draft-ietf-rmcat-sbd-01.txt   draft-ietf-rmcat-sbd-02.txt 
RTP Media Congestion Avoidance D. Hayes, Ed. RTP Media Congestion Avoidance Techniques D. Hayes, Ed.
Techniques University of Oslo Internet-Draft University of Oslo
Internet-Draft S. Ferlin Intended status: Experimental S. Ferlin
Intended status: Experimental Simula Research Laboratory Expires: April 21, 2016 Simula Research Laboratory
Expires: January 2, 2016 M. Welzl M. Welzl
K. Kiorth
University of Oslo University of Oslo
July 1, 2015 October 19, 2015
Shared Bottleneck Detection for Coupled Congestion Control for RTP Shared Bottleneck Detection for Coupled Congestion Control for RTP
Media. Media.
draft-ietf-rmcat-sbd-01 draft-ietf-rmcat-sbd-02
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 by a data receiver based on continuous that are calculated by a data receiver based on continuous
measurements and regularly fed to a grouping algorithm that runs measurements and regularly fed to a grouping algorithm that runs
wherever the knowledge is needed. This mechanism complements the wherever the knowledge is needed. This mechanism complements the
coupled congestion control mechanism in draft-welzl-rmcat-coupled-cc. coupled congestion control mechanism in draft-welzl-rmcat-coupled-cc.
Status of this Memo Status of This Memo
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Table of Contents Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. The signals . . . . . . . . . . . . . . . . . . . . . . . 3 1.1. The signals . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.1. Packet Loss . . . . . . . . . . . . . . . . . . . . . 3 1.1.1. Packet Loss . . . . . . . . . . . . . . . . . . . . . 3
1.1.2. Packet Delay . . . . . . . . . . . . . . . . . . . . . 3 1.1.2. Packet Delay . . . . . . . . . . . . . . . . . . . . 3
1.1.3. Path Lag . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.3. Path Lag . . . . . . . . . . . . . . . . . . . . . . 4
2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4 2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1. Parameters and their Effect . . . . . . . . . . . . . . . 5 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. Key metrics and their calculation . . . . . . . . . . . . 9 3.1. Key metrics and their calculation . . . . . . . . . . . . 9
3.1.1. Mean delay . . . . . . . . . . . . . . . . . . . . . . 9 3.1.1. Mean delay . . . . . . . . . . . . . . . . . . . . . 9
3.1.2. Skewness Estimate . . . . . . . . . . . . . . . . . . 9 3.1.2. Skewness Estimate . . . . . . . . . . . . . . . . . . 9
3.1.3. Variability Estimate . . . . . . . . . . . . . . . . . 10 3.1.3. Variability Estimate . . . . . . . . . . . . . . . . 10
3.1.4. Oscillation Estimate . . . . . . . . . . . . . . . . . 11 3.1.4. Oscillation Estimate . . . . . . . . . . . . . . . . 11
3.1.5. Packet loss . . . . . . . . . . . . . . . . . . . . . 11 3.1.5. Packet loss . . . . . . . . . . . . . . . . . . . . . 11
3.2. Flow Grouping . . . . . . . . . . . . . . . . . . . . . . 12 3.2. Flow Grouping . . . . . . . . . . . . . . . . . . . . . . 12
3.2.1. Flow Grouping Algorithm . . . . . . . . . . . . . . . 12 3.2.1. Flow Grouping Algorithm . . . . . . . . . . . . . . . 12
3.2.2. Using the flow group signal . . . . . . . . . . . . . 13 3.2.2. Using the flow group signal . . . . . . . . . . . . . 13
3.3. Removing Noise from the Estimates . . . . . . . . . . . . 13 3.3. Removing Noise from the Estimates . . . . . . . . . . . . 13
3.3.1. PDV noise . . . . . . . . . . . . . . . . . . . . . . 14 3.3.1. Oscillation noise . . . . . . . . . . . . . . . . . . 14
3.3.2. Oscillation noise . . . . . . . . . . . . . . . . . . 14 3.3.2. Clock skew . . . . . . . . . . . . . . . . . . . . . 14
3.3.3. Clock skew . . . . . . . . . . . . . . . . . . . . . . 15 3.4. Reducing lag and Improving Responsiveness . . . . 14
3.4. Reducing lag and Improving Responsiveness . . . . . . . . 15 3.4.1. Improving the response of the skewness estimate . 15
3.4.1. Improving the response of the skewness estimate . . . 16 3.4.2. Improving the response of the variability estimate 17
3.4.2. Improving the response of the variability estimate . . 16 4. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 17
4. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 17 4.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 17
4.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 17 5. Implementation status . . . . . . . . . . . . . . . . . . . . 18
5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 17 6. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 18
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 17 7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 18
7. Security Considerations . . . . . . . . . . . . . . . . . . . 17 8. Security Considerations . . . . . . . . . . . . . . . . . . . 18
8. Change history . . . . . . . . . . . . . . . . . . . . . . . . 18 9. Change history . . . . . . . . . . . . . . . . . . . . . . . 18
9. References . . . . . . . . . . . . . . . . . . . . . . . . . . 18 10. References . . . . . . . . . . . . . . . . . . . . . . . . . 19
9.1. Normative References . . . . . . . . . . . . . . . . . . . 18 10.1. Normative References . . . . . . . . . . . . . . . . . . 19
9.2. Informative References . . . . . . . . . . . . . . . . . . 18 10.2. Informative References . . . . . . . . . . . . . . . . . 19
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 19 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 20
1. Introduction 1. Introduction
In the Internet, it is not normally known if flows (e.g., TCP In the Internet, it is not normally known if flows (e.g., TCP
connections or UDP data streams) traverse the same bottlenecks. Even connections or UDP data streams) traverse the same bottlenecks. Even
flows that have the same sender and receiver may take different paths flows that have the same sender and receiver may take different paths
and share a bottleneck or not. Flows that share a bottleneck link and share a bottleneck or not. Flows that share a bottleneck link
usually compete with one another for their share of the capacity. usually compete with one another for their share of the capacity.
This competition has the potential to increase packet loss and This competition has the potential to increase packet loss and
delays. This is especially relevant for interactive applications delays. This is especially relevant for interactive applications
skipping to change at page 3, line 50 skipping to change at page 3, line 50
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 synchronised. However, since the and receiver clocks to be synchronised. 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
OWD measurement is sufficient. Clock skew is not usually significant OWD measurement is sufficient. Clock skew is not usually significant
over the time intervals used by this SBD mechanism (see [RFC6817] A.2 over the time intervals used by this SBD mechanism (see [RFC6817] A.2
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 3.3.3 outlines a way circumstances where it is significant, Section 3.3.2 outlines a way
of adjusting the calculations to cater for it. of 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.
1.1.3. Path Lag 1.1.3. Path Lag
skipping to change at page 4, line 29 skipping to change at page 4, line 29
2. Definitions 2. Definitions
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119]. document are to be interpreted as described in RFC 2119 [RFC2119].
Acronyms used in this document: Acronyms used in this document:
OWD -- One Way Delay OWD -- One Way Delay
PDV -- Packet Delay Variation
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.
skipping to change at page 5, line 30 skipping to change at page 5, line 26
min_T(...) -- the minimum recorded measurement of the variable in min_T(...) -- the minimum recorded measurement of the variable in
parentheses taken over the interval T parentheses taken over the interval T
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_VM(...) -- the count of valid values of the variable in
parentheses given M records parentheses given M records
PC -- a boolean variable indicating the particular flow PB -- a boolean variable indicating the particular flow
was identified as experiencing congestion in the was identified transiting a bottleneck in the
previous interval T (i.e. Previously Congested) 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
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.
freq_est -- a measure of low frequency oscillation in the OWD freq_est -- a measure of low frequency oscillation in the OWD
measurements. measurements.
p_l, p_f, p_pdv, p_mad, c_s, c_h, p_s, p_d, p_v -- various p_l, p_f, p_mad, c_s, c_h, p_s, p_d, p_v -- various thresholds
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 PDV and freq_est. It will also increase the the efficacy of freq_est. It will also increase the response
response time of the mechanism. Making T too small will make time of the mechanism. Making T too small will make the
the metrics noisier. metrics noisier.
N & M N should be large enough provide a stable estimate of N & M N should be large enough to provide a stable estimate of
oscillations in OWD and average PDV. Usually M=N, though oscillations in OWD. Usually M=N, though having M<N may be
having M<N may be beneficial in certain circumstances. M*T beneficial in certain circumstances. M*T needs to be long
needs to be long enough provide stable estimates of skewness enough to provide stable estimates of skewness and MAD.
and MAD (if used).
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 experiencing congestion or not. It should be a flow is transiting a bottleneck or not. It should be
slightly negative so that a very lightly loaded path does not slightly negative so that a very lightly loaded path does not
give a false indication. Setting c_s more negative makes the give a false indication. Setting c_s more negative makes the
SBD mechanism less sensitive to transient and light SBD mechanism less sensitive to transient and slight
congestion episodes. bottlenecks.
c_s c_h adds hysteresis to the congestion determination. It c_h c_h adds hysteresis to the botteneck 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 congestion 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 skew_est|var_est|freq_est
measure is greater than p_s|p_f|p_d|(p_pdv|p_mad). Adjusting measure is greater than p_s|p_f|p_d|p_mad. Adjusting these
these is a compromise between false grouping of flows that do is a compromise between false grouping of flows that do not
not share a bottleneck and false splitting of flows that do. share a bottleneck and false splitting of flows that do.
Making them larger can help if the measures are very noisy, Making them larger can help if the measures are very noisy,
but reducing the noise in the statistical measures by but reducing the noise in the statistical measures by
adjusting T and N|M may be a better solution. 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 3.3: c_s = -0.01, p_f = p_s = p_d = algorithm outlined in Section 3.3: c_s=-0.01, p_f=p_d=0.1, p_s=0.15,
0.1, p_pdv = 0.2, p_v = 0.2 (or p_mad=0.1, p_v=0.7). M=50, F=25, and p_mad=0.1, p_v=0.7. M=30, F=20, and c_h = 0.3 are additional
c_h = 0.3 are additional parameters defined in the document. These parameters defined in the document. These are values that seem to
are values that seem to work well over a wide range of practical work well over a wide range of practical Internet conditions.
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:
variability (estimate var_est, see Section 3.1.3) variability (estimate var_est, see Section 3.1.3)
skewness (estimate skew_est, see Section 3.1.2) skewness (estimate skew_est, see Section 3.1.2)
oscillation (estimate freq_est, see Section 3.1.4) oscillation (estimate freq_est, see Section 3.1.4)
with packet loss (estimate pkt_loss, see Section 3.1.5) used as a with packet loss (estimate pkt_loss, see Section 3.1.5) used as a
supplementary statistic. supplementary statistic.
Summary statistics help to address both the noise and the path lag Summary statistics help to address both the noise and the path lag
problems by describing the general shape over a relatively long problems by describing the general shape over a relatively long
period of time. This is sufficient for their application in coupled period of time. Each summary statistic portrays a "view" of the
congestion control for RTP Media. They can be signalled from a bottleneck link characteristics, and when used together, they provide
receiver, which measures the OWD and calculates the summary a robust discrimination for grouping flows. They can be signalled
from a receiver, which measures the OWD and calculates the summary
statistics, to a sender, which is the entity that is transmitting the statistics, to a sender, which is the entity that is transmitting the
media stream. An RTP Media device may be both a sender and a media stream. An RTP Media device may be both a sender and a
receiver. SBD can be performed at either a sender or a receiver or receiver. SBD can be performed at either a sender or a receiver or
both. both.
+----+ +----+
| H2 | | H2 |
+----+ +----+
| |
| L2 | L2
skipping to change at page 8, line 25 skipping to change at page 8, line 25
A network with 3 hosts (H1, H2, H3) and 3 links (L1, L2, L3). A network with 3 hosts (H1, H2, H3) and 3 links (L1, L2, L3).
Figure 1 Figure 1
In Figure 1, there are two possible cases for shared bottleneck In Figure 1, there are two possible cases for shared bottleneck
detection: a sender-based and a receiver-based case. detection: a sender-based and a receiver-based case.
1. Sender-based: consider a situation where host H1 sends media 1. Sender-based: consider a situation where host H1 sends media
streams to hosts H2 and H3, and L1 is a shared bottleneck. H2 streams to hosts H2 and H3, and L1 is a shared bottleneck. H2
and H3 measure the OWD and calculate summary statistics, which and H3 measure the OWD and calculate summary statistics, which
they send to H1 every T. H1, having this knowledge, can determine they send to H1 every T. H1, having this knowledge, can
the shared bottleneck and accordingly control the send rates. determine the shared bottleneck and accordingly control the send
rates.
2. Receiver-based: consider that H2 is also sending media to H3, and 2. Receiver-based: 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. This the summary statistics related to H1 and H2, respectively. This
case is applicable when send rates are controlled by the case is applicable when send rates are controlled by the
receiver; then, the signal from H3 to the senders contains the receiver; then, the signal from H3 to the senders contains the
sending rate. sending rate.
A discussion of the required signalling for the receiver-based case A discussion of the required signalling for the receiver-based case
is beyond the scope of this document. For the sender-based case, the is beyond the scope of this document. For the sender-based case, the
messages and their data format will be defined here in future messages and their data format will be defined here in future
versions of this document. We envision that an initialization versions of this document.
message from the sender to the receiver could specify which key
metrics are requested out of a possibly extensible set (pkt_loss, We envisige the following exchange during initialisation:
var_est, skew_est, freq_est). The grouping algorithm described in
this document requires all four of these metrics, and receivers MUST o An initialization message from the sender to the receiver will
be able to provide them, but future algorithms may be able to exploit contain the following information:
other metrics (e.g. metrics based on explicit network signals).
Moreover, the initialization message could specify T, N, and the * A protocol identifier (SBD=01). This is to future proof the
necessary resolution and precision (number of bits per field). message exchange so that potential advances in SBD technology
can be easily deployed. All following initialisation elements
relate to the mechanism outlined in this document which will
have the identifier SBD=01.
* A list of which key metrics should be collected and relayed
back to the sender out of a possibly extensible set (pkt_loss,
var_est, skew_est, freq_est). The grouping algorithm described
in this document requires all four of these metrics, and
receivers MUST be able to provide them, but future algorithms
may be able to exploit other metrics (e.g. metrics based on
explicit network signals).
* The values of T, N, M, and the necessary resolution and
precision of the relayed statistics.
o A response message from the receiver acknowledges this message
with a list of key metrics it supports (subset of the senders
list) and is able to relay back to the sender.
o This initialisation exchange may be repeated to finalize the
agreed metrics should not all be supported by all receivers.
3.1. Key metrics and their calculation 3.1. Key metrics and their calculation
Measurements are calculated over a base interval, T. T should be long Measurements are calculated over a base interval, T and summarized
enough to provide enough samples for a good estimate of skewness, but over N or M such intervals. All summary statistics can be calculated
short enough so that a measure of the oscillation can be made from N incrementally.
of these estimates. Reference [Hayes-LCN14] uses T = 350ms and
N=M=50, which are values that seem to work well over a wide range of
practical Internet conditions.
3.1.1. Mean delay 3.1.1. 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. Generally M=N: setting M to be less than N allows the where M <= N. Setting M to be less than N allows the mechanism to be
mechanism to be more responsive to changes, but potentially at the more responsive to changes, but potentially at the expense of a
expense of a higher error rate (see Section 3.4 for a discussion on higher error rate (see Section 3.4 for a discussion on improving the
improving the responsiveness of the mechanism.) responsiveness of the mechanism.)
3.1.2. Skewness Estimate 3.1.2. 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 skewness is estimated using two counters, counting the number of The base for the skewness calculation is estimated using a counter
one way delay samples (OWD) above and below the mean: initialised every T. It increments for one way delay samples (OWD)
below the mean and decrements for OWD above the mean. So for each
skew_base_T = sum_T(OWD < mean_delay) - sum_T(OWD > mean_delay) OWD sample:
where
if (OWD < mean_delay) 1 else 0 if (OWD < mean_delay) skew_base_T++
if (OWD > mean_delay) 1 else 0 if (OWD > mean_delay) skew_base_T--
and mean_delay does not include the mean of the current T The mean_delay does not include the mean of the current T interval to
interval. 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.
3.1.3. Variability Estimate 3.1.3. Variability Estimate
Packet Delay Variation (PDV) ([RFC5481] and [ITU-Y1540]) is used as Mean Absolute Deviation (MAD) delay is a robust variability measure
an estimator of the variability of the delay signal. We define PDV that copes well with different send rates. It can be implemented in
as follows: an online manner as follows:
PDV = PDV_max = max_T(OWD) - E_T(OWD) var_base_T = sum_T(|OWD - E_T(OWD)|)
var_est = E_M(PDV) = sum_M(PDV) / M where
This modifies PDV as outlined in [RFC5481] to provide a summary |x| is the absolute value of x
statistic version that best aids the grouping decisions of the
algorithm (see [Hayes-LCN14] section IVB).
Generally the maximum is sampled well during congestion, though it is E_T(OWD) is the mean OWD calculated in the previous T
more sensitive to path and operating system noise. The use of PDV =
PDV_min = E_T(OWD) - min_T(OWD) would be less sensitive to this var_est = MAD_MT = sum_MT(var_base_T)/num_MT(OWD)
noise, but is not well sampled during congestion at the bottleneck
and therefore not recommended. For calculation of freq_est p_v=0.7
For the grouping threshold p_mad=0.1
3.1.4. Oscillation Estimate 3.1.4. 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 normalising the significant mean, calculated by counting and normalising 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 = 0.2 is a good value. have found that p_v = 0.7 is a good value.
Freq_est is a number between 0 and 1. Freq_est can be approximated Freq_est is a number between 0 and 1. Freq_est can be approximated
incrementally as follows: incrementally as follows:
With each new calculation of E_T(OWD) a decision is made as to With each new calculation of E_T(OWD) a decision is made as to
whether this value of E_T(OWD) significantly crosses the current whether this value of E_T(OWD) significantly crosses the current
long term mean, mean_delay, with respect to the previous long term mean, mean_delay, with respect to the previous
significant mean crossing. significant mean crossing.
A cyclic buffer, last_N_crossings, records a 1 if there is a A cyclic buffer, last_N_crossings, records a 1 if there is a
skipping to change at page 11, line 40 skipping to change at page 11, line 40
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.
3.1.5. Packet loss 3.1.5. Packet loss
The proportion of packets lost is used as a supplementary measure: The proportion of packets lost over the period NT is used as a
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.
3.2. Flow Grouping 3.2. Flow Grouping
3.2.1. Flow Grouping Algorithm 3.2.1. 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
otherwise be possible. otherwise be possible.
Flows determined to be experiencing congestion are successively Flows determined to be transiting a bottleneck are successively
divided into groups based on freq_est, var_est, and skew_est. divided into groups based on freq_est, var_est, skew_est and
pkt_loss.
The first step is to determine which flows are experiencing The first step is to determine which flows are transiting a
congestion. This is important, since if a flow is not experiencing bottleneck. This is important, since if a flow is not transiting a
congestion its delay based metrics will not describe the bottleneck, bottleneck its delay based metrics will not describe the bottleneck,
but the "noise" from the rest of the path. Skewness, with proportion but the "noise" from the rest of the path. Skewness, with proportion
of packets 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 where: 1. Grouping will be performed on flows that are inferred to be
traversing a bottleneck by:
skew_est < c_s skew_est < c_s
|| ( skew_est < c_h && PC ) || ( skew_est < c_h & PB ) || pkt_loss > p_l
|| 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 congestion. C_s = 0.0 was used in [Hayes-LCN14]. A value detecting a bottleneck. C_s = 0.0 was used in [Hayes-LCN14]. A
of c_s = 0.05 is a little more sensitive, and c_s = -0.05 is a little value of c_s = 0.05 is a little more sensitive, and c_s = -0.05 is a
less sensitive. C_h controls the hysteresis on flows that were little less sensitive. C_h controls the hysteresis on flows that
grouped as experiencing congestion last time. were grouped as transiting a bottleneck last time. If the test
result is TRUE, PB=TRUE, otherwise PB=FALSE.
These flows, flows experiencing congestion, are then progressively These flows, flows transiting a bottleneck, are then progressively
divided into groups based on the freq_est, PDV, and skew_est summary divided into groups based on the freq_est, var_est, and skew_est
statistics. The process proceeds according to the following steps: summary statistics. The process proceeds according to the following
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
3. Group flows whose difference in sorted E_N(PDV) (highest to 3. Group flows whose difference in sorted E_M(var_est) (highest to
lowest) is less than a threshold: lowest) is less than a threshold:
diff(var_est) < (p_pdv * var_est) diff(var_est) < (p_mad * var_est)
The threshold, (p_pdv * var_est), is with respect to the highest The threshold, (p_mad * var_est), is with respect to the highest
value in the difference. value in the difference.
4. Group flows whose difference in sorted skew_est or pkt_loss is 4. Group flows whose difference in sorted skew_est is less than a
less than a threshold: threshold:
if pkt_loss < p_l
diff(skew_est) < p_s diff(skew_est) < p_s
otherwise 5. When packet loss is high enough to be reliable (pkt_loss > p_l),
group flows whose difference is less than a threshold
diff(pkt_loss) < (p_d * pkt_loss) diff(pkt_loss) < (p_d * pkt_loss)
The threshold, (p_d * pkt_loss), is with respect to the The threshold, (p_d * pkt_loss), is with respect to the highest
highest value in the difference. value in the difference.
This procedure involves sorting estimates from highest to lowest. It This procedure involves sorting estimates from highest to lowest. It
is simple to implement, and efficient for small numbers of flows (up is simple to implement, and efficient for small numbers of flows (up
to 10-20). to 10-20).
3.2.2. Using the flow group signal 3.2.2. Using the flow group signal
A grouping decisions is made every T from the second T, though they Grouping decisions can be made every T from the second T, however
will not attain their full design accuracy until after the N'th T they will not attain their full design accuracy until after the
interval. 2*N'th T interval. We recommend that grouping decisions are not made
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 draft and will be specific to the this are beyond the scope of this draft and will be specific to the
coupled congestion controllers objectives. coupled congestion controllers objectives.
3.3. Removing Noise from the Estimates 3.3. Removing Noise from the Estimates
The following describe small changes to the calculation of the key The following describe small changes to the calculation of the key
metrics that help remove noise from them. Currently these "tweaks" metrics that help remove noise from them. Currently these "tweaks"
are described separately to keep the main description succinct. In are described separately to keep the main description succinct. In
future revisions of the draft these enhancements may replace the future revisions of the draft these enhancements may replace the
original key metric calculations. original key metric calculations.
3.3.1. PDV noise 3.3.1. Oscillation noise
Usually during congestion the max_T(OWD) is quite well sampled as the
delay distribution is skewed toward the maximum. However max_T(OWD)
is subject to delay noise from other queues along the path as well as
the host operating system. Min_T(OWD) is less prone to noise along
the path and from the host operating system, but is not well sampled
during congestion (i.e. when there is a bottleneck). Flows with very
different packet send rates exacerbate the problem.
An alternative delay variation measure that is less sensitive to
extreme values and different send rates is Mean Absolute Deviation
(MAD). It can be implemented in an online manner as follows:
var_base_T = sum_T(|OWD - E_T(OWD)|)
where
|x| is the absolute value of x
E_T(OWD) is the mean OWD calculated in the previous T
var_est = MAD_MT = sum_MT(var_base_T)/num_MT(OWD)
For calculation of freq_est p_v=0.7 (MAD is a smaller number than
PDV)
For the grouping threshold p_mad=0.1 instead of p_pdv (MAD is less
noisy so the test can be tighter)
Note that the method for improving responsiveness of MAD_MT is the
same as that described in Section 3.4.1 for skew_est.
3.3.2. Oscillation noise
When a path has no congestion, 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 PDV to PDV = NaN (a value representing an invalid 1. Set the current var_base_T = NaN (a value representing an invalid
record, i.e. Not a Number) for flows that are deemed to not be record, i.e. Not a Number) for flows that are deemed to not be
experiencing congestion by the first skew_est based grouping test transiting a bottleneck by the first skew_est based grouping test
(see Section 3.2.1). (see Section 3.2.1).
2. Then var_est = sum_M(PDV != NaN) / num_VM(PDV) 2. Then var_est = sum_MT(var_base_T != NaN) / num_MT(OWD)
3. For freq_est, only record a significant mean crossing if flow is 3. For freq_est, only record a significant mean crossing if flow
experiencing congestion. deemed to be transiting a bottleneck.
These three changes will remove the non-congestion noise from These three changes can help to remove the non-bottleneck noise from
freq_est. A similar adjustment can be made for MAD based var_est. freq_est.
3.3.3. Clock skew 3.3.2. 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 is most sensitive to significant errors in the estimators. Skew_est is most sensitive to
this type of noise. In circumstances where clock skew is high, this type of noise. In circumstances where clock skew is high,
making M < N can reduce this error. basing skew_est only on the previous T's mean provides a noisier but
reliable signal.
A better method is to estimate the effect the clock skew is having on A better method is to estimate the effect the clock skew is having on
the summary statistics, and then adjust statistics accordingly. A the summary statistics, and then adjust statistics accordingly. A
simple online method of doing this based on min_T(OWD) will be simple online method of doing this based on min_T(OWD) will be
described here in a subsequent version of the draft. described here in a subsequent version of the draft.
3.4. Reducing lag and Improving Responsiveness 3.4. Reducing lag and Improving Responsiveness
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
skipping to change at page 16, line 25 skipping to change at page 16, line 5
+ sum([(M-F):1].*numsampT(F+1:M))) + sum([(M-F):1].*numsampT(F+1:M)))
where numsampT is an array of the number of OWD samples in each T where numsampT is an array of the number of OWD samples in each T
(i.e. num_T(OWD)), and numsampT(1) is the most recent; skew_base_T(1) (i.e. num_T(OWD)), and numsampT(1) is the most recent; skew_base_T(1)
is the most recent calculation of skew_base_T; 1:F refers to the is the most recent calculation of skew_base_T; 1:F refers to the
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. array scalar dot product operator.
To calculate this weighted skew_est incrementally:
Notation: F_ - flat portion, D_ - declining portion, W_ - weighted
component
Initialise: sum_skewbase = 0, F_skewbase=0, W_D_skewbase=0
skewbase_hist = buffer length M initialize to 0
numsampT = buffer length M initialzed to 0
Steps per iteration:
1. old_skewbase = skewbase_hist(M)
2. old_numsampT = numsampT(M)
3. cycle(skewbase_hist)
4. cycle(numsampT)
5. numsampT(1) = num_T(OWD)
6. skewbase_hist(1) = skew_base_T
7. F_skewbase = F_skewbase + skew_base_T - skewbase_hist(F+1)
8. W_D_skewbase = W_D_skewbase + (M-F)*skewbase_hist(F+1)
- sum_skewbase
9. W_D_numsamp = W_D_numsamp + (M-F)*numsampT(F+1) - sum_numsamp
+ F_numsamp
10. F_numsamp = F_numsamp + numsampT(1) - numsampT(F+1)
11. sum_skewbase = sum_skewbase + skewbase_hist(F+1) - old_skewbase
12. sum_numsamp = sum_numsamp + numsampT(1) - old_numsampT
13. skew_est = ((M-F+1)*F_skewbase + W_D_skewbase) /
((M-F+1)*F_numsamp+W_D_numsamp)
Where cycle(....) refers to the operation on a cyclic buffer where
the start of the buffer is now the next element in the buffer.
3.4.2. Improving the response of the variability estimate 3.4.2. Improving the response of the variability estimate
The weighted moving average for var_est can be calculated as follows: Similarly the weighted moving average for var_est can be calculated
as follows:
var_est = ((M-F+1)*sum(PDV(1:F)) + sum([(M-F):1].*PDV(F+1:M))) var_est = ((M-F+1)*sum(var_base_T(1:F))
/ (F*(M-F+1) + sum([(M-F):1]) + sum([(M-F):1].*var_base_T(F+1:M)))
where 1:F refers to the integer values 1 through to F, and [(M-F):1] / ((M-F+1)*sum(numsampT(1:F))
refers to an array of the integer values (M-F) declining through to
1; and ".*" is the array scalar dot product operator. When removing + sum([(M-F):1].*numsampT(F+1:M)))
oscillation noise (see Section 3.3.2) this calculation must be
adjusted to allow for invalid PDV records. where numsampT is an array of the number of OWD samples in each T
(i.e. num_T(OWD)), and numsampT(1) is the most recent; skew_base_T(1)
is the most recent calculation of skew_base_T; 1:F refers to the
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
array scalar dot product operator. When removing oscillation noise
(see Section 3.3.1) this calculation must be adjusted to allow for
invalid var_base_T records.
Var_est can be calculated incrementally in the same way as skew_est
in Section 3.4.1. However, note that the buffer numsampT is used for
both calculations so the operations on it should not be repeated.
4. Measuring OWD 4. Measuring OWD
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 draft relies on differences The SBD mechanism described in this draft 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
skipping to change at page 17, line 29 skipping to change at page 18, line 8
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 lower the time resolution, the more care delays. In general, the lower 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. skewness calculation.
Typical RTP media flows use sub-millisecond timers, which should be Typical RTP media flows use sub-millisecond timers, which should be
adequate in most situations. adequate in most situations.
5. Acknowledgements 5. Implementation status
The University of Oslo is currently working on an implementation of
this in the Chromium browser.
6. Acknowledgements
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.
6. IANA Considerations 7. IANA Considerations
This memo includes no request to IANA. This memo includes no request to IANA.
7. Security Considerations 8. 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 shared bottleneck indications Non-authenticated RTCP packets carrying shared bottleneck indications
and summary statistics could allow attackers to alter the bottleneck and summary statistics could allow attackers to alter the bottleneck
sharing characteristics for private gain or disruption of other sharing characteristics for private gain or disruption of other
parties communication. parties communication.
8. Change history 9. Change history
Changes made to this document: Changes made to this document:
WG-01->WG-02 : Removed ambiguity associated with the term
"congestion". Expanded the description of
initialisation messages. Removed PDV metric.
Added description of incremental weighted metric
calculations for skew_est. Various clarifications
based on implementation work. Fixed typos and
tuned parameters.
WG-00->WG-01 : Moved unbiased skew section to replace skew WG-00->WG-01 : Moved unbiased skew section to replace skew
estimate, more robust variability estimator, the estimate, more robust variability estimator, the
term variance replaced with variability, clock term variance replaced with variability, clock
drift term corrected to clock skew, revision to drift term corrected to clock skew, revision to
clock skew section with a place holder, description clock skew section with a place holder, description
of parameters. of parameters.
02->WG-00 : Fixed missing 0.5 in 3.3.2 and missing brace in 02->WG-00 : Fixed missing 0.5 in 3.3.2 and missing brace in
3.3.3 3.3.3
01->02 : New section describing improvements to the key 01->02 : New section describing improvements to the key
metric calculations that help to remove noise, metric calculations that help to remove noise,
bias, and reduce lag. Some revisions to the bias, and reduce lag. Some revisions to the
notation to make it clearer. Some tightening of notation to make it clearer. Some tightening of
the thresholds. the thresholds.
00->01 : Revisions to terminology for clarity 00->01 : Revisions to terminology for clarity
9. References 10. References
9.1. Normative References 10.1. 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, March 1997. Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/
RFC2119, March 1997,
<http://www.rfc-editor.org/info/rfc2119>.
9.2. Informative References 10.2. 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)
(LCN) p150-158, September 2014, <http://heim.ifi.uio.no/ p150-158, September 2014, <http://heim.ifi.uio.no/davihay/
davihay/
hayes14__pract_passiv_shared_bottl_detec-abstract.html>. hayes14__pract_passiv_shared_bottl_detec-abstract.html>.
[I-D.welzl-rmcat-coupled-cc] [I-D.welzl-rmcat-coupled-cc]
Welzl, M., Islam, S., and S. Gjessing, "Coupled congestion Welzl, M., Islam, S., and S. Gjessing, "Coupled congestion
control for RTP media", draft-welzl-rmcat-coupled-cc-04 control for RTP media", draft-welzl-rmcat-coupled-cc-04
(work in progress), October 2014. (work in progress), October 2014.
[ITU-Y1540] [ITU-Y1540]
ITU-T, "Internet Protocol Data Communication Service - IP ITU-T, "Internet Protocol Data Communication Service - IP
Packet Transfer and Availability Performance Parameters", Packet Transfer and Availability Performance Parameters",
Series Y: Global Information Infrastructure, Internet Series Y: Global Information Infrastructure, Internet
Protocol Aspects and Next-Generation Networks , Protocol Aspects and Next-Generation Networks , March
March 2011, 2011, <http://www.itu.int/rec/T-REC-Y.1540-201103-I/en>.
<http://www.itu.int/rec/T-REC-Y.1540-201103-I/en>.
[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, July 2003. Applications", STD 64, RFC 3550, DOI 10.17487/RFC3550,
July 2003, <http://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
July 2006. 10.17487/RFC4585, July 2006,
<http://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, February 2008. (RTP/SAVPF)", RFC 5124, DOI 10.17487/RFC5124, February
2008, <http://www.rfc-editor.org/info/rfc5124>.
[RFC5481] Morton, A. and B. Claise, "Packet Delay Variation [RFC5481] Morton, A. and B. Claise, "Packet Delay Variation
Applicability Statement", RFC 5481, March 2009. Applicability Statement", RFC 5481, DOI 10.17487/RFC5481,
March 2009, <http://www.rfc-editor.org/info/rfc5481>.
[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,
December 2012. DOI 10.17487/RFC6817, December 2012,
<http://www.rfc-editor.org/info/rfc6817>.
Authors' Addresses Authors' Addresses
David Hayes (editor) David Hayes (editor)
University of Oslo University of Oslo
PO Box 1080 Blindern PO Box 1080 Blindern
Oslo, N-0316 Oslo N-0316
Norway Norway
Phone: +47 2284 5566 Phone: +47 2284 5566
Email: davihay@ifi.uio.no Email: davihay@ifi.uio.no
Simone Ferlin Simone Ferlin
Simula Research Laboratory Simula Research Laboratory
P.O.Box 134 P.O.Box 134
Lysaker, 1325 Lysaker 1325
Norway Norway
Phone: +47 4072 0702 Phone: +47 4072 0702
Email: ferlin@simula.no 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 Phone: +47 2285 2420
Email: michawe@ifi.uio.no Email: michawe@ifi.uio.no
Kristian Hiorth
University of Oslo
PO Box 1080 Blindern
Oslo N-0316
Norway
Email: kristahi@ifi.uio.no
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