draft-ietf-ippm-loss-pattern-05.txt		draft-ietf-ippm-loss-pattern-06.txt
	IP Performance Metrics (IPPM) WG Rajeev Koodli		IPPM Working Group Rajeev Koodli
	INTERNET DRAFT Nokia Research Center		INTERNET DRAFT Nokia Research Center
	20 July 2001 R. Ravikanth		5 December 2001 R. Ravikanth
	Axiowave		Axiowave
	One-way Loss Pattern Sample Metrics		One-way Loss Pattern Sample Metrics
	<draft-ietf-ippm-loss-pattern-05.txt>		draft-ietf-ippm-loss-pattern-06.txt
	STATUS OF THIS MEMO		Status of This Memo
	This document is an Internet-Draft and is in full conformance with all		This document is an Internet-Draft and is in full conformance with
	provisions of Section 10 of RFC2026.		all provisions of Section 10 of RFC2026.
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	This memo provides information for the Internet community. This memo		This memo provides information for the Internet community. This memo
	does not specify an Internet standard of any kind. Distribution of		does not specify an Internet standard of any kind. Distribution of
	this memo is unlimited.		this memo is unlimited.
	Abstract		Abstract
	The Internet exhibits certain specific types of behavior (e.g., bursty		The Internet exhibits certain specific types of behavior (e.g.,
	packet loss) that can affect the performance seen by the users as		bursty packet loss) that can affect the performance seen by the users
	well as the operators. Currently, the focus has been on specifying		as well as the operators. Previously, the focus of the IPPM had
	base metrics such as delay, loss and connectivity under the		been on specifying base metrics such as delay, loss and connectivity
	framework described in [frame-work]. It is useful to capture		under the framework described in [10]. However, specific Internet
	specific Internet behaviors under the umbrella of IPPM framework,		behaviors can also be captured under the umbrella of IPPM framework,
	specifying new concepts while reusing existing guidelines as much as		specifying new concepts while reusing existing guidelines as much as
	possible. This draft proposes the use of "derived metrics" to		possible. This document defines metrics derived from the previously
	accomplish this, specifically providing means for capturing the loss		specified base metrics to capture loss patterns experienced by
	pattern on the Internet.		streams of packets on the Internet.
			Contents
			Status of This Memo i
			Abstract i
			1. Introduction 2
			2. Terminology 2
			3. The Approach 2
			4. Basic Definitions 3
			5. Definitions for Samples of One-way Loss Distance, and One-way
			Loss Period 4
			5.1. Metric Names . . . . . . . . . . . . . . . . . . . . . . 4
			5.1.1. Type-P-One-Way-Loss-Distance-Stream . . . . . . . 4
			5.1.2. Type-P-One-Way-Loss-Period-Stream . . . . . . . . 4
			5.2. Metric Parameters . . . . . . . . . . . . . . . . . . . . 4
			5.3. Metric Units . . . . . . . . . . . . . . . . . . . . . . 4
			5.3.1. Type-P-One-Way-Loss-Distance-Stream . . . . . . . 4
			5.3.2. Type-P-One-Way-Loss-Period-Stream . . . . . . . . 4
			5.4. Definitions . . . . . . . . . . . . . . . . . . . . . . . 5
			5.4.1. Type-P-One-Way-Loss-Distance-Stream . . . . . . . 5
			5.4.2. Type-P-One-Way-Loss-Period-Stream . . . . . . . . 5
			5.4.3. Examples . . . . . . . . . . . . . . . . . . . . 5
			5.5. Methodologies . . . . . . . . . . . . . . . . . . . . . . 6
			5.6. Discussion . . . . . . . . . . . . . . . . . . . . . . . 7
			5.7. Sampling Considerations . . . . . . . . . . . . . . . . . 7
			5.8. Errors and Uncertainties . . . . . . . . . . . . . . . . 7
			6. Statistics 8
			6.1. Type-P-One-Way-Loss-Noticeable-Rate . . . . . . . . . . . 8
			6.2. Type-P-One-Way-Loss-Period-Total . . . . . . . . . . . . 8
			6.3. Type-P-One-Way-Loss-Period-Lengths . . . . . . . . . . . 9
			6.4. Type-P-One-Way-Inter-Loss-Period-Lengths . . . . . . . . 9
			6.5. Examples . . . . . . . . . . . . . . . . . . . . . . . . 9
			7. Security Considerations: 10
			8. IANA Considerations 11
			9. Acknowledgements 11
			Addresses 13
	1. Introduction		1. Introduction
	In certain real-time applications (such as packet voice and video),
	the loss pattern or loss distribution is a key parameter		In certain real-time applications (such as packet voice and
	that determines the performance observed by the users. For the same		video), the loss pattern or loss distribution is a key parameter
	loss rate, two different loss distributions could potentially produce		that determines the performance observed by the users. For the
	widely different perceptions of performance. The impact of loss pattern		same loss rate, two different loss distributions could potentially
	is also extremely important for non-real-time applications that use		produce widely different perceptions of performance. The impact
	an adaptive protocol such as TCP. There is ample evidence in the		of loss pattern is also extremely important for non-real-time
	literature indicating the importance and existence of loss burstiness		applications that use an adaptive protocol such as TCP. Refer
	and its effect on packet voice and video applications		to [2], [3], [5], [12] for evidence as to the importance and
	[Bolot], [Borella], [Handley], [Yajnik].		existence of loss burstiness and its effect on packet voice and video
			applications.
	In this document, we propose two derived metrics, called "loss distance"		In this document, we propose two derived metrics, called "loss
	and "loss period", with associated statistics, to capture packet loss		distance" and "loss period", with associated statistics, to capture
	patterns. The loss period metric captures the frequency and length		packet loss patterns. The loss period metric captures the frequency
	(burstiness) of loss once it starts, and the loss distance metric		and length (burstiness) of loss once it starts, and the loss
	captures the spacing between the loss periods. It is important to note		distance metric captures the spacing between the loss periods. It is
	that these metrics are derived based on the base metric		important to note that these metrics are derived based on the base
	Type-P-One-Way-packet-Loss.		metric Type-P-One-Way-packet-Loss.
	2. The Approach		2. Terminology
	This document closely follows the guidelines specified in [frame-work].		The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
	Specifically, the concepts of "singleton, sample, statistic",		NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", "OPTIONAL", and
			"silently ignore" in this document are to be interpreted as described
			in RFC 2119 [4].
			3. The Approach
			This document closely follows the guidelines specified in [10].
			Specifically, the concepts of singleton, sample, statistic,
	measurement principles, Type-P packets, as well as standard-formed		measurement principles, Type-P packets, as well as standard-formed
	packets all apply. However, since the draft proposes to capture		packets all apply. However, since the draft proposes to capture
	specific Internet behaviors, modifications to the sampling process		specific Internet behaviors, modifications to the sampling process
	may be needed. Indeed, this is mentioned in [AKZ], where it is noted		MAY be needed. Indeed, this is mentioned in [1], where it is
	that alternate sampling procedures may be useful depending on specific		noted that alternate sampling procedures may be useful depending
	circumstances. This draft proposes that the specific behaviors be		on specific circumstances. This draft proposes that the specific
	captured as "derived" metrics from the base metrics the behaviors		behaviors be captured as "derived" metrics from the base metrics the
	are related to. The reasons for adopting this position are the		behaviors are related to. The reasons for adopting this position are
	following		the following:
	- it provides consistent usage of singleton metric definition for		- it provides consistent usage of singleton metric definition for
	different behaviors (e.g., a single definition of packet loss		different behaviors (e.g., a single definition of packet loss is
	is needed for capturing burst of losses, 'm out of n' losses		needed for capturing burst of losses, 'm out of n' losses etc.)
	etc. Otherwise, the metrics would have to be fundamentally
	different)
	- it allows re-use of the methodologies specified for the singleton		- it allows re-use of the methodologies specified for the singleton
	metric with modifications whenever necessary		metric with modifications whenever necessary
	- it clearly separates few base metrics from many Internet behaviors
	Following the guidelines in [frame-work], this		- it clearly separates few base metrics from many Internet
	translates to deriving sample metrics from the respective		behaviors
	singletons. The process of deriving sample metrics from the singletons
	is specified in [frame-work], [AKZ], and others.		Following the guidelines in [10], this translates to deriving
			sample metrics from the respective singletons. The process
			of deriving sample metrics from the singletons is specified
			in [10], [1], and others.
	In the following sections, we apply this approach to a particular		In the following sections, we apply this approach to a particular
	Internet behavior, namely the packet loss process.		Internet behavior, namely the packet loss process.
	3. Basic Definitions:		4. Basic Definitions
	3.1. Bursty loss:
	The loss involving consecutive packets of a stream.
	3.2. Loss Distance:
	The difference in sequence numbers of two successively lost		Sequence number: Consecutive packets in a time series sample
	packets which may or may not be separated by successfully		are given sequence numbers that are consecutive
	received packets.		integers. This document does not specify exactly
			how to associate sequence numbers with packets. The
			sequence numbers could be contained within test
			packets themselves, or they could be derived through
			post-processing of the sample.
	Example. Let packet with sequence number 50 be considered lost		Bursty loss: The loss involving consecutive packets of a stream.
	immediately after packet with sequence number 20 was
	considered lost. The loss distance is 30.
	Note that this definition does not specify exactly how to		Loss Distance: The difference in sequence numbers of two
	associate sequence numbers with test packets. In other words, from		successively lost packets which may or may not be
	a timeseries sample of test packets, one may derive the sequence		separated by successfully received packets.
	numbers. However, these sequence numbers must be consecutive
	integers.
	3.3. Loss period:		Example: In a packet stream, the packet with sequence number 20
			is considered lost, followed by the packet with
			sequence number 50. The loss distance is 30.
	Let P_i be the i'th packet.		Loss period: Let P_i be the i'th packet. Define f(P_i) = 1 if P_i
	Define f(P_i) = 1 if P_i is lost, 0 otherwise.		is lost, 0 otherwise. Then, a loss period begins if
	Then, a loss period begins if f(P_i) = 1 and f(P_(i-1)) = 0		f(P_i) = 1 and f(P_(i-1)) = 0
	Example. Consider the following sequence of lost (denoted by x)		Example: Consider the following sequence of lost (denoted by x)
	and received (denoted by r) packets.		and received (denoted by r) packets.
	r r r x r r x x x r x r r x x x		r r r x r r x x x r x r r x x x
	Then, with i assigned as follows		Then, with `i' assigned as follows,
	1 1 1 1 1 1		1 1 1 1 1 1
	i: 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5		i: 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
	f(P_i) is,		f(P_i) is,
	f(P_i): 0 0 0 1 0 0 1 1 1 0 1 0 0 1 1 1		f(P_i): 0 0 0 1 0 0 1 1 1 0 1 0 0 1 1 1
	and there are four loss periods in the above sequence		and there are four loss periods in the above sequence
	beginning at P_3, P_6, P_10, and P_13.		beginning at P_3, P_6, P_10, and P_13.
	4. Definitions for Samples of One-way Loss Distance,		5. Definitions for Samples of One-way Loss Distance, and One-way
	and One-way Loss Period.		Loss Period
	4.1 Metric Name:		5.1. Metric Names
	4.1.1 Type-P-One-Way-Loss-Distance-Stream		5.1.1. Type-P-One-Way-Loss-Distance-Stream
	4.1.2 Type-P-One-Way-Loss-Period-Stream
	4.2 Metric Parameters		5.1.2. Type-P-One-Way-Loss-Period-Stream
	+ Src, the IP address of a host		5.2. Metric Parameters
	+ Dst, the IP address of a host
	+ T0, a time
	+ Tf, a time
	+ lambda, a rate in reciprocal of seconds
	+ Path, the path from Src to Dst (See [AKZ] for comments)
	4.3 Metric Units		Src, the IP address of a host
	4.3.1 Type-P-One-Way-Loss-Distance-Stream:		Dst, the IP address of a host
	A sequence of pairs of the form <loss distance, loss>, where loss		T0, a time
	is derived from the sequence of <time, loss> in [AKZ], and loss
			Tf, a time
			lambda, a rate of any sampling method chosen in reciprocal of
			seconds
			5.3. Metric Units
			5.3.1. Type-P-One-Way-Loss-Distance-Stream
			A sequence of pairs of the form <loss distance, loss>, where
			loss is derived from the sequence of <time, loss> in [1], and loss
	distance is either zero or a positive integer.		distance is either zero or a positive integer.
	4.3.2 Type-P-One-Way-Loss-Period-Stream		5.3.2. Type-P-One-Way-Loss-Period-Stream
	A sequence of pairs of the form <loss period, loss>, where loss is		A sequence of pairs of the form <loss period, loss>, where loss is
	derived from the sequence of <time, loss> in [AKZ], and loss period		derived from the sequence of <time, loss> in [1], and loss period is
	an integer.		an integer.
	4.4. Definitions:		5.4. Definitions
	4.4.1 Type-P-One-Way-Loss-Distance-Stream		5.4.1. Type-P-One-Way-Loss-Distance-Stream
	When a packet is considered lost (using the definition in [AKZ]), we		When a packet is considered lost (using the definition in [1]),
	look at its sequence number and compare it with that of the		we look at its sequence number and compare it with that of the
	previously lost packet. The difference is the loss distance between		previously lost packet. The difference is the loss distance between
	the lost packet and the previously lost packet. The sample would		the lost packet and the previously lost packet. The sample would
	consist of <loss distance, loss> pairs. This definition assumes that		consist of <loss distance, loss> pairs. This definition assumes that
	sequence numbers of successive test packets increase monotonically by		sequence numbers of successive test packets increase monotonically by
	one. The loss distance associated with the very first packet loss is		one. The loss distance associated with the very first packet loss is
	considered to be zero.		considered to be zero.
	The sequence number of a test packet can be derived from the timeseries		The sequence number of a test packet can be derived from the
	sample collected by performing the loss measurement according to the		timeseries sample collected by performing the loss measurement
	methodology in [AKZ]. For example, if a loss sample consists of		according to the methodology in [1]. For example, if a loss sample
	{<T0,0>, <T1,0>, <T2,1>, <T3,0>, <T4,0>}, the sequence numbers of the		consists of <T0,0>, <T1,0>, <T2,1>, <T3,0>, <T4,0>, the sequence
	five test packets sent at T0, T1, T2, T3, and T4 can be 0, 1, 2, 3 and		numbers of the five test packets sent at T0, T1, T2, T3, and T4 can
	4 respectively, or 100, 101, 102, 103 and 104 respectively, etc.		be 0, 1, 2, 3 and 4 respectively, or 100, 101, 102, 103 and 104
			respectively, etc.
	4.4.2 Type-P-One-Way-Loss-Period-Stream		5.4.2. Type-P-One-Way-Loss-Period-Stream
	We start a counter 'n' at an initial value of zero. This counter is		We start a counter 'n' at an initial value of zero. This
	incremented by one each time a lost packet satisfies the Definition 3.3.		counter is incremented by one each time a lost packet satisfies the
	The metric is defined as <loss period, loss> where		definition outlined in 4. The metric is defined as <loss period,
	"loss" is derived from the sequence of <time, loss> in		loss> where "loss" is derived from the sequence of <time, loss> in
	Type-P-One-Way-Loss-Stream [AKZ], and		Type-P-One-Way-Loss-Stream [1], and loss period is set to zero when
	loss period is set to zero when "loss" is zero in		"loss" is zero in Type-P-One-Way-Loss-Stream, and loss period is set
	Type-P-One-Way-Loss-Stream, and loss period is set to 'n' (above)		to 'n' (above) when "loss" is one in Type-P-One-Way-Loss-Stream.
	when "loss" is one in Type-P-One-Way-Loss-Stream.
	Essentially, when a packet is lost, the current value of "n" indicates		Essentially, when a packet is lost, the current value of "n"
	the loss period to which this packet belongs. For a packet that is		indicates the loss period to which this packet belongs. For a packet
	received successfully, the loss period is defined to be zero.		that is received successfully, the loss period is defined to be zero.
	4.4.3 Example:		5.4.3. Examples
	Let the following set of pairs represent a Type-P-One-Way-Loss-Stream.		Let the following set of pairs represent a Type-P-One-Way-Loss-Stream.
	{<T1,0>,<T2,1>,<T3,0>,<T4,0>,<T5,1>,<T6,0>,<T7,1>,<T8,0>,<T9,1>,		{<T1,0>,<T2,1>,<T3,0>,<T4,0>,<T5,1>,<T6,0>,<T7,1>,<T8,0>,<T9,1>,<T10,1>}
	<T10,1>}
	where T1, T2,..,T10 are in increasing order.		where T1, T2,..,T10 are in increasing order.
	Packets sent at T2, T5, T7, T9, T10 are lost. The two derived metrics		Packets sent at T2, T5, T7, T9, T10 are lost. The two derived
	can be obtained from this sample as follows.		metrics can be obtained from this sample as follows.
	(i) Type-P-One-Way-Loss-Distance-Stream:		(i) Type-P-One-Way-Loss-Distance-Stream:
	Since packet 2 is the first lost packet, the associated loss distance		Since packet 2 is the first lost packet, the associated loss
	is zero. For the next lost packet (packet 5), loss distance is 5-2 or 3.		distance is zero. For the next lost packet (packet 5), loss distance
	Similarly, for the remaining lost packets (packets 7, 9, and 10) their		is 5-2 or 3. Similarly, for the remaining lost packets (packets
	loss distances are 2, 2, and 1 respectively. Therefore, the		7, 9, and 10) their loss distances are 2, 2, and 1 respectively.
	Type-P-One-Way-Loss-Distance-Stream is:		Therefore, the Type-P-One-Way-Loss-Distance-Stream is:
	{<0,0>,<0,1>,<0,0>,<0,0>,<3,1>,<0,0>,<2,1>,<0,0>,<2,1>,<1,1>}		{<0,0>,<0,1>,<0,0>,<0,0>,<3,1>,<0,0>,<2,1>,<0,0>,<2,1>,<1,1>}
	(ii) The Type-P-One-Way-Loss-Period-Stream:		(ii) The Type-P-One-Way-Loss-Period-Stream:
	The packet 2 sets the counter 'n' to 1, which is incremented by one		The packet 2 sets the counter 'n' to 1, which is incremented
	for packets 5, 7 and 9 according to Definition 3.3. However, for		by one for packets 5, 7 and 9 according to the definition in 4.
	packet 10, the counter remains at 4 satisfying Definition 3.3 again.		However, for packet 10, the counter remains at 4, again satisfying
	Thus, the Type-P-One-Way-Loss-Period-Stream is:		the definition in 4. Thus, the Type-P-One-Way-Loss-Period-Stream is:
	{<0,0>,<1,1>,<0,0>,<0,0>,<2,1>,<0,0>,<3,1>,<0,0>,<4,1>,<4,1>}		{<0,0>,<1,1>,<0,0>,<0,0>,<2,1>,<0,0>,<3,1>,<0,0>,<4,1>,<4,1>}
	4.5. Methodologies:		5.5. Methodologies
	The same methodology outlined in [AKZ] can be used to conduct the		The same methodology outlined in [1] can be used to conduct the
	sample experiments.		sample experiments. A synopsis is listed below.
	4.6 Discussion:		Generally, for a given Type-P, one possible methodology would
			proceed as follows:
			- Arrange that Src and Dst have clocks that are synchronized with
			each other. The degree of synchronization is a parameter of the
			methodology, and depends on the threshold used to determine loss
			(see below).
			- At the Src host, select Src and Dst IP addresses, and form a test
			packet of Type-P with these addresses.
			- At the Dst host, arrange to receive the packet.
			- At the Src host, place a timestamp in the prepared Type-P packet,
			and send it towards Dst.
			- If the packet arrives within a reasonable period of time, the
			one-way packet-loss is taken to be zero.
			- If the packet fails to arrive within a reasonable period of
			time, the one-way packet-loss is taken to be one. Note that the
			threshold of "reasonable" here is a parameter of the methodology.
			5.6. Discussion
	The Loss-Distance-Stream metric allows one to study the separation		The Loss-Distance-Stream metric allows one to study the separation
	between packet losses. This could be useful in determining a		between packet losses. This could be useful in determining a "spread
	"spread factor" associated with the packet loss rate. For		factor" associated with the packet loss rate. In conjunction, the
	example, for a given packet loss rate, this metric		Loss-Period-Stream metric allows the study of loss burstiness for
	indicates how the losses are spread. On the other hand,		each occurrence of loss. A single loss period of length 'n' can
	the Loss-Period-Stream metric allows the study of loss burstiness		account for a significant portion of the overall loss rate. Note
	for each occurrence of loss. Note that a single loss period of		that it is possible to measure distance between loss bursts separated
	length 'n' can account for a significant portion of the overall		by one or more successfully received packets. (Refer to Sections 6.4
	loss rate. Note also that it is possible to measure distance between		and 6.5).
	loss bursts seprated by one or more successfully received packets: See
	Section 5.4, and 5.5
	4.7 Sampling Considerations:		5.7. Sampling Considerations
	The proposed metrics can be used independent of the		The proposed metrics can be used independent of the particular
	particular sampling method used. We note that Poisson sampling		sampling method used. We note that Poisson sampling may not
	may not yield appropriate values for these metrics for		yield appropriate values for these metrics for certain real-time
	certain real-time applications such as voice over IP, as well as to		applications such as voice over IP, as well as to TCP-based
	TCP-based applications. For real-time applications, it may be more		applications. For real-time applications, it may be more appropriate
	appropriate to use the ON-OFF [Sriram] model, in which an ON period		to use the ON-OFF [11] model, in which an ON period starts with
	starts with certain probability 'p', during which certain number of		certain probability 'p', during which certain number of packets
	packets are transmitted with mean 'lambda-on' according to geometric		are transmitted with mean 'lambda-on' according to geometric
	distribution and an OFF period starts with probability '1-p' and		distribution and an OFF period starts with probability '1-p' and
	lasts for a period of time based on exponential distribution with		lasts for a period of time based on exponential distribution with
	rate 'lambda-off'.		rate 'lambda-off'.
	For TCP-based applications, one may use the model proposed in		For TCP-based applications, one may use the model proposed in [7].
	[Padhye1]. See [Padhye2] for an application of the model.		See [8] for an application of the model.
	5. Statistics:		5.8. Errors and Uncertainties
	5.1 Type-P-One-Way-Loss-Noticeable-Rate		The measurement aspects, including the packet size, loss
			threshold, type of the test machine chosen etc, invariably influence
			the packet loss metric itself and hence the derived metrics described
			in this document. Thus, when making assessment of the results
			pertaining to the metrics outlined in this document, attention must
			be paid to these matters. See [1] for a detailed consideration of
			errors and uncertainties regarding the measurement of base packet
			loss metric.
	Define loss of a packet to be "noticeable" [RK97] if the distance		6. Statistics
	between the lost packet and the previously lost packet is no
	greater than delta, a positive integer, where delta is the
	"loss constraint".
	Example. Let delta = 99. Let us assume that packet 50 is lost		6.1. Type-P-One-Way-Loss-Noticeable-Rate
	followed by a bursty loss of length 3 starting from
	packet 125.
	All the *four* losses are noticeable.
	Given a Type-P-One-Way-Loss-Distance-Stream, this statistic		Define loss of a packet to be "noticeable" [6] if the distance
	can be computed simply as the number of losses that violate some		between the lost packet and the previously lost packet is no greater
	constraint delta, divided by the number of losses. (Alternately, it		than delta, a positive integer, where delta is the "loss constraint".
	can also be defined as the number of "noticeable losses" to the number
	of successfully received packets). This statistic is useful when the		Example: Let delta = 99. Let us assume that packet 50 is lost
			followed by a bursty loss of length 3 starting from packet 125. All
			the three losses starting from packet 125 are noticeable.
			Given a Type-P-One-Way-Loss-Distance-Stream, this statistic can be
			computed simply as the number of losses that violate some constraint
			delta, divided by the number of losses. (Alternatively, it can also
			be defined as the number of "noticeable losses" to the number of
			successfully received packets). This statistic is useful when the
	actual distance between successive losses is important. For example,		actual distance between successive losses is important. For example,
	many multimedia codecs can sustain losses by "concealing" the effect		many multimedia codecs can sustain losses by "concealing" the effect
	of loss by making use of past history information. Their ability to		of loss by making use of past history information. Their ability to
	do so degrades with poor history resulting from losses separated by		do so degrades with poor history resulting from losses separated by
	close distances. By chosing delta based on this sensitivity, one can		close distances. By choosing delta based on this sensitivity, one
	measure how "noticeable" a loss might be for quality purposes.		can measure how "noticeable" a loss might be for quality purposes.
	The noticeable loss requires a certain "spread factor" for losses		The noticeable loss requires a certain "spread factor" for losses
	in the timeseries. In the above example where loss constraint is equal		in the timeseries. In the above example where loss constraint is
	to 99, a loss rate of one percent with a spread of 100 between		equal to 99, a loss rate of one percent with a spread of 100 between
	losses (e.g., 100, 200, 300, 400, 500 out of 500 packets) may be more		losses (e.g., 100, 200, 300, 400, 500 out of 500 packets) may be more
	desirable for some applications compared to the same loss rate with a		desirable for some applications compared to the same loss rate with a
	spread that violates the loss constraint		spread that violates the loss constraint (e.g., 100, 175, 275, 290,
	(e.g., 100, 175, 275, 290, 400: losses occuring at 175 and 290		400: losses occurring at 175 and 290 violate delta = 99).
	violate delta = 99).
	5.2 Type-P-One-Way-Loss-Period-Total		6.2. Type-P-One-Way-Loss-Period-Total
	This represents the total number of loss periods, and can be derived		This represents the total number of loss periods, and can be
	from the loss period metric Type-P-One-Way-Loss-Period-Stream as		derived from the loss period metric Type-P-One-Way-Loss-Period-Stream
	follows:		as follows:
	Type-P-One-Way-Loss-Period-Total = maximum value of the first entry		Type-P-One-Way-Loss-Period-Total = maximum value of the first
	of the set of pairs, <loss period, loss>, representing the loss metric		entry of the set of pairs, <loss period, loss>, representing the loss
	Type-P-One-Way-Loss-Period-Stream.		metric Type-P-One-Way-Loss-Period-Stream.
	Note that this statistic does not describe the duration of each loss		Note that this statistic does not describe the duration of each
	period itself. If this statistic is large, it does not mean that the		loss period itself. If this statistic is large, it does not mean
	losses are more spread out than they are otherwise; one or more		that the losses are more spread out than they are otherwise; one
	loss periods may include bursty losses. This statistic is generally		or more loss periods may include bursty losses. This statistic is
	useful in gathering first order of approximation of loss spread.		generally useful in gathering first order of approximation of loss
			spread.
	5.3 Type-P-One-Way-Loss-Period-Lengths		6.3. Type-P-One-Way-Loss-Period-Lengths
	This statistic is a sequence of pairs <loss period, length>, with the		This statistic is a sequence of pairs <loss period,
	"loss period" entry ranging from 1 - Type-P-One-Way-Loss-Period-Total.		length>, with the "loss period" entry ranging from 1 -
	Thus the total number of pairs in this statistic equals		Type-P-One-Way-Loss-Period-Total. Thus the total number of
	Type-P-One-Way-Loss-Period-Total. In each pair, the "length" is		pairs in this statistic equals Type-P-One-Way-Loss-Period-Total. In
	obtained by counting the number of pairs, <loss period, loss>, in the		each pair, the "length" is obtained by counting the number of pairs,
	metric Type-P-One-Way-Loss-Period-Stream which have first entry equal		<loss period, loss>, in the metric Type-P-One-Way-Loss-Period-Stream
	to "loss period."		which have first entry equal to "loss period."
	Since this statistic represents the number of packets lost in each		Since this statistic represents the number of packets lost in each
	loss period, it is an indicator of burstiness of each loss period. In		loss period, it is an indicator of burstiness of each loss period.
	conjunction with loss-period-total statistic, this statistic is generally		In conjunction with loss-period-total statistic, this statistic is
	useful in observing which loss periods are potentially more influential		generally useful in observing which loss periods are potentially more
	than others from a quality perspective.		influential than others from a quality perspective.
	5.4 Type-P-One-Way-Inter-Loss-Period-Lengths		6.4. Type-P-One-Way-Inter-Loss-Period-Lengths
	This statistic measures distance between successive loss periods. It		This statistic measures distance between successive loss
	takes the form of a set of pairs		periods. It takes the form of a set of pairs <loss period,
	<loss period, inter-loss-period-length>, with the		inter-loss-period-length>, with the "loss period" entry ranging from
	"loss period" entry ranging from 1 - Type-P-One-Way-Loss-Period-Total,		1 - Type-P-One-Way-Loss-Period-Total, and "inter-loss-period-length"
	and "inter-loss-period-length" is the loss distance between the last		is the loss distance between the last packet considered lost in "loss
	packet considered lost in "loss period" 'i-1', and the first packet		period" 'i-1', and the first packet considered lost in "loss period"
	considered lost in "loss period" 'i', where 'i' ranges from 2 to		'i', where 'i' ranges from 2 to Type-P-One-Way-Loss-Period-Total.
	Type-P-One-Way-Loss-Period-Total. The "inter-loss-period-length"		The "inter-loss-period-length" associated with the first "loss
	associated with the first "loss period" is defined to be zero.		period" is defined to be zero.
	This statistic allows one to consider, for example, two loss periods each		This statistic allows one to consider, for example, two loss
	of length greater than one (implying loss burst), but separated by a		periods each of length greater than one (implying loss burst), but
	distance of 2 to belong to the same loss burst if such a consideration		separated by a distance of 2 to belong to the same loss burst if such
	is deemed useful. When the Inter-Loss-Period-Length between two bursty		a consideration is deemed useful. When the Inter-Loss-Period-Length
	loss periods is smaller, it could affect the loss concealing ability of		between two bursty loss periods is smaller, it could affect the loss
	multimedia codecs since there is relatively smaller history. When it is		concealing ability of multimedia codecs since there is relatively
	larger, an application may be able to rebuild its history which could		smaller history. When it is larger, an application may be able to
	dampen the effect of an impending loss (period).		rebuild its history which could dampen the effect of an impending
			loss (period).
	5.5 Example		6.5. Examples
	We continue with the same example as in Section 4.4.3. The three		We continue with the same example as in Section 5.4.3. The three
	statistics defined above will have the following values.		statistics defined above will have the following values.
	+ Let delta = 2.		- Let delta = 2. In Type-P-One-Way-Loss-Distance-Stream
	In Type-P-One-Way-Loss-Distance-Stream
	{<0,0>,<0,1>,<0,0>,<0,0>,<3,1>,<0,0>,<2,1>,<0,0>,<2,1>,<1,1>}, there
	are 3 loss distances that violate the delta of 2. Thus,
	Type-P-One-Way-Loss-Noticeable-Rate = 3/5		{<0,0>,<0,1>,<0,0>,<0,0>,<3,1>,<0,0>,<2,1>,<0,0>,<2,1>,<1,1>},
	((number of noticeable losses)/(number of total losses))		there are 3 loss distances that violate the delta of 2. Thus,
	+ In Type-P-One-Way-Loss-Period-Stream		Type-P-One-Way-Loss-Noticeable-Rate = 3/5 ((number of noticeable
	{<0,0>,<1,1>,<0,0>,<0,0>,<2,1>,<0,0>,<3,1>,<0,0>,<4,1>,<4,1>}, the		losses)/(number of total losses))
	largest of the first entry in the sequence of <loss period,loss>
	pairs is 4. Thus,		- In Type-P-One-Way-Loss-Period-Stream
			{<0,0>,<1,1>,<0,0>,<0,0>,<2,1>,<0,0>,<3,1>,<0,0>,<4,1>,<4,1>},
			the largest of the first entry in the sequence of <loss
			period,loss> pairs is 4. Thus,
	Type-P-One-Way-Loss-Period-Total = 4		Type-P-One-Way-Loss-Period-Total = 4
	+ In Type-P-One-Way-Loss-Period-Stream		- In Type-P-One-Way-Loss-Period-Stream
	{<0,0>,<1,1>,<0,0>,<0,0>,<2,1>,<0,0>,<3,1>,<0,0>,<4,1>,<4,1>}, the
	lengths of individual loss periods are 1, 1, 1 and 2 respectively.
	Thus,
	Type-P-One-Way-Loss-Period-Lengths = {<1,1>,<2,1>,<3,1>,<4,2>}		{<0,0>,<1,1>,<0,0>,<0,0>,<2,1>,<0,0>,<3,1>,<0,0>,<4,1>,<4,1>},
	+ In Type-P-One-Way-Loss-Period-Stream		the lengths of individual loss periods are 1, 1, 1 and 2
	{<0,0>,<1,1>,<0,0>,<0,0>,<2,1>,<0,0>,<3,1>,<0,0>,<4,1>,<4,1>}, the		respectively. Thus,
	loss periods 1 and 2 are separated by 3 (5-2), loss periods 2 and 3
	are separated by 2 (7-5), and 3 and 4 are separated by 2 (9-7).
	Thus,
	Type-P-One-Way-Inter-Loss-Period-Lengths = {<1,0>,<2,3>,<3,2>,<4,2>}
	6. Security Considerations		Type-P-One-Way-Loss-Period-Lengths =
	Since this draft proposes sample metrics based on the base loss metric		{<1,1>,<2,1>,<3,1>,<4,2>}
	defined in [AKZ], it inherits the security considerations mentioned in
	[AKZ].
	7. Acknowledgements		- In Type-P-One-Way-Loss-Period-Stream
	Many thanks to Matt Zekauskas for the constructive feedback on the draft.		{<0,0>,<1,1>,<0,0>,<0,0>,<2,1>,<0,0>,<3,1>,<0,0>,<4,1>,<4,1>},
	Thanks to Guy Almes for encouraging the work, and Vern Paxson for the
	comments during the IETF meetings. Thanks to Steve Glass for making the
	presentation at the Oslo meeting.
	8. References		the loss periods 1 and 2 are separated by 3 (5-2), loss periods
			2 and 3 are separated by 2 (7-5), and 3 and 4 are separated by 2
			(9-7). Thus,
			Type-P-One-Way-Inter-Loss-Period-Lengths =
	[AKZ] G. Almes and S. Kalindindi and M. Zekauskas, "A One-way Packet		{<1,0>,<2,3>,<3,2>,<4,2>}
	Loss Metric for IPPM", RFC 2680, September 1999
	[Bolot] J.-C. Bolot and A. vega Garcia, "The case for FEC-based		7. Security Considerations:
	error control for Packet Audio in the Internet", ACM Multimedia
			Since this draft proposes sample metrics based on the base loss
			metric defined in [1], it inherits the security considerations
			mentioned in [1].
			Conducting Internet measurements raises both security and privacy
			concerns. This document does not specify a particular implementation
			of metrics, so it does not directly affect the security of the
			Internet nor of applications which run on the Internet. However,
			implementations of these metrics must be mindful of security and
			privacy concerns.
			The derived sample metrics in this document are based on the loss
			metric defined in RFC-2680 [1], and thus they inherit the security
			considerations of that document. The reader should consult [1] for a
			more detailed treatment of security considerations.
			Nevertheless, there are a few things to highlight. First,
			the lambda specified in the Type-P-Loss-Distance-Stream and
			Type-P-Loss-Period-Stream controls the rate at which test packets
			are sent, and therefore if it is set inappropriately large could
			perturb the network under test, cause congestion, or at worst be a
			denial-of-service attack to the network under test.
			Second, privacy of user data is not a concern, since the
			underlying metric is intended to be implemented using test packets
			that contain no user information. Even if packets contained user
			information, the derived metrics do not release data sent by the
			user. Third, the results could be perturbed by attempting to corrupt
			or disrupt the underlying stream, for example adding extra packets
			that look just like test packets.
			In general, legitimate measurements must have their parameters
			selected carefully in order to avoid interfering with normal traffic
			in the network. Such measurements should also be authorized and
			authenticated in some way so that attacks can be identified and
			intercepted.
			8. IANA Considerations
			Since this document does not define a specific protocol, nor does
			it define any well-known values, there are no IANA considerations for
			this document.
			9. Acknowledgements
			Matt Zekauskas provided insightful feedback and the text for the
			Security Considerations section. We sincerely thank him for his
			painstaking review and for supporting this work along with Merike
			Kaeo. Thanks to Guy Almes for encouraging the work, and Vern Paxson
			for the comments during the IETF meetings. Thanks to Steve Glass for
			making the presentation at the Oslo meeting.
			References
			[1] G. Almes and S. Kalindindi and M. Zekauskas, "A One-way Packet
			Loss Metric for IPPM", RFC 2680, September 1999.
			[2] J.-C. Bolot and A. vega Garcia, "The case for FEC-based error
			control for Packet Audio in the Internet", ACM Multimedia
	Systems, 1997.		Systems, 1997.
	[Borella] M. S. Borella, D. Swider, S. Uludag, and G. B. Brewster,		[3] M. S. Borella, D. Swider, S. Uludag, and G. B. Brewster,
	"Internet Packet Loss: Measurement and Implications for End-to-End		"Internet Packet Loss: Measurement and Implications for
	QoS," Proceedings, International Conference on Parallel Processing,		End-to-End QoS," Proceedings, International Conference on
	August 1998.		Parallel Processing, August 1998.
	[Handley] M. Handley, "An examination of MBONE performance",		[4] S. Bradner, "Key words for use in RFCs to Indicate Requirement
	Technical Report, USC/ISI, ISI/RR-97-450, July 1997		Levels," RFC 2119, Internet Engineering Task Force, March 1997.
	[RK97] R. Koodli, "Scheduling Support for Multi-tier Quality of		[5] M. Handley, "An examination of MBONE performance", Technical
			Report, USC/ISI, ISI/RR-97-450, July 1997
			[6] R. Koodli, "Scheduling Support for Multi-tier Quality of
	Service in Continuous Media Applications", PhD dissertation,		Service in Continuous Media Applications", PhD dissertation,
	Electrical and Computer Engineering Department, University of		Electrical and Computer Engineering Department, University of
	Massachusetts, Amherst, MA 01003.		Massachusetts, Amherst, MA 01003, September 1997.
	[Padhye1] J. Padhye, V. Firoiu, J. Kurose and D. Towsley, "Modeling		[7] J. Padhye, V. Firoiu, J. Kurose and D. Towsley, "Modeling TCP
	TCP throughput: a simple model and its empirical validation", in		throughput: a simple model and its empirical validation", in
	Proceedings of SIGCOMM'98, 1998.		Proceedings of SIGCOMM'98, 1998.
	[Padhye2] J. Padhye, J. Kurose, D. Towsley and R. Koodli, "A		[8] J. Padhye, J. Kurose, D. Towsley and R. Koodli, "A TCP-friendly
	TCP-friendly rate adjustment protocol for continuous media flows		rate adjustment protocol for continuous media flows over
	over best-effort networks", short paper presentation in		best-effort networks", short paper presentation in ACM
	ACM SIGMETRICS'99. Available as Umass Computer Science tech report		SIGMETRICS'99. Available as Umass Computer Science tech report
	from ftp://gaia.cs.umass.edu/pub/Padhye98-tcp-friendly-TR.ps.gz		from ftp://gaia.cs.umass.edu/pub/Padhye98-tcp-friendly-TR.ps.gz
	[Paxson] V. Paxson, "End-to-end Internet packet dynamics", Computer		[9] V. Paxson, "End-to-end Internet packet dynamics", IEEE/ACM
	Communication review, Proceedings of ACM SIGCOMM'97 Conference,		Transactions on Networking, 7(3), pages 277-292, June 1999.
	Cannes, France, September 1997, 27(4), pages 139-152, October 1997
	[frame-work] V. Paxson, G. Almes, J. Mahdavi, and M. Mathis,		[10] V. Paxson, G. Almes, J. Mahdavi, and M. Mathis, "Framework for
	"Framework for IP Performance Metrics", RFC 2330, May 1998.		IP Performance Metrics", RFC 2330, May 1998.
	[Sriram] K. Sriram and W. Whitt, "Characterizing superposition		[11] K. Sriram and W. Whitt, "Characterizing superposition arrival
	arrival processes in packet multiplexers for voice and data", IEEE		processes in packet multiplexers for voice and data", IEEE
	Journal on Selected Areas of Communication, September 1986, pages		Journal on Selected Areas of Communication, pages 833-846,
	833-846		September 1986,
	[Yajnik] M. Yajnik, J. Kurose and D. Towsley, "Packet loss		[12] M. Yajnik, J. Kurose and D. Towsley, "Packet loss correlation
	correlation in the MBONE multicast network", Proceedings of IEEE		in the MBONE multicast network", Proceedings of IEEE Global
	Global Internet, London, UK, November 1996.		Internet, London, UK, November 1996.
	Author's Addresses		Addresses
	Rajeev Koodli		Questions about this memo can be directed to the authors:
	Nokia Research Center
	313, Fairchild Drive
	Mountain View, CA 94043
	Phone: +1 650-625-2359
	Email: rajeev.koodli@nokia.com
	Rayadurgam Ravikanth		Rajeev Koodli Rayadurgam Ravikanth
	Axiowave Networks Inc.		Communications Systems Lab Axiowave Networks Inc.
	100 Nickerson Road		Nokia Research Center 100 Nickerson Road
	Marlborough, MA- 01752		313 Fairchild Drive Marlborough, MA- 01752
	Email: rravikanth@axiowave.com		Mountain View, California 94043 USA
			USA Email: rravikanth@axiowave.com
			Phone: +1-650 625-2359
			EMail: rajeev.koodli@nokia.com
			Fax: +1 650 625-2502
End of changes.
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