draft-ietf-rmcat-sbd-00.txt		draft-ietf-rmcat-sbd-01.txt
	RTP Media Congestion Avoidance D. Hayes, Ed.		RTP Media Congestion Avoidance D. Hayes, Ed.
	Techniques University of Oslo		Techniques University of Oslo
	Internet-Draft S. Ferlin		Internet-Draft S. Ferlin
	Intended status: Experimental Simula Research Laboratory		Intended status: Experimental Simula Research Laboratory
	Expires: November 9, 2015 M. Welzl		Expires: January 2, 2016 M. Welzl
	University of Oslo		University of Oslo
	May 8, 2015		July 1, 2015
	Shared Bottleneck Detection for Coupled Congestion Control for RTP		Shared Bottleneck Detection for Coupled Congestion Control for RTP
	Media.		Media.
	draft-ietf-rmcat-sbd-00		draft-ietf-rmcat-sbd-01
	Abstract		Abstract
	This document describes a mechanism to detect whether end-to-end data		This document describes a mechanism to detect whether end-to-end data
	flows share a common bottleneck. It relies on summary statistics		flows share a common bottleneck. It relies on summary statistics
	that are calculated by a data receiver based on continuous		that are calculated by a data receiver based on continuous
	measurements and regularly fed to a grouping algorithm that runs		measurements and regularly fed to a grouping algorithm that runs
	wherever the knowledge is needed. This mechanism complements the		wherever the knowledge is needed. This mechanism complements the
	coupled congestion control mechanism in draft-welzl-rmcat-coupled-cc.		coupled congestion control mechanism in draft-welzl-rmcat-coupled-cc.
	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 November 9, 2015.		This Internet-Draft will expire on January 2, 2016.
	Copyright Notice		Copyright Notice
	Copyright (c) 2015 IETF Trust and the persons identified as the		Copyright (c) 2015 IETF Trust and the persons identified as the
	document authors. All rights reserved.		document authors. All rights reserved.
	This document is subject to BCP 78 and the IETF Trust's Legal		This document is subject to BCP 78 and the IETF Trust's Legal
	Provisions Relating to IETF Documents		Provisions Relating to IETF Documents
	(http://trustee.ietf.org/license-info) in effect on the date of		(http://trustee.ietf.org/license-info) in effect on the date of
	publication of this document. Please review these documents		publication of this document. Please review these documents
	skipping to change at page 2, line 18		skipping to change at page 2, line 18
	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 signals . . . . . . . . . . . . . . . . . . . . . . . 3
	1.1.1. Packet Loss . . . . . . . . . . . . . . . . . . . . . 3		1.1.1. Packet Loss . . . . . . . . . . . . . . . . . . . . . 3
	1.1.2. Packet Delay . . . . . . . . . . . . . . . . . . . . . 3		1.1.2. Packet Delay . . . . . . . . . . . . . . . . . . . . . 3
	1.1.3. Path Lag . . . . . . . . . . . . . . . . . . . . . . . 4		1.1.3. Path Lag . . . . . . . . . . . . . . . . . . . . . . . 4
	2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4		2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4
	2.1. Parameter Values . . . . . . . . . . . . . . . . . . . . . 5		2.1. Parameters and their Effect . . . . . . . . . . . . . . . 5
	3. Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 6		2.2. Recommended Parameter Values . . . . . . . . . . . . . . . 7
	3.1. Key metrics and their calculation . . . . . . . . . . . . 7		3. Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 7
	3.1.1. Mean delay . . . . . . . . . . . . . . . . . . . . . . 7		3.1. Key metrics and their calculation . . . . . . . . . . . . 9
	3.1.2. Skewness Estimate . . . . . . . . . . . . . . . . . . 8		3.1.1. Mean delay . . . . . . . . . . . . . . . . . . . . . . 9
	3.1.3. Variance Estimate . . . . . . . . . . . . . . . . . . 9		3.1.2. Skewness Estimate . . . . . . . . . . . . . . . . . . 9
	3.1.4. Oscillation Estimate . . . . . . . . . . . . . . . . . 9		3.1.3. Variability Estimate . . . . . . . . . . . . . . . . . 10
	3.1.5. Packet loss . . . . . . . . . . . . . . . . . . . . . 10		3.1.4. Oscillation Estimate . . . . . . . . . . . . . . . . . 11
	3.2. Flow Grouping . . . . . . . . . . . . . . . . . . . . . . 10		3.1.5. Packet loss . . . . . . . . . . . . . . . . . . . . . 11
	3.2.1. Flow Grouping Algorithm . . . . . . . . . . . . . . . 10		3.2. Flow Grouping . . . . . . . . . . . . . . . . . . . . . . 12
	3.2.2. Using the flow group signal . . . . . . . . . . . . . 12		3.2.1. Flow Grouping Algorithm . . . . . . . . . . . . . . . 12
	3.3. Removing Noise from the Estimates . . . . . . . . . . . . 12		3.2.2. Using the flow group signal . . . . . . . . . . . . . 13
	3.3.1. Oscillation noise . . . . . . . . . . . . . . . . . . 12		3.3. Removing Noise from the Estimates . . . . . . . . . . . . 13
	3.3.2. Clock drift . . . . . . . . . . . . . . . . . . . . . 13		3.3.1. PDV noise . . . . . . . . . . . . . . . . . . . . . . 14
	3.3.3. Bias in the skewness measure . . . . . . . . . . . . . 14		3.3.2. Oscillation noise . . . . . . . . . . . . . . . . . . 14
	3.4. Reducing lag and Improving Responsiveness . . . . . . . . 14		3.3.3. Clock skew . . . . . . . . . . . . . . . . . . . . . . 15
	3.4.1. Improving the response of the skewness estimate . . . 15		3.4. Reducing lag and Improving Responsiveness . . . . . . . . 15
	3.4.2. Improving the response of the variance estimate . . . 15		3.4.1. Improving the response of the skewness estimate . . . 16
	4. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 16		3.4.2. Improving the response of the variability estimate . . 16
	4.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 16		4. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 17
	5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 16		4.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 17
	6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 16		5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 17
	7. Security Considerations . . . . . . . . . . . . . . . . . . . 16		6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 17
	8. Change history . . . . . . . . . . . . . . . . . . . . . . . . 17		7. Security Considerations . . . . . . . . . . . . . . . . . . . 17
	9. References . . . . . . . . . . . . . . . . . . . . . . . . . . 17		8. Change history . . . . . . . . . . . . . . . . . . . . . . . . 18
	9.1. Normative References . . . . . . . . . . . . . . . . . . . 17		9. References . . . . . . . . . . . . . . . . . . . . . . . . . . 18
	9.2. Informative References . . . . . . . . . . . . . . . . . . 17		9.1. Normative References . . . . . . . . . . . . . . . . . . . 18
	Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 18		9.2. Informative References . . . . . . . . . . . . . . . . . . 18
			Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 19
	1. Introduction		1. Introduction
	In the Internet, it is not normally known if flows (e.g., TCP		In the Internet, it is not normally known if flows (e.g., TCP
	connections or UDP data streams) traverse the same bottlenecks. Even		connections or UDP data streams) traverse the same bottlenecks. Even
	flows that have the same sender and receiver may take different paths		flows that have the same sender and receiver may take different paths
	and share a bottleneck or not. Flows that share a bottleneck link		and share a bottleneck or not. Flows that share a bottleneck link
	usually compete with one another for their share of the capacity.		usually compete with one another for their share of the capacity.
	This competition has the potential to increase packet loss and		This competition has the potential to increase packet loss and
	delays. This is especially relevant for interactive applications		delays. This is especially relevant for interactive applications
	skipping to change at page 3, line 47		skipping to change at page 3, line 47
	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 synchronised. However, since the		and receiver clocks to be synchronised. However, since the
	statistics being collected are relative to the mean OWD, a relative		statistics being collected are relative to the mean OWD, a relative
	OWD measurement is sufficient. Clock drift is not usually		OWD measurement is sufficient. Clock skew is not usually significant
	significant over the time intervals used by this SBD mechanism (see		over the time intervals used by this SBD mechanism (see [RFC6817] A.2
	[RFC6817] A.2 for a discussion on clock drift and OWD measurements).		for a discussion on clock skew and OWD measurements). However, in
	However, in circumstances where it is significant, Section 3.3.2		circumstances where it is significant, Section 3.3.3 outlines a way
	outlines a way of adjusting the calculations to cater for it.		of adjusting the calculations to cater for it.
	Each packet arriving at the bottleneck buffer may experience very		Each packet arriving at the bottleneck buffer may experience very
	different queue lengths, and therefore different waiting times. A		different queue lengths, and therefore different waiting times. A
	single OWD sample does not, therefore, characterize the path well.		single OWD sample does not, therefore, characterize the path well.
	However, multiple OWD measurements do reflect the distribution of		However, multiple OWD measurements do reflect the distribution of
	delays experienced at the bottleneck.		delays experienced at the bottleneck.
	1.1.3. Path Lag		1.1.3. Path Lag
	Flows that share a common bottleneck may traverse different paths,		Flows that share a common bottleneck may traverse different paths,
	skipping to change at page 4, line 31		skipping to change at page 4, line 31
	The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",		The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
	"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this		"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
	document are to be interpreted as described in RFC 2119 [RFC2119].		document are to be interpreted as described in RFC 2119 [RFC2119].
	Acronyms used in this document:		Acronyms used in this document:
	OWD -- One Way Delay		OWD -- One Way Delay
	PDV -- Packet Delay Variation		PDV -- Packet Delay Variation
			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		N -- 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
	interval N*T
	E_T(...) -- the expectation or mean of the measurements of the		sum_NT(...) -- summation of all measurements taken over the
	variable in parentheses over T		interval N*T
	E_N(...) -- The expectation or mean of the last N values of the		E_T(...) -- the expectation or mean of the measurements of the
	variable in parentheses		variable in parentheses over T
	E_M(...) -- The expectation or mean of the last M values of the		E_N(...) -- the expectation or mean of the last N values of the
	variable in parentheses, where M <= N.		variable in parentheses
			E_M(...) -- the expectation or mean of the last M values of the
			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
	parentheses taken over the interval T		parentheses taken over the interval T
	min_T(...) -- the minimum recorded measurement of the variable in		min_T(...) -- the minimum recorded measurement of the variable in
	parentheses taken over the interval T		parentheses taken over the interval T
	num_T(...) -- the count of measurements of the variable in		num_T(...) -- the count of measurements of the variable in
	parentheses taken in the interval T		parentheses taken in the interval T
	num_VM(...) -- the count of valid values of the variable in		num_VM(...) -- the count of valid values of the variable in
	parentheses given M records		parentheses given M records
	PC -- a boolean variable indicating the particular flow was		PC -- a boolean variable indicating the particular flow
	identified as experiencing congestion in the previous		was identified as experiencing congestion in the
	interval T (i.e. Previously Congested)		previous interval T (i.e. Previously Congested)
	CD_T -- an estimate of the effect of Clock Drift on the mean		skew_est -- a measure of skewness in a OWD distribution.
	OWD per T
	CD_Adj(...) -- Mean OWD adjusted for clock drift		var_est -- a measure of variability in OWD measurements.
	p_l, p_f, p_pdv, c_s, c_h, p_s, p_d, p_v -- various thresholds		freq_est -- a measure of low frequency oscillation in the OWD
	used in the mechanism.		measurements.
	N, M, and F -- number of values (calculated over T).		p_l, p_f, p_pdv, p_mad, c_s, c_h, p_s, p_d, p_v -- various
			thresholds used in the mechanism
	2.1. Parameter Values		M and F -- number of values related to N
			2.1. Parameters and their Effect
			T T should be long enough so that there are enough packets
			received during T for a useful estimate of short term mean
			OWD and variation statistics. Making T too large can limit
			the efficacy of PDV and freq_est. It will also increase the
			response time of the mechanism. Making T too small will make
			the metrics noisier.
			N & M N should be large enough provide a stable estimate of
			oscillations in OWD and average PDV. Usually M=N, though
			having M<N may be beneficial in certain circumstances. M*T
			needs to be long enough provide stable estimates of skewness
			and MAD (if used).
			F F determines the number of intervals over which statistics
			are considered to be equally weighted. When F=M recent and
			older measurements are considered equal. Making F<M can
			increase the responsiveness of the SBD mechanism. If F is
			too small, statistics will be too noisy.
			c_s c_s is the threshold in skew_est used for determining whether
			a flow is experiencing congestion or not. It should be
			slightly negative so that a very lightly loaded path does not
			give a false indication. Setting c_s more negative makes the
			SBD mechanism less sensitive to transient and light
			congestion episodes.
			c_s c_h adds hysteresis to the congestion determination. It
			should be large enough to avoid constant switching in the
			determination, but low enough to ensure that grouping is not
			attempted when there is no congestion and the delay and loss
			signals cannot be relied upon.
			p_v p_v determines the sensitivity of freq_est to noise. Making
			it smaller will yield higher but noisier values for freq_est.
			Making it too large will render it ineffective for
			determining groups.
			p_* Flows are separated when the skew_est|var_est|freq_est
			measure is greater than p_s|p_f|p_d|(p_pdv|p_mad). Adjusting
			these is a compromise between false grouping of flows that do
			not share a bottleneck and false splitting of flows that do.
			Making them larger can help if the measures are very noisy,
			but reducing the noise in the statistical measures by
			adjusting T and N|M may be a better solution.
			2.2. Recommended Parameter Values
	Reference [Hayes-LCN14] uses T=350ms, N=50, p_l = 0.1. The other		Reference [Hayes-LCN14] uses T=350ms, N=50, p_l = 0.1. The other
	parameters have been tightened to reflect minor enhancements to the		parameters have been tightened to reflect minor enhancements to the
	algorithm outlined in Section 3.3: c_s = -0.01, p_f = p_s = p_d =		algorithm outlined in Section 3.3: c_s = -0.01, p_f = p_s = p_d =
	0.1, p_pdv = 0.2, p_v = 0.2. M=50, F=10, and c_h = 0.3 are		0.1, p_pdv = 0.2, p_v = 0.2 (or p_mad=0.1, p_v=0.7). M=50, F=25, and
	additional parameters defined in the document. These are values that		c_h = 0.3 are additional parameters defined in the document. These
	seem to work well over a wide range of practical Internet conditions,		are values that seem to work well over a wide range of practical
	but are the subject of ongoing tests.		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 from flows		that the distribution of delay measurements of packets that traverse
	that share a common bottleneck have similar shape characteristics.		a common bottleneck have similar shape characteristics. These shape
	These shape characteristics are described using 3 key summary		characteristics are described using 3 key summary statistics:
	statistics:
	variance (estimate var_est, see Section 3.1.3)		variability (estimate var_est, see Section 3.1.3)
	skewness (estimate skew_est, see Section 3.1.2)		skewness (estimate skew_est, see Section 3.1.2)
	oscillation (estimate freq_est, see Section 3.1.4)		oscillation (estimate freq_est, see Section 3.1.4)
	with packet loss (estimate pkt_loss, see Section 3.1.5) used as a		with packet loss (estimate pkt_loss, see Section 3.1.5) used as a
	supplementary statistic.		supplementary statistic.
	Summary statistics help to address both the noise and the path lag		Summary statistics help to address both the noise and the path lag
	problems by describing the general shape over a relatively long		problems by describing the general shape over a relatively long
	period of time. This is sufficient for their application in coupled		period of time. This is sufficient for their application in coupled
	congestion control for RTP Media. They can be signalled from a		congestion control for RTP Media. They can be signalled from a
	receiver, which measures the OWD and calculates the summary		receiver, which measures the OWD and calculates the summary
	statistics, to a sender, which is the entity that is transmitting the		statistics, to a sender, which is the entity that is transmitting the
	media stream. An RTP Media device may be both a sender and a		media stream. An RTP Media device may be both a sender and a
	receiver. SBD can be performed at either Sender or receiver or both.		receiver. SBD can be performed at either a sender or a receiver or
			both.
	+----+		+----+
	| H2 |		| H2 |
	+----+		+----+
	|		|
	| L2		| L2
	|		|
	+----+ L1 | L3 +----+		+----+ L1 | L3 +----+
	| H1 |------|------| H3 |		| H1 |------|------| H3 |
	+----+ +----+		+----+ +----+
	skipping to change at page 7, line 50		skipping to change at page 9, line 28
	necessarily be synchronized. However, it is a base measure for the 3		necessarily be synchronized. However, it is a base measure for the 3
	summary statistics. The mean delay, E_T(OWD), is the average one way		summary statistics. The mean delay, E_T(OWD), is the average one way
	delay measured over T.		delay measured over T.
	To facilitate the other calculations, the last N E_T(OWD) values will		To facilitate the other calculations, the last N E_T(OWD) values will
	need to be stored in a cyclic buffer along with the moving average of		need to be stored in a cyclic buffer along with the moving average of
	E_T(OWD):		E_T(OWD):
	mean_delay = E_M(E_T(OWD)) = sum_M(E_T(OWD)) / M		mean_delay = E_M(E_T(OWD)) = sum_M(E_T(OWD)) / M
	where M <= N. Generally M=N, setting M to be less than N allows the		where M <= N. Generally M=N: setting M to be less than N allows the
	mechanism to be more responsive to changes, but potentially at the		mechanism to be more responsive to changes, but potentially at the
	expense of a higher error rate (see Section 3.4 for a discussion on		expense of a higher error rate (see Section 3.4 for a discussion on
	improving the responsiveness of the mechanism.)		improving the responsiveness of the mechanism.)
	3.1.2. Skewness Estimate		3.1.2. Skewness Estimate
	Skewness is difficult to calculate efficiently and accurately.		Skewness is difficult to calculate efficiently and accurately.
	Ideally it should be calculated over the entire period (M * T) from		Ideally it should be calculated over the entire period (M * T) from
	the mean OWD over that period. However this would require storing		the mean OWD over that period. However this would require storing
	every delay measurement over the period. Instead, an estimate is		every delay measurement over the period. Instead, an estimate is
	made over T using the previous calculation of mean_delay.		made over M * T based on a calculation every T using the previous T's
	Comparisons are made using the mean of M skew estimates (an		calculation of mean_delay.
	alternative that removes bias in the mean is given in Section 3.3.3).
	The skewness is estimated using two counters, counting the number of		The skewness is estimated using two counters, counting the number of
	one way delay samples (OWD) above and below the mean:		one way delay samples (OWD) above and below the mean:
	skew_est_T = (sum_T(OWD < mean_delay)		skew_base_T = sum_T(OWD < mean_delay) - sum_T(OWD > mean_delay)
	- sum_T(OWD > mean_delay)) / num_T(OWD)
	where		where
	if (OWD < mean_delay) 1 else 0		if (OWD < mean_delay) 1 else 0
	if (OWD > mean_delay) 1 else 0		if (OWD > mean_delay) 1 else 0
	skew_est_T is a number between -1 and 1		and mean_delay does not include the mean of the current T
			interval.
	skew_est = E_M(skew_est_T) = sum_M(skew_est_T) / M		skew_est = sum_MT(skew_base_T)/num_MT(OWD)
	For implementation ease, mean_delay does not include the mean of the		where skew_est is a number between -1 and 1
	current T interval.
	Note: Care must be taken when implementing the comparisons to ensure		Note: Care must be taken when implementing the comparisons to ensure
	that rounding does not bias skew_est. It is important that the mean		that rounding does not bias skew_est. It is important that the mean
	is calculated with a higher precision than the samples.		is calculated with a higher precision than the samples.
	3.1.3. Variance Estimate		3.1.3. Variability Estimate
	Packet Delay Variation (PDV) ([RFC5481] and [ITU-Y1540]) is used as		Packet Delay Variation (PDV) ([RFC5481] and [ITU-Y1540]) is used as
	an estimator of the variance of the delay signal. We define PDV as		an estimator of the variability of the delay signal. We define PDV
	follows:		as follows:
	PDV = PDV_max = max_T(OWD) - E_T(OWD)		PDV = PDV_max = max_T(OWD) - E_T(OWD)
	var_est = E_M(PDV) = sum_M(PDV) / M		var_est = E_M(PDV) = sum_M(PDV) / M
	This modifies PDV as outlined in [RFC5481] to provide a summary		This modifies PDV as outlined in [RFC5481] to provide a summary
	statistic version that best aids the grouping decisions of the		statistic version that best aids the grouping decisions of the
	algorithm (see [Hayes-LCN14] section IVB).		algorithm (see [Hayes-LCN14] section IVB).
	The use of PDV = PDV_min = E_T(OWD) - min_T(OWD) is currently being		Generally the maximum is sampled well during congestion, though it is
	investigated as an alternative that is less sensitive to noise. The		more sensitive to path and operating system noise. The use of PDV =
	drawback of using PDV_min is that it does not distinguish between		PDV_min = E_T(OWD) - min_T(OWD) would be less sensitive to this
	groups of flows with similar values of skew_est as well as PDV_max		noise, but is not well sampled during congestion at the bottleneck
	(see [Hayes-LCN14] section IVB).		and therefore not recommended.
	3.1.4. Oscillation Estimate		3.1.4. Oscillation Estimate
	An estimate of the low frequency oscillation of the delay signal is		An estimate of the low frequency oscillation of the delay signal is
	calculated by counting and normalising the significant mean,		calculated by counting and normalising the significant mean,
	E_T(OWD), crossings of mean_delay:		E_T(OWD), crossings of mean_delay:
	freq_est = number_of_crossings / N		freq_est = number_of_crossings / N
	Where		where we define a significant mean crossing as a crossing that
	we define a significant mean crossing as a crossing that
	extends p_v * var_est from mean_delay. In our experiments we		extends p_v * var_est from mean_delay. In our experiments we
	have found that p_v = 0.2 is a good value.		have found that p_v = 0.2 is a good value.
	Freq_est is a number between 0 and 1. Freq_est can be approximated		Freq_est is a number between 0 and 1. Freq_est can be approximated
	incrementally as follows:		incrementally as follows:
	With each new calculation of E_T(OWD) a decision is made as to		With each new calculation of E_T(OWD) a decision is made as to
	whether this value of E_T(OWD) significantly crosses the current		whether this value of E_T(OWD) significantly crosses the current
	long term mean, mean_delay, with respect to the previous		long term mean, mean_delay, with respect to the previous
	significant mean crossing.		significant mean crossing.
	A cyclic buffer, last_N_crossings, records a 1 if there is a		A cyclic buffer, last_N_crossings, records a 1 if there is a
	significant mean crossing, otherwise a 0.		significant mean crossing, otherwise a 0.
	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 subtracted from when a non-zero		significant mean crossing and decremented when a non-zero value is
	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.
	3.1.5. Packet loss		3.1.5. Packet loss
	The proportion of packets lost is used as a supplementary measure:		The proportion of packets lost is used as a supplementary measure:
	pkt_loss = sum_NT(lost packets) / sum_NT(total packets)		pkt_loss = sum_NT(lost packets) / sum_NT(total packets)
	Note: When pkt_loss is small it is very variable, however, when		Note: When pkt_loss is small it is very variable, however, when
	pkt_loss is high it becomes a stable measure for making grouping		pkt_loss is high it becomes a stable measure for making grouping
	decisions.		decisions..
	3.2. Flow Grouping		3.2. Flow Grouping
	3.2.1. Flow Grouping Algorithm		3.2.1. Flow Grouping Algorithm
	The following grouping algorithm is RECOMMENDED for SBD in the RMCAT		The following grouping algorithm is RECOMMENDED for SBD in the RMCAT
	context and is sufficient and efficient for small to moderate numbers		context and is sufficient and efficient for small to moderate numbers
	of flows. For very large numbers of flows (e.g. hundreds), a more		of flows. For very large numbers of flows (e.g. hundreds), a more
	complex clustering algorithm may be substituted.		complex clustering algorithm may be substituted.
	skipping to change at page 11, line 51		skipping to change at page 13, line 30
	diff(skew_est) < p_s		diff(skew_est) < p_s
	otherwise		otherwise
	diff(pkt_loss) < (p_d * pkt_loss)		diff(pkt_loss) < (p_d * pkt_loss)
	The threshold, (p_d * pkt_loss), is with respect to the		The threshold, (p_d * pkt_loss), is with respect to the
	highest value in the difference.		highest 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,		is simple to implement, and efficient for small numbers of flows (up
	such as are expected in RTCWEB.		to 10-20).
	3.2.2. Using the flow group signal		3.2.2. Using the flow group signal
	A grouping decisions is made every T from the second T, though they		A grouping decisions is made every T from the second T, though they
	will not attain their full design accuracy until after the N'th T		will not attain their full design accuracy until after the N'th T
	interval.		interval.
	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
	skipping to change at page 12, line 26		skipping to change at page 14, line 5
	coupled congestion controllers objectives.		coupled congestion controllers objectives.
	3.3. Removing Noise from the Estimates		3.3. Removing Noise from the Estimates
	The following describe small changes to the calculation of the key		The following describe small changes to the calculation of the key
	metrics that help remove noise from them. Currently these "tweaks"		metrics that help remove noise from them. Currently these "tweaks"
	are described separately to keep the main description succinct. In		are described separately to keep the main description succinct. In
	future revisions of the draft these enhancements may replace the		future revisions of the draft these enhancements may replace the
	original key metric calculations.		original key metric calculations.
	3.3.1. Oscillation noise		3.3.1. PDV noise
	When a path has no congestion, the PDV will be very small and the		Usually during congestion the max_T(OWD) is quite well sampled as the
			delay distribution is skewed toward the maximum. However max_T(OWD)
			is subject to delay noise from other queues along the path as well as
			the host operating system. Min_T(OWD) is less prone to noise along
			the path and from the host operating system, but is not well sampled
			during congestion (i.e. when there is a bottleneck). Flows with very
			different packet send rates exacerbate the problem.
			An alternative delay variation measure that is less sensitive to
			extreme values and different send rates is Mean Absolute Deviation
			(MAD). It can be implemented in an online manner as follows:
			var_base_T = sum_T(|OWD - E_T(OWD)|)
			where
			|x| is the absolute value of x
			E_T(OWD) is the mean OWD calculated in the previous T
			var_est = MAD_MT = sum_MT(var_base_T)/num_MT(OWD)
			For calculation of freq_est p_v=0.7 (MAD is a smaller number than
			PDV)
			For the grouping threshold p_mad=0.1 instead of p_pdv (MAD is less
			noisy so the test can be tighter)
			Note that the method for improving responsiveness of MAD_MT is the
			same as that described in Section 3.4.1 for skew_est.
			3.3.2. Oscillation noise
			When a path has no congestion, var_est will be very small and the
	recorded significant mean crossings will be the result of path noise.		recorded significant mean crossings will be the result of path noise.
	Thus up to N-1 meaningless mean crossings can be a source of error at		Thus up to N-1 meaningless mean crossings can be a source of error at
	the point a link becomes a bottleneck and flows traversing it begin		the point a link becomes a bottleneck and flows traversing it begin
	to be grouped.		to be grouped.
	To remove this source of noise from freq_est:		To remove this source of noise from freq_est:
	1. Set the current PDV to PDV = NaN (a value representing an invalid		1. Set the current PDV to PDV = NaN (a value representing an invalid
	record, ie Not a Number) for flows that are deemed to not be		record, i.e. Not a Number) for flows that are deemed to not be
	experiencing congestion by the first skew_est based grouping test		experiencing congestion by the first skew_est based grouping test
	(see Section 3.2.1).		(see Section 3.2.1).
	2. Then var_est = sum_M(PDV != NaN) / num_VM(PDV)		2. Then var_est = sum_M(PDV != NaN) / num_VM(PDV)
	3. For freq_est, only record a significant mean crossing if flow is		3. For freq_est, only record a significant mean crossing if flow is
	experiencing congestion.		experiencing congestion.
	These three changes will remove the non-congestion noise from		These three changes will remove the non-congestion noise from
	freq_est.		freq_est. A similar adjustment can be made for MAD based var_est.
	3.3.2. Clock drift		3.3.3. Clock skew
	Generally sender and receiver clock drift will be too small to cause		Generally sender and receiver clock skew will be too small to cause
	significant errors in the estimators. Skew_est is most sensitive to		significant errors in the estimators. Skew_est is most sensitive to
	this type of noise. In circumstances where clock drift is high,		this type of noise. In circumstances where clock skew is high,
	making M < N can reduce this error.		making M < N can reduce this error.
	A better method is to estimate the effect the clock drift is having		A better method is to estimate the effect the clock skew is having on
	on the E_N(E_T(OWD)), and then adjust mean_delay accordingly. A		the summary statistics, and then adjust statistics accordingly. A
	simple method of doing this follows:		simple online method of doing this based on min_T(OWD) will be
			described here in a subsequent version of the draft.
	First divide the N E_T(OWD) values into two halves (N/2 in each)
	-- old and new.
	Calculate a mean of the old half:
	Older_mean = E_old(E_T(OWD)) / N/2
	Calculate a mean of the new (most recent) half:
	Newer_mean = E_new(E_T(OWD)) / N/2
	A linear estimate of the Clock Drift per T estimates is:
	CD_T = (Newer_mean - Older_mean)/N/2
	An adjusted mean estimate then is:
	mean_delay = CD_Adj(E_M(E_T(OWD))) = E_M(E_T(OWD)) + CD_T *
	(M/2 + 0.5)
	CD_Adj can be thought of as a prediction of what the long term mean
	will be in the current measurement period T. It is used as the basis
	for skew_est and freq_est.
	3.3.3. Bias in the skewness measure
	If successive calculations of skew_est are made with very different
	numbers of samples (num_T(OWD)), the simple calculation of
	E_M(skew_est) used for grouping decisions will be biased by the
	intervals that have few samples samples. This bias can be corrected
	if necessary as follows.
	skew_base_T = sum_T(OWD < mean_delay) - sum_T(OWD > mean_delay)
	skew_est = sum_MT(skew_base_T)/num_MT(OWD)
	This calculation requires slightly more state, since an
	implementation will need to maintain two cyclic buffers storing
	skew_base_T and num_T(OWD) respectively to manage the rolling
	summations (note only one cyclic buffer is needed for the calculation
	of skew_est outlined previously).
	3.4. Reducing lag and Improving Responsiveness		3.4. Reducing lag and Improving Responsiveness
	Measurement based shared bottleneck detection makes decisions in the		Measurement based shared bottleneck detection makes decisions in the
	present based on what has been measured in the past. This means that		present based on what has been measured in the past. This means that
	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 less accurate		1. Reducing N and/or M -- but at the expense of having less accurate
	metrics, and/or		metrics, and/or
	2. Exploiting the fact that more recent measurements are more		2. Exploiting the fact that more recent measurements are more
	valuable than older measurements and weighting them accordingly.		valuable than older measurements and weighting them accordingly.
	Although more recent measurements are more valuable, older		Although more recent measurements are more valuable, older
	measurements are still needed to gain an accurate estimate of the		measurements are still needed to gain an accurate estimate of the
	distribution descriptor we are measuring. Unfortunately, the simple		distribution descriptor we are measuring. Unfortunately, the simple
	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
	skipping to change at page 15, line 8		skipping to change at page 16, line 8
	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.
	3.4.1. Improving the response of the skewness estimate		3.4.1. 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 3.3.3, can be calculated as follows:		Section 3.1.2, 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 (ie		where numsampT is an array of the number of OWD samples in each T
	num_T(OWD)), and numsampT(1) is the most recent; skew_base_T(1) is		(i.e. num_T(OWD)), and numsampT(1) is the most recent; skew_base_T(1)
	the most recent calculation of skew_base_T; 1:F refers to the integer		is the most recent calculation of skew_base_T; 1:F refers to the
	values 1 through to F, and [(M-F):1] refers to an array of the		integer values 1 through to F, and [(M-F):1] refers to an array of
	integer values (M-F) declining through to 1; and ".*" is the array		the integer values (M-F) declining through to 1; and ".*" is the
	scalar dot product operator.		array scalar dot product operator.
	3.4.2. Improving the response of the variance estimate		3.4.2. Improving the response of the variability estimate
	The weighted moving average for var_est can be calculated as follows:		The weighted moving average for var_est can be calculated as follows:
	var_est = ((M-F+1)*sum(PDV(1:F)) + sum([(M-F):1].*PDV(F+1:M)))		var_est = ((M-F+1)*sum(PDV(1:F)) + sum([(M-F):1].*PDV(F+1:M)))
	/ (F*(M-F+1) + sum([(M-F):1])		/ (F*(M-F+1) + sum([(M-F):1])
	where 1:F refers to the integer values 1 through to F, and [(M-F):1]		where 1:F refers to the integer values 1 through to F, and [(M-F):1]
	refers to an array of the integer values (M-F) declining through to		refers to an array of the integer values (M-F) declining through to
	1; and ".*" is the array scalar dot product operator. When removing		1; and ".*" is the array scalar dot product operator. When removing
	oscillation noise (see Section 3.3.1) this calculation must be		oscillation noise (see Section 3.3.2) this calculation must be
	adjusted to allow for invalid PDV records.		adjusted to allow for invalid PDV records.
	4. Measuring OWD		4. Measuring OWD
	This section discusses the OWD measurements required for this		This section discusses the OWD measurements required for this
	algorithm to detect shared bottlenecks.		algorithm to detect shared bottlenecks.
	The SBD mechanism described in this draft relies on differences		The SBD mechanism described in this draft relies on differences
	between OWD measurements to avoid the practical problems with		between OWD measurements to avoid the practical problems with
	measuring absolute OWD (see [Hayes-LCN14] section IIIC). Since all		measuring absolute OWD (see [Hayes-LCN14] section IIIC). Since all
	skipping to change at page 17, line 9		skipping to change at page 18, line 9
	Non-authenticated RTCP packets carrying shared bottleneck indications		Non-authenticated RTCP packets carrying shared bottleneck indications
	and summary statistics could allow attackers to alter the bottleneck		and summary statistics could allow attackers to alter the bottleneck
	sharing characteristics for private gain or disruption of other		sharing characteristics for private gain or disruption of other
	parties communication.		parties communication.
	8. Change history		8. Change history
	Changes made to this document:		Changes made to this document:
	02->WG-00 : Fixed missing 0.5 in 3.3.2 and missing brace in 3.3.3		WG-00->WG-01 : Moved unbiased skew section to replace skew
			estimate, more robust variability estimator, the
			term variance replaced with variability, clock
			drift term corrected to clock skew, revision to
			clock skew section with a place holder, description
			of parameters.
	01->02 : New section describing improvements to the key metric		02->WG-00 : Fixed missing 0.5 in 3.3.2 and missing brace in
	calculations that help to remove noise, bias, and		3.3.3
	reduce lag. Some revisions to the notation to make
	it clearer. Some tightening of the thresholds.
	00->01 : Revisions to terminology for clarity		01->02 : New section describing improvements to the key
			metric calculations that help to remove noise,
			bias, and reduce lag. Some revisions to the
			notation to make it clearer. Some tightening of
			the thresholds.
			00->01 : Revisions to terminology for clarity
	9. References		9. References
	9.1. Normative References		9.1. Normative References
	[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate		[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
	Requirement Levels", BCP 14, RFC 2119, March 1997.		Requirement Levels", BCP 14, RFC 2119, March 1997.
	9.2. Informative References		9.2. Informative References
End of changes. 61 change blocks.
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