 1/draftietfrmcatsbd01.txt 20151019 10:15:14.962969172 0700
+++ 2/draftietfrmcatsbd02.txt 20151019 10:15:15.006970245 0700
@@ 1,101 +1,102 @@
RTP Media Congestion Avoidance D. Hayes, Ed.
Techniques University of Oslo
InternetDraft S. Ferlin
Intended status: Experimental Simula Research Laboratory
Expires: January 2, 2016 M. Welzl
+RTP Media Congestion Avoidance Techniques D. Hayes, Ed.
+InternetDraft University of Oslo
+Intended status: Experimental S. Ferlin
+Expires: April 21, 2016 Simula Research Laboratory
+ M. Welzl
+ K. Kiorth
University of Oslo
 July 1, 2015
+ October 19, 2015
Shared Bottleneck Detection for Coupled Congestion Control for RTP
Media.
 draftietfrmcatsbd01
+ draftietfrmcatsbd02
Abstract
This document describes a mechanism to detect whether endtoend data
flows share a common bottleneck. It relies on summary statistics
that are calculated by a data receiver based on continuous
measurements and regularly fed to a grouping algorithm that runs
wherever the knowledge is needed. This mechanism complements the
coupled congestion control mechanism in draftwelzlrmcatcoupledcc.
Status of this Memo
+Status of This Memo
This InternetDraft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
InternetDrafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as InternetDrafts. The list of current Internet
Drafts is at http://datatracker.ietf.org/drafts/current/.
InternetDrafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use InternetDrafts as reference
material or to cite them other than as "work in progress."
 This InternetDraft will expire on January 2, 2016.
+ This InternetDraft will expire on April 21, 2016.
Copyright Notice
Copyright (c) 2015 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(http://trustee.ietf.org/licenseinfo) in effect on the date of
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
to this document. Code Components extracted from this document must
include Simplified BSD License text as described in Section 4.e of
the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
Table of Contents
 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
+ 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. The signals . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.1. Packet Loss . . . . . . . . . . . . . . . . . . . . . 3
 1.1.2. Packet Delay . . . . . . . . . . . . . . . . . . . . . 3
 1.1.3. Path Lag . . . . . . . . . . . . . . . . . . . . . . . 4
+ 1.1.2. Packet Delay . . . . . . . . . . . . . . . . . . . . 3
+ 1.1.3. Path Lag . . . . . . . . . . . . . . . . . . . . . . 4
2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4
 2.1. Parameters and their Effect . . . . . . . . . . . . . . . 5
 2.2. Recommended Parameter Values . . . . . . . . . . . . . . . 7
+ 2.1. Parameters and their Effect . . . . . . . . . . . . . . . 6
+ 2.2. Recommended Parameter Values . . . . . . . . . . . . . . 7
3. Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 7
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
 3.1.3. Variability Estimate . . . . . . . . . . . . . . . . . 10
 3.1.4. Oscillation Estimate . . . . . . . . . . . . . . . . . 11
+ 3.1.3. Variability Estimate . . . . . . . . . . . . . . . . 10
+ 3.1.4. Oscillation Estimate . . . . . . . . . . . . . . . . 11
3.1.5. Packet loss . . . . . . . . . . . . . . . . . . . . . 11
3.2. Flow Grouping . . . . . . . . . . . . . . . . . . . . . . 12
3.2.1. Flow Grouping Algorithm . . . . . . . . . . . . . . . 12
3.2.2. Using the flow group signal . . . . . . . . . . . . . 13
3.3. Removing Noise from the Estimates . . . . . . . . . . . . 13
 3.3.1. PDV noise . . . . . . . . . . . . . . . . . . . . . . 14
 3.3.2. Oscillation noise . . . . . . . . . . . . . . . . . . 14
 3.3.3. Clock skew . . . . . . . . . . . . . . . . . . . . . . 15
 3.4. Reducing lag and Improving Responsiveness . . . . . . . . 15
 3.4.1. Improving the response of the skewness estimate . . . 16
 3.4.2. Improving the response of the variability estimate . . 16
+ 3.3.1. Oscillation noise . . . . . . . . . . . . . . . . . . 14
+ 3.3.2. Clock skew . . . . . . . . . . . . . . . . . . . . . 14
+ 3.4. Reducing lag and Improving Responsiveness . . . . 14
+ 3.4.1. Improving the response of the skewness estimate . 15
+ 3.4.2. Improving the response of the variability estimate 17
4. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 17
4.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 17
 5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 17
 6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 17
 7. Security Considerations . . . . . . . . . . . . . . . . . . . 17
 8. Change history . . . . . . . . . . . . . . . . . . . . . . . . 18
 9. References . . . . . . . . . . . . . . . . . . . . . . . . . . 18
 9.1. Normative References . . . . . . . . . . . . . . . . . . . 18
 9.2. Informative References . . . . . . . . . . . . . . . . . . 18
 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 19
+ 5. Implementation status . . . . . . . . . . . . . . . . . . . . 18
+ 6. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 18
+ 7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 18
+ 8. Security Considerations . . . . . . . . . . . . . . . . . . . 18
+ 9. Change history . . . . . . . . . . . . . . . . . . . . . . . 18
+ 10. References . . . . . . . . . . . . . . . . . . . . . . . . . 19
+ 10.1. Normative References . . . . . . . . . . . . . . . . . . 19
+ 10.2. Informative References . . . . . . . . . . . . . . . . . 19
+ Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 20
1. Introduction
In the Internet, it is not normally known if flows (e.g., TCP
connections or UDP data streams) traverse the same bottlenecks. Even
flows that have the same sender and receiver may take different paths
and share a bottleneck or not. Flows that share a bottleneck link
usually compete with one another for their share of the capacity.
This competition has the potential to increase packet loss and
delays. This is especially relevant for interactive applications
@@ 128,21 +129,21 @@
device. The noise is often significantly increased if the roundtrip
time is used. The cleanest signal is obtained by using OneWayDelay
(OWD).
Measuring absolute OWD is difficult since it requires both the sender
and receiver clocks to be synchronised. However, since the
statistics being collected are relative to the mean OWD, a relative
OWD measurement is sufficient. Clock skew is not usually significant
over the time intervals used by this SBD mechanism (see [RFC6817] A.2
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.
Each packet arriving at the bottleneck buffer may experience very
different queue lengths, and therefore different waiting times. A
single OWD sample does not, therefore, characterize the path well.
However, multiple OWD measurements do reflect the distribution of
delays experienced at the bottleneck.
1.1.3. Path Lag
@@ 155,22 +156,20 @@
2. Definitions
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
Acronyms used in this document:
OWD  One Way Delay
 PDV  Packet Delay Variation

MAD  Mean Absolute Deviation
RTT  Round Trip Time
SBD  Shared Bottleneck Detection
Conventions used in this document:
T  the base time interval over which measurements are
made.
@@ 201,113 +201,121 @@
min_T(...)  the minimum recorded measurement of the variable in
parentheses taken over the interval T
num_T(...)  the count of measurements of the variable in
parentheses taken in the interval T
num_VM(...)  the count of valid values of the variable in
parentheses given M records
 PC  a boolean variable indicating the particular flow
 was identified as experiencing congestion in the
 previous interval T (i.e. Previously Congested)
+ PB  a boolean variable indicating the particular flow
+ was identified transiting a bottleneck in the
+ previous interval T (i.e. Previously Bottleneck)
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_base_T  a variable used as an intermediate step in
+ calculating var_est.
+
freq_est  a measure of low frequency oscillation in the OWD
measurements.
 p_l, p_f, p_pdv, p_mad, c_s, c_h, p_s, p_d, p_v  various
 thresholds used in the mechanism
+ p_l, p_f, p_mad, c_s, c_h, p_s, p_d, p_v  various thresholds
+ used in the mechanism
M and F  number of values related to N
+ .
+
2.1. Parameters and their Effect
+
T T should be long enough so that there are enough packets
received during T for a useful estimate of short term mean
OWD and variation statistics. Making T too large can limit
 the efficacy of PDV and freq_est. It will also increase the
 response time of the mechanism. Making T too small will make
 the metrics noisier.
+ the efficacy of freq_est. It will also increase the response
+ time of the mechanism. Making T too small will make the
+ metrics noisier.
 N & M N should be large enough provide a stable estimate of
 oscillations in OWD and average PDV. Usually M=N, though
 having M mean_delay)

 where
+ The base for the skewness calculation is estimated using a counter
+ initialised every T. It increments for one way delay samples (OWD)
+ below the mean and decrements for OWD above the mean. So for each
+ OWD sample:
 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
 interval.
+ The mean_delay does not include the mean of the current T interval to
+ enable it to be calculated iteratively.
skew_est = sum_MT(skew_base_T)/num_MT(OWD)
where skew_est is a number between 1 and 1
Note: Care must be taken when implementing the comparisons to ensure
that rounding does not bias skew_est. It is important that the mean
is calculated with a higher precision than the samples.
3.1.3. Variability Estimate
 Packet Delay Variation (PDV) ([RFC5481] and [ITUY1540]) is used as
 an estimator of the variability of the delay signal. We define PDV
 as follows:
+ Mean Absolute Deviation (MAD) delay is a robust variability measure
+ that copes well with different send rates. It can be implemented in
+ 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
 statistic version that best aids the grouping decisions of the
 algorithm (see [HayesLCN14] section IVB).
+ x is the absolute value of x
 Generally the maximum is sampled well during congestion, though it is
 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
 noise, but is not well sampled during congestion at the bottleneck
 and therefore not recommended.
+ 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
+
+ For the grouping threshold p_mad=0.1
3.1.4. Oscillation Estimate
An estimate of the low frequency oscillation of the delay signal is
calculated by counting and normalising the significant mean,
E_T(OWD), crossings of mean_delay:
freq_est = number_of_crossings / N
where we define a significant mean crossing as a crossing that
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
incrementally as follows:
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
long term mean, mean_delay, with respect to the previous
significant mean crossing.
A cyclic buffer, last_N_crossings, records a 1 if there is a
@@ 458,184 +483,155 @@
removed from the last_N_crossings.
This approximation of freq_est was not used in [HayesLCN14], which
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
are almost identical to when the full calculation is performed every
T.
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)
Note: When pkt_loss is small it is very variable, however, when
pkt_loss is high it becomes a stable measure for making grouping
 decisions..
+ decisions.
3.2. Flow Grouping
3.2.1. Flow Grouping Algorithm
The following grouping algorithm is RECOMMENDED for SBD in the RMCAT
context and is sufficient and efficient for small to moderate numbers
of flows. For very large numbers of flows (e.g. hundreds), a more
complex clustering algorithm may be substituted.
Since no single metric is precise enough to group flows (due to
noise), the algorithm uses multiple metrics. Each metric offers a
different "view" of the bottleneck link characteristics, and used
together they enable a more precise grouping of flows than would
otherwise be possible.
 Flows determined to be experiencing congestion are successively
 divided into groups based on freq_est, var_est, and skew_est.
+ Flows determined to be transiting a bottleneck are successively
+ divided into groups based on freq_est, var_est, skew_est and
+ pkt_loss.
 The first step is to determine which flows are experiencing
 congestion. This is important, since if a flow is not experiencing
 congestion its delay based metrics will not describe the bottleneck,
+ The first step is to determine which flows are transiting a
+ bottleneck. This is important, since if a flow is not transiting a
+ bottleneck its delay based metrics will not describe the bottleneck,
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_h && PC )

  pkt_loss > p_l
+  ( skew_est < c_h & PB )  pkt_loss > p_l
The parameter c_s controls how sensitive the mechanism is in
 detecting congestion. C_s = 0.0 was used in [HayesLCN14]. A value
 of c_s = 0.05 is a little more sensitive, and c_s = 0.05 is a little
 less sensitive. C_h controls the hysteresis on flows that were
 grouped as experiencing congestion last time.
+ detecting a bottleneck. C_s = 0.0 was used in [HayesLCN14]. A
+ value of c_s = 0.05 is a little more sensitive, and c_s = 0.05 is a
+ little less sensitive. C_h controls the hysteresis on flows that
+ 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
 divided into groups based on the freq_est, PDV, and skew_est summary
 statistics. The process proceeds according to the following steps:
+ These flows, flows transiting a bottleneck, are then progressively
+ divided into groups based on the freq_est, var_est, and skew_est
+ summary statistics. The process proceeds according to the following
+ steps:
2. Group flows whose difference in sorted freq_est is less than a
threshold:
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:
 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.
 4. Group flows whose difference in sorted skew_est or pkt_loss is
 less than a threshold:

 if pkt_loss < p_l
+ 4. Group flows whose difference in sorted skew_est is less than a
+ threshold:
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)
 The threshold, (p_d * pkt_loss), is with respect to the
 highest value in the difference.
+ The threshold, (p_d * pkt_loss), is with respect to the highest
+ value in the difference.
This procedure involves sorting estimates from highest to lowest. It
is simple to implement, and efficient for small numbers of flows (up
to 1020).
3.2.2. Using the flow group signal
 A grouping decisions is made every T from the second T, though they
 will not attain their full design accuracy until after the N'th T
 interval.
+ Grouping decisions can be made every T from the second T, however
+ they will not attain their full design accuracy until after the
+ 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
bottlenecks to fluctuate. A coupled congestion controller MAY decide
only to couple groups that remain stable, say grouped together 90% of
the time, depending on its objectives. Recommendations concerning
this are beyond the scope of this draft and will be specific to the
coupled congestion controllers objectives.
3.3. Removing Noise from the Estimates
The following describe small changes to the calculation of the key
metrics that help remove noise from them. Currently these "tweaks"
are described separately to keep the main description succinct. In
future revisions of the draft these enhancements may replace the
original key metric calculations.
3.3.1. PDV 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
+3.3.1. 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.
Thus up to N1 meaningless mean crossings can be a source of error at
the point a link becomes a bottleneck and flows traversing it begin
to be grouped.
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
 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).
 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
 experiencing congestion.
+ 3. For freq_est, only record a significant mean crossing if flow
+ deemed to be transiting a bottleneck.
 These three changes will remove the noncongestion noise from
 freq_est. A similar adjustment can be made for MAD based var_est.
+ These three changes can help to remove the nonbottleneck noise from
+ freq_est.
3.3.3. Clock skew
+3.3.2. Clock skew
Generally sender and receiver clock skew will be too small to cause
significant errors in the estimators. Skew_est is most sensitive to
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
the summary statistics, and then adjust statistics accordingly. A
simple online method of doing this based on min_T(OWD) will be
described here in a subsequent version of the draft.
3.4. Reducing lag and Improving Responsiveness
Measurement based shared bottleneck detection makes decisions in the
present based on what has been measured in the past. This means that
@@ 675,33 +671,90 @@
+ sum([(MF):1].*numsampT(F+1:M)))
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 [(MF):1] refers to an array of
the integer values (MF) declining through to 1; and ".*" is the
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 + (MF)*skewbase_hist(F+1)
+  sum_skewbase
+
+ 9. W_D_numsamp = W_D_numsamp + (MF)*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 = ((MF+1)*F_skewbase + W_D_skewbase) /
+ ((MF+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
 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 = ((MF+1)*sum(PDV(1:F)) + sum([(MF):1].*PDV(F+1:M)))
+ var_est = ((MF+1)*sum(var_base_T(1:F))
 / (F*(MF+1) + sum([(MF):1])
+ + sum([(MF):1].*var_base_T(F+1:M)))
 where 1:F refers to the integer values 1 through to F, and [(MF):1]
 refers to an array of the integer values (MF) declining through to
 1; and ".*" is the array scalar dot product operator. When removing
 oscillation noise (see Section 3.3.2) this calculation must be
 adjusted to allow for invalid PDV records.
+ / ((MF+1)*sum(numsampT(1:F))
+
+ + sum([(MF):1].*numsampT(F+1:M)))
+
+ 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 [(MF):1] refers to an array of
+ the integer values (MF) 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
This section discusses the OWD measurements required for this
algorithm to detect shared bottlenecks.
The SBD mechanism described in this draft relies on differences
between OWD measurements to avoid the practical problems with
measuring absolute OWD (see [HayesLCN14] section IIIC). Since all
summary statistics are relative to the mean OWD and sender/receiver
@@ 713,130 +766,156 @@
The SBD mechanism requires timing information precise enough to be
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
delays. In general, the lower the time resolution, the more care
that needs to be taken to ensure rounding errors do not bias the
skewness calculation.
Typical RTP media flows use submillisecond timers, which should be
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 partfunded by the European Community under its Seventh
Framework Programme through the Reducing Internet Transport Latency
(RITE) project (ICT317700). The views expressed are solely those of
the authors.
6. IANA Considerations
+7. IANA Considerations
This memo includes no request to IANA.
7. Security Considerations
+8. Security Considerations
The security considerations of RFC 3550 [RFC3550], RFC 4585
[RFC4585], and RFC 5124 [RFC5124] are expected to apply.
Nonauthenticated RTCP packets carrying shared bottleneck indications
and summary statistics could allow attackers to alter the bottleneck
sharing characteristics for private gain or disruption of other
parties communication.
8. Change history
+9. Change history
Changes made to this document:
+ WG01>WG02 : 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.
+
WG00>WG01 : Moved unbiased skew section to replace skew
estimate, more robust variability estimator, the
term variance replaced with variability, clock
drift term corrected to clock skew, revision to
clock skew section with a place holder, description
of parameters.
02>WG00 : Fixed missing 0.5 in 3.3.2 and missing brace in
3.3.3
01>02 : New section describing improvements to the key
metric calculations that help to remove noise,
bias, and reduce lag. Some revisions to the
notation to make it clearer. Some tightening of
the thresholds.
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
 Requirement Levels", BCP 14, RFC 2119, March 1997.
+ Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/
+ RFC2119, March 1997,
+ .
9.2. Informative References
+10.2. Informative References
[HayesLCN14]
Hayes, D., Ferlin, S., and M. Welzl, "Practical Passive
Shared Bottleneck Detection using Shape Summary
 Statistics", Proc. the IEEE Local Computer Networks
 (LCN) p150158, September 2014, .
[ID.welzlrmcatcoupledcc]
Welzl, M., Islam, S., and S. Gjessing, "Coupled congestion
control for RTP media", draftwelzlrmcatcoupledcc04
(work in progress), October 2014.
[ITUY1540]
ITUT, "Internet Protocol Data Communication Service  IP
Packet Transfer and Availability Performance Parameters",
Series Y: Global Information Infrastructure, Internet
 Protocol Aspects and NextGeneration Networks ,
 March 2011,
 .
+ Protocol Aspects and NextGeneration Networks , March
+ 2011, .
[RFC3550] Schulzrinne, H., Casner, S., Frederick, R., and V.
Jacobson, "RTP: A Transport Protocol for RealTime
 Applications", STD 64, RFC 3550, July 2003.
+ Applications", STD 64, RFC 3550, DOI 10.17487/RFC3550,
+ July 2003, .
[RFC4585] Ott, J., Wenger, S., Sato, N., Burmeister, C., and J. Rey,
"Extended RTP Profile for Realtime Transport Control
 Protocol (RTCP)Based Feedback (RTP/AVPF)", RFC 4585,
 July 2006.
+ Protocol (RTCP)Based Feedback (RTP/AVPF)", RFC 4585, DOI
+ 10.17487/RFC4585, July 2006,
+ .
[RFC5124] Ott, J. and E. Carrara, "Extended Secure RTP Profile for
Realtime Transport Control Protocol (RTCP)Based Feedback
 (RTP/SAVPF)", RFC 5124, February 2008.
+ (RTP/SAVPF)", RFC 5124, DOI 10.17487/RFC5124, February
+ 2008, .
[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, .
[RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
"Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
 December 2012.
+ DOI 10.17487/RFC6817, December 2012,
+ .
Authors' Addresses
David Hayes (editor)
University of Oslo
PO Box 1080 Blindern
 Oslo, N0316
+ Oslo N0316
Norway
Phone: +47 2284 5566
Email: davihay@ifi.uio.no
Simone Ferlin
Simula Research Laboratory
P.O.Box 134
 Lysaker, 1325
+ Lysaker 1325
Norway
Phone: +47 4072 0702
Email: ferlin@simula.no
Michael Welzl
University of Oslo
PO Box 1080 Blindern
 Oslo, N0316
+ Oslo N0316
Norway
Phone: +47 2285 2420
Email: michawe@ifi.uio.no
+
+ Kristian Hiorth
+ University of Oslo
+ PO Box 1080 Blindern
+ Oslo N0316
+ Norway
+
+ Email: kristahi@ifi.uio.no