draft-ietf-rmcat-sbd-08.txt   draft-ietf-rmcat-sbd-09.txt 
RTP Media Congestion Avoidance Techniques D. Hayes, Ed. RTP Media Congestion Avoidance Techniques D. Hayes, Ed.
Internet-Draft S. Ferlin Internet-Draft Simula Research Laboratory
Intended status: Experimental Simula Research Laboratory Intended status: Experimental S. Ferlin
Expires: January 4, 2018 M. Welzl Expires: May 30, 2018
M. Welzl
K. Hiorth K. Hiorth
University of Oslo University of Oslo
July 3, 2017 November 26, 2017
Shared Bottleneck Detection for Coupled Congestion Control for RTP Shared Bottleneck Detection for Coupled Congestion Control for RTP
Media. Media.
draft-ietf-rmcat-sbd-08 draft-ietf-rmcat-sbd-09
Abstract Abstract
This document describes a mechanism to detect whether end-to-end data This document describes a mechanism to detect whether end-to-end data
flows share a common bottleneck. It relies on summary statistics flows share a common bottleneck. It relies on summary statistics
that are calculated based on continuous measurements and used as that are calculated based on continuous measurements and used as
input to a grouping algorithm that runs wherever the knowledge is input to a grouping algorithm that runs wherever the knowledge is
needed. This mechanism complements the coupled congestion control needed. This mechanism complements the coupled congestion control
mechanism in draft-ietf-rmcat-coupled-cc. mechanism in draft-ietf-rmcat-coupled-cc.
Status of This Memo Status of This Memo
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provisions of BCP 78 and BCP 79. provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on January 4, 2018. This Internet-Draft will expire on May 30, 2018.
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Table of Contents Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. The basic mechanism . . . . . . . . . . . . . . . . . . . 3 1.1. The Basic Mechanism . . . . . . . . . . . . . . . . . . . 3
1.2. The signals . . . . . . . . . . . . . . . . . . . . . . . 3 1.2. The Signals . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1. Packet loss . . . . . . . . . . . . . . . . . . . . . 3 1.2.1. Packet Loss . . . . . . . . . . . . . . . . . . . . . 3
1.2.2. Packet delay . . . . . . . . . . . . . . . . . . . . 4 1.2.2. Packet Delay . . . . . . . . . . . . . . . . . . . . 4
1.2.3. Path lag . . . . . . . . . . . . . . . . . . . . . . 4 1.2.3. Path Lag . . . . . . . . . . . . . . . . . . . . . . 4
2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4 2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1. Parameters and their effect . . . . . . . . . . . . . . . 6 2.1. Parameters and Their Effect . . . . . . . . . . . . . . . 6
2.2. Recommended parameter values . . . . . . . . . . . . . . 7 2.2. Recommended Parameter Values . . . . . . . . . . . . . . 7
3. Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3. Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.1. SBD feedback requirements . . . . . . . . . . . . . . . . 8 3.1. SBD Feedback Requirements . . . . . . . . . . . . . . . . 8
3.1.1. Feedback when all the logic is placed at the sender . 9 3.1.1. Feedback When All the Logic is Placed at the Sender . 9
3.1.2. Feedback when the statistics are calculated at the 3.1.2. Feedback When the Statistics are Calculated at the
receiver and SBD performed at the sender . . . . . . 9 Receiver and SBD Performed at the Sender . . . . . . 9
3.1.3. Feedback when bottlenecks can be determined at both 3.1.3. Feedback When Bottlenecks can be Determined at Both
senders and receivers . . . . . . . . . . . . . . . . 10 Senders and Receivers . . . . . . . . . . . . . . . . 10
3.2. Key metrics and their calculation . . . . . . . . . . . . 10 3.2. Key Metrics and Their Calculation . . . . . . . . . . . . 10
3.2.1. Mean delay . . . . . . . . . . . . . . . . . . . . . 10 3.2.1. Mean Delay . . . . . . . . . . . . . . . . . . . . . 10
3.2.2. Skewness estimate . . . . . . . . . . . . . . . . . . 11 3.2.2. Skewness Estimate . . . . . . . . . . . . . . . . . . 11
3.2.3. Variability estimate . . . . . . . . . . . . . . . . 12 3.2.3. Variability Estimate . . . . . . . . . . . . . . . . 12
3.2.4. Oscillation estimate . . . . . . . . . . . . . . . . 12 3.2.4. Oscillation Estimate . . . . . . . . . . . . . . . . 12
3.2.5. Packet loss . . . . . . . . . . . . . . . . . . . . . 13 3.2.5. Packet Loss . . . . . . . . . . . . . . . . . . . . . 13
3.3. Flow Grouping . . . . . . . . . . . . . . . . . . . . . . 13 3.3. Flow Grouping . . . . . . . . . . . . . . . . . . . . . . 13
3.3.1. Flow grouping algorithm . . . . . . . . . . . . . . . 13 3.3.1. Flow Grouping Algorithm . . . . . . . . . . . . . . . 13
3.3.2. Using the flow group signal . . . . . . . . . . . . . 16 3.3.2. Using the Flow Group Signal . . . . . . . . . . . . . 16
4. Enhancements to the basic SBD algorithm . . . . . . . . . . . 16 4. Enhancements to the Basic SBD Algorithm . . . . . . . . . . . 16
4.1. Reducing lag and improving responsiveness . . . . . . . . 16 4.1. Reducing Lag and Improving Responsiveness . . . . . . . . 16
4.1.1. Improving the response of the skewness estimate . . . 17 4.1.1. Improving the Response of the Skewness Estimate . . . 17
4.1.2. Improving the response of the variability estimate . 19 4.1.2. Improving the Response of the Variability Estimate . 19
4.2. Removing oscillation noise . . . . . . . . . . . . . . . 19 4.2. Removing Oscillation Noise . . . . . . . . . . . . . . . 19
5. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 20 5. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 20
5.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 20 5.1. Time-stamp Resolution . . . . . . . . . . . . . . . . . . 20
5.2. Clock skew . . . . . . . . . . . . . . . . . . . . . . . 20 5.2. Clock Skew . . . . . . . . . . . . . . . . . . . . . . . 20
6. Expected feedback from experiments . . . . . . . . . . . . . 20 6. Expected Feedback from Experiments . . . . . . . . . . . . . 20
7. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 21 7. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 21
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 21 8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 21
9. Security Considerations . . . . . . . . . . . . . . . . . . . 21 9. Security Considerations . . . . . . . . . . . . . . . . . . . 21
10. Change history . . . . . . . . . . . . . . . . . . . . . . . 21 10. Change history . . . . . . . . . . . . . . . . . . . . . . . 21
11. References . . . . . . . . . . . . . . . . . . . . . . . . . 22 11. References . . . . . . . . . . . . . . . . . . . . . . . . . 22
11.1. Normative References . . . . . . . . . . . . . . . . . . 22 11.1. Normative References . . . . . . . . . . . . . . . . . . 22
11.2. Informative References . . . . . . . . . . . . . . . . . 23 11.2. Informative References . . . . . . . . . . . . . . . . . 23
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 24 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 24
1. Introduction 1. Introduction
skipping to change at page 3, line 24 skipping to change at page 3, line 25
capacity. This competition has the potential to increase packet loss capacity. This competition has the potential to increase packet loss
and delays. This is especially relevant for interactive applications and delays. This is especially relevant for interactive applications
that communicate simultaneously with multiple peers (such as multi- that communicate simultaneously with multiple peers (such as multi-
party video). For RTP media applications such as RTCWEB, party video). For RTP media applications such as RTCWEB,
[I-D.ietf-rmcat-coupled-cc] describes a scheme that combines the [I-D.ietf-rmcat-coupled-cc] describes a scheme that combines the
congestion controllers of flows in order to honor their priorities congestion controllers of flows in order to honor their priorities
and avoid unnecessary packet loss as well as delay. This mechanism and avoid unnecessary packet loss as well as delay. This mechanism
relies on some form of Shared Bottleneck Detection (SBD); here, a relies on some form of Shared Bottleneck Detection (SBD); here, a
measurement-based SBD approach is described. measurement-based SBD approach is described.
1.1. The basic mechanism 1.1. The Basic Mechanism
The mechanism groups flows that have similar statistical The mechanism groups flows that have similar statistical
characteristics together. Section 3.3.1 describes a simple method characteristics together. Section 3.3.1 describes a simple method
for achieving this, however, a major part of this draft is concerned for achieving this, however, a major part of this draft is concerned
with collecting suitable statistics for this purpose. with collecting suitable statistics for this purpose.
1.2. The signals 1.2. The Signals
The current Internet is unable to explicitly inform endpoints as to The current Internet is unable to explicitly inform endpoints as to
which flows share bottlenecks, so endpoints need to infer this from which flows share bottlenecks, so endpoints need to infer this from
whatever information is available to them. The mechanism described whatever information is available to them. The mechanism described
here currently utilizes packet loss and packet delay, but is not here currently utilizes packet loss and packet delay, but is not
restricted to these. As ECN becomes more prevalent it too will restricted to these. As ECN becomes more prevalent it too will
become a valuable base signal. become a valuable base signal.
1.2.1. Packet loss 1.2.1. Packet Loss
Packet loss is often a relatively rare signal. Therefore, on its own Packet loss is often a relatively rare signal. Therefore, on its own
it is of limited use for SBD, however, it is a valuable supplementary it is of limited use for SBD, however, it is a valuable supplementary
measure when it is more prevalent. measure when it is more prevalent.
1.2.2. Packet delay 1.2.2. Packet Delay
End-to-end delay measurements include noise from every device along End-to-end delay measurements include noise from every device along
the path in addition to the delay perturbation at the bottleneck the path in addition to the delay perturbation at the bottleneck
device. The noise is often significantly increased if the round-trip device. The noise is often significantly increased if the round-trip
time is used. The cleanest signal is obtained by using One-Way-Delay time is used. The cleanest signal is obtained by using One-Way-Delay
(OWD). (OWD).
Measuring absolute OWD is difficult since it requires both the sender Measuring absolute OWD is difficult since it requires both the sender
and receiver clocks to be synchronized. However, since the and receiver clocks to be synchronized. However, since the
statistics being collected are relative to the mean OWD, a relative statistics being collected are relative to the mean OWD, a relative
skipping to change at page 4, line 28 skipping to change at page 4, line 28
for a discussion on clock skew and OWD measurements). However, in for a discussion on clock skew and OWD measurements). However, in
circumstances where it is significant, Section 5.2 outlines a way of circumstances where it is significant, Section 5.2 outlines a way of
adjusting the calculations to cater for it. adjusting the calculations to cater for it.
Each packet arriving at the bottleneck buffer may experience very Each packet arriving at the bottleneck buffer may experience very
different queue lengths, and therefore different waiting times. A different queue lengths, and therefore different waiting times. A
single OWD sample does not, therefore, characterize the path well. single OWD sample does not, therefore, characterize the path well.
However, multiple OWD measurements do reflect the distribution of However, multiple OWD measurements do reflect the distribution of
delays experienced at the bottleneck. delays experienced at the bottleneck.
1.2.3. Path lag 1.2.3. Path Lag
Flows that share a common bottleneck may traverse different paths, Flows that share a common bottleneck may traverse different paths,
and these paths will often have different base delays. This makes it and these paths will often have different base delays. This makes it
difficult to correlate changes in delay or loss. This technique uses difficult to correlate changes in delay or loss. This technique uses
the long term shape of the delay distribution as a base for the long term shape of the delay distribution as a base for
comparison to counter this. comparison to counter this.
2. Definitions 2. Definitions
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
skipping to change at page 5, line 7 skipping to change at page 5, line 7
OWD -- One Way Delay OWD -- One Way Delay
MAD -- Mean Absolute Deviation MAD -- Mean Absolute Deviation
RTT -- Round Trip Time RTT -- Round Trip Time
SBD -- Shared Bottleneck Detection SBD -- Shared Bottleneck Detection
Conventions used in this document: Conventions used in this document:
T -- the base time interval over which measurements are T -- the base time interval over which measurements are
made. made
N -- the number of base time, T, intervals used in some
calculations.
M -- the number of base time, T, intervals used in some N -- the number of base time, T, intervals used in some
calculations. calculations
sum_T(...) -- summation of all the measurements of the variable M -- the number of base time, T, intervals used in some
in parentheses taken over the interval T calculations, where M <= N
sum(...) -- summation of terms of the variable in parentheses sum(...) -- summation of terms of the variable in parentheses
sum_N(...) -- summation of N terms of the variable in parentheses sum_T(...) -- summation of all the measurements of the variable
in parentheses taken over the interval T
sum_NT(...) -- summation of all measurements taken over the sum_NT(...) -- summation of all measurements taken over the
interval N*T interval N*T
sum_MT(...) -- summation of all measurements taken over the sum_MT(...) -- summation of all measurements taken over the
interval M*T interval M*T
E_T(...) -- the expectation or mean of the measurements of the E_T(...) -- the expectation or mean of the measurements of the
variable in parentheses over T variable in parentheses over T
E_N(...) -- the expectation or mean of the last N values of the E_N(...) -- the expectation or mean of the last N values of
variable in parentheses the variable in parentheses
E_M(...) -- the expectation or mean of the last M values of the E_M(...) -- the expectation or mean of the last M values of
variable in parentheses, where M <= N. the variable in parentheses
num_T(...) -- the count of measurements of the variable in num_T(...) -- the count of measurements of the variable in
parentheses taken in the interval T parentheses taken in the interval T
num_VM(...) -- the count of valid values of the variable in num_MT(...) -- the count of measurements of the variable in
parentheses given M records parentheses taken in the interval NT
PB -- a boolean variable indicating the particular flow PB -- a boolean variable indicating the particular flow
was identified transiting a bottleneck in the was identified transiting a bottleneck in the
previous interval T (i.e. Previously Bottleneck) previous interval T (i.e. Previously Bottleneck)
skew_est -- a measure of skewness in a OWD distribution. skew_est -- a measure of skewness in a OWD distribution
skew_base_T -- a variable used as an intermediate step in skew_base_T -- a variable used as an intermediate step in
calculating skew_est. calculating skew_est
var_est -- a measure of variability in OWD measurements. var_est -- a measure of variability in OWD measurements
var_base_T -- a variable used as an intermediate step in
calculating var_est
var_base_T -- a variable used as an intermediate step in freq_est -- a measure of low frequency oscillation in the OWD
calculating var_est. measurements
freq_est -- a measure of low frequency oscillation in the OWD pkt_loss -- a measure of the proportion of packets lost
measurements.
p_l, p_f, p_mad, c_s, c_h, p_s, p_d, p_v -- various thresholds p_l, p_f, p_mad, c_s, c_h, p_s, p_d, p_v -- various thresholds
used in the mechanism used in the mechanism
M and F -- number of values related to N M and F -- number of values related to N
2.1. Parameters and their effect 2.1. Parameters and Their Effect
T T should be long enough so that there are enough packets T T should be long enough so that there are enough packets
received during T for a useful estimate of short term mean received during T for a useful estimate of short term mean
OWD and variation statistics. Making T too large can limit OWD and variation statistics. Making T too large can limit
the efficacy of freq_est. It will also increase the response the efficacy of freq_est. It will also increase the response
time of the mechanism. Making T too small will make the time of the mechanism. Making T too small will make the
metrics noisier. metrics noisier.
N & M N should be large enough to provide a stable estimate of N & M N should be large enough to provide a stable estimate of
oscillations in OWD. Usually M=N, though having M<N may be oscillations in OWD. Usually M=N, though having M<N may be
beneficial in certain circumstances. M*T needs to be long beneficial in certain circumstances. M*T needs to be long
enough to provide stable estimates of skewness and MAD. enough to provide stable estimates of skewness and MAD.
F F determines the number of intervals over which statistics F F determines the number of intervals over which statistics
are considered to be equally weighted. When F=M recent and are considered to be equally weighted. When F=M recent and
older measurements are considered equal. Making F<M can older measurements are considered equal. Making F<M can
increase the responsiveness of the SBD mechanism. If F is increase the responsiveness of the SBD mechanism. If F is
too small, statistics will be too noisy. too small, statistics will be too noisy.
c_s c_s is the threshold in skew_est used for determining whether c_s c_s is the threshold in skew_est used for determining whether
a flow is transiting a bottleneck or not. It should be a flow is transiting a bottleneck or not. Lower values of
slightly negative so that a very lightly loaded path does not c_s require bottlenecks to be more congested to be considered
give a false indication. Setting c_s more negative makes the for grouping by the mechanism. c_s should be set within the
SBD mechanism less sensitive to transient and slight range of +0.2 to -0.1; low enough so that lightly loaded
bottlenecks. paths do not give a false indication.
p_l p_l is the threshold in pkt_loss used for determining whether
a flow is transiting a bottleneck or not. When pkt_loss is
high it becomes a better indicator of congestion than
skew_est.
c_h c_h adds hysteresis to the bottleneck determination. It c_h c_h adds hysteresis to the bottleneck determination. It
should be large enough to avoid constant switching in the should be large enough to avoid constant switching in the
determination, but low enough to ensure that grouping is not determination, but low enough to ensure that grouping is not
attempted when there is no bottleneck and the delay and loss attempted when there is no bottleneck and the delay and loss
signals cannot be relied upon. signals cannot be relied upon.
p_v p_v determines the sensitivity of freq_est to noise. Making p_v p_v determines the sensitivity of freq_est to noise. Making
it smaller will yield higher but noisier values for freq_est. it smaller will yield higher but noisier values for freq_est.
Making it too large will render it ineffective for Making it too large will render it ineffective for
determining groups. determining groups.
p_* Flows are separated when the skew_est|var_est|freq_est p_* Flows are separated when the
measure is greater than p_s|p_f|p_d|p_mad. Adjusting these skew_est|var_est|freq_est|pkt_loss measure is greater than
is a compromise between false grouping of flows that do not p_s|p_mad|p_f|p_d. Adjusting these is a compromise between
share a bottleneck and false splitting of flows that do. false grouping of flows that do not share a bottleneck and
Making them larger can help if the measures are very noisy, false splitting of flows that do. Making them larger can
but reducing the noise in the statistical measures by help if the measures are very noisy, but reducing the noise
adjusting T and N|M may be a better solution. in the statistical measures by adjusting T and N|M may be a
better solution.
2.2. Recommended parameter values 2.2. Recommended Parameter Values
Reference [Hayes-LCN14] uses T=350ms, N=50, p_l=0.1. The other Reference [Hayes-LCN14] uses T=350ms, N=50, p_l=0.1. The other
parameters have been tightened to reflect minor enhancements to the parameters have been tightened to reflect minor enhancements to the
algorithm outlined in Section 4: c_s=-0.01, p_f=p_d=0.1, p_s=0.15, algorithm outlined in Section 4: c_s=0.1, p_f=p_d=0.1, p_s=0.15,
p_mad=0.1, p_v=0.7. M=30, F=20, and c_h = 0.3 are additional p_mad=0.1, p_v=0.7. M=30, F=20, and c_h = 0.3 are additional
parameters defined in the document. These are values that seem to parameters defined in the document. These are values that seem to
work well over a wide range of practical Internet conditions. work well over a wide range of practical Internet conditions.
3. Mechanism 3. Mechanism
The mechanism described in this document is based on the observation The mechanism described in this document is based on the observation
that the distribution of delay measurements of packets that traverse that the distribution of delay measurements of packets that traverse
a common bottleneck have similar shape characteristics. These shape a common bottleneck have similar shape characteristics. These shape
characteristics are described using 3 key summary statistics: characteristics are described using 3 key summary statistics:
skipping to change at page 8, line 35 skipping to change at page 8, line 37
raw data, or the calculated summary statistics, periodically to raw data, or the calculated summary statistics, periodically to
H1 every T. H1, having this knowledge, can determine the shared H1 every T. H1, having this knowledge, can determine the shared
bottleneck and accordingly control the send rates. bottleneck and accordingly control the send rates.
2. Receiver-side: consider that H2 is also sending media to H3, and 2. Receiver-side: consider that H2 is also sending media to H3, and
L3 is a shared bottleneck. If H3 sends summary statistics to H1 L3 is a shared bottleneck. If H3 sends summary statistics to H1
and H2, neither H1 nor H2 alone obtain enough knowledge to detect and H2, neither H1 nor H2 alone obtain enough knowledge to detect
this shared bottleneck; H3 can however determine it by combining this shared bottleneck; H3 can however determine it by combining
the summary statistics related to H1 and H2, respectively. the summary statistics related to H1 and H2, respectively.
3.1. SBD feedback requirements 3.1. SBD Feedback Requirements
There are three possible scenarios each with different feedback There are three possible scenarios each with different feedback
requirements: requirements:
1. Both summary statistic calculations and SBD are performed at 1. Both summary statistic calculations and SBD are performed at
senders only. When sender-based congestion control is senders only. When sender-based congestion control is
implemented, this method is RECOMMENDED. implemented, this method is RECOMMENDED.
2. Summary statistics calculated on the receivers and SBD at the 2. Summary statistics calculated on the receivers and SBD at the
senders. senders.
3. Summary statistic calculations on receivers, and SBD performed at 3. Summary statistic calculations on receivers, and SBD performed at
both senders and receivers (beyond the current scope, but allows both senders and receivers (beyond the current scope, but allows
cooperative detection of bottlenecks). cooperative detection of bottlenecks).
All three possibilities are discussed for completeness in this All three possibilities are discussed for completeness in this
document, however, it is expected that feedback will take the form document, however, it is expected that feedback will take the form of
scenario 1 and operate in conjunction with sender-based congestion scenario 1 and operate in conjunction with sender-based congestion
control mechanisms. control mechanisms.
3.1.1. Feedback when all the logic is placed at the sender 3.1.1. Feedback When All the Logic is Placed at the Sender
Having the sender calculate the summary statistics and determine the Having the sender calculate the summary statistics and determine the
shared bottlenecks based on them has the advantage of placing most of shared bottlenecks based on them has the advantage of placing most of
the functionality in one place -- the sender. the functionality in one place -- the sender.
For every packet, the sender requires accurate relative OWD For every packet, the sender requires accurate relative OWD
measurements of adequate precision, along with an indication of lost measurements of adequate precision, along with an indication of lost
packets (or the proportion of packets lost over an interval). These packets (or the proportion of packets lost over an interval). These
can be provided by [I-D.dt-rmcat-feedback-message]. can be provided by [I-D.ietf-avtcore-cc-feedback-message].
Sums, var_base_T and skew_base_T are calculated incrementally as Sums, var_base_T and skew_base_T are calculated incrementally as
relative OWD measurements are determined from the feedback messages. relative OWD measurements are determined from the feedback messages.
When the mechanism has received sufficient measurements to cover the When the mechanism has received sufficient measurements to cover the
T base time interval for all flows, the summary statistics (see T base time interval for all flows, the summary statistics (see
Section 3.2) are calculated for that T interval and flows are grouped Section 3.2) are calculated for that T interval and flows are grouped
(see Section 3.3.1). The exact timing of these calculations will (see Section 3.3.1). The exact timing of these calculations will
depend on the frequency of the feedback message. depend on the frequency of the feedback message.
3.1.2. Feedback when the statistics are calculated at the receiver and 3.1.2. Feedback When the Statistics are Calculated at the Receiver and
SBD performed at the sender SBD Performed at the Sender
This scenario minimizes feedback, but requires receivers to send This scenario minimizes feedback, but requires receivers to send
selected summary statistics at an agreed regular interval. We selected summary statistics at an agreed regular interval. We
envisage the following exchange of information to initialize the envisage the following exchange of information to initialize the
system: system:
o An initialization message from the sender to the receiver will o An initialization message from the sender to the receiver will
contain the following information: contain the following information:
* A protocol identifier (SBD=01). This is to future proof the * A protocol identifier (SBD=01). This is to future proof the
skipping to change at page 10, line 18 skipping to change at page 10, line 18
o A response message from the receiver acknowledges this message o A response message from the receiver acknowledges this message
with a list of key metrics it supports (subset of the senders with a list of key metrics it supports (subset of the senders
list) and is able to relay back to the sender. list) and is able to relay back to the sender.
This initialization exchange may be repeated to finalize the agreed This initialization exchange may be repeated to finalize the agreed
metrics should not all be supported by all receivers. metrics should not all be supported by all receivers.
After initialization the agreed summary statistics are fed back to After initialization the agreed summary statistics are fed back to
the sender (nominally every T). the sender (nominally every T).
3.1.3. Feedback when bottlenecks can be determined at both senders and 3.1.3. Feedback When Bottlenecks can be Determined at Both Senders and
receivers Receivers
This type of mechanism is currently beyond the scope of SBD in RMCAT. This type of mechanism is currently beyond the scope of SBD in RMCAT.
It is mentioned here to ensure more advanced sender/receiver It is mentioned here to ensure more advanced sender/receiver
cooperative shared bottleneck determination mechanisms remain cooperative shared bottleneck determination mechanisms remain
possible in the future. possible in the future.
It is envisaged that such a mechanism would be initialized in a It is envisaged that such a mechanism would be initialized in a
similar manner to that described in Section 3.1.2. similar manner to that described in Section 3.1.2.
After initialization both summary statistics and shared bottleneck After initialization both summary statistics and shared bottleneck
determinations should be exchanged, nominally every T. determinations should be exchanged, nominally every T.
3.2. Key metrics and their calculation 3.2. Key Metrics and Their Calculation
Measurements are calculated over a base interval, T and summarized Measurements are calculated over a base interval, T and summarized
over N or M such intervals. All summary statistics can be calculated over N or M such intervals. All summary statistics can be calculated
incrementally. incrementally.
3.2.1. Mean delay 3.2.1. Mean Delay
The mean delay is not a useful signal for comparisons between flows The mean delay is not a useful signal for comparisons between flows
since flows may traverse quite different paths and clocks will not since flows may traverse quite different paths and clocks will not
necessarily be synchronized. However, it is a base measure for the 3 necessarily be synchronized. However, it is a base measure for the 3
summary statistics. The mean delay, E_T(OWD), is the average one way summary statistics. The mean delay, E_T(OWD), is the average one way
delay measured over T. delay measured over T.
To facilitate the other calculations, the last N E_T(OWD) values will To facilitate the other calculations, the last N E_T(OWD) values will
need to be stored in a cyclic buffer along with the moving average of need to be stored in a cyclic buffer along with the moving average of
E_T(OWD): E_T(OWD):
mean_delay = E_M(E_T(OWD)) = sum_M(E_T(OWD)) / M mean_delay = E_M(E_T(OWD)) = sum_M(E_T(OWD)) / M
where M <= N. Setting M to be less than N allows the mechanism to be where M <= N. Setting M to be less than N allows the mechanism to be
more responsive to changes, but potentially at the expense of a more responsive to changes, but potentially at the expense of a
higher error rate (see Section 4.1 for a discussion on improving the higher error rate (see Section 4.1 for a discussion on improving the
responsiveness of the mechanism.) responsiveness of the mechanism.)
3.2.2. Skewness estimate 3.2.2. Skewness Estimate
Skewness is difficult to calculate efficiently and accurately. Skewness is difficult to calculate efficiently and accurately.
Ideally it should be calculated over the entire period (M * T) from Ideally it should be calculated over the entire period (M * T) from
the mean OWD over that period. However this would require storing the mean OWD over that period. However this would require storing
every delay measurement over the period. Instead, an estimate is every delay measurement over the period. Instead, an estimate is
made over M * T based on a calculation every T using the previous T's made over M * T based on a calculation every T using the previous T's
calculation of mean_delay. calculation of mean_delay.
The base for the skewness calculation is estimated using a counter The base for the skewness calculation is estimated using a counter
initialized every T. It increments for one way delay samples (OWD) initialized every T. It increments for one way delay samples (OWD)
skipping to change at page 12, line 5 skipping to change at page 12, line 5
enable it to be calculated iteratively. enable it to be calculated iteratively.
skew_est = sum_MT(skew_base_T)/num_MT(OWD) skew_est = sum_MT(skew_base_T)/num_MT(OWD)
where skew_est is a number between -1 and 1 where skew_est is a number between -1 and 1
Note: Care must be taken when implementing the comparisons to ensure Note: Care must be taken when implementing the comparisons to ensure
that rounding does not bias skew_est. It is important that the mean that rounding does not bias skew_est. It is important that the mean
is calculated with a higher precision than the samples. is calculated with a higher precision than the samples.
3.2.3. Variability estimate 3.2.3. Variability Estimate
Mean Absolute Deviation (MAD) delay is a robust variability measure Mean Absolute Deviation (MAD) delay is a robust variability measure
that copes well with different send rates. It can be implemented in that copes well with different send rates. It can be implemented in
an online manner as follows: an online manner as follows:
var_base_T = sum_T(|OWD - E_T(OWD)|) var_base_T = sum_T(|OWD - E_T(OWD)|)
where where
|x| is the absolute value of x |x| is the absolute value of x
E_T(OWD) is the mean OWD calculated in the previous T E_T(OWD) is the mean OWD calculated in the previous T
var_est = MAD_MT = sum_MT(var_base_T)/num_MT(OWD) var_est = MAD_MT = sum_MT(var_base_T)/num_MT(OWD)
For calculation of freq_est p_v=0.7 3.2.4. Oscillation Estimate
For the grouping threshold p_mad=0.1
3.2.4. Oscillation estimate
An estimate of the low frequency oscillation of the delay signal is An estimate of the low frequency oscillation of the delay signal is
calculated by counting and normalizing the significant mean, calculated by counting and normalizing the significant mean,
E_T(OWD), crossings of mean_delay: E_T(OWD), crossings of mean_delay:
freq_est = number_of_crossings / N freq_est = number_of_crossings / N
where we define a significant mean crossing as a crossing that where we define a significant mean crossing as a crossing that
extends p_v * var_est from mean_delay. In our experiments we extends p_v * var_est from mean_delay. In our experiments we
have found that p_v = 0.7 is a good value. have found that p_v = 0.7 is a good value.
skipping to change at page 13, line 11 skipping to change at page 13, line 5
The counter, number_of_crossings, is incremented when there is a The counter, number_of_crossings, is incremented when there is a
significant mean crossing and decremented when a non-zero value is significant mean crossing and decremented when a non-zero value is
removed from the last_N_crossings. removed from the last_N_crossings.
This approximation of freq_est was not used in [Hayes-LCN14], which This approximation of freq_est was not used in [Hayes-LCN14], which
calculated freq_est every T using the current E_N(E_T(OWD)). Our calculated freq_est every T using the current E_N(E_T(OWD)). Our
tests show that this approximation of freq_est yields results that tests show that this approximation of freq_est yields results that
are almost identical to when the full calculation is performed every are almost identical to when the full calculation is performed every
T. T.
3.2.5. Packet loss 3.2.5. Packet Loss
The proportion of packets lost over the period NT is used as a The proportion of packets lost over the period NT is used as a
supplementary measure: supplementary measure:
pkt_loss = sum_NT(lost packets) / sum_NT(total packets) pkt_loss = sum_NT(lost packets) / sum_NT(total packets)
Note: When pkt_loss is small it is very variable, however, when Note: When pkt_loss is small it is very variable, however, when
pkt_loss is high it becomes a stable measure for making grouping pkt_loss is high it becomes a stable measure for making grouping
decisions. decisions.
3.3. Flow Grouping 3.3. Flow Grouping
3.3.1. Flow grouping algorithm 3.3.1. Flow Grouping Algorithm
The following grouping algorithm is RECOMMENDED for SBD in the RMCAT The following grouping algorithm is RECOMMENDED for SBD in the RMCAT
context and is sufficient and efficient for small to moderate numbers context and is sufficient and efficient for small to moderate numbers
of flows. For very large numbers of flows (e.g. hundreds), a more of flows. For very large numbers of flows (e.g. hundreds), a more
complex clustering algorithm may be substituted. complex clustering algorithm may be substituted.
Since no single metric is precise enough to group flows (due to Since no single metric is precise enough to group flows (due to
noise), the algorithm uses multiple metrics. Each metric offers a noise), the algorithm uses multiple metrics. Each metric offers a
different "view" of the bottleneck link characteristics, and used different "view" of the bottleneck link characteristics, and used
together they enable a more precise grouping of flows than would together they enable a more precise grouping of flows than would
skipping to change at page 14, line 13 skipping to change at page 14, line 13
of packet loss as a supplementary measure, is used to do this: of packet loss as a supplementary measure, is used to do this:
1. Grouping will be performed on flows that are inferred to be 1. Grouping will be performed on flows that are inferred to be
traversing a bottleneck by: traversing a bottleneck by:
skew_est < c_s skew_est < c_s
|| ( skew_est < c_h & PB ) || pkt_loss > p_l || ( skew_est < c_h & PB ) || pkt_loss > p_l
The parameter c_s controls how sensitive the mechanism is in The parameter c_s controls how sensitive the mechanism is in
detecting a bottleneck. C_s = 0.0 was used in [Hayes-LCN14]. A detecting a bottleneck. c_s = 0.0 was used in [Hayes-LCN14]. A value
value of c_s = 0.05 is a little more sensitive, and c_s = -0.05 is a of c_s = 0.1 is a little more sensitive, and c_s = -0.1 is a little
little less sensitive. C_h controls the hysteresis on flows that less sensitive. c_h controls the hysteresis on flows that were
were grouped as transiting a bottleneck last time. If the test grouped as transiting a bottleneck last time. If the test result is
result is TRUE, PB=TRUE, otherwise PB=FALSE. TRUE, PB=TRUE, otherwise PB=FALSE.
These flows, flows transiting a bottleneck, are then progressively These flows, flows transiting a bottleneck, are then progressively
divided into groups based on the freq_est, var_est, and skew_est divided into groups based on the freq_est, var_est, and skew_est
summary statistics. The process proceeds according to the following summary statistics. The process proceeds according to the following
steps: steps:
2. Group flows whose difference in sorted freq_est is less than a 2. Group flows whose difference in sorted freq_est is less than a
threshold: threshold:
diff(freq_est) < p_f diff(freq_est) < p_f
skipping to change at page 16, line 5 skipping to change at page 16, line 5
/ \ | / \ |
.-----v--. .-v------. .----v---. .-----v--. .-v------. .----v---.
5. Divide by | g_1aiA | ... | g_1aiZ | ... | g_nzxZ | 5. Divide by | g_1aiA | ... | g_1aiZ | ... | g_nzxZ |
pkt_loss '--------' '--------' '--------' pkt_loss '--------' '--------' '--------'
(when applicable) (when applicable)
Simple grouping algorithm. Simple grouping algorithm.
Figure 2 Figure 2
3.3.2. Using the flow group signal 3.3.2. Using the Flow Group Signal
Grouping decisions can be made every T from the second T, however Grouping decisions can be made every T from the second T, however
they will not attain their full design accuracy until after the they will not attain their full design accuracy until after the
2*N'th T interval. We recommend that grouping decisions are not made 2*N'th T interval. We recommend that grouping decisions are not made
until 2*M T intervals. until 2*M T intervals.
Network conditions, and even the congestion controllers, can cause Network conditions, and even the congestion controllers, can cause
bottlenecks to fluctuate. A coupled congestion controller MAY decide bottlenecks to fluctuate. A coupled congestion controller MAY decide
only to couple groups that remain stable, say grouped together 90% of only to couple groups that remain stable, say grouped together 90% of
the time, depending on its objectives. Recommendations concerning the time, depending on its objectives. Recommendations concerning
this are beyond the scope of this document and will be specific to this are beyond the scope of this document and will be specific to
the coupled congestion controllers objectives. the coupled congestion controller's objectives.
4. Enhancements to the basic SBD algorithm 4. Enhancements to the Basic SBD Algorithm
The SBD algorithm as specified in Section 3 was found to work well The SBD algorithm as specified in Section 3 was found to work well
for a broad variety of conditions. The following enhancements to the for a broad variety of conditions. The following enhancements to the
basic mechanisms have been found to significantly improve the basic mechanisms have been found to significantly improve the
algorithm's performance under some circumstances and SHOULD be algorithm's performance under some circumstances and SHOULD be
implemented. These "tweaks" are described separately to keep the implemented. These "tweaks" are described separately to keep the
main description succinct. main description succinct.
4.1. Reducing lag and improving responsiveness 4.1. Reducing Lag and Improving Responsiveness
This section describes how to improve the responsiveness of the basic This section describes how to improve the responsiveness of the basic
algorithm. algorithm.
Measurement based shared bottleneck detection makes decisions in the Measurement based shared bottleneck detection makes decisions in the
present based on what has been measured in the past. This means that present based on what has been measured in the past. This means that
there is always a lag in responding to changing conditions. This there is always a lag in responding to changing conditions. This
mechanism is based on summary statistics taken over (N*T) seconds. mechanism is based on summary statistics taken over (N*T) seconds.
This mechanism can be made more responsive to changing conditions by: This mechanism can be made more responsive to changing conditions by:
skipping to change at page 17, line 9 skipping to change at page 17, line 9
exponentially weighted moving average weights drop off too quickly exponentially weighted moving average weights drop off too quickly
for our requirements and have an infinite tail. A simple linearly for our requirements and have an infinite tail. A simple linearly
declining weighted moving average also does not provide enough weight declining weighted moving average also does not provide enough weight
to the most recent measurements. We propose a piecewise linear to the most recent measurements. We propose a piecewise linear
distribution of weights, such that the first section (samples 1:F) is distribution of weights, such that the first section (samples 1:F) is
flat as in a simple moving average, and the second section (samples flat as in a simple moving average, and the second section (samples
F+1:M) is linearly declining weights to the end of the averaging F+1:M) is linearly declining weights to the end of the averaging
window. We choose integer weights, which allows incremental window. We choose integer weights, which allows incremental
calculation without introducing rounding errors. calculation without introducing rounding errors.
4.1.1. Improving the response of the skewness estimate 4.1.1. Improving the Response of the Skewness Estimate
The weighted moving average for skew_est, based on skew_est in The weighted moving average for skew_est, based on skew_est in
Section 3.2.2, can be calculated as follows: Section 3.2.2, can be calculated as follows:
skew_est = ((M-F+1)*sum(skew_base_T(1:F)) skew_est = ((M-F+1)*sum(skew_base_T(1:F))
+ sum([(M-F):1].*skew_base_T(F+1:M))) + sum([(M-F):1].*skew_base_T(F+1:M)))
/ ((M-F+1)*sum(numsampT(1:F)) / ((M-F+1)*sum(numsampT(1:F))
skipping to change at page 19, line 5 skipping to change at page 19, line 5
11. sum_skewbase = sum_skewbase + skewbase_hist(F+1) - old_skewbase 11. sum_skewbase = sum_skewbase + skewbase_hist(F+1) - old_skewbase
12. sum_numsamp = sum_numsamp + numsampT(1) - old_numsampT 12. sum_numsamp = sum_numsamp + numsampT(1) - old_numsampT
13. skew_est = ((M-F+1)*F_skewbase + W_D_skewbase) / 13. skew_est = ((M-F+1)*F_skewbase + W_D_skewbase) /
((M-F+1)*F_numsamp+W_D_numsamp) ((M-F+1)*F_numsamp+W_D_numsamp)
Where cycle(....) refers to the operation on a cyclic buffer where Where cycle(....) refers to the operation on a cyclic buffer where
the start of the buffer is now the next element in the buffer. the start of the buffer is now the next element in the buffer.
4.1.2. Improving the response of the variability estimate 4.1.2. Improving the Response of the Variability Estimate
Similarly the weighted moving average for var_est can be calculated Similarly the weighted moving average for var_est can be calculated
as follows: as follows:
var_est = ((M-F+1)*sum(var_base_T(1:F)) var_est = ((M-F+1)*sum(var_base_T(1:F))
+ sum([(M-F):1].*var_base_T(F+1:M))) + sum([(M-F):1].*var_base_T(F+1:M)))
/ ((M-F+1)*sum(numsampT(1:F)) / ((M-F+1)*sum(numsampT(1:F))
skipping to change at page 19, line 31 skipping to change at page 19, line 31
integer values 1 through to F, and [(M-F):1] refers to an array of integer values 1 through to F, and [(M-F):1] refers to an array of
the integer values (M-F) declining through to 1; and ".*" is the the integer values (M-F) declining through to 1; and ".*" is the
array scalar dot product operator. When removing oscillation noise array scalar dot product operator. When removing oscillation noise
(see Section 4.2) this calculation must be adjusted to allow for (see Section 4.2) this calculation must be adjusted to allow for
invalid var_base_T records. invalid var_base_T records.
Var_est can be calculated incrementally in the same way as skew_est Var_est can be calculated incrementally in the same way as skew_est
in Section 4.1.1. However, note that the buffer numsampT is used for in Section 4.1.1. However, note that the buffer numsampT is used for
both calculations so the operations on it should not be repeated. both calculations so the operations on it should not be repeated.
4.2. Removing oscillation noise 4.2. Removing Oscillation Noise
When a path has no bottleneck, var_est will be very small and the When a path has no bottleneck, var_est will be very small and the
recorded significant mean crossings will be the result of path noise. recorded significant mean crossings will be the result of path noise.
Thus up to N-1 meaningless mean crossings can be a source of error at Thus up to N-1 meaningless mean crossings can be a source of error at
the point a link becomes a bottleneck and flows traversing it begin the point a link becomes a bottleneck and flows traversing it begin
to be grouped. to be grouped.
To remove this source of noise from freq_est: To remove this source of noise from freq_est:
1. Set the current var_base_T = NaN (a value representing an invalid 1. Set the current var_base_T = NaN (a value representing an invalid
skipping to change at page 20, line 20 skipping to change at page 20, line 20
This section discusses the OWD measurements required for this This section discusses the OWD measurements required for this
algorithm to detect shared bottlenecks. algorithm to detect shared bottlenecks.
The SBD mechanism described in this document relies on differences The SBD mechanism described in this document relies on differences
between OWD measurements to avoid the practical problems with between OWD measurements to avoid the practical problems with
measuring absolute OWD (see [Hayes-LCN14] section IIIC). Since all measuring absolute OWD (see [Hayes-LCN14] section IIIC). Since all
summary statistics are relative to the mean OWD and sender/receiver summary statistics are relative to the mean OWD and sender/receiver
clock offsets should be approximately constant over the measurement clock offsets should be approximately constant over the measurement
periods, the offset is subtracted out in the calculation. periods, the offset is subtracted out in the calculation.
5.1. Time stamp resolution 5.1. Time-stamp Resolution
The SBD mechanism requires timing information precise enough to be The SBD mechanism requires timing information precise enough to be
able to make comparisons. As a rule of thumb, the time resolution able to make comparisons. As a rule of thumb, the time resolution
should be less than one hundredth of a typical path's range of should be less than one hundredth of a typical path's range of
delays. In general, the coarser the time resolution, the more care delays. In general, the coarser the time resolution, the more care
that needs to be taken to ensure rounding errors do not bias the that needs to be taken to ensure rounding errors do not bias the
skewness calculation. Timing information described by skewness calculation. Timing information described by
[I-D.dt-rmcat-feedback-message] should be sufficient for the sender [I-D.ietf-avtcore-cc-feedback-message] should be sufficient for the
to calculate relative OWD. sender to calculate relative OWD.
5.2. Clock skew 5.2. Clock Skew
Generally sender and receiver clock skew will be too small to cause Generally sender and receiver clock skew will be too small to cause
significant errors in the estimators. Skew_est and freq_est are the significant errors in the estimators. Skew_est and freq_est are the
most sensitive to this type of noise due to their use of a mean OWD most sensitive to this type of noise due to their use of a mean OWD
calculated over a longer interval. In circumstances where clock skew calculated over a longer interval. In circumstances where clock skew
is high, basing skew_est only on the previous T's mean and ignoring is high, basing skew_est only on the previous T's mean and ignoring
freq_est provides a noisier but reliable signal. freq_est provides a noisier but reliable signal.
A more sophisticated method is to estimate the effect the clock skew A more sophisticated method is to estimate the effect the clock skew
is having on the summary statistics, and then adjust statistics is having on the summary statistics, and then adjust statistics
accordingly. There are a number of techniques in the literature, accordingly. There are a number of techniques in the literature,
including [Zhang-Infocom02]. including [Zhang-Infocom02].
6. Expected feedback from experiments 6. Expected Feedback from Experiments
The algorithm described in this memo has so far been evaluated using The algorithm described in this memo has so far been evaluated using
simulations. Real network tests using the proposed congestion simulations and small scale experiments. Real network tests using
control algorithms will help confirm the default parameter choice. RMCAT congestion control algorithms will help confirm the default
For example, the time interval T may need to be made longer if the parameter choice. For example, the time interval T may need to be
packet rate is very low. Implementers and testers are invited to made longer if the packet rate is very low. Implementers and testers
document their findings in an Internet draft. are invited to document their findings in an Internet draft.
7. Acknowledgments 7. Acknowledgments
This work was part-funded by the European Community under its Seventh This work was part-funded by the European Community under its Seventh
Framework Programme through the Reducing Internet Transport Latency Framework Programme through the Reducing Internet Transport Latency
(RITE) project (ICT-317700). The views expressed are solely those of (RITE) project (ICT-317700). The views expressed are solely those of
the authors. the authors.
8. IANA Considerations 8. IANA Considerations
This memo includes no request to IANA. This memo includes no request to IANA.
9. Security Considerations 9. Security Considerations
The security considerations of RFC 3550 [RFC3550], RFC 4585 The security considerations of RFC 3550 [RFC3550], RFC 4585
[RFC4585], and RFC 5124 [RFC5124] are expected to apply. [RFC4585], and RFC 5124 [RFC5124] are expected to apply.
Non-authenticated RTCP packets carrying OWD measurements, shared Non-authenticated RTCP packets carrying OWD measurements, shared
bottleneck indications, and/or summary statistics could allow bottleneck indications, and/or summary statistics could allow
attackers to alter the bottleneck sharing characteristics for private attackers to alter the bottleneck sharing characteristics for private
gain or disruption of other parties communication. gain or disruption of other parties' communication.
10. Change history 10. Change history
Changes made to this document: Changes made to this document:
WG-08->WG-09 : Removed definitions that are no longer used. Added
pkt_loss definition. Refined c_s recommendation.
WG-07->WG-08 : Updates addressing https://www.ietf.org/mail- WG-07->WG-08 : Updates addressing https://www.ietf.org/mail-
archive/web/rmcat/current/msg01671.html Mainly archive/web/rmcat/current/msg01671.html Mainly
clarifications. clarifications.
WG-06->WG-07 : Updates addressing WG-06->WG-07 : Updates addressing
https://mailarchive.ietf.org/arch/msg/ https://mailarchive.ietf.org/arch/msg/
rmcat/80B6q4nI7carGcf_ddBwx7nKvOw. Mainly rmcat/80B6q4nI7carGcf_ddBwx7nKvOw. Mainly
clarifications. Figure 2 to supplement grouping clarifications. Figure 2 to supplement grouping
algorithm description. algorithm description.
skipping to change at page 22, line 49 skipping to change at page 23, line 8
00->01 : Revisions to terminology for clarity 00->01 : Revisions to terminology for clarity
11. References 11. References
11.1. Normative References 11.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997, DOI 10.17487/RFC2119, March 1997,
<http://www.rfc-editor.org/info/rfc2119>. <https://www.rfc-editor.org/info/rfc2119>.
11.2. Informative References 11.2. Informative References
[Hayes-LCN14] [Hayes-LCN14]
Hayes, D., Ferlin, S., and M. Welzl, "Practical Passive Hayes, D., Ferlin, S., and M. Welzl, "Practical Passive
Shared Bottleneck Detection using Shape Summary Shared Bottleneck Detection using Shape Summary
Statistics", Proc. the IEEE Local Computer Networks Statistics", Proc. the IEEE Local Computer Networks
(LCN) pp150-158, September 2014, (LCN) pp150-158, September 2014,
<http://heim.ifi.uio.no/davihay/ <http://heim.ifi.uio.no/davihay/
hayes14__pract_passiv_shared_bottl_detec-abstract.html>. hayes14__pract_passiv_shared_bottl_detec-abstract.html>.
[I-D.dt-rmcat-feedback-message] [I-D.ietf-avtcore-cc-feedback-message]
Sarker, Z., Perkins, C., Singh, V., and M. Ramalho, "RTP Sarker, Z., Perkins, C., Singh, V., and M. Ramalho, "RTP
Control Protocol (RTCP) Feedback for Congestion Control", Control Protocol (RTCP) Feedback for Congestion Control",
draft-dt-rmcat-feedback-message-02 (work in progress), May draft-ietf-avtcore-cc-feedback-message-00 (work in
2017. progress), October 2017.
[I-D.ietf-rmcat-coupled-cc] [I-D.ietf-rmcat-coupled-cc]
Islam, S., Welzl, M., and S. Gjessing, "Coupled congestion Islam, S., Welzl, M., and S. Gjessing, "Coupled congestion
control for RTP media", draft-ietf-rmcat-coupled-cc-06 control for RTP media", draft-ietf-rmcat-coupled-cc-07
(work in progress), March 2017. (work in progress), September 2017.
[RFC3550] Schulzrinne, H., Casner, S., Frederick, R., and V. [RFC3550] Schulzrinne, H., Casner, S., Frederick, R., and V.
Jacobson, "RTP: A Transport Protocol for Real-Time Jacobson, "RTP: A Transport Protocol for Real-Time
Applications", STD 64, RFC 3550, DOI 10.17487/RFC3550, Applications", STD 64, RFC 3550, DOI 10.17487/RFC3550,
July 2003, <http://www.rfc-editor.org/info/rfc3550>. July 2003, <https://www.rfc-editor.org/info/rfc3550>.
[RFC4585] Ott, J., Wenger, S., Sato, N., Burmeister, C., and J. Rey, [RFC4585] Ott, J., Wenger, S., Sato, N., Burmeister, C., and J. Rey,
"Extended RTP Profile for Real-time Transport Control "Extended RTP Profile for Real-time Transport Control
Protocol (RTCP)-Based Feedback (RTP/AVPF)", RFC 4585, Protocol (RTCP)-Based Feedback (RTP/AVPF)", RFC 4585,
DOI 10.17487/RFC4585, July 2006, DOI 10.17487/RFC4585, July 2006,
<http://www.rfc-editor.org/info/rfc4585>. <https://www.rfc-editor.org/info/rfc4585>.
[RFC5124] Ott, J. and E. Carrara, "Extended Secure RTP Profile for [RFC5124] Ott, J. and E. Carrara, "Extended Secure RTP Profile for
Real-time Transport Control Protocol (RTCP)-Based Feedback Real-time Transport Control Protocol (RTCP)-Based Feedback
(RTP/SAVPF)", RFC 5124, DOI 10.17487/RFC5124, February (RTP/SAVPF)", RFC 5124, DOI 10.17487/RFC5124, February
2008, <http://www.rfc-editor.org/info/rfc5124>. 2008, <https://www.rfc-editor.org/info/rfc5124>.
[RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind, [RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
"Low Extra Delay Background Transport (LEDBAT)", RFC 6817, "Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
DOI 10.17487/RFC6817, December 2012, DOI 10.17487/RFC6817, December 2012,
<http://www.rfc-editor.org/info/rfc6817>. <https://www.rfc-editor.org/info/rfc6817>.
[Zhang-Infocom02] [Zhang-Infocom02]
Zhang, L., Liu, Z., and H. Xia, "Clock synchronization Zhang, L., Liu, Z., and H. Xia, "Clock synchronization
algorithms for network measurements", Proc. the IEEE algorithms for network measurements", Proc. the IEEE
International Conference on Computer Communications International Conference on Computer Communications
(INFOCOM) pp160-169, September 2002, (INFOCOM) pp160-169, September 2002,
<http://dx.doi.org/10.1109/INFCOM.2002.1019257>. <http://dx.doi.org/10.1109/INFCOM.2002.1019257>.
Authors' Addresses Authors' Addresses
David Hayes (editor) David Hayes (editor)
Simula Research Laboratory Simula Research Laboratory
P.O. Box 134 P.O. Box 134
Lysaker 1325 Lysaker 1325
Norway Norway
Phone: +47 2284 5566
Email: davidh@simula.no Email: davidh@simula.no
Simone Ferlin Simone Ferlin
Simula Research Laboratory
P.O.Box 134
Lysaker 1325
Norway
Phone: +47 4072 0702 Email: simone@ferlin.io
Email: ferlin@simula.no
Michael Welzl Michael Welzl
University of Oslo University of Oslo
PO Box 1080 Blindern PO Box 1080 Blindern
Oslo N-0316 Oslo N-0316
Norway Norway
Phone: +47 2285 2420
Email: michawe@ifi.uio.no Email: michawe@ifi.uio.no
Kristian Hiorth Kristian Hiorth
University of Oslo University of Oslo
PO Box 1080 Blindern PO Box 1080 Blindern
Oslo N-0316 Oslo N-0316
Norway Norway
Email: kristahi@ifi.uio.no Email: kristahi@ifi.uio.no
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