draft-ietf-ippm-loss-pattern-04.txt   draft-ietf-ippm-loss-pattern-05.txt 
IP Performance Metrics (IPPM) WG Rajeev Koodli IP Performance Metrics (IPPM) WG Rajeev Koodli
INTERNET DRAFT Nokia Research Center INTERNET DRAFT Nokia Research Center
21 November 2000 R. Ravikanth 20 July 2001 R. Ravikanth
Axiowave Axiowave
One-way Loss Pattern Sample Metrics One-way Loss Pattern Sample Metrics
<draft-ietf-ippm-loss-pattern-04.txt> <draft-ietf-ippm-loss-pattern-05.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 all
provisions of Section 10 of RFC2026. provisions of Section 10 of RFC2026.
Internet-Drafts are working documents of the Internet Engineering Task Internet-Drafts are working documents of the Internet Engineering Task
Force (IETF), its areas, and its working groups. Note that other groups Force (IETF), its areas, and its working groups. Note that other groups
may also distribute working documents as Internet- Drafts. may also distribute working documents as Internet- Drafts.
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- 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 needed for capturing burst of losses, 'm out of n' losses is needed for capturing burst of losses, 'm out of n' losses
etc. Otherwise, the metrics would have to be fundamentally etc. Otherwise, the metrics would have to be fundamentally
different) 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 - it clearly separates few base metrics from many Internet behaviors
Following the guidelines in [frame-work], this Following the guidelines in [frame-work], this
translates to deriving *sample* metrics from the respective translates to deriving sample metrics from the respective
singletons. The process of deriving sample metrics from the singletons singletons. The process of deriving sample metrics from the singletons
is specified in [frame-work], [AKZ], and others. is specified in [frame-work], [AKZ], 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: 3. Basic Definitions:
3.1. Bursty loss: 3.1. Bursty loss:
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packets which may or may not be separated by successfully packets which may or may not be separated by successfully
received packets. received packets.
Example. Let packet with sequence number 50 be considered lost Example. Let packet with sequence number 50 be considered lost
immediately after packet with sequence number 20 was immediately after packet with sequence number 20 was
considered lost. The loss distance is 30. considered lost. The loss distance is 30.
Note that this definition does not specify exactly how to Note that this definition does not specify exactly how to
associate sequence numbers with test packets. In other words, from associate sequence numbers with test packets. In other words, from
a timeseries sample of test packets, one may derive the sequence a timeseries sample of test packets, one may derive the sequence
numbers. However, these sequence numbers must to be consecutive numbers. However, these sequence numbers must be consecutive
integers. integers.
Typo in last sentence.
3.3. Loss period: 3.3. Loss period:
Let P_i be the i'th packet. Let P_i be the i'th packet.
Define f(P_i) = 1 if P_i is lost, 0 otherwise. Define f(P_i) = 1 if P_i is lost, 0 otherwise.
Then, a loss period begins if f(P_i) = 1 and f(P_(i-1)) = 0 Then, a loss period begins if 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
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Example. Let delta = 99. Let us assume that packet 50 is lost Example. Let delta = 99. Let us assume that packet 50 is lost
followed by a bursty loss of length 3 starting from followed by a bursty loss of length 3 starting from
packet 125. packet 125.
All the *four* losses are noticeable. All the *four* losses are noticeable.
Given a Type-P-One-Way-Loss-Distance-Stream, this statistic Given a Type-P-One-Way-Loss-Distance-Stream, this statistic
can be computed simply as the number of losses that violate some can be computed simply as the number of losses that violate some
constraint delta, divided by the number of losses. (Alternately, it constraint delta, divided by the number of losses. (Alternately, it
can also be defined as the number of "noticeable losses" to the number 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, many multimedia codecs can sustain
losses by "concealing" the effect of loss by making use of past of successfully received packets). This statistic is useful when the
history information. Their ability to do so degrades with poor actual distance between successive losses is important. For example,
history resulting from losses separated by close distances. By chosing many multimedia codecs can sustain losses by "concealing" the effect
delta based on this sensitivity, one can measure how "noticeable" a of loss by making use of past history information. Their ability to
loss might be for quality purposes. The noticeable loss requires do so degrades with poor history resulting from losses separated by
a certain "spread factor" for losses in the timeseries. In the above close distances. By chosing delta based on this sensitivity, one can
example where loss constraint is equal to 99, a loss rate of one measure how "noticeable" a loss might be for quality purposes.
percent with a spread of 100 between losses (e.g., 100, 200, 300, The noticeable loss requires a certain "spread factor" for losses
400, 500 out of 500 packets) may be more desirable for some in the timeseries. In the above example where loss constraint is equal
applications compared to the same loss rate with a spread that to 99, a loss rate of one percent with a spread of 100 between
violates the loss constraint (e.g., 100, 175, 275, 290, 400: losses losses (e.g., 100, 200, 300, 400, 500 out of 500 packets) may be more
occuring at 175 and 290 violate delta = 99). desirable for some applications compared to the same loss rate with a
spread that violates the loss constraint
(e.g., 100, 175, 275, 290, 400: losses occuring at 175 and 290
violate delta = 99).
5.2 Type-P-One-Way-Loss-Period-Total 5.2 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 derived
from the loss period metric Type-P-One-Way-Loss-Period-Stream as from the loss period metric Type-P-One-Way-Loss-Period-Stream as
follows: 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 entry
of the set of pairs, <loss period, loss>, representing the loss metric of the set of pairs, <loss period, loss>, representing the loss metric
Type-P-One-Way-Loss-Period-Stream. Type-P-One-Way-Loss-Period-Stream.
Note that this statistic does not describe the duration of each loss
period itself. If this statistic is large, it does not mean that the
losses are more spread out than they are otherwise; one or more
loss periods may include bursty losses. This statistic is generally
useful in gathering first order of approximation of loss spread.
5.3 Type-P-One-Way-Loss-Period-Lengths 5.3 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, length>, with the
"loss period" entry ranging from 1 - Type-P-One-Way-Loss-Period-Total. "loss period" entry ranging from 1 - Type-P-One-Way-Loss-Period-Total.
Thus the total number of pairs in this statistic equals Thus the total number of pairs in this statistic equals
Type-P-One-Way-Loss-Period-Total. In each pair, the "length" is Type-P-One-Way-Loss-Period-Total. In each pair, the "length" is
obtained by counting the number of pairs, <loss period, loss>, in the obtained by counting the number of pairs, <loss period, loss>, in the
metric Type-P-One-Way-Loss-Period-Stream which have first entry equal metric Type-P-One-Way-Loss-Period-Stream which have first entry equal
to "loss period." to "loss period."
Thus, this statistic represents the number of packets lost in each Since this statistic represents the number of packets lost in each
loss period. loss period, it is an indicator of burstiness of each loss period. In
conjunction with loss-period-total statistic, this statistic is generally
useful in observing which loss periods are potentially more influential
than others from a quality perspective.
5.4 Type-P-One-Way-Inter-Loss-Period-Lengths 5.4 Type-P-One-Way-Inter-Loss-Period-Lengths
This statistic measures distance between successive loss periods. It This statistic measures distance between successive loss periods. It
takes the form of a set of pairs takes the form of a set of pairs
<loss period, inter-loss-period-length>, with the <loss period, inter-loss-period-length>, with the
"loss period" entry ranging from 1 - Type-P-One-Way-Loss-Period-Total, "loss period" entry ranging from 1 - Type-P-One-Way-Loss-Period-Total,
and "inter-loss-period-length" is the loss distance between the last and "inter-loss-period-length" is the loss distance between the last
packet considered lost in "loss period" 'i-1', and the first packet packet considered lost in "loss period" 'i-1', and the first packet
considered lost in "loss period" 'i', where 'i' ranges from 2 to considered lost in "loss period" 'i', where 'i' ranges from 2 to
Type-P-One-Way-Loss-Period-Total. The "inter-loss-period-length" Type-P-One-Way-Loss-Period-Total. The "inter-loss-period-length"
associated with the first "loss period" is defined to be zero. This associated with the first "loss period" is defined to be zero.
statistic allows one to consider, for example, two loss periods each
This statistic allows one to consider, for example, two loss periods each
of length greater than one (implying loss burst), but separated by a of length greater than one (implying loss burst), but separated by a
distance of 2 to belong to the same loss burst if such a consideration distance of 2 to belong to the same loss burst if such a consideration
is deemed useful. When the Inter-Loss-Period-Length between two bursty
is deemed useful. loss periods is smaller, it could affect the loss concealing ability of
multimedia codecs since there is relatively smaller history. When it is
larger, an application may be able to rebuild its history which could
dampen the effect of an impending loss (period).
5.5 Example 5.5 Example
We continue with the same example as in Section 4.4.3. The three We continue with the same example as in Section 4.4.3. 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 {<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, are 3 loss distances that violate the delta of 2. Thus,
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