draft-ietf-rmcat-sbd-05.txt		draft-ietf-rmcat-sbd-06.txt
	RTP Media Congestion Avoidance Techniques D. Hayes, Ed.		RTP Media Congestion Avoidance Techniques D. Hayes, Ed.
	Internet-Draft S. Ferlin		Internet-Draft S. Ferlin
	Intended status: Experimental Simula Research Laboratory		Intended status: Experimental Simula Research Laboratory
	Expires: March 21, 2017 M. Welzl		Expires: August 19, 2017 M. Welzl
	K. Hiorth		K. Hiorth
	University of Oslo		University of Oslo
	September 17, 2016		February 15, 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-05		draft-ietf-rmcat-sbd-06
	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.
	skipping to change at page 1, line 39 ¶		skipping to change at page 1, line 39 ¶
	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
	working documents as Internet-Drafts. The list of current Internet-		working documents as Internet-Drafts. The list of current Internet-
	Drafts is at http://datatracker.ietf.org/drafts/current/.		Drafts is at http://datatracker.ietf.org/drafts/current/.
	Internet-Drafts are draft documents valid for a maximum of six months		Internet-Drafts are draft documents valid for a maximum of six months
	and may be updated, replaced, or obsoleted by other documents at any		and may be updated, replaced, or obsoleted by other documents at any
	time. It is inappropriate to use Internet-Drafts as reference		time. It is inappropriate to use Internet-Drafts as reference
	material or to cite them other than as "work in progress."		material or to cite them other than as "work in progress."
	This Internet-Draft will expire on March 21, 2017.		This Internet-Draft will expire on August 19, 2017.
	Copyright Notice		Copyright Notice
	Copyright (c) 2016 IETF Trust and the persons identified as the		Copyright (c) 2017 IETF Trust and the persons identified as the
	document authors. All rights reserved.		document authors. All rights reserved.
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	Provisions Relating to IETF Documents		Provisions Relating to IETF Documents
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	to this document. Code Components extracted from this document must		to this document. Code Components extracted from this document must
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	described in the Simplified BSD License.		described in the Simplified BSD License.
	Table of Contents		Table of Contents
	1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3		1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
	1.1. The signals . . . . . . . . . . . . . . . . . . . . . . . 3		1.1. The basic mechanism . . . . . . . . . . . . . . . . . . . 3
	1.1.1. Packet Loss . . . . . . . . . . . . . . . . . . . . . 3		1.2. The signals . . . . . . . . . . . . . . . . . . . . . . . 3
	1.1.2. Packet Delay . . . . . . . . . . . . . . . . . . . . 3		1.2.1. Packet loss . . . . . . . . . . . . . . . . . . . . . 3
	1.1.3. Path Lag . . . . . . . . . . . . . . . . . . . . . . 4		1.2.2. Packet delay . . . . . . . . . . . . . . . . . . . . 3
			1.2.3. Path lag . . . . . . . . . . . . . . . . . . . . . . 4
	2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4		2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4
	2.1. Parameters and their Effect . . . . . . . . . . . . . . . 7		2.1. Parameters and their effect . . . . . . . . . . . . . . . 7
	2.2. Recommended Parameter Values . . . . . . . . . . . . . . 8		2.2. Recommended parameter values . . . . . . . . . . . . . . 8
	3. Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 8		3. Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 8
	3.1. SBD feedback requirements . . . . . . . . . . . . . . . . 9		3.1. SBD feedback requirements . . . . . . . . . . . . . . . . 9
	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 . . . . . . 10		receiver and SBD performed at the sender . . . . . . 10
	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 . . . . . . . . . . . . 11		3.2. Key metrics and their calculation . . . . . . . . . . . . 11
	3.2.1. Mean delay . . . . . . . . . . . . . . . . . . . . . 11		3.2.1. Mean delay . . . . . . . . . . . . . . . . . . . . . 11
	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 . . . . . . . . . . . . . 15		3.3.2. Using the flow group signal . . . . . . . . . . . . . 15
	3.4. Removing Noise from the Estimates . . . . . . . . . . . . 15		4. Enhancements to the basic SBD algorithm . . . . . . . . . . . 15
	3.4.1. Oscillation noise . . . . . . . . . . . . . . . . . . 15		4.1. Reducing lag and improving responsiveness . . . . . . . . 15
	3.4.2. Clock skew . . . . . . . . . . . . . . . . . . . . . 15		4.1.1. Improving the response of the skewness estimate . . . 16
	3.5. Reducing lag and Improving Responsiveness . . . . . . . . 16		4.1.2. Improving the response of the variability estimate . 18
	3.5.1. Improving the response of the skewness estimate . . . 16		4.2. Removing oscillation noise . . . . . . . . . . . . . . . 18
	3.5.2. Improving the response of the variability estimate . 19		5. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 19
	4. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 19		5.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 19
	4.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 19		5.2. Clock skew . . . . . . . . . . . . . . . . . . . . . . . 19
	5. Expected feedback from experiments . . . . . . . . . . . . . 20		6. Expected feedback from experiments . . . . . . . . . . . . . 19
	6. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 20		7. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 20
	7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 20		8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 20
	8. Security Considerations . . . . . . . . . . . . . . . . . . . 20		9. Security Considerations . . . . . . . . . . . . . . . . . . . 20
	9. Change history . . . . . . . . . . . . . . . . . . . . . . . 20		10. Change history . . . . . . . . . . . . . . . . . . . . . . . 20
	10. References . . . . . . . . . . . . . . . . . . . . . . . . . 21		11. References . . . . . . . . . . . . . . . . . . . . . . . . . 21
	10.1. Normative References . . . . . . . . . . . . . . . . . . 21		11.1. Normative References . . . . . . . . . . . . . . . . . . 21
	10.2. Informative References . . . . . . . . . . . . . . . . . 21		11.2. Informative References . . . . . . . . . . . . . . . . . 21
	Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 22		Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 22
	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 may or may not share a bottleneck. Flows that share a bottleneck		and may or may not share a bottleneck. Flows that share a bottleneck
	link usually compete with one another for their share of the		link usually compete with one another for their share of the
	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 signals		1.1. The basic mechanism
			The mechanism groups flows that have similar statistical
			characteristics together. Section 3.3.1 describes a simple method
			for achieving this, however, a major part of this draft is concerned
			with collecting suitable statistics for this purpose.
			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.
	1.1.1. 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.
	1.1.2. 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
	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 3.4.2 outlines a way		circumstances where it is significant, Section 5.2 outlines a way of
	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.
	1.1.3. 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 4 ¶		skipping to change at page 5, line 15 ¶
	N -- the number of base time, T, intervals used in some		N -- the number of base time, T, intervals used in some
	calculations.		calculations.
	M -- the number of base time, T, intervals used in some		M -- the number of base time, T, intervals used in some
	calculations.		calculations.
	sum_T(...) -- summation of all the measurements of the variable		sum_T(...) -- summation of all the measurements of the variable
	in parentheses taken over the interval T		in parentheses taken over the interval T
	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_N(...) -- summation of N terms of the variable in parentheses
	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
			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 the
	variable in parentheses		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 the
	variable in parentheses, where M <= N.		variable in parentheses, where M <= N.
	max_T(...) -- the maximum recorded measurement of the variable in		max_T(...) -- the maximum recorded measurement of the variable in
	skipping to change at page 7, line 5 ¶		skipping to change at page 7, line 5 ¶
	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_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
	skipping to change at page 8, line 5 ¶		skipping to change at page 8, line 5 ¶
	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_mad. Adjusting these		measure is greater than p_s|p_f|p_d|p_mad. Adjusting these
	is a compromise between false grouping of flows that do not		is a compromise between false grouping of flows that do 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 3.4: c_s=-0.01, p_f=p_d=0.1, p_s=0.15,		algorithm outlined in Section 4: c_s=-0.01, 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 9, line 36 ¶		skipping to change at page 9, line 36 ¶
	1. Both summary statistic calculations and SBD are performed at		1. Both summary statistic calculations and SBD are performed at
	senders only.		senders only.
	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).
			Note that the mechanism bases its calculations on the interval T. It
			does not require T to be the feedback interval, only that
			calculations can be performed over measurements made in that
			interval.
	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.
	The sender requires precise accurate OWD measurements for every		The sender requires precise accurate OWD measurements for every
	packet, along with an indication of lost packets (or the proportion		packet, along with an indication of lost packets (or the proportion
	of packets lost over the interval T). The mechanism performs its		of packets lost over the interval T). The mechanism performs its
	calculations every T and requires measurements to be available for		calculations every T and requires measurements to be available for
	this.		this.
	An initialization message may be required to agree on the feedback		It is expected that the draft-ietf-rmcat-feedback-message will
	interval.		provide the necessary feedback for both SBD and congestion
			controllers.
	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
	skipping to change at page 10, line 41 ¶		skipping to change at page 10, line 45 ¶
	precision of the relayed statistics.		precision of the relayed statistics.
	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 will be fed back		After initialization the agreed summary statistics will be fed back
	to the sender every T.		to 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 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
	skipping to change at page 11, line 30 ¶		skipping to change at page 11, line 35 ¶
	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 3.5 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 13 ¶		skipping to change at page 12, line 20 ¶
	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		For calculation of freq_est p_v=0.7
	For the grouping threshold p_mad=0.1		For the grouping threshold p_mad=0.1
	3.2.4. Oscillation Estimate		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 31 ¶		skipping to change at page 13, line 39 ¶
	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 29 ¶		skipping to change at page 14, line 39 ¶
	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
	3. Group flows whose difference in sorted E_M(var_est) (highest to		3. Subdivide freq_est groups by grouping flows whose difference in
	lowest) is less than a threshold:		sorted E_M(var_est) (highest to lowest) is less than a threshold:
	diff(var_est) < (p_mad * var_est)		diff(var_est) < (p_mad * var_est)
	The threshold, (p_mad * 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 is less than a		4. Subdivide var_est groups by grouping flows whose difference in
	threshold:		sorted skew_est is less than a threshold:
	diff(skew_est) < p_s		diff(skew_est) < p_s
	5. When packet loss is high enough to be reliable (pkt_loss > p_l),		5. When packet loss is high enough to be reliable (pkt_loss > p_l),
	group flows whose difference is less than a threshold		Subdivide skew_est groups by grouping 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 highest		The threshold, (p_d * pkt_loss), is with respect to the 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).
	skipping to change at page 15, line 19 ¶		skipping to change at page 15, line 32 ¶
	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 controllers objectives.
	3.4. Removing Noise from the Estimates		4. Enhancements to the basic SBD algorithm
	The following describe small changes to the calculation of the key
	metrics that help remove noise from them. These "tweaks" are
	described separately to keep the main description succinct.
	3.4.1. Oscillation noise
	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 N-1 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 var_base_T = NaN (a value representing an invalid
	record, i.e. Not a Number) for flows that are deemed to not be
	transiting a bottleneck by the first skew_est based grouping test
	(see Section 3.3.1).
	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
	deemed to be transiting a bottleneck.
	These three changes can help to remove the non-bottleneck noise from
	freq_est.
	3.4.2. Clock skew
	Generally sender and receiver clock skew will be too small to cause		The SBD algorithm as specified in Section 3 was found to work well
	significant errors in the estimators. Skew_est and freq_est are the		for a broad variety of conditions. The following enhancements to the
	most sensitive to this type of noise due to their use of a mean OWD		basic mechanisms have been found to significantly improve the
	calculated over a longer interval. In circumstances where clock skew		algorithm's performance under some circumstances and SHOULD be
	is high, basing skew_est only on the previous T's mean and ignoring		implemented. These "tweaks" are described separately to keep the
	freq_est provides a noisier but reliable signal.		main description succinct.
	A more sophisticated method is to estimate the effect the clock skew		4.1. Reducing lag and improving responsiveness
	is having on the summary statistics, and then adjust statistics
	accordingly. There are a number of techniques in the literature,
	including [Zhang-Infocom02].
	3.5. Reducing lag and Improving Responsiveness		This section describes how to improve the responsiveness of the basic
			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:
	1. Reducing N and/or M -- but at the expense of having less accurate		1. Reducing N and/or M -- but at the expense of having less accurate
	metrics, and/or		metrics, and/or
	skipping to change at page 16, line 40 ¶		skipping to change at page 16, line 24 ¶
	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.
	3.5.1. 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))
	+ 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:		To calculate this weighted skew_est incrementally:
	skipping to change at page 18, line 10 ¶		skipping to change at page 17, line 10 ¶
	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:		To calculate this weighted skew_est incrementally:
	Notation: F_ - flat portion, D_ - declining portion, W_ - weighted		Notation: F_ - flat portion, D_ - declining portion, W_ - weighted
	component		component
	Initialise: sum_skewbase = 0, F_skewbase=0, W_D_skewbase=0		Initialize: sum_skewbase = 0, F_skewbase=0, W_D_skewbase=0
	skewbase_hist = buffer length M initialize to 0		skewbase_hist = buffer length M initialize to 0
	numsampT = buffer length M initialized to 0		numsampT = buffer length M initialized to 0
	Steps per iteration:		Steps per iteration:
	1. old_skewbase = skewbase_hist(M)		1. old_skewbase = skewbase_hist(M)
	2. old_numsampT = numsampT(M)		2. old_numsampT = numsampT(M)
	skipping to change at page 19, line 5 ¶		skipping to change at page 18, 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.
	3.5.2. 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))
	+ 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. When removing oscillation noise		array scalar dot product operator. When removing oscillation noise
	(see Section 3.4.1) 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 3.5.1. 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. Measuring OWD		4.2. Removing oscillation noise
			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 N-1 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 var_base_T = NaN (a value representing an invalid
			record, i.e. Not a Number) for flows that are deemed to not be
			transiting a bottleneck by the first skew_est based grouping test
			(see Section 3.3.1).
			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
			deemed to be transiting a bottleneck.
			These three changes can help to remove the non-bottleneck noise from
			freq_est.
			5. 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 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.
	4.1. 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 lower 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.		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. Expected feedback from experiments		5.2. Clock skew
			Generally sender and receiver clock skew will be too small to cause
			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
			calculated over a longer interval. In circumstances where clock skew
			is high, basing skew_est only on the previous T's mean and ignoring
			freq_est provides a noisier but reliable signal.
			A more sophisticated method is to estimate the effect the clock skew
			is having on the summary statistics, and then adjust statistics
			accordingly. There are a number of techniques in the literature,
			including [Zhang-Infocom02].
			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. Real network tests using the proposed congestion
	control algorithms will help confirm the default parameter choice.		control algorithms will help confirm the default parameter choice.
	For example, the time interval T may need to be made longer if the		For example, the time interval T may need to be made longer if the
	packet rate is very low. Implementers and testers are invited to		packet rate is very low. Implementers and testers are invited to
	document their findings in an Internet draft.		document their findings in an Internet draft.
	6. 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.
	7. IANA Considerations		8. IANA Considerations
	This memo includes no request to IANA.		This memo includes no request to IANA.
	8. 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 shared bottleneck indications		Non-authenticated RTCP packets carrying OWD measurements, shared
	and summary statistics could allow attackers to alter the bottleneck		bottleneck indications, and/or summary statistics could allow
	sharing characteristics for private gain or disruption of other		attackers to alter the bottleneck sharing characteristics for private
	parties communication.		gain or disruption of other parties communication.
	9. Change history		10. Change history
	Changes made to this document:		Changes made to this document:
			WG-05->WG-06 : Updates addressing WG reviews
			https://mailarchive.ietf.org/arch/msg/rmcat/-
			1JdrTMq1Y5T6ZNlOkrQJQ27TzE and
			https://mailarchive.ietf.org/arch/msg/rmcat/
			eI2Q1f8NL2SxbJgjFLR4_rEmJ_g. This has mainly
			involved minor clarifications, including the moving
			of 3.4.1 and 3.5 into the new Section 4, and 3.4.1
			into Section 5
	WG-04->WG-05 : Fix ToC formatting. Add section on expected		WG-04->WG-05 : Fix ToC formatting. Add section on expected
	feedback from experiments replacing short section		feedback from experiments replacing short section
	on implementation status. Added comment on ECN as		on implementation status. Added comment on ECN as
	a signal. Clarification of lost packet signaling.		a signal. Clarification of lost packet signaling.
	Change term "draft" to "document" where		Change term "draft" to "document" where
	appropriate. American spelling. Some tightening		appropriate. American spelling. Some tightening
	of the text.		of the text.
	WG-03->WG-04 : Add M to terminology table, suggest skew_est based		WG-03->WG-04 : Add M to terminology table, suggest skew_est based
	on previous T and no freq_est in clock skew		on previous T and no freq_est in clock skew
	section, feedback requirements as a separate sub		section, feedback requirements as a separate sub
	section.		section.
	WG-02->WG-03 : Correct misspelled author		WG-02->WG-03 : Correct misspelled author
	WG-01->WG-02 : Removed ambiguity associated with the term		WG-01->WG-02 : Removed ambiguity associated with the term
	"congestion". Expanded the description of		"congestion". Expanded the description of
	initialisation messages. Removed PDV metric.		initialization messages. Removed PDV metric.
	Added description of incremental weighted metric		Added description of incremental weighted metric
	calculations for skew_est. Various clarifications		calculations for skew_est. Various clarifications
	based on implementation work. Fixed typos and		based on implementation work. Fixed typos and
	tuned parameters.		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
	skipping to change at page 21, line 35 ¶		skipping to change at page 21, line 35 ¶
	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
	10. References		11. References
	10.1. 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>.		<http://www.rfc-editor.org/info/rfc2119>.
	10.2. 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.ietf-rmcat-coupled-cc]		[I-D.ietf-rmcat-coupled-cc]
End of changes. 56 change blocks.
	117 lines changed or deleted		151 lines changed or added
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