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) [RFC793] 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, 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 (see Section 2.5
for the estimation of the goodput); (2) the cumulative average
goodput of the flow from Category II, goodput_Cat_II (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 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 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, which is determined when there is no AQM
scheme (i.e., a drop-tail queue). 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:
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 theoretical
analysis of the AQM control law and the input parameter space within
which the control law operates, or 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 ScenarioFigure 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 Requirements14. 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>.
[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>.
[RFC7679] Almes, G., Kalidindi, S., Zekauskas, M., and A. Morton,
Ed., "A One-Way Delay Metric for IP Performance Metrics
(IPPM)", STD 81, RFC 7679, DOI 10.17487/RFC7679, January
2016, <http://www.rfc-editor.org/info/rfc7679>.
[RFC7680] Almes, G., Kalidindi, S., Zekauskas, M., and A. Morton,
Ed., "A One-Way Loss Metric for IP Performance Metrics
(IPPM)", STD 82, RFC 7680, DOI 10.17487/RFC7680, January
2016, <http://www.rfc-editor.org/info/rfc7680>.
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
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S., Estrin, D., Floyd, S., Jacobson, V., Minshall, G.,
Partridge, C., Peterson, L., Ramakrishnan, K., Shenker,
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Over Satellite Channels using Standard Mechanisms",
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of Explicit Congestion Notification (ECN) to IP",
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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
