wdiff rfc7928v1.txt rfc7928.txt

Internet Engineering Task Force (IETF) N. Kuhn, Ed.
Request for Comments: 7928 CNES, Telecom Bretagne
Category: Informational P. Natarajan, Ed.
ISSN: 2070-1721 Cisco Systems
N. Khademi, Ed.
University of Oslo
D. Ros
Simula Research Laboratory AS
July 2016

Characterization Guidelines for Active Queue Management (AQM)

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
characterizations of AQM schemes that can be used in lab testing
during development, prior to deployment.

Status of This Memo

This document is not an Internet Standards Track specification; it is
published for informational purposes.

This document is a product of the Internet Engineering Task Force
(IETF). It represents the consensus of the IETF community. It has
received public review and has been approved for publication by the
Internet Engineering Steering Group (IESG). Not all documents
approved by the IESG are a candidate for any level of Internet
Standard; see Section 2 of RFC 5741.

Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
http://www.rfc-editor.org/info/rfc7928.

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
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
to this document. Code Components extracted from this document must
include Simplified BSD License text as described in Section 4.e of
the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.

Table of Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3 4
1.1. Reducing the Latency and Maximizing the Goodput . . . . . 5
1.2. Goals of This Document . . . . . . . . . . . . . . . . . 5
1.3. Requirements Language . . . . . . . . . . . . . . . . . . 6
1.4. Glossary . . . . . . . . . . . . . . . . . . . . . . . . 6 7
2. End-to-End Metrics . . . . . . . . . . . . . . . . . . . . . 7
2.1. Flow Completion Time . . . . . . . . . . . . . . . . . . 7 8
2.2. Flow Startup Time . . . . . . . . . . . . . . . . . . . . 8
2.3. Packet Loss . . . . . . . . . . . . . . . . . . . . . . . 8 9
2.4. Packet Loss Synchronization . . . . . . . . . . . . . . . 9
2.5. Goodput . . . . . . . . . . . . . . . . . . . . . . . . . 9 10
2.6. Latency and Jitter . . . . . . . . . . . . . . . . . . . 10 11
2.7. Discussion on the Trade-Off between Latency and Goodput . 10 11
3. Generic Setup for Evaluations . . . . . . . . . . . . . . . . 11 12
3.1. Topology and Notations . . . . . . . . . . . . . . . . . 11 12
3.2. Buffer Size . . . . . . . . . . . . . . . . . . . . . . . 13 14
3.3. Congestion Controls . . . . . . . . . . . . . . . . . . . 13 14
4. Methodology, Metrics, AQM Comparisons, Packet Sizes,
Scheduling, and ECN . . . . . . . . . . . . . . . . . . . . . 14
4.1. Methodology . . . . . . . . . . . . . . . . . . . . . . . 14
4.2. Comments on Metrics Measurement . . . . . . . . . . . . . 14 15
4.3. Comparing AQM Schemes . . . . . . . . . . . . . . . . . . 15
4.3.1. Performance Comparison . . . . . . . . . . . . . . . 15
4.3.2. Deployment Comparison . . . . . . . . . . . . . . . . 16
4.4. Packet Sizes and Congestion Notification . . . . . . . . 16
4.5. Interaction with ECN . . . . . . . . . . . . . . . . . . 16 17
4.6. Interaction with Scheduling . . . . . . . . . . . . . . . 17
5. Transport Protocols . . . . . . . . . . . . . . . . . . . . . 17 18
5.1. TCP-Friendly Sender . . . . . . . . . . . . . . . . . . . 18 19
5.1.1. TCP-Friendly Sender with the Same Initial Congestion
Window . . . . . . . . . . . . . . . . . . . . . . . 18 19
5.1.2. TCP-Friendly Sender with Different Initial Congestion
Windows . . . . . . . . . . . . . . . . . . . . . . . 18 19
5.2. Aggressive Transport Sender . . . . . . . . . . . . . . . 19
5.3. Unresponsive Transport Sender . . . . . . . . . . . . . . 19 20
5.4. Less-than-Best-Effort Transport Sender . . . . . . . . . 20
6. Round-Trip Time Fairness . . . . . . . . . . . . . . . . . . 20 21
6.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . 20 21
6.2. Recommended Tests . . . . . . . . . . . . . . . . . . . . 21
6.3. Metrics to Evaluate the RTT Fairness . . . . . . . . . . 21 22
7. Burst Absorption . . . . . . . . . . . . . . . . . . . . . . 21 22
7.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . 21 22
7.2. Recommended Tests . . . . . . . . . . . . . . . . . . . . 22 23
8. Stability . . . . . . . . . . . . . . . . . . . . . . . . . . 23 24
8.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . 23 24
8.2. Recommended Tests . . . . . . . . . . . . . . . . . . . . 24
8.2.1. Definition of the Congestion Level . . . . . . . . . 24 25
8.2.2. Mild Congestion . . . . . . . . . . . . . . . . . . . 25
8.2.3. Medium Congestion . . . . . . . . . . . . . . . . . . 25
8.2.4. Heavy Congestion . . . . . . . . . . . . . . . . . . 25
8.2.5. Varying the Congestion Level . . . . . . . . . . . . 25 26
8.2.6. Varying Available Capacity . . . . . . . . . . . . . 25 26
8.3. Parameter Sensitivity and Stability Analysis . . . . . . 26 27
9. Various Traffic Profiles . . . . . . . . . . . . . . . . . . 27
9.1. Traffic Mix . . . . . . . . . . . . . . . . . . . . . . . 27 28
9.2. Bidirectional Traffic . . . . . . . . . . . . . . . . . . 28
10. Example of a Multi-AQM Scenario . . . . . . . . . . . . . . . 28 29
10.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . 28 29
10.2. Details on the Evaluation Scenario . . . . . . . . . . . 28 29
11. Implementation Cost . . . . . . . . . . . . . . . . . . . . . 29 30
11.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . 29 30
11.2. Recommended Discussion . . . . . . . . . . . . . . . . . 29 30
12. Operator Control and Auto-Tuning . . . . . . . . . . . . . . 30
12.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . 30
12.2. Recommended Discussion . . . . . . . . . . . . . . . . . 30 31
13. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
14. Security Considerations . . . . . . . . . . . . . . . . . . . 32
15. References . . . . . . . . . . . . . . . . . . . . . . . . . 32
15.1. Normative References . . . . . . . . . . . . . . . . . . 32
15.2. Informative References . . . . . . . . . . . . . . . . . 32 33
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 36 37
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 36 38

1. Introduction

Active Queue Management (AQM) addresses the concerns arising from
using unnecessarily large and unmanaged buffers to improve network
and application performance, such as those presented in Section 1.2
of the AQM recommendations document [RFC7567]. Several AQM
algorithms have been proposed in the past years, most notably Random
Early Detection (RED) [FLOY1993], BLUE [FENG2002], Proportional
Integral controller (PI) [HOLLO2001], and more recently, Controlled
Delay (CoDel) [CODEL] and Proportional Integral controller Enhanced
(PIE) [AQMPIE]. 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. 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.

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 switches and
routers, a global memory space is often shared between the available
interfaces, and thus, the maximum buffer size for any given interface
may vary over 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 a 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, 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 are also robust under a wide variety of operating
conditions. This document provides characterization guidelines that
can be used to assess the applicability, performance, and
deployability of an AQM, whether it is a candidate for
standardization at IETF or not.

The AQM algorithm implemented in a router can be separated from the
scheduling of packets sent out by the router as discussed in the AQM
recommendations document [RFC7567]. 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 the 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
the impact of the number of hash buckets.

1.1. Reducing the Latency and Maximizing the Goodput

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. To ensure the safety deployment of an AQM, its
behavior should be assessed in a variety of scenarios. Whenever
possible, solutions ought to aim at both maximizing goodput and
minimizing latency.

1.2. Goals of This Document

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, but should be used to compare a proposal
with drop-tail.

This memo details generic characterization scenarios against which
any AQM proposal should be evaluated, irrespective of whether or not
an AQM is standardized by the IETF. This document recommends the
relevant scenarios and metrics to be considered. This 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.

These guidelines do not define and are not bound to a particular
deployment scenario 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 IEEE 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
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.

1.3. 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].

1.4. Glossary

o Application-limited traffic: A type of traffic that does not have
an unlimited amount of data to transmit.

o AQM: The Active Queue Management (AQM) algorithm implemented in a
router can be separated 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 [RFC7567].

o BDP: Bandwidth Delay Product.

o Buffer: A physical volume of memory in which a queue or set of
queues are stored.

o Buffer Occupancy: The amount of data stored in a buffer, measured
in bytes or packets.

o Buffer Size: The maximum buffer occupancy, that is the maximum
amount of data that may be stored in a buffer, measured in bytes
or packets.

o Initial Window 10 (IW10): TCP initial congestion window set to 10
packets.

o Latency: One-way delay of packets across Internet paths. This
definition suits transport layer definition of the latency, which
should not be confused with an application-layer view of the
latency.

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]. The goodput should be determined for
each flow and not for aggregates of flows.

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
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. The definition of the flow
size may be a source of contradictions, thus, this metric can
consider a flow as a single file. 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] )

Where flow size is the size of the transport-layer payload in bits
and goodput is the transport-layer payload transfer time (described
in Section 2.5).

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, rather than assessing the time needed to
download each object.

2.2. Flow Startup Time

The flow startup time is the time between when the request was sent
from the client and when 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, and Secure Socket Layer (SSL) handshakes.

2.3. Packet Loss

Packet loss can occur en route, this can impact the end-to-end
performance measured at the receiver end.

The tester should evaluate the loss experienced at the receiver end
using one of two metrics:

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 advise the tester:

o To check that for every packet, a corresponding packet was
received within a reasonable time, as presented in the document
that proposes a metric for one-way packet loss across Internet
paths [RFC2680].

o To keep a count of all packets sent, and a count of the non-
duplicate packets received, as discussed in [RFC2544], which
presents a benchmarking methodology.

The interval between consecutive losses, which is also called a
"gap", is a metric of interest for Voice over IP (VoIP) traffic
[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] and [RFC7567]). The "degree" of packet-loss
synchronization between flows should be assessed, with and without
the AQM under consideration.

Loss synchronization among flows may be quantified by several
slightly different metrics that capture different aspects of the same
issue [HASS2008]. However, in real-world measurements the choice of
metric could be imposed by practical considerations -- e.g., whether
fine-grained information on packet losses at the bottleneck is
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. This metric
can be used in real-world experiments to characterize synchronization
along arbitrary Internet paths [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.

2.5. Goodput

The goodput has been defined as the number of bits per the unit of
time forwarded to the correct destination interface, minus any bits
lost or retransmitted, such as proposed in Section 3.17 of [RFC2647],
which describes the benchmarking terminology for firewall
performances. This definition requires 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 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 to evaluate to what
extent an AQM is able to maintain a high bottleneck utilization.
This metric should also be 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 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 (IEEE 802.11 WLANs), heavy congestion levels, or the
transport layer's rate reduction by the 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) serves well in all use cases
[RFC5481].

The end-to-end latency includes components other than just the
queuing delay, such as the signal-processing delay, transmission
delay, and 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 end-to-end
latency and jitter, and therefore these metrics 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 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 at
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 can be
considered whenever it is more appropriate for the presentation of
the results, since 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 a per-packet basis whenever possible, depending on the
features provided by the hardware and software and the impact of
sampling itself on the hardware performance.

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 a
queuing delay (that is, the average per-packet delay in excess of
minimum RTT), the y-axis the goodput. Ellipses are computed 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.

3.1. Topology and Notations

+--------------+ +--------------+
|sender A_i | |receive B_i |
|--------------| |--------------|
| SEN.Flow1.1 +---------+ +-----------+ REC.Flow1.1 |
| + | | | | + |
| | | | | | | |
| + | | | | + |
| SEN.Flow1.X +-----+ | | +--------+ REC.Flow1.X |
+--------------+ | | | | +--------------+
+ +-+---+---+ +--+--+---+ +
| |Router L | |Router R | |
| |---------| |---------| |
| | AQM | | | |
| | BuffSize| | BuffSize| |
| | (Bsize) +-----+ (Bsize) | |
| +-----+--++ ++-+------+ |
+ | | | | +
+--------------+ | | | | +--------------+
|sender A_n | | | | | |receive B_n |
|--------------| | | | | |--------------|
| 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 The traffic profile is a set of flows with similar characteristics
-- RTT, congestion control scheme, transport protocol, etc.;

o Senders 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);

o Sender A_i is instantiated for each traffic profile. A
corresponding receiver B_i is instantiated for receiving the flows
in the profile;

o Flows share 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 the case of
an 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
bidirectional traffic between A and B (downlink and uplink) with
the AQM mechanism deployed in one direction only. The tester may
additionally consider a scenario with the AQM mechanism being
deployed in both directions. In each scenario, the tester should
investigate the impact of the drop policy of the AQM on TCP ACK
packets and its impact on the performance (Section 9.2).

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 [TCPEVAL]. Testers ought to pay attention to the topology
used to evaluate an AQM scheme when comparing it 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 being 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:

o Standard TCP congestion control: The base-line congestion control
is TCP NewReno with selective acknowledgment (SACK) [RFC5681].

o Aggressive congestion controls: A base-line congestion control for
this category is TCP CUBIC [CUBIC].

o Less-than-Best-Effort (LBE) congestion controls: An Per [RFC6297], an
LBE congestion
control 'results service "results in smaller bandwidth and/or delay impact on
standard TCP than standard TCP itself, when sharing a bottleneck
with it.': a it." A base-line congestion control for this category is Low
Extra Delay Background Transport (LEDBAT) [RFC6817].

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

This document presents the end-to-end metrics that ought to be used
to evaluate the trade-off between latency and goodput as described 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
advise considering 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.

These guidelines do not aim to detail the way these metrics can be
measured, since that 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 (for example, RED) or queuing-delay based scheme (for
example, CoDel, PIE). AQM schemes expose different control knobs
associated with different semantics. For example, while both PIE and
CoDel are queuing-delay based schemes and each expose a knob to
control the queuing delay -- PIE's "queuing delay reference" vs.
CoDel's "queuing 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. Similar control parameters and implications: Testers should be
aware of the control parameters of the different schemes that
control similar behavior. Testers should also be aware of the
input value ranges and corresponding implications. For example,
consider two different schemes -- (A) queue-length based AQM
scheme, and (B) queuing-delay based scheme. A and B are likely
to have different kinds of control inputs to control the target
delay -- the target queue length in A vs. target queuing delay in
B, for example. Setting parameter values such as 100 MB for A
vs. 10 ms for B will have different implications depending on
evaluation context. Such context-dependent implications must be
considered before drawing conclusions on performance comparisons.
Also, it would be preferable if an AQM proposal listed such
parameters and 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 have 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]. For example, control packets such as
DNS requests/responses, TCP SYNs/ACKs are small, but their loss can
severely impact application performance. An AQM scheme may therefore
be biased towards small packets by dropping them with lower
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 an
AQM scheme by transmitting smaller packets, and this could result in
unsafe deployments and unhealthy transport and/or application
designs.

An AQM scheme should adhere to the recommendations outlined in the
Best Current Practice for dropping and marking packets [RFC7141], [BCP41], and
should not provide undue advantage to flows with smaller packets,
such as discussed in Section 4.4 of the AQM recommendation document
[RFC7567]. In order to evaluate if an AQM scheme is biased towards
flows with smaller size packets, traffic can be generated, as defined
in Section 8.2.2, where half of the flows have smaller packets (e.g.,
500-byte packets) than the other half of the flow (e.g., 1500-byte
packets). In this case, the metrics reported could be the same as in
Section 6.3, where Category I is the set of flows with smaller
packets and Category II the one with larger packets. The
bidirectional scenario could also be considered (Section 9.2).

4.5. Interaction with ECN

ECN [RFC3168] is an alternative that allows AQM schemes to signal to
receivers about network congestion that does not use packet drops.
There are benefits to providing ECN support for an AQM scheme
[WELZ2015].

If the tested AQM scheme can support ECN, the testers must discuss
and describe the support of ECN, such as discussed in the AQM
recommendation document [RFC7567]. Also, the AQM's ECN support can
be studied and verified by replicating tests in Section 6.2 with ECN
turned ON at the TCP senders. The results can be used not only to
evaluate the performance of the tested AQM with and without ECN
markings, but also 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, such
as discussed in Section 2.1 of the AQM recommendation document
[RFC7567].

The scheduling and the AQM conjointly impact the end-to-end
performance. Therefore, the AQM proposal must discuss the
feasibility of adding scheduling combined with the AQM algorithm. It
can be explained 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 help in tackling the
bufferbloat, but also are robust under a wide variety of operating
conditions. This is out of the scope of this document, which focuses
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 the Transmission
Control Protocol (TCP) [RFC0793] with a limited number of
applications. TCP is a widely deployed transport. It fills up
available buffers until a sender transferring 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. A variety of senders generate different classes of traffic,
which may not react to congestion signals (aka non-responsive flows
in Section 3 of the AQM recommendation document [RFC7567]) or may not
reduce their sending rate as expected (aka Transport Flows that are
less responsive than TCP, such as proposed in Section 3 of the AQM
recommendation document [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, and
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.

The transmission of the non-application-limited flow must start first
and the transmission of the application-limited flow starts after the
non-application-limited flow has reached steady state. The steady
state can be assumed when the goodput is stable.

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-than-standard
increase factor upon a successful transmission and/or a lower-than-
standard decrease factor upon a unsuccessful transmission (e.g., in
case of congestion controls with the 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 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
may also lead to a similar situation, such that the AQM scheme needs
to adapt to unresponsive traffic (see Section 3 of the AQM
recommendation document [RFC7567]). To this end, these guidelines
propose the two following scenarios:

o 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
that transmits data between sender A and receiver B. The graph
described in Section 2.7 could be generated.

o 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 traffic. It
consists of a long-lived UDP flow from unresponsive application
and a single long-lived, non-application-limited (unlimited data
available to the transport sender from the 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 control that "results in smaller bandwidth and/or delay
impact on standard TCP than standard TCP itself, when sharing a
bottleneck with it" [RFC6297]. There are potential fateful
interactions when AQM and LBE techniques are combined [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 setting the target delay and
gain values of LEDBAT to 5 ms and 10, respectively [TRAN2014]. Other
LBE congestion control schemes may also be considered and are listed
in the IETF survey of LBE protocols [RFC6297].

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 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. In this scenario, a
flow traversing on a shorter RTT path may react faster to congestion
and recover faster from it compared to another flow on a longer RTT
path. The introduction of AQM schemes may potentially improve the
RTT fairness.

Introducing an AQM scheme may cause 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 are divided
into two categories. Category I whose RTT between sender A and
receiver B should be 100 ms. Category II, in which the RTT between
sender A and receiver B should be in the range [5 ms, 560 ms]
inclusive. The maximum value for the RTT represents the RTT of a
satellite link [RFC2488].

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); (see Section 2.5
for the estimation of the goodput); (2) the cumulative average
goodput of the flow from Category II, goodput_Cat_II (Section 2.5); (see Section 2.5
for the estimation of the goodput); (3) the ratio goodput_Cat_II/
goodput_Cat_I; and (4) the average packet drop rate for each category
(Section 2.3).

7. Burst Absorption

"AQM mechanisms might 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 Service Level Agreements (SLAs) and can 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
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, the tester must evaluate how the AQM performs with
a traffic mixed that mix. The traffic mix could be composed of (from sender A
to receiver B):

o Burst of packets at the beginning of a transmission, such as web
traffic with IW10;

o Applications that send large bursts of data, such as bursty video
frames;

o Background traffic, such as Constant Bit Rate (CBR) UDP traffic
and/or 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

A new web page download could start after the previous web page
download is finished. Each web page could be composed of at least 50
objects and the size of each object should be at least 1 KB. Six TCP
parallel connections should be generated to download the objects,
each parallel connection 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, and 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 or not 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.

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 a per-phase
basis as well.

Wherever the notion of time has been 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 which is determined when there is no AQM
scheme (i.e., a drop-tail queue was chosen instead
of an AQM scheme. queue). When the bottleneck is shared
among non-
application-limited 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:

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 timescale. The following phases may be considered: phase I --
mild congestion during 0-20 s; phase II -- medium congestion during
20-40 s; phase III -- heavy congestion during 40-60 s; 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 timescale. As an example, the following phases may be
considered: phase I -- 100 Mbps during 0-20 s; phase II -- 10 Mbps
during 20-40 s; phase I again, and so on.

o Experiment 2: The capacity varies between two values within a
short timescale. As an example, the following phases may be
considered: phase I -- 100 Mbps during 0-100 ms; phase II -- 10
Mbps during 100-200 ms; 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 a time-
interval value should be stated and elaborated.

The tester may additionally evaluate the two mentioned scenarios
(short-term and long-term capacity variations), during and/or
including the 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 needs to not
adversely interact with the dynamics of the transport protocols that
they control.

AQM proposals should provide background material showing control
theoretic theoretical
analysis of the AQM control law and the input parameter space within
which the control law operates as expected; operates, or could they should 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.

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 bidirectional 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 (either unidirectional or bidirectional)

Various traffic mixes can be considered. These guidelines recommend
examining at least the following example: 1 bidirectional VoIP; 6 web
page downloads (such as those 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. Bidirectional 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, as defined in Section 3.1. The amount of asymmetry between
the uplink and the downlink depends on the context. These guidelines
recommend considering 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.

10. Example of a 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 A | |AQM M | |No AQM |
+---------+ +--+------+ +--+-----+
+---------+ | | +-----------+
|senders B|-------------+ +---|receivers B|
+---------+ +-----------+

Figure 3: Topology for the Multi-AQM Scenario

Figure 3 describes topology options for evaluating multi-AQM
scenarios. The AQM schemes are applied in sequence and impact the
induced latency reduction, the induced goodput maximization, and the
trade-off between these two. Note that AQM schemes A and B
introduced in Routers L and M could be (I) same scheme with identical
parameter values, (ii) same scheme with different parameter values,
or (iii) two different schemes. To best understand the interactions
and implications, the mild congestion scenario as described in
Section 8.2.2 is recommended such that the number of flows is equally
shared among senders A and B. Other relevant combinations of
congestion levels could also be considered. We recommend measuring
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 may 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.

AQM proposals should provide pseudocode 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, and 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 discussions 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 queuing-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 AQM logic on the device. If the device provides
indirect means to estimate the queuing delay (for example, timestamps
and 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 behavior of an AQM. Exposing all these parameters as
control parameters to a network operator (or user) can easily result
in an 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.

"AQM algorithms SHOULD NOT require tuning of initial or configuration
parameters in common use cases." such as stated in Section 4 of the
AQM recommendation document [RFC7567]. A scheme ought to 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. Summary

Figure 4 lists the scenarios for an extended characterization of an
AQM scheme. This table comes along with a set of requirements to
present more clearly the weight and importance of each scenario. The
requirements listed here are informational and their relevance may
depend on the deployment scenario.

+------------------------------------------------------------------+
|Scenario |Sec. |Informational requirement |
+------------------------------------------------------------------+
+------------------------------------------------------------------+
|Interaction with ECN | 4.5 |must be discussed if supported |
+------------------------------------------------------------------+
|Interaction with Scheduling| 4.6 |should 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 |
| Bidirectional traffic | 9.2 |scenario may be considered |
+------------------------------------------------------------------+
|Multi-AQM | 10.2 |scenario may be considered |
+------------------------------------------------------------------+

Figure 4: Summary of the Scenarios and their Requirements

14. Security Considerations

Some security considerations for AQM are identified in [RFC7567].
This document, by itself, presents no new privacy or security issues.

15. References

15.1. Normative References

[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", RFC 2119, 1997.

[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>.

[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>.

[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>.

[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>.

15.2. 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,
DOI 10.1016/j.bjp.2013.11.008, 2014.

[AQMPIE] Pan, R., Natarajan, P., Baker, F., and G. White, "PIE: A
Lightweight Control Scheme To Address the Bufferbloat
Problem", Work in Progress, draft-ietf-aqm-pie-08, June
2016.

[BB2011] Cerf, V., Jacobson, V., Weaver, N., and J. Gettys,
"BufferBloat: what's wrong with the internet?", ACM
Queue Vol. 55, DOI 10.1145/2076450.2076464, 2012.

[BCP41] Floyd, S., "Congestion Control Principles", BCP 41,
RFC 2914, September 2000.

Briscoe, B. and J. Manner, "Byte and Packet Congestion
Notification", BCP 41, RFC 7141, February 2014.

<http://www.rfc-editor.org/info/bcp41>
[CODEL] Nichols, K., Jacobson, V., McGregor, A., and J. Iyengar,
"Controlled Delay Active Queue Management", Work in
Progress, draft-ietf-aqm-codel-04, June 2016.

[CUBIC] Rhee, I., Xu, L., Ha, S., Zimmermann, A., Eggert, L., and
R. Scheffenegger, "CUBIC for Fast Long-Distance Networks",
Work in Progress, draft-ietf-tcpm-cubic-01, January 2016.

[FENG2002]
Feng, W., Shin, K., Kandlur, D., and D. Saha, "The BLUE
active queue management algorithms", IEEE Transactions on
Networking Vol.10 Issue 4, DOI 10.1109/TNET.2002.801399,
2002, <http://ieeexplore.ieee.org/xpl/
articleDetails.jsp?arnumber=1026008>.

[FLOY1993]
Floyd, S. and V. Jacobson, "Random Early Detection (RED)
Gateways for Congestion Avoidance", IEEE Transactions on
Networking Vol. 1 Issue 4, DOI 10.1109/90.251892, 1993,
<http://ieeexplore.ieee.org/xpl/
articleDetails.jsp?arnumber=251892>.

[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, pp.115-128 Vol. 60,
DOI 10.1109/INFCOMW.2013.6562885, 2014.

[HASS2008]
Hassayoun, S. and D. Ros, "Loss Synchronization and Router
Buffer Sizing with High-Speed Versions of TCP",
IEEE INFOCOM Workshops, DOI 10.1109/INFOCOM.2008.4544632,
2008, <http://ieeexplore.ieee.org/xpl/
articleDetails.jsp?arnumber=4544632>.

[HOEI2015]
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", Work in Progress draft-ietf-aqm-fq-
codel-06, Progress, draft-ietf-aqm-
fq-codel-06, March 2016.

[HOLLO2001]
Hollot, C., Misra, V., Towsley, V., and W. Gong, "On
Designing Improved Controller for AQM Routers Supporting
TCP Flows", IEEE INFOCOM, DOI 10.1109/INFCOM.2001.916670,
2001, <http://ieeexplore.ieee.org/xpl/
articleDetails.jsp?arnumber=916670>.

[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, DOI 10.1109/INFCOM.2000.832487, 2000,
<http://ieeexplore.ieee.org/xpl/
articleDetails.jsp?arnumber=832487>.

[RFC0793] Postel, J., "Transmission Control Protocol", STD 7,
RFC 793, DOI 10.17487/RFC0793, September 1981,
<http://www.rfc-editor.org/info/rfc793>.

[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>.

[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>.

[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>.

[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", BCP 41, RFC 7141, DOI 10.17487/RFC7141,
February 2014, <http://www.rfc-editor.org/info/rfc7141>.

[TCPEVAL] Hayes, D., Ros, D., Andrew, L., and S. Floyd, "Common TCP
Evaluation Suite", Work in Progress, draft-irtf-iccrg-
tcpeval-01, July 2014.

[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,
DOI 10.1109/ICC.2014.6883487, 2014,
<http://ieeexplore.ieee.org/xpl/
articleDetails.jsp?arnumber=6883487>.

[WELZ2015]
Welzl, M. and G. Fairhurst, "The Benefits to Applications
of using Explicit Congestion Notification (ECN)", Work in
Progress, draft-welzl-ecn-benefits-02, March 2015.

[WINS2014]
Winstein, K., "Transport Architectures for an Evolving
Internet", PhD thesis, Massachusetts Institute of
Technology, June 2014.

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).

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. Morton,
R. Pan, G. Skinner, D. Taht, and M. Welzl for detailed and wise
feedback on this document.

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 of America

Email: prenatar@cisco.com

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