draft-ietf-rmcat-sbd-01.txt		draft-ietf-rmcat-sbd-02.txt
	RTP Media Congestion Avoidance D. Hayes, Ed.		RTP Media Congestion Avoidance Techniques D. Hayes, Ed.
	Techniques University of Oslo		Internet-Draft University of Oslo
	Internet-Draft S. Ferlin		Intended status: Experimental S. Ferlin
	Intended status: Experimental Simula Research Laboratory		Expires: April 21, 2016 Simula Research Laboratory
	Expires: January 2, 2016 M. Welzl		M. Welzl
			K. Kiorth
	University of Oslo		University of Oslo
	July 1, 2015		October 19, 2015
	Shared Bottleneck Detection for Coupled Congestion Control for RTP		Shared Bottleneck Detection for Coupled Congestion Control for RTP
	Media.		Media.
	draft-ietf-rmcat-sbd-01		draft-ietf-rmcat-sbd-02
	Abstract		Abstract
	This document describes a mechanism to detect whether end-to-end data		This document describes a mechanism to detect whether end-to-end data
	flows share a common bottleneck. It relies on summary statistics		flows share a common bottleneck. It relies on summary statistics
	that are calculated by a data receiver based on continuous		that are calculated by a data receiver based on continuous
	measurements and regularly fed to a grouping algorithm that runs		measurements and regularly fed to a grouping algorithm that runs
	wherever the knowledge is needed. This mechanism complements the		wherever the knowledge is needed. This mechanism complements the
	coupled congestion control mechanism in draft-welzl-rmcat-coupled-cc.		coupled congestion control mechanism in draft-welzl-rmcat-coupled-cc.
	Status of this Memo		Status of This Memo
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	provisions of BCP 78 and BCP 79.		provisions of BCP 78 and BCP 79.
	Internet-Drafts are working documents of the Internet Engineering		Internet-Drafts are working documents of the Internet Engineering
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	Copyright Notice		Copyright Notice
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	document authors. All rights reserved.		document authors. All rights reserved.
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	Table of Contents		Table of Contents
	1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3		1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
	1.1. The signals . . . . . . . . . . . . . . . . . . . . . . . 3		1.1. The signals . . . . . . . . . . . . . . . . . . . . . . . 3
	1.1.1. Packet Loss . . . . . . . . . . . . . . . . . . . . . 3		1.1.1. Packet Loss . . . . . . . . . . . . . . . . . . . . . 3
	1.1.2. Packet Delay . . . . . . . . . . . . . . . . . . . . . 3		1.1.2. Packet Delay . . . . . . . . . . . . . . . . . . . . 3
	1.1.3. Path Lag . . . . . . . . . . . . . . . . . . . . . . . 4		1.1.3. Path Lag . . . . . . . . . . . . . . . . . . . . . . 4
	2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4		2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4
	2.1. Parameters and their Effect . . . . . . . . . . . . . . . 5		2.1. Parameters and their Effect . . . . . . . . . . . . . . . 6
	2.2. Recommended Parameter Values . . . . . . . . . . . . . . . 7		2.2. Recommended Parameter Values . . . . . . . . . . . . . . 7
	3. Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 7		3. Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 7
	3.1. Key metrics and their calculation . . . . . . . . . . . . 9		3.1. Key metrics and their calculation . . . . . . . . . . . . 9
	3.1.1. Mean delay . . . . . . . . . . . . . . . . . . . . . . 9		3.1.1. Mean delay . . . . . . . . . . . . . . . . . . . . . 9
	3.1.2. Skewness Estimate . . . . . . . . . . . . . . . . . . 9		3.1.2. Skewness Estimate . . . . . . . . . . . . . . . . . . 9
	3.1.3. Variability Estimate . . . . . . . . . . . . . . . . . 10		3.1.3. Variability Estimate . . . . . . . . . . . . . . . . 10
	3.1.4. Oscillation Estimate . . . . . . . . . . . . . . . . . 11		3.1.4. Oscillation Estimate . . . . . . . . . . . . . . . . 11
	3.1.5. Packet loss . . . . . . . . . . . . . . . . . . . . . 11		3.1.5. Packet loss . . . . . . . . . . . . . . . . . . . . . 11
	3.2. Flow Grouping . . . . . . . . . . . . . . . . . . . . . . 12		3.2. Flow Grouping . . . . . . . . . . . . . . . . . . . . . . 12
	3.2.1. Flow Grouping Algorithm . . . . . . . . . . . . . . . 12		3.2.1. Flow Grouping Algorithm . . . . . . . . . . . . . . . 12
	3.2.2. Using the flow group signal . . . . . . . . . . . . . 13		3.2.2. Using the flow group signal . . . . . . . . . . . . . 13
	3.3. Removing Noise from the Estimates . . . . . . . . . . . . 13		3.3. Removing Noise from the Estimates . . . . . . . . . . . . 13
	3.3.1. PDV noise . . . . . . . . . . . . . . . . . . . . . . 14		3.3.1. Oscillation noise . . . . . . . . . . . . . . . . . . 14
	3.3.2. Oscillation noise . . . . . . . . . . . . . . . . . . 14		3.3.2. Clock skew . . . . . . . . . . . . . . . . . . . . . 14
	3.3.3. Clock skew . . . . . . . . . . . . . . . . . . . . . . 15		3.4. Reducing lag and Improving Responsiveness . . . . 14
	3.4. Reducing lag and Improving Responsiveness . . . . . . . . 15		3.4.1. Improving the response of the skewness estimate . 15
	3.4.1. Improving the response of the skewness estimate . . . 16		3.4.2. Improving the response of the variability estimate 17
	3.4.2. Improving the response of the variability estimate . . 16		4. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 17
	4. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 17		4.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 17
	4.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 17		5. Implementation status . . . . . . . . . . . . . . . . . . . . 18
	5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 17		6. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 18
	6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 17		7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 18
	7. Security Considerations . . . . . . . . . . . . . . . . . . . 17		8. Security Considerations . . . . . . . . . . . . . . . . . . . 18
	8. Change history . . . . . . . . . . . . . . . . . . . . . . . . 18		9. Change history . . . . . . . . . . . . . . . . . . . . . . . 18
	9. References . . . . . . . . . . . . . . . . . . . . . . . . . . 18		10. References . . . . . . . . . . . . . . . . . . . . . . . . . 19
	9.1. Normative References . . . . . . . . . . . . . . . . . . . 18		10.1. Normative References . . . . . . . . . . . . . . . . . . 19
	9.2. Informative References . . . . . . . . . . . . . . . . . . 18		10.2. Informative References . . . . . . . . . . . . . . . . . 19
	Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 19		Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 20
	1. Introduction		1. Introduction
	In the Internet, it is not normally known if flows (e.g., TCP		In the Internet, it is not normally known if flows (e.g., TCP
	connections or UDP data streams) traverse the same bottlenecks. Even		connections or UDP data streams) traverse the same bottlenecks. Even
	flows that have the same sender and receiver may take different paths		flows that have the same sender and receiver may take different paths
	and share a bottleneck or not. Flows that share a bottleneck link		and share a bottleneck or not. Flows that share a bottleneck link
	usually compete with one another for their share of the capacity.		usually compete with one another for their share of the capacity.
	This competition has the potential to increase packet loss and		This competition has the potential to increase packet loss and
	delays. This is especially relevant for interactive applications		delays. This is especially relevant for interactive applications
	skipping to change at page 3, line 50		skipping to change at page 3, line 50
	device. The noise is often significantly increased if the round-trip		device. The noise is often significantly increased if the round-trip
	time is used. The cleanest signal is obtained by using One-Way-Delay		time is used. The cleanest signal is obtained by using One-Way-Delay
	(OWD).		(OWD).
	Measuring absolute OWD is difficult since it requires both the sender		Measuring absolute OWD is difficult since it requires both the sender
	and receiver clocks to be synchronised. However, since the		and receiver clocks to be synchronised. However, since the
	statistics being collected are relative to the mean OWD, a relative		statistics being collected are relative to the mean OWD, a relative
	OWD measurement is sufficient. Clock skew is not usually significant		OWD measurement is sufficient. Clock skew is not usually significant
	over the time intervals used by this SBD mechanism (see [RFC6817] A.2		over the time intervals used by this SBD mechanism (see [RFC6817] A.2
	for a discussion on clock skew and OWD measurements). However, in		for a discussion on clock skew and OWD measurements). However, in
	circumstances where it is significant, Section 3.3.3 outlines a way		circumstances where it is significant, Section 3.3.2 outlines a way
	of adjusting the calculations to cater for it.		of adjusting the calculations to cater for it.
	Each packet arriving at the bottleneck buffer may experience very		Each packet arriving at the bottleneck buffer may experience very
	different queue lengths, and therefore different waiting times. A		different queue lengths, and therefore different waiting times. A
	single OWD sample does not, therefore, characterize the path well.		single OWD sample does not, therefore, characterize the path well.
	However, multiple OWD measurements do reflect the distribution of		However, multiple OWD measurements do reflect the distribution of
	delays experienced at the bottleneck.		delays experienced at the bottleneck.
	1.1.3. Path Lag		1.1.3. Path Lag
	skipping to change at page 4, line 29		skipping to change at page 4, line 29
	2. Definitions		2. Definitions
	The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",		The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
	"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this		"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
	document are to be interpreted as described in RFC 2119 [RFC2119].		document are to be interpreted as described in RFC 2119 [RFC2119].
	Acronyms used in this document:		Acronyms used in this document:
	OWD -- One Way Delay		OWD -- One Way Delay
	PDV -- Packet Delay Variation
	MAD -- Mean Absolute Deviation		MAD -- Mean Absolute Deviation
	RTT -- Round Trip Time		RTT -- Round Trip Time
	SBD -- Shared Bottleneck Detection		SBD -- Shared Bottleneck Detection
	Conventions used in this document:		Conventions used in this document:
	T -- the base time interval over which measurements are		T -- the base time interval over which measurements are
	made.		made.
	skipping to change at page 5, line 30		skipping to change at page 5, line 26
	min_T(...) -- the minimum recorded measurement of the variable in		min_T(...) -- the minimum recorded measurement of the variable in
	parentheses taken over the interval T		parentheses taken over the interval T
	num_T(...) -- the count of measurements of the variable in		num_T(...) -- the count of measurements of the variable in
	parentheses taken in the interval T		parentheses taken in the interval T
	num_VM(...) -- the count of valid values of the variable in		num_VM(...) -- the count of valid values of the variable in
	parentheses given M records		parentheses given M records
	PC -- a boolean variable indicating the particular flow		PB -- a boolean variable indicating the particular flow
	was identified as experiencing congestion in the		was identified transiting a bottleneck in the
	previous interval T (i.e. Previously Congested)		previous interval T (i.e. Previously Bottleneck)
	skew_est -- a measure of skewness in a OWD distribution.		skew_est -- a measure of skewness in a OWD distribution.
			skew_base_T -- a variable used as an intermediate step in
			calculating skew_est.
	var_est -- a measure of variability in OWD measurements.		var_est -- a measure of variability in OWD measurements.
			var_base_T -- a variable used as an intermediate step in
			calculating var_est.
	freq_est -- a measure of low frequency oscillation in the OWD		freq_est -- a measure of low frequency oscillation in the OWD
	measurements.		measurements.
	p_l, p_f, p_pdv, p_mad, c_s, c_h, p_s, p_d, p_v -- various		p_l, p_f, p_mad, c_s, c_h, p_s, p_d, p_v -- various thresholds
	thresholds used in the mechanism		used in the mechanism
	M and F -- number of values related to N		M and F -- number of values related to N
			.
	2.1. Parameters and their Effect		2.1. Parameters and their Effect
	T T should be long enough so that there are enough packets		T T should be long enough so that there are enough packets
	received during T for a useful estimate of short term mean		received during T for a useful estimate of short term mean
	OWD and variation statistics. Making T too large can limit		OWD and variation statistics. Making T too large can limit
	the efficacy of PDV and freq_est. It will also increase the		the efficacy of freq_est. It will also increase the response
	response time of the mechanism. Making T too small will make		time of the mechanism. Making T too small will make the
	the metrics noisier.		metrics noisier.
	N & M N should be large enough provide a stable estimate of		N & M N should be large enough to provide a stable estimate of
	oscillations in OWD and average PDV. Usually M=N, though		oscillations in OWD. Usually M=N, though having M<N may be
	having M<N may be beneficial in certain circumstances. M*T		beneficial in certain circumstances. M*T needs to be long
	needs to be long enough provide stable estimates of skewness		enough to provide stable estimates of skewness and MAD.
	and MAD (if used).
	F F determines the number of intervals over which statistics		F F determines the number of intervals over which statistics
	are considered to be equally weighted. When F=M recent and		are considered to be equally weighted. When F=M recent and
	older measurements are considered equal. Making F<M can		older measurements are considered equal. Making F<M can
	increase the responsiveness of the SBD mechanism. If F is		increase the responsiveness of the SBD mechanism. If F is
	too small, statistics will be too noisy.		too small, statistics will be too noisy.
	c_s c_s is the threshold in skew_est used for determining whether		c_s c_s is the threshold in skew_est used for determining whether
	a flow is experiencing congestion or not. It should be		a flow is transiting a bottleneck or not. It should be
	slightly negative so that a very lightly loaded path does not		slightly negative so that a very lightly loaded path does not
	give a false indication. Setting c_s more negative makes the		give a false indication. Setting c_s more negative makes the
	SBD mechanism less sensitive to transient and light		SBD mechanism less sensitive to transient and slight
	congestion episodes.		bottlenecks.
	c_s c_h adds hysteresis to the congestion determination. It		c_h c_h adds hysteresis to the botteneck determination. It
	should be large enough to avoid constant switching in the		should be large enough to avoid constant switching in the
	determination, but low enough to ensure that grouping is not		determination, but low enough to ensure that grouping is not
	attempted when there is no congestion and the delay and loss		attempted when there is no bottleneck and the delay and loss
	signals cannot be relied upon.		signals cannot be relied upon.
	p_v p_v determines the sensitivity of freq_est to noise. Making		p_v p_v determines the sensitivity of freq_est to noise. Making
	it smaller will yield higher but noisier values for freq_est.		it smaller will yield higher but noisier values for freq_est.
	Making it too large will render it ineffective for		Making it too large will render it ineffective for
	determining groups.		determining groups.
	p_* Flows are separated when the skew_est|var_est|freq_est		p_* Flows are separated when the skew_est|var_est|freq_est
	measure is greater than p_s|p_f|p_d|(p_pdv|p_mad). Adjusting		measure is greater than p_s|p_f|p_d|p_mad. Adjusting these
	these is a compromise between false grouping of flows that do		is a compromise between false grouping of flows that do not
	not share a bottleneck and false splitting of flows that do.		share a bottleneck and false splitting of flows that do.
	Making them larger can help if the measures are very noisy,		Making them larger can help if the measures are very noisy,
	but reducing the noise in the statistical measures by		but reducing the noise in the statistical measures by
	adjusting T and N|M may be a better solution.		adjusting T and N|M may be a better solution.
	2.2. Recommended Parameter Values		2.2. Recommended Parameter Values
	Reference [Hayes-LCN14] uses T=350ms, N=50, p_l = 0.1. The other		Reference [Hayes-LCN14] uses T=350ms, N=50, p_l=0.1. The other
	parameters have been tightened to reflect minor enhancements to the		parameters have been tightened to reflect minor enhancements to the
	algorithm outlined in Section 3.3: c_s = -0.01, p_f = p_s = p_d =		algorithm outlined in Section 3.3: c_s=-0.01, p_f=p_d=0.1, p_s=0.15,
	0.1, p_pdv = 0.2, p_v = 0.2 (or p_mad=0.1, p_v=0.7). M=50, F=25, and		p_mad=0.1, p_v=0.7. M=30, F=20, and c_h = 0.3 are additional
	c_h = 0.3 are additional parameters defined in the document. These		parameters defined in the document. These are values that seem to
	are values that seem to work well over a wide range of practical		work well over a wide range of practical Internet conditions.
	Internet conditions.
	3. Mechanism		3. Mechanism
	The mechanism described in this document is based on the observation		The mechanism described in this document is based on the observation
	that the distribution of delay measurements of packets that traverse		that the distribution of delay measurements of packets that traverse
	a common bottleneck have similar shape characteristics. These shape		a common bottleneck have similar shape characteristics. These shape
	characteristics are described using 3 key summary statistics:		characteristics are described using 3 key summary statistics:
	variability (estimate var_est, see Section 3.1.3)		variability (estimate var_est, see Section 3.1.3)
	skewness (estimate skew_est, see Section 3.1.2)		skewness (estimate skew_est, see Section 3.1.2)
	oscillation (estimate freq_est, see Section 3.1.4)		oscillation (estimate freq_est, see Section 3.1.4)
	with packet loss (estimate pkt_loss, see Section 3.1.5) used as a		with packet loss (estimate pkt_loss, see Section 3.1.5) used as a
	supplementary statistic.		supplementary statistic.
	Summary statistics help to address both the noise and the path lag		Summary statistics help to address both the noise and the path lag
	problems by describing the general shape over a relatively long		problems by describing the general shape over a relatively long
	period of time. This is sufficient for their application in coupled		period of time. Each summary statistic portrays a "view" of the
	congestion control for RTP Media. They can be signalled from a		bottleneck link characteristics, and when used together, they provide
	receiver, which measures the OWD and calculates the summary		a robust discrimination for grouping flows. They can be signalled
			from a receiver, which measures the OWD and calculates the summary
	statistics, to a sender, which is the entity that is transmitting the		statistics, to a sender, which is the entity that is transmitting the
	media stream. An RTP Media device may be both a sender and a		media stream. An RTP Media device may be both a sender and a
	receiver. SBD can be performed at either a sender or a receiver or		receiver. SBD can be performed at either a sender or a receiver or
	both.		both.
	+----+		+----+
	| H2 |		| H2 |
	+----+		+----+
	|		|
	| L2		| L2
	skipping to change at page 8, line 25		skipping to change at page 8, line 25
	A network with 3 hosts (H1, H2, H3) and 3 links (L1, L2, L3).		A network with 3 hosts (H1, H2, H3) and 3 links (L1, L2, L3).
	Figure 1		Figure 1
	In Figure 1, there are two possible cases for shared bottleneck		In Figure 1, there are two possible cases for shared bottleneck
	detection: a sender-based and a receiver-based case.		detection: a sender-based and a receiver-based case.
	1. Sender-based: consider a situation where host H1 sends media		1. Sender-based: consider a situation where host H1 sends media
	streams to hosts H2 and H3, and L1 is a shared bottleneck. H2		streams to hosts H2 and H3, and L1 is a shared bottleneck. H2
	and H3 measure the OWD and calculate summary statistics, which		and H3 measure the OWD and calculate summary statistics, which
	they send to H1 every T. H1, having this knowledge, can determine		they send to H1 every T. H1, having this knowledge, can
	the shared bottleneck and accordingly control the send rates.		determine the shared bottleneck and accordingly control the send
			rates.
	2. Receiver-based: consider that H2 is also sending media to H3, and		2. Receiver-based: consider that H2 is also sending media to H3, and
	L3 is a shared bottleneck. If H3 sends summary statistics to H1		L3 is a shared bottleneck. If H3 sends summary statistics to H1
	and H2, neither H1 nor H2 alone obtain enough knowledge to detect		and H2, neither H1 nor H2 alone obtain enough knowledge to detect
	this shared bottleneck; H3 can however determine it by combining		this shared bottleneck; H3 can however determine it by combining
	the summary statistics related to H1 and H2, respectively. This		the summary statistics related to H1 and H2, respectively. This
	case is applicable when send rates are controlled by the		case is applicable when send rates are controlled by the
	receiver; then, the signal from H3 to the senders contains the		receiver; then, the signal from H3 to the senders contains the
	sending rate.		sending rate.
	A discussion of the required signalling for the receiver-based case		A discussion of the required signalling for the receiver-based case
	is beyond the scope of this document. For the sender-based case, the		is beyond the scope of this document. For the sender-based case, the
	messages and their data format will be defined here in future		messages and their data format will be defined here in future
	versions of this document. We envision that an initialization		versions of this document.
	message from the sender to the receiver could specify which key
	metrics are requested out of a possibly extensible set (pkt_loss,		We envisige the following exchange during initialisation:
	var_est, skew_est, freq_est). The grouping algorithm described in
	this document requires all four of these metrics, and receivers MUST		o An initialization message from the sender to the receiver will
	be able to provide them, but future algorithms may be able to exploit		contain the following information:
	other metrics (e.g. metrics based on explicit network signals).
	Moreover, the initialization message could specify T, N, and the		* A protocol identifier (SBD=01). This is to future proof the
	necessary resolution and precision (number of bits per field).		message exchange so that potential advances in SBD technology
			can be easily deployed. All following initialisation elements
			relate to the mechanism outlined in this document which will
			have the identifier SBD=01.
			* A list of which key metrics should be collected and relayed
			back to the sender out of a possibly extensible set (pkt_loss,
			var_est, skew_est, freq_est). The grouping algorithm described
			in this document requires all four of these metrics, and
			receivers MUST be able to provide them, but future algorithms
			may be able to exploit other metrics (e.g. metrics based on
			explicit network signals).
			* The values of T, N, M, and the necessary resolution and
			precision of the relayed statistics.
			o A response message from the receiver acknowledges this message
			with a list of key metrics it supports (subset of the senders
			list) and is able to relay back to the sender.
			o This initialisation exchange may be repeated to finalize the
			agreed metrics should not all be supported by all receivers.
	3.1. Key metrics and their calculation		3.1. Key metrics and their calculation
	Measurements are calculated over a base interval, T. T should be long		Measurements are calculated over a base interval, T and summarized
	enough to provide enough samples for a good estimate of skewness, but		over N or M such intervals. All summary statistics can be calculated
	short enough so that a measure of the oscillation can be made from N		incrementally.
	of these estimates. Reference [Hayes-LCN14] uses T = 350ms and
	N=M=50, which are values that seem to work well over a wide range of
	practical Internet conditions.
	3.1.1. Mean delay		3.1.1. Mean delay
	The mean delay is not a useful signal for comparisons between flows		The mean delay is not a useful signal for comparisons between flows
	since flows may traverse quite different paths and clocks will not		since flows may traverse quite different paths and clocks will not
	necessarily be synchronized. However, it is a base measure for the 3		necessarily be synchronized. However, it is a base measure for the 3
	summary statistics. The mean delay, E_T(OWD), is the average one way		summary statistics. The mean delay, E_T(OWD), is the average one way
	delay measured over T.		delay measured over T.
	To facilitate the other calculations, the last N E_T(OWD) values will		To facilitate the other calculations, the last N E_T(OWD) values will
	need to be stored in a cyclic buffer along with the moving average of		need to be stored in a cyclic buffer along with the moving average of
	E_T(OWD):		E_T(OWD):
	mean_delay = E_M(E_T(OWD)) = sum_M(E_T(OWD)) / M		mean_delay = E_M(E_T(OWD)) = sum_M(E_T(OWD)) / M
	where M <= N. Generally M=N: setting M to be less than N allows the		where M <= N. Setting M to be less than N allows the mechanism to be
	mechanism to be more responsive to changes, but potentially at the		more responsive to changes, but potentially at the expense of a
	expense of a higher error rate (see Section 3.4 for a discussion on		higher error rate (see Section 3.4 for a discussion on improving the
	improving the responsiveness of the mechanism.)		responsiveness of the mechanism.)
	3.1.2. Skewness Estimate		3.1.2. Skewness Estimate
	Skewness is difficult to calculate efficiently and accurately.		Skewness is difficult to calculate efficiently and accurately.
	Ideally it should be calculated over the entire period (M * T) from		Ideally it should be calculated over the entire period (M * T) from
	the mean OWD over that period. However this would require storing		the mean OWD over that period. However this would require storing
	every delay measurement over the period. Instead, an estimate is		every delay measurement over the period. Instead, an estimate is
	made over M * T based on a calculation every T using the previous T's		made over M * T based on a calculation every T using the previous T's
	calculation of mean_delay.		calculation of mean_delay.
	The skewness is estimated using two counters, counting the number of		The base for the skewness calculation is estimated using a counter
	one way delay samples (OWD) above and below the mean:		initialised every T. It increments for one way delay samples (OWD)
			below the mean and decrements for OWD above the mean. So for each
	skew_base_T = sum_T(OWD < mean_delay) - sum_T(OWD > mean_delay)		OWD sample:
	where
	if (OWD < mean_delay) 1 else 0		if (OWD < mean_delay) skew_base_T++
	if (OWD > mean_delay) 1 else 0		if (OWD > mean_delay) skew_base_T--
	and mean_delay does not include the mean of the current T		The mean_delay does not include the mean of the current T interval to
	interval.		enable it to be calculated iteratively.
	skew_est = sum_MT(skew_base_T)/num_MT(OWD)		skew_est = sum_MT(skew_base_T)/num_MT(OWD)
	where skew_est is a number between -1 and 1		where skew_est is a number between -1 and 1
	Note: Care must be taken when implementing the comparisons to ensure		Note: Care must be taken when implementing the comparisons to ensure
	that rounding does not bias skew_est. It is important that the mean		that rounding does not bias skew_est. It is important that the mean
	is calculated with a higher precision than the samples.		is calculated with a higher precision than the samples.
	3.1.3. Variability Estimate		3.1.3. Variability Estimate
	Packet Delay Variation (PDV) ([RFC5481] and [ITU-Y1540]) is used as		Mean Absolute Deviation (MAD) delay is a robust variability measure
	an estimator of the variability of the delay signal. We define PDV		that copes well with different send rates. It can be implemented in
	as follows:		an online manner as follows:
	PDV = PDV_max = max_T(OWD) - E_T(OWD)		var_base_T = sum_T(|OWD - E_T(OWD)|)
	var_est = E_M(PDV) = sum_M(PDV) / M		where
	This modifies PDV as outlined in [RFC5481] to provide a summary		|x| is the absolute value of x
	statistic version that best aids the grouping decisions of the
	algorithm (see [Hayes-LCN14] section IVB).
	Generally the maximum is sampled well during congestion, though it is		E_T(OWD) is the mean OWD calculated in the previous T
	more sensitive to path and operating system noise. The use of PDV =
	PDV_min = E_T(OWD) - min_T(OWD) would be less sensitive to this		var_est = MAD_MT = sum_MT(var_base_T)/num_MT(OWD)
	noise, but is not well sampled during congestion at the bottleneck
	and therefore not recommended.		For calculation of freq_est p_v=0.7
			For the grouping threshold p_mad=0.1
	3.1.4. Oscillation Estimate		3.1.4. Oscillation Estimate
	An estimate of the low frequency oscillation of the delay signal is		An estimate of the low frequency oscillation of the delay signal is
	calculated by counting and normalising the significant mean,		calculated by counting and normalising the significant mean,
	E_T(OWD), crossings of mean_delay:		E_T(OWD), crossings of mean_delay:
	freq_est = number_of_crossings / N		freq_est = number_of_crossings / N
	where we define a significant mean crossing as a crossing that		where we define a significant mean crossing as a crossing that
	extends p_v * var_est from mean_delay. In our experiments we		extends p_v * var_est from mean_delay. In our experiments we
	have found that p_v = 0.2 is a good value.		have found that p_v = 0.7 is a good value.
	Freq_est is a number between 0 and 1. Freq_est can be approximated		Freq_est is a number between 0 and 1. Freq_est can be approximated
	incrementally as follows:		incrementally as follows:
	With each new calculation of E_T(OWD) a decision is made as to		With each new calculation of E_T(OWD) a decision is made as to
	whether this value of E_T(OWD) significantly crosses the current		whether this value of E_T(OWD) significantly crosses the current
	long term mean, mean_delay, with respect to the previous		long term mean, mean_delay, with respect to the previous
	significant mean crossing.		significant mean crossing.
	A cyclic buffer, last_N_crossings, records a 1 if there is a		A cyclic buffer, last_N_crossings, records a 1 if there is a
	skipping to change at page 11, line 40		skipping to change at page 11, line 40
	removed from the last_N_crossings.		removed from the last_N_crossings.
	This approximation of freq_est was not used in [Hayes-LCN14], which		This approximation of freq_est was not used in [Hayes-LCN14], which
	calculated freq_est every T using the current E_N(E_T(OWD)). Our		calculated freq_est every T using the current E_N(E_T(OWD)). Our
	tests show that this approximation of freq_est yields results that		tests show that this approximation of freq_est yields results that
	are almost identical to when the full calculation is performed every		are almost identical to when the full calculation is performed every
	T.		T.
	3.1.5. Packet loss		3.1.5. Packet loss
	The proportion of packets lost is used as a supplementary measure:		The proportion of packets lost over the period NT is used as a
			supplementary measure:
	pkt_loss = sum_NT(lost packets) / sum_NT(total packets)		pkt_loss = sum_NT(lost packets) / sum_NT(total packets)
	Note: When pkt_loss is small it is very variable, however, when		Note: When pkt_loss is small it is very variable, however, when
	pkt_loss is high it becomes a stable measure for making grouping		pkt_loss is high it becomes a stable measure for making grouping
	decisions..		decisions.
	3.2. Flow Grouping		3.2. Flow Grouping
	3.2.1. Flow Grouping Algorithm		3.2.1. Flow Grouping Algorithm
	The following grouping algorithm is RECOMMENDED for SBD in the RMCAT		The following grouping algorithm is RECOMMENDED for SBD in the RMCAT
	context and is sufficient and efficient for small to moderate numbers		context and is sufficient and efficient for small to moderate numbers
	of flows. For very large numbers of flows (e.g. hundreds), a more		of flows. For very large numbers of flows (e.g. hundreds), a more
	complex clustering algorithm may be substituted.		complex clustering algorithm may be substituted.
	Since no single metric is precise enough to group flows (due to		Since no single metric is precise enough to group flows (due to
	noise), the algorithm uses multiple metrics. Each metric offers a		noise), the algorithm uses multiple metrics. Each metric offers a
	different "view" of the bottleneck link characteristics, and used		different "view" of the bottleneck link characteristics, and used
	together they enable a more precise grouping of flows than would		together they enable a more precise grouping of flows than would
	otherwise be possible.		otherwise be possible.
	Flows determined to be experiencing congestion are successively		Flows determined to be transiting a bottleneck are successively
	divided into groups based on freq_est, var_est, and skew_est.		divided into groups based on freq_est, var_est, skew_est and
			pkt_loss.
	The first step is to determine which flows are experiencing		The first step is to determine which flows are transiting a
	congestion. This is important, since if a flow is not experiencing		bottleneck. This is important, since if a flow is not transiting a
	congestion its delay based metrics will not describe the bottleneck,		bottleneck its delay based metrics will not describe the bottleneck,
	but the "noise" from the rest of the path. Skewness, with proportion		but the "noise" from the rest of the path. Skewness, with proportion
	of packets loss as a supplementary measure, is used to do this:		of packet loss as a supplementary measure, is used to do this:
	1. Grouping will be performed on flows where:		1. Grouping will be performed on flows that are inferred to be
			traversing a bottleneck by:
	skew_est < c_s		skew_est < c_s
	|| ( skew_est < c_h && PC )		|| ( skew_est < c_h & PB ) || pkt_loss > p_l
	|| pkt_loss > p_l
	The parameter c_s controls how sensitive the mechanism is in		The parameter c_s controls how sensitive the mechanism is in
	detecting congestion. C_s = 0.0 was used in [Hayes-LCN14]. A value		detecting a bottleneck. C_s = 0.0 was used in [Hayes-LCN14]. A
	of c_s = 0.05 is a little more sensitive, and c_s = -0.05 is a little		value of c_s = 0.05 is a little more sensitive, and c_s = -0.05 is a
	less sensitive. C_h controls the hysteresis on flows that were		little less sensitive. C_h controls the hysteresis on flows that
	grouped as experiencing congestion last time.		were grouped as transiting a bottleneck last time. If the test
			result is TRUE, PB=TRUE, otherwise PB=FALSE.
	These flows, flows experiencing congestion, are then progressively		These flows, flows transiting a bottleneck, are then progressively
	divided into groups based on the freq_est, PDV, and skew_est summary		divided into groups based on the freq_est, var_est, and skew_est
	statistics. The process proceeds according to the following steps:		summary statistics. The process proceeds according to the following
			steps:
	2. Group flows whose difference in sorted freq_est is less than a		2. Group flows whose difference in sorted freq_est is less than a
	threshold:		threshold:
	diff(freq_est) < p_f		diff(freq_est) < p_f
	3. Group flows whose difference in sorted E_N(PDV) (highest to		3. Group flows whose difference in sorted E_M(var_est) (highest to
	lowest) is less than a threshold:		lowest) is less than a threshold:
	diff(var_est) < (p_pdv * var_est)		diff(var_est) < (p_mad * var_est)
	The threshold, (p_pdv * var_est), is with respect to the highest		The threshold, (p_mad * var_est), is with respect to the highest
	value in the difference.		value in the difference.
	4. Group flows whose difference in sorted skew_est or pkt_loss is		4. Group flows whose difference in sorted skew_est is less than a
	less than a threshold:		threshold:
	if pkt_loss < p_l
	diff(skew_est) < p_s		diff(skew_est) < p_s
	otherwise		5. When packet loss is high enough to be reliable (pkt_loss > p_l),
			group flows whose difference is less than a threshold
	diff(pkt_loss) < (p_d * pkt_loss)		diff(pkt_loss) < (p_d * pkt_loss)
	The threshold, (p_d * pkt_loss), is with respect to the		The threshold, (p_d * pkt_loss), is with respect to the highest
	highest value in the difference.		value in the difference.
	This procedure involves sorting estimates from highest to lowest. It		This procedure involves sorting estimates from highest to lowest. It
	is simple to implement, and efficient for small numbers of flows (up		is simple to implement, and efficient for small numbers of flows (up
	to 10-20).		to 10-20).
	3.2.2. Using the flow group signal		3.2.2. Using the flow group signal
	A grouping decisions is made every T from the second T, though they		Grouping decisions can be made every T from the second T, however
	will not attain their full design accuracy until after the N'th T		they will not attain their full design accuracy until after the
	interval.		2*N'th T interval. We recommend that grouping decisions are not made
			until 2*M T intervals.
	Network conditions, and even the congestion controllers, can cause		Network conditions, and even the congestion controllers, can cause
	bottlenecks to fluctuate. A coupled congestion controller MAY decide		bottlenecks to fluctuate. A coupled congestion controller MAY decide
	only to couple groups that remain stable, say grouped together 90% of		only to couple groups that remain stable, say grouped together 90% of
	the time, depending on its objectives. Recommendations concerning		the time, depending on its objectives. Recommendations concerning
	this are beyond the scope of this draft and will be specific to the		this are beyond the scope of this draft and will be specific to the
	coupled congestion controllers objectives.		coupled congestion controllers objectives.
	3.3. Removing Noise from the Estimates		3.3. Removing Noise from the Estimates
	The following describe small changes to the calculation of the key		The following describe small changes to the calculation of the key
	metrics that help remove noise from them. Currently these "tweaks"		metrics that help remove noise from them. Currently these "tweaks"
	are described separately to keep the main description succinct. In		are described separately to keep the main description succinct. In
	future revisions of the draft these enhancements may replace the		future revisions of the draft these enhancements may replace the
	original key metric calculations.		original key metric calculations.
	3.3.1. PDV noise		3.3.1. Oscillation noise
	Usually during congestion the max_T(OWD) is quite well sampled as the
	delay distribution is skewed toward the maximum. However max_T(OWD)
	is subject to delay noise from other queues along the path as well as
	the host operating system. Min_T(OWD) is less prone to noise along
	the path and from the host operating system, but is not well sampled
	during congestion (i.e. when there is a bottleneck). Flows with very
	different packet send rates exacerbate the problem.
	An alternative delay variation measure that is less sensitive to
	extreme values and different send rates is Mean Absolute Deviation
	(MAD). It can be implemented in an online manner as follows:
	var_base_T = sum_T(|OWD - E_T(OWD)|)
	where
	|x| is the absolute value of x
	E_T(OWD) is the mean OWD calculated in the previous T
	var_est = MAD_MT = sum_MT(var_base_T)/num_MT(OWD)
	For calculation of freq_est p_v=0.7 (MAD is a smaller number than
	PDV)
	For the grouping threshold p_mad=0.1 instead of p_pdv (MAD is less
	noisy so the test can be tighter)
	Note that the method for improving responsiveness of MAD_MT is the
	same as that described in Section 3.4.1 for skew_est.
	3.3.2. Oscillation noise
	When a path has no congestion, var_est will be very small and the		When a path has no bottleneck, var_est will be very small and the
	recorded significant mean crossings will be the result of path noise.		recorded significant mean crossings will be the result of path noise.
	Thus up to N-1 meaningless mean crossings can be a source of error at		Thus up to N-1 meaningless mean crossings can be a source of error at
	the point a link becomes a bottleneck and flows traversing it begin		the point a link becomes a bottleneck and flows traversing it begin
	to be grouped.		to be grouped.
	To remove this source of noise from freq_est:		To remove this source of noise from freq_est:
	1. Set the current PDV to PDV = NaN (a value representing an invalid		1. Set the current var_base_T = NaN (a value representing an invalid
	record, i.e. Not a Number) for flows that are deemed to not be		record, i.e. Not a Number) for flows that are deemed to not be
	experiencing congestion by the first skew_est based grouping test		transiting a bottleneck by the first skew_est based grouping test
	(see Section 3.2.1).		(see Section 3.2.1).
	2. Then var_est = sum_M(PDV != NaN) / num_VM(PDV)		2. Then var_est = sum_MT(var_base_T != NaN) / num_MT(OWD)
	3. For freq_est, only record a significant mean crossing if flow is		3. For freq_est, only record a significant mean crossing if flow
	experiencing congestion.		deemed to be transiting a bottleneck.
	These three changes will remove the non-congestion noise from		These three changes can help to remove the non-bottleneck noise from
	freq_est. A similar adjustment can be made for MAD based var_est.		freq_est.
	3.3.3. Clock skew		3.3.2. Clock skew
	Generally sender and receiver clock skew will be too small to cause		Generally sender and receiver clock skew will be too small to cause
	significant errors in the estimators. Skew_est is most sensitive to		significant errors in the estimators. Skew_est is most sensitive to
	this type of noise. In circumstances where clock skew is high,		this type of noise. In circumstances where clock skew is high,
	making M < N can reduce this error.		basing skew_est only on the previous T's mean provides a noisier but
			reliable signal.
	A better method is to estimate the effect the clock skew is having on		A better method is to estimate the effect the clock skew is having on
	the summary statistics, and then adjust statistics accordingly. A		the summary statistics, and then adjust statistics accordingly. A
	simple online method of doing this based on min_T(OWD) will be		simple online method of doing this based on min_T(OWD) will be
	described here in a subsequent version of the draft.		described here in a subsequent version of the draft.
	3.4. Reducing lag and Improving Responsiveness		3.4. Reducing lag and Improving Responsiveness
	Measurement based shared bottleneck detection makes decisions in the		Measurement based shared bottleneck detection makes decisions in the
	present based on what has been measured in the past. This means that		present based on what has been measured in the past. This means that
	skipping to change at page 16, line 25		skipping to change at page 16, line 5
	+ sum([(M-F):1].*numsampT(F+1:M)))		+ sum([(M-F):1].*numsampT(F+1:M)))
	where numsampT is an array of the number of OWD samples in each T		where numsampT is an array of the number of OWD samples in each T
	(i.e. num_T(OWD)), and numsampT(1) is the most recent; skew_base_T(1)		(i.e. num_T(OWD)), and numsampT(1) is the most recent; skew_base_T(1)
	is the most recent calculation of skew_base_T; 1:F refers to the		is the most recent calculation of skew_base_T; 1:F refers to the
	integer values 1 through to F, and [(M-F):1] refers to an array of		integer values 1 through to F, and [(M-F):1] refers to an array of
	the integer values (M-F) declining through to 1; and ".*" is the		the integer values (M-F) declining through to 1; and ".*" is the
	array scalar dot product operator.		array scalar dot product operator.
			To calculate this weighted skew_est incrementally:
			Notation: F_ - flat portion, D_ - declining portion, W_ - weighted
			component
			Initialise: sum_skewbase = 0, F_skewbase=0, W_D_skewbase=0
			skewbase_hist = buffer length M initialize to 0
			numsampT = buffer length M initialzed to 0
			Steps per iteration:
			1. old_skewbase = skewbase_hist(M)
			2. old_numsampT = numsampT(M)
			3. cycle(skewbase_hist)
			4. cycle(numsampT)
			5. numsampT(1) = num_T(OWD)
			6. skewbase_hist(1) = skew_base_T
			7. F_skewbase = F_skewbase + skew_base_T - skewbase_hist(F+1)
			8. W_D_skewbase = W_D_skewbase + (M-F)*skewbase_hist(F+1)
			- sum_skewbase
			9. W_D_numsamp = W_D_numsamp + (M-F)*numsampT(F+1) - sum_numsamp
			+ F_numsamp
			10. F_numsamp = F_numsamp + numsampT(1) - numsampT(F+1)
			11. sum_skewbase = sum_skewbase + skewbase_hist(F+1) - old_skewbase
			12. sum_numsamp = sum_numsamp + numsampT(1) - old_numsampT
			13. skew_est = ((M-F+1)*F_skewbase + W_D_skewbase) /
			((M-F+1)*F_numsamp+W_D_numsamp)
			Where cycle(....) refers to the operation on a cyclic buffer where
			the start of the buffer is now the next element in the buffer.
	3.4.2. Improving the response of the variability estimate		3.4.2. Improving the response of the variability estimate
	The weighted moving average for var_est can be calculated as follows:		Similarly the weighted moving average for var_est can be calculated
			as follows:
	var_est = ((M-F+1)*sum(PDV(1:F)) + sum([(M-F):1].*PDV(F+1:M)))		var_est = ((M-F+1)*sum(var_base_T(1:F))
	/ (F*(M-F+1) + sum([(M-F):1])		+ sum([(M-F):1].*var_base_T(F+1:M)))
	where 1:F refers to the integer values 1 through to F, and [(M-F):1]		/ ((M-F+1)*sum(numsampT(1:F))
	refers to an array of the integer values (M-F) declining through to
	1; and ".*" is the array scalar dot product operator. When removing		+ sum([(M-F):1].*numsampT(F+1:M)))
	oscillation noise (see Section 3.3.2) this calculation must be
	adjusted to allow for invalid PDV records.		where numsampT is an array of the number of OWD samples in each T
			(i.e. num_T(OWD)), and numsampT(1) is the most recent; skew_base_T(1)
			is the most recent calculation of skew_base_T; 1:F refers to the
			integer values 1 through to F, and [(M-F):1] refers to an array of
			the integer values (M-F) declining through to 1; and ".*" is the
			array scalar dot product operator. When removing oscillation noise
			(see Section 3.3.1) this calculation must be adjusted to allow for
			invalid var_base_T records.
			Var_est can be calculated incrementally in the same way as skew_est
			in Section 3.4.1. However, note that the buffer numsampT is used for
			both calculations so the operations on it should not be repeated.
	4. Measuring OWD		4. Measuring OWD
	This section discusses the OWD measurements required for this		This section discusses the OWD measurements required for this
	algorithm to detect shared bottlenecks.		algorithm to detect shared bottlenecks.
	The SBD mechanism described in this draft relies on differences		The SBD mechanism described in this draft relies on differences
	between OWD measurements to avoid the practical problems with		between OWD measurements to avoid the practical problems with
	measuring absolute OWD (see [Hayes-LCN14] section IIIC). Since all		measuring absolute OWD (see [Hayes-LCN14] section IIIC). Since all
	summary statistics are relative to the mean OWD and sender/receiver		summary statistics are relative to the mean OWD and sender/receiver
	skipping to change at page 17, line 29		skipping to change at page 18, line 8
	The SBD mechanism requires timing information precise enough to be		The SBD mechanism requires timing information precise enough to be
	able to make comparisons. As a rule of thumb, the time resolution		able to make comparisons. As a rule of thumb, the time resolution
	should be less than one hundredth of a typical path's range of		should be less than one hundredth of a typical path's range of
	delays. In general, the lower the time resolution, the more care		delays. In general, the lower the time resolution, the more care
	that needs to be taken to ensure rounding errors do not bias the		that needs to be taken to ensure rounding errors do not bias the
	skewness calculation.		skewness calculation.
	Typical RTP media flows use sub-millisecond timers, which should be		Typical RTP media flows use sub-millisecond timers, which should be
	adequate in most situations.		adequate in most situations.
	5. Acknowledgements		5. Implementation status
			The University of Oslo is currently working on an implementation of
			this in the Chromium browser.
			6. Acknowledgements
	This work was part-funded by the European Community under its Seventh		This work was part-funded by the European Community under its Seventh
	Framework Programme through the Reducing Internet Transport Latency		Framework Programme through the Reducing Internet Transport Latency
	(RITE) project (ICT-317700). The views expressed are solely those of		(RITE) project (ICT-317700). The views expressed are solely those of
	the authors.		the authors.
	6. IANA Considerations		7. IANA Considerations
	This memo includes no request to IANA.		This memo includes no request to IANA.
	7. Security Considerations		8. Security Considerations
	The security considerations of RFC 3550 [RFC3550], RFC 4585		The security considerations of RFC 3550 [RFC3550], RFC 4585
	[RFC4585], and RFC 5124 [RFC5124] are expected to apply.		[RFC4585], and RFC 5124 [RFC5124] are expected to apply.
	Non-authenticated RTCP packets carrying shared bottleneck indications		Non-authenticated RTCP packets carrying shared bottleneck indications
	and summary statistics could allow attackers to alter the bottleneck		and summary statistics could allow attackers to alter the bottleneck
	sharing characteristics for private gain or disruption of other		sharing characteristics for private gain or disruption of other
	parties communication.		parties communication.
	8. Change history		9. Change history
	Changes made to this document:		Changes made to this document:
			WG-01->WG-02 : Removed ambiguity associated with the term
			"congestion". Expanded the description of
			initialisation messages. Removed PDV metric.
			Added description of incremental weighted metric
			calculations for skew_est. Various clarifications
			based on implementation work. Fixed typos and
			tuned parameters.
	WG-00->WG-01 : Moved unbiased skew section to replace skew		WG-00->WG-01 : Moved unbiased skew section to replace skew
	estimate, more robust variability estimator, the		estimate, more robust variability estimator, the
	term variance replaced with variability, clock		term variance replaced with variability, clock
	drift term corrected to clock skew, revision to		drift term corrected to clock skew, revision to
	clock skew section with a place holder, description		clock skew section with a place holder, description
	of parameters.		of parameters.
	02->WG-00 : Fixed missing 0.5 in 3.3.2 and missing brace in		02->WG-00 : Fixed missing 0.5 in 3.3.2 and missing brace in
	3.3.3		3.3.3
	01->02 : New section describing improvements to the key		01->02 : New section describing improvements to the key
	metric calculations that help to remove noise,		metric calculations that help to remove noise,
	bias, and reduce lag. Some revisions to the		bias, and reduce lag. Some revisions to the
	notation to make it clearer. Some tightening of		notation to make it clearer. Some tightening of
	the thresholds.		the thresholds.
	00->01 : Revisions to terminology for clarity		00->01 : Revisions to terminology for clarity
	9. References		10. References
	9.1. Normative References		10.1. Normative References
	[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate		[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
	Requirement Levels", BCP 14, RFC 2119, March 1997.		Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/
			RFC2119, March 1997,
			<http://www.rfc-editor.org/info/rfc2119>.
	9.2. Informative References		10.2. Informative References
	[Hayes-LCN14]		[Hayes-LCN14]
	Hayes, D., Ferlin, S., and M. Welzl, "Practical Passive		Hayes, D., Ferlin, S., and M. Welzl, "Practical Passive
	Shared Bottleneck Detection using Shape Summary		Shared Bottleneck Detection using Shape Summary
	Statistics", Proc. the IEEE Local Computer Networks		Statistics", Proc. the IEEE Local Computer Networks (LCN)
	(LCN) p150-158, September 2014, <http://heim.ifi.uio.no/		p150-158, September 2014, <http://heim.ifi.uio.no/davihay/
	davihay/
	hayes14__pract_passiv_shared_bottl_detec-abstract.html>.		hayes14__pract_passiv_shared_bottl_detec-abstract.html>.
	[I-D.welzl-rmcat-coupled-cc]		[I-D.welzl-rmcat-coupled-cc]
	Welzl, M., Islam, S., and S. Gjessing, "Coupled congestion		Welzl, M., Islam, S., and S. Gjessing, "Coupled congestion
	control for RTP media", draft-welzl-rmcat-coupled-cc-04		control for RTP media", draft-welzl-rmcat-coupled-cc-04
	(work in progress), October 2014.		(work in progress), October 2014.
	[ITU-Y1540]		[ITU-Y1540]
	ITU-T, "Internet Protocol Data Communication Service - IP		ITU-T, "Internet Protocol Data Communication Service - IP
	Packet Transfer and Availability Performance Parameters",		Packet Transfer and Availability Performance Parameters",
	Series Y: Global Information Infrastructure, Internet		Series Y: Global Information Infrastructure, Internet
	Protocol Aspects and Next-Generation Networks ,		Protocol Aspects and Next-Generation Networks , March
	March 2011,		2011, <http://www.itu.int/rec/T-REC-Y.1540-201103-I/en>.
	<http://www.itu.int/rec/T-REC-Y.1540-201103-I/en>.
	[RFC3550] Schulzrinne, H., Casner, S., Frederick, R., and V.		[RFC3550] Schulzrinne, H., Casner, S., Frederick, R., and V.
	Jacobson, "RTP: A Transport Protocol for Real-Time		Jacobson, "RTP: A Transport Protocol for Real-Time
	Applications", STD 64, RFC 3550, July 2003.		Applications", STD 64, RFC 3550, DOI 10.17487/RFC3550,
			July 2003, <http://www.rfc-editor.org/info/rfc3550>.
	[RFC4585] Ott, J., Wenger, S., Sato, N., Burmeister, C., and J. Rey,		[RFC4585] Ott, J., Wenger, S., Sato, N., Burmeister, C., and J. Rey,
	"Extended RTP Profile for Real-time Transport Control		"Extended RTP Profile for Real-time Transport Control
	Protocol (RTCP)-Based Feedback (RTP/AVPF)", RFC 4585,		Protocol (RTCP)-Based Feedback (RTP/AVPF)", RFC 4585, DOI
	July 2006.		10.17487/RFC4585, July 2006,
			<http://www.rfc-editor.org/info/rfc4585>.
	[RFC5124] Ott, J. and E. Carrara, "Extended Secure RTP Profile for		[RFC5124] Ott, J. and E. Carrara, "Extended Secure RTP Profile for
	Real-time Transport Control Protocol (RTCP)-Based Feedback		Real-time Transport Control Protocol (RTCP)-Based Feedback
	(RTP/SAVPF)", RFC 5124, February 2008.		(RTP/SAVPF)", RFC 5124, DOI 10.17487/RFC5124, February
			2008, <http://www.rfc-editor.org/info/rfc5124>.
	[RFC5481] Morton, A. and B. Claise, "Packet Delay Variation		[RFC5481] Morton, A. and B. Claise, "Packet Delay Variation
	Applicability Statement", RFC 5481, March 2009.		Applicability Statement", RFC 5481, DOI 10.17487/RFC5481,
			March 2009, <http://www.rfc-editor.org/info/rfc5481>.
	[RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,		[RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
	"Low Extra Delay Background Transport (LEDBAT)", RFC 6817,		"Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
	December 2012.		DOI 10.17487/RFC6817, December 2012,
			<http://www.rfc-editor.org/info/rfc6817>.
	Authors' Addresses		Authors' Addresses
	David Hayes (editor)		David Hayes (editor)
	University of Oslo		University of Oslo
	PO Box 1080 Blindern		PO Box 1080 Blindern
	Oslo, N-0316		Oslo N-0316
	Norway		Norway
	Phone: +47 2284 5566		Phone: +47 2284 5566
	Email: davihay@ifi.uio.no		Email: davihay@ifi.uio.no
	Simone Ferlin		Simone Ferlin
	Simula Research Laboratory		Simula Research Laboratory
	P.O.Box 134		P.O.Box 134
	Lysaker, 1325		Lysaker 1325
	Norway		Norway
	Phone: +47 4072 0702		Phone: +47 4072 0702
	Email: ferlin@simula.no		Email: ferlin@simula.no
	Michael Welzl		Michael Welzl
	University of Oslo		University of Oslo
	PO Box 1080 Blindern		PO Box 1080 Blindern
	Oslo, N-0316		Oslo N-0316
	Norway		Norway
	Phone: +47 2285 2420		Phone: +47 2285 2420
	Email: michawe@ifi.uio.no		Email: michawe@ifi.uio.no
			Kristian Hiorth
			University of Oslo
			PO Box 1080 Blindern
			Oslo N-0316
			Norway
			Email: kristahi@ifi.uio.no
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