, deployment scenario, etc. For
example:
o Traffic and congestion levels are higher during peak hours than
off-peak hours.
o In the presence of a scheduler, the draining rate of a queue can
vary depending on the occupancy of other queues: a low load on a
high priority queue implies a higher draining rate for the lower
priority queues.
o The capacity available can vary over time (e.g., a lossy channel,
a link supporting traffic in a higher diffserv class).
Whether the target context is a not stable environment, the ability
of an AQM scheme to maintain its control over the queuing delay and
buffer occupancy can be challenged. This document proposes
guidelines to assess the behavior of AQM schemes under varying
congestion levels and varying draining rates.
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8.2. Recommended tests
Note that the traffic profiles explained below comprises non
application-limited TCP flows. For each of the below scenarios, the
graphs described in Section 2.7 SHOULD be generated, and the goodput
of the various flows should be cumulated. For Section 8.2.5 and
Section 8.2.6 they SHOULD incorporate the results in per-phase basis
as well.
Wherever the notion of time has explicitly mentioned in this
subsection, time 0 starts from the moment all TCP flows have already
reached their congestion avoidance phase.
8.2.1. Definition of the congestion Level
In these guidelines, the congestion levels are represented by the
projected packet drop rate, had a drop-tail queue was chosen instead
of an AQM scheme. When the bottleneck is shared among non
application-limited TCP flows. l_r, the loss rate projection can be
expressed as a function of N, the number of bulk TCP flows, and S,
the sum of the bandwidth-delay product and the maximum buffer size,
both expressed in packets, based on Eq. 3 of [MORR2000]:
l_r = 0.76 * N^2 / S^2
N = S * SQRT(1/0.76) * SQRT (l_r)
These guidelines use the loss rate to define the different congestion
levels, but they do not stipulate that in other circumstances,
measuring the congestion level gives you an accurate estimation of
the loss rate or vice-versa.
8.2.2. Mild congestion
This scenario can be used to evaluate how an AQM scheme reacts to a
light load of incoming traffic resulting in mild congestion -- packet
drop rates around 0.1%. The number of bulk flows required to achieve
this congestion level, N_mild, is then:
N_mild = ROUND (0.036*S)
8.2.3. Medium congestion
This scenario can be used to evaluate how an AQM scheme reacts to
incoming traffic resulting in medium congestion -- packet drop rates
around 0.5%. The number of bulk flows required to achieve this
congestion level, N_med, is then:
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N_med = ROUND (0.081*S)
8.2.4. Heavy congestion
This scenario can be used to evaluate how an AQM scheme reacts to
incoming traffic resulting in heavy congestion -- packet drop rates
around 1%. The number of bulk flows required to achieve this
congestion level, N_heavy, is then:
N_heavy = ROUND (0.114*S)
8.2.5. Varying the congestion level
This scenario can be used to evaluate how an AQM scheme reacts to
incoming traffic resulting in various levels of congestion during the
experiment. In this scenario, the congestion level varies within a
large time-scale. The following phases may be considered: phase I -
mild congestion during 0-20s; phase II - medium congestion during
20-40s; phase III - heavy congestion during 40-60s; phase I again,
and so on.
8.2.6. Varying available capacity
This scenario can be used to help characterize how the AQM behaves
and adapts to bandwidth changes. The experiments are not meant to
reflect the exact conditions of Wi-Fi environments since it is hard
to design repetitive experiments or accurate simulations for such
scenarios.
To emulate varying draining rates, the bottleneck capacity between
nodes 'Router L' and 'Router R' varies over the course of the
experiment as follows:
o Experiment 1: the capacity varies between two values within a
large time-scale. As an example, the following phases may be
considered: phase I - 100Mbps during 0-20s; phase II - 10Mbps
during 20-40s; phase I again, and so on.
o Experiment 2: the capacity varies between two values within a
short time-scale. As an example, the following phases may be
considered: phase I - 100Mbps during 0-100ms; phase II - 10Mbps
during 100-200ms; phase I again, and so on.
The tester MAY choose a phase time-interval value different than what
is stated above, if the network's path conditions (such as bandwidth-
delay product) necessitate. In this case the choice of such time-
interval value SHOULD be stated and elaborated.
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The tester MAY additionally evaluate the two mentioned scenarios
(short-term and long-term capacity variations), during and/or
including TCP slow-start phase.
More realistic fluctuating capacity patterns MAY be considered. The
tester MAY choose to incorporate realistic scenarios with regards to
common fluctuation of bandwidth in state-of-the-art technologies.
The scenario consists of TCP NewReno flows between sender A and
receiver B. To better assess the impact of draining rates on the AQM
behavior, the tester MUST compare its performance with those of drop-
tail and SHOULD provide a reference document for their proposal
discussing performance and deployment compared to those of drop-tail.
Burst traffic, such as presented in Section 7.2, could also be
considered to assess the impact of varying available capacity on the
burst absorption of the AQM.
8.3. Parameter sensitivity and stability analysis
The control law used by an AQM is the primary means by which the
queuing delay is controlled. Hence understanding the control law is
critical to understanding the behavior of the AQM scheme. The
control law could include several input parameters whose values
affect the AQM scheme's output behavior and its stability.
Additionally, AQM schemes may auto-tune parameter values in order to
maintain stability under different network conditions (such as
different congestion levels, draining rates or network environments).
The stability of these auto-tuning techniques is also important to
understand.
Transports operating under the control of AQM experience the effect
of multiple control loops that react over different timescales. It
is therefore important that proposed AQM schemes are seen to be
stable when they are deployed at multiple points of potential
congestion along an Internet path. The pattern of congestion signals
(loss or ECN-marking) arising from AQM methods also need to not
adversely interact with the dynamics of the transport protocols that
they control.
AQM proposals SHOULD provide background material showing control
theoretic analysis of the AQM control law and the input parameter
space within which the control law operates as expected; or could use
another way to discuss the stability of the control law. For
parameters that are auto-tuned, the material SHOULD include stability
analysis of the auto-tuning mechanism(s) as well. Such analysis
helps to understand an AQM control law better and the network
conditions/deployments under which the AQM is stable.
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9. Various Traffic Profiles
This section provides guidelines to assess the performance of an AQM
proposal for various traffic profiles such as traffic with different
applications or bi-directional traffic.
9.1. Traffic mix
This scenario can be used to evaluate how an AQM scheme reacts to a
traffic mix consisting of different applications such as:
o Bulk TCP transfer
o Web traffic
o VoIP
o Constant Bit Rate (CBR) UDP traffic
o Adaptive video streaming
Various traffic mixes can be considered. These guidelines RECOMMEND
to examine at least the following example: 1 bi-directional VoIP; 6
Web pages download (such as detailed in Section 7.2); 1 CBR; 1
Adaptive Video; 5 bulk TCP. Any other combinations could be
considered and should be carefully documented.
For each scenario, the graph described in Section 2.7 could be
generated for each class of traffic. Metrics such as end-to-end
latency, jitter and flow completion time MAY be reported.
9.2. Bi-directional traffic
Control packets such as DNS requests/responses, TCP SYNs/ACKs are
small, but their loss can severely impact the application
performance. The scenario proposed in this section will help in
assessing whether the introduction of an AQM scheme increases the
loss probability of these important packets.
For this scenario, traffic MUST be generated in both downlink and
uplink, such as defined in Section 3.1. These guidelines RECOMMEND
to consider a mild congestion level and the traffic presented in
Section 8.2.2 in both directions. In this case, the metrics reported
MUST be the same as in Section 8.2 for each direction.
The traffic mix presented in Section 9.1 MAY also be generated in
both directions.
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10. Multi-AQM Scenario
10.1. Motivation
Transports operating under the control of AQM experience the effect
of multiple control loops that react over different timescales. It
is therefore important that proposed AQM schemes are seen to be
stable when they are deployed at multiple points of potential
congestion along an Internet path. The pattern of congestion signals
(loss or ECN-marking) arising from AQM methods also need to not
adversely interact with the dynamics of the transport protocols that
they control.
10.2. Details on the evaluation scenario
+---------+ +-----------+
|senders A|---+ +---|receivers A|
+---------+ | | +-----------+
+-----+---+ +---------+ +--+-----+
|Router L |--|Router M |--|Router R|
|AQM | |AQM | |No AQM |
+---------+ +--+------+ +--+-----+
+---------+ | | +-----------+
|senders B|-------------+ +---|receivers B|
+---------+ +-----------+
Figure 3: Topology for the Multi-AQM scenario
This scenario can be used to evaluate how having AQM schemes in
sequence impact the induced latency reduction, the induced goodput
maximization and the trade-off between these two. The topology
presented in Figure 3 could be used. AQM schemes introduced in
Router L and Router M should be the same; any other configurations
could be considered. For this scenario, it is recommended to
consider a mild congestion level, the number of flows specified in
Section 8.2.2 being equally shared among senders A and B. Any other
relevant combination of congestion levels could be considered. We
recommend to measure the metrics presented in Section 8.2.
11. Implementation cost
11.1. Motivation
Successful deployment of AQM is directly related to its cost of
implementation. Network devices can need hardware or software
implementations of the AQM mechanism. Depending on a device's
capabilities and limitations, the device may or may not be able to
implement some or all parts of their AQM logic.
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AQM proposals SHOULD provide pseudo-code for the complete AQM scheme,
highlighting generic implementation-specific aspects of the scheme
such as "drop-tail" vs. "drop-head", inputs (e.g., current queuing
delay, queue length), computations involved, need for timers, etc.
This helps to identify costs associated with implementing the AQM
scheme on a particular hardware or software device. This also
facilitates discsusions around which kind of devices can easily
support the AQM and which cannot.
11.2. Recommended discussion
AQM proposals SHOULD highlight parts of their AQM logic that are
device dependent and discuss if and how AQM behavior could be
impacted by the device. For example, a queueing-delay based AQM
scheme requires current queuing delay as input from the device. If
the device already maintains this value, then it can be trivial to
implement the their AQM logic on the device. If the device provides
indirect means to estimate the queuing delay (for example:
timestamps, dequeuing rate), then the AQM behavior is sensitive to
the precision of the queuing delay estimations are for that device.
Highlighting the sensitivity of an AQM scheme to queuing delay
estimations helps implementers to identify appropriate means of
implementing the mechanism on a device.
12. Operator Control and Auto-tuning
12.1. Motivation
One of the biggest hurdles of RED deployment was/is its parameter
sensitivity to operating conditions -- how difficult it is to tune
RED parameters for a deployment to achieve acceptable benefit from
using RED. Fluctuating congestion levels and network conditions add
to the complexity. Incorrect parameter values lead to poor
performance.
Any AQM scheme is likely to have parameters whose values affect the
control law and behaviour of an AQM. Exposing all these parameters
as control parameters to a network operator (or user) can easily
result in a unsafe AQM deployment. Unexpected AQM behavior ensues
when parameter values are set improperly. A minimal number of
control parameters minimizes the number of ways a user can break a
system where an AQM scheme is deployed at. Fewer control parameters
make the AQM scheme more user-friendly and easier to deploy and
debug.
[RFC7567] states "AQM algorithms SHOULD NOT require tuning of initial
or configuration parameters in common use cases." A scheme ought to
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expose only those parameters that control the macroscopic AQM
behavior such as queue delay threshold, queue length threshold, etc.
Additionally, the safety of an AQM scheme is directly related to its
stability under varying operating conditions such as varying traffic
profiles and fluctuating network conditions, as described in
Section 8. Operating conditions vary often and hence the AQM needs
to remain stable under these conditions without the need for
additional external tuning. If AQM parameters require tuning under
these conditions, then the AQM must self-adapt necessary parameter
values by employing auto-tuning techniques.
12.2. Recommended discussion
In order to understand an AQM's deployment considerations and
performance under a specific environment, AQM proposals SHOULD
describe the parameters that control the macroscopic AQM behavior,
and identify any parameters that require tuning to operational
conditions. It could be interesting to also discuss that even if an
AQM scheme may not adequately auto-tune its parameters, the resulting
performance may not be optimal, but close to something reasonable.
If there are any fixed parameters within the AQM, their setting
SHOULD be discussed and justified, to help understand whether a fixed
parameter value is applicable for a particular environment.
If an AQM scheme is evaluated with parameter(s) that were externally
tuned for optimization or other purposes, these values MUST be
disclosed.
13. Conclusion
Figure 4 lists the scenarios and their requirements.
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+------------------------------------------------------------------+
|Scenario |Sec. |Requirement |
+------------------------------------------------------------------+
+------------------------------------------------------------------+
|Interaction with ECN | 4.5 |MUST be discussed if supported |
+------------------------------------------------------------------+
|Interaction with Scheduling| 4.6 |Feasibility MUST be discussed |
+------------------------------------------------------------------+
|Transport Protocols |5. | |
| TCP-friendly sender | 5.1 |Scenario MUST be considered |
| Aggressive sender | 5.2 |Scenario MUST be considered |
| Unresponsive sender | 5.3 |Scenario MUST be considered |
| LBE sender | 5.4 |Scenario MAY be considered |
+------------------------------------------------------------------+
|Round Trip Time Fairness | 6.2 |Scenario MUST be considered |
+------------------------------------------------------------------+
|Burst Absorption | 7.2 |Scenario MUST be considered |
+------------------------------------------------------------------+
|Stability |8. | |
| Varying congestion levels | 8.2.5|Scenario MUST be considered |
| Varying available capacity| 8.2.6|Scenario MUST be considered |
| Parameters and stability | 8.3 |This SHOULD be discussed |
+------------------------------------------------------------------+
|Various Traffic Profiles |9. | |
| Traffic mix | 9.1 |Scenario is RECOMMENDED |
| Bi-directional traffic | 9.2 |Scenario MAY be considered |
+------------------------------------------------------------------+
|Multi-AQM | 10.2 |Scenario MAY be considered |
+------------------------------------------------------------------+
|Implementation Cost | 11.2 |Pseudo-code SHOULD be provided |
+------------------------------------------------------------------+
|Operator Control | 12.2 |Tuning SHOULD NOT be required |
+------------------------------------------------------------------+
Figure 4: Summary of the scenarios and their requirements
14. Acknowledgements
This work has been partially supported by the European Community
under its Seventh Framework Programme through the Reducing Internet
Transport Latency (RITE) project (ICT-317700).
15. Contributors
Many thanks to S. Akhtar, A.B. Bagayoko, F. Baker, R. Bless, D.
Collier-Brown, G. Fairhurst, J. Gettys, P. Goltsman, T. Hoiland-
Jorgensen, K. Kilkki, C. Kulatunga, W. Lautenschlager, A.C.
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Morton, R. Pan, G. Skinner, D. Taht and M. Welzl for detailed and
wise feedback on this document.
16. IANA Considerations
This memo includes no request to IANA.
17. Security Considerations
Some security considerations for AQM are identified in [RFC7567].This
document, by itself, presents no new privacy nor security issues.
18. References
18.1. Normative References
[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-01 (work in progress), January 2016.
[I-D.irtf-iccrg-tcpeval]
Hayes, D., Ros, D., Andrew, L., and S. Floyd, "Common TCP
Evaluation Suite", draft-irtf-iccrg-tcpeval-01 (work in
progress), July 2014.
[RFC0793] Postel, J., "Transmission Control Protocol", STD 7,
RFC 793, DOI 10.17487/RFC0793, September 1981,
<http://www.rfc-editor.org/info/rfc793>.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", RFC 2119, 1997.
[RFC2488] Allman, M., Glover, D., and L. Sanchez, "Enhancing TCP
Over Satellite Channels using Standard Mechanisms",
BCP 28, RFC 2488, DOI 10.17487/RFC2488, January 1999,
<http://www.rfc-editor.org/info/rfc2488>.
[RFC2544] Bradner, S. and J. McQuaid, "Benchmarking Methodology for
Network Interconnect Devices", RFC 2544,
DOI 10.17487/RFC2544, March 1999,
<http://www.rfc-editor.org/info/rfc2544>.
[RFC2647] Newman, D., "Benchmarking Terminology for Firewall
Performance", RFC 2647, DOI 10.17487/RFC2647, August 1999,
<http://www.rfc-editor.org/info/rfc2647>.
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[RFC2679] Almes, G., Kalidindi, S., and M. Zekauskas, "A One-way
Delay Metric for IPPM", RFC 2679, DOI 10.17487/RFC2679,
September 1999, <http://www.rfc-editor.org/info/rfc2679>.
[RFC2680] Almes, G., Kalidindi, S., and M. Zekauskas, "A One-way
Packet Loss Metric for IPPM", RFC 2680,
DOI 10.17487/RFC2680, September 1999,
<http://www.rfc-editor.org/info/rfc2680>.
[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>.
[RFC3611] Friedman, T., Ed., Caceres, R., Ed., and A. Clark, Ed.,
"RTP Control Protocol Extended Reports (RTCP XR)",
RFC 3611, DOI 10.17487/RFC3611, November 2003,
<http://www.rfc-editor.org/info/rfc3611>.
[RFC5348] Floyd, S., Handley, M., Padhye, J., and J. Widmer, "TCP
Friendly Rate Control (TFRC): Protocol Specification",
RFC 5348, DOI 10.17487/RFC5348, September 2008,
<http://www.rfc-editor.org/info/rfc5348>.
[RFC5481] Morton, A. and B. Claise, "Packet Delay Variation
Applicability Statement", RFC 5481, DOI 10.17487/RFC5481,
March 2009, <http://www.rfc-editor.org/info/rfc5481>.
[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>.
[RFC6297] Welzl, M. and D. Ros, "A Survey of Lower-than-Best-Effort
Transport Protocols", RFC 6297, DOI 10.17487/RFC6297, June
2011, <http://www.rfc-editor.org/info/rfc6297>.
[RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
"Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
DOI 10.17487/RFC6817, December 2012,
<http://www.rfc-editor.org/info/rfc6817>.
[RFC7141] Briscoe, B. and J. Manner, "Byte and Packet Congestion
Notification", RFC 7141, 2014.
[RFC7567] Baker, F., Ed. and G. Fairhurst, Ed., "IETF
Recommendations Regarding Active Queue Management",
BCP 197, RFC 7567, DOI 10.17487/RFC7567, July 2015,
<http://www.rfc-editor.org/info/rfc7567>.
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18.2. Informative References
[ANEL2014]
Anelli, P., Diana, R., and E. Lochin, "FavorQueue: a
Parameterless Active Queue Management to Improve TCP
Traffic Performance", Computer Networks vol. 60, 2014.
[BB2011] "BufferBloat: what's wrong with the internet?", ACM
Queue vol. 9, 2011.
[GONG2014]
Gong, Y., Rossi, D., Testa, C., Valenti, S., and D. Taht,
"Fighting the bufferbloat: on the coexistence of AQM and
low priority congestion control", Computer Networks,
Elsevier, 2014, 60, pp.115 - 128 , 2014.
[HASS2008]
Hassayoun, S. and D. Ros, "Loss Synchronization and Router
Buffer Sizing with High-Speed Versions of TCP", IEEE
INFOCOM Workshops , 2008.
[HOEI2015]
Hoeiland-Joergensen, T., McKenney, P., Taht, D., Gettys,
J., and E. Dumazet, "FlowQueue-Codel", IETF (Work-in-
Progress) , January 2015.
[JAY2006] Jay, P., Fu, Q., and G. Armitage, "A preliminary analysis
of loss synchronisation between concurrent TCP flows",
Australian Telecommunication Networks and Application
Conference (ATNAC) , 2006.
[MORR2000]
Morris, R., "Scalable TCP congestion control", IEEE
INFOCOM , 2000.
[NICH2012]
Nichols, K. and V. Jacobson, "Controlling Queue Delay",
ACM Queue , 2012.
[PAN2013] Pan, R., Natarajan, P., Piglione, C., Prabhu, MS.,
Subramanian, V., Baker, F., and B. VerSteeg, "PIE: A
lightweight control scheme to address the bufferbloat
problem", IEEE HPSR , 2013.
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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, April 1998.
[TRAN2014]
Trang, S., Kuhn, N., Lochin, E., Baudoin, C., Dubois, E.,
and P. Gelard, "On The Existence Of Optimal LEDBAT
Parameters", IEEE ICC 2014 - Communication QoS,
Reliability and Modeling Symposium , 2014.
[WELZ2015]
Welzl, M. and G. Fairhurst, "The Benefits to Applications
of using Explicit Congestion Notification (ECN)", IETF
(Work-in-Progress) , June 2015.
[WINS2014]
Winstein, K., "Transport Architectures for an Evolving
Internet", PhD thesis, Massachusetts Institute of
Technology , 2014.
Authors' Addresses
Nicolas Kuhn (editor)
CNES, Telecom Bretagne
18 avenue Edouard Belin
Toulouse 31400
France
Phone: +33 5 61 27 32 13
Email: nicolas.kuhn@cnes.fr
Preethi Natarajan (editor)
Cisco Systems
510 McCarthy Blvd
Milpitas, California
United States
Email: prenatar@cisco.com
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Naeem Khademi (editor)
University of Oslo
Department of Informatics, PO Box 1080 Blindern
N-0316 Oslo
Norway
Phone: +47 2285 24 93
Email: naeemk@ifi.uio.no
David Ros
Simula Research Laboratory AS
P.O. Box 134
Lysaker, 1325
Norway
Phone: +33 299 25 21 21
Email: dros@simula.no
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