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
	This Internet-Draft is submitted in full conformance with the		This Internet-Draft is submitted in full conformance with the
	provisions of BCP 78 and BCP 79.		provisions of BCP 78 and BCP 79.
	Internet-Drafts are working documents of the Internet Engineering		Internet-Drafts are working documents of the Internet Engineering
	Task Force (IETF). Note that other groups may also distribute		Task Force (IETF). Note that other groups may also distribute
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	Copyright Notice		Copyright Notice
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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
End of changes. 82 change blocks.
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