 1/draftietfippmlosspattern04.txt 20060204 23:45:35.000000000 +0100
+++ 2/draftietfippmlosspattern05.txt 20060204 23:45:35.000000000 +0100
@@ 1,18 +1,18 @@
IP Performance Metrics (IPPM) WG Rajeev Koodli
INTERNET DRAFT Nokia Research Center
21 November 2000 R. Ravikanth
+20 July 2001 R. Ravikanth
Axiowave
Oneway Loss Pattern Sample Metrics

+
STATUS OF THIS MEMO
This document is an InternetDraft and is in full conformance with all
provisions of Section 10 of RFC2026.
InternetDrafts are working documents of the Internet Engineering Task
Force (IETF), its areas, and its working groups. Note that other groups
may also distribute working documents as Internet Drafts.
@@ 82,21 +82,21 @@
 it provides consistent usage of singleton metric definition for
different behaviors (e.g., a single definition of packet loss
is needed for capturing burst of losses, 'm out of n' losses
etc. Otherwise, the metrics would have to be fundamentally
different)
 it allows reuse of the methodologies specified for the singleton
metric with modifications whenever necessary
 it clearly separates few base metrics from many Internet behaviors
Following the guidelines in [framework], 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
is specified in [framework], [AKZ], and others.
In the following sections, we apply this approach to a particular
Internet behavior, namely the packet loss process.
3. Basic Definitions:
3.1. Bursty loss:
@@ 108,25 +108,23 @@
packets which may or may not be separated by successfully
received packets.
Example. Let packet with sequence number 50 be considered lost
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
associate sequence numbers with test packets. In other words, from
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.
Typo in last sentence.

3.3. Loss period:
Let P_i be the i'th packet.
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_(i1)) = 0
Example. Consider the following sequence of lost (denoted by x)
and received (denoted by r) packets.
r r r x r r x x x r x r r x x x
@@ 286,77 +284,89 @@
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 *four* losses are noticeable.
Given a TypePOneWayLossDistanceStream, this statistic
can be computed simply as the number of losses that violate some
constraint delta, divided by the number of losses. (Alternately, 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, many multimedia codecs can sustain
losses by "concealing" the effect of loss by making use of past
history information. Their ability to do so degrades with poor
history resulting from losses separated by close distances. By chosing
delta based on this sensitivity, one can measure how "noticeable" a
loss might be for quality purposes. The noticeable loss requires
a certain "spread factor" for losses in the timeseries. In the above
example where loss constraint is 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 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).
+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 history information. Their ability to
+do so degrades with poor history resulting from losses separated by
+close distances. By chosing delta based on this sensitivity, one can
+measure how "noticeable" a loss might be for quality purposes.
+The noticeable loss requires a certain "spread factor" for losses
+in the timeseries. In the above example where loss constraint is 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
+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 TypePOneWayLossPeriodTotal
This represents the total number of loss periods, and can be derived
from the loss period metric TypePOneWayLossPeriodStream as
follows:
TypePOneWayLossPeriodTotal = maximum value of the first entry
of the set of pairs, , representing the loss metric
TypePOneWayLossPeriodStream.
+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 TypePOneWayLossPeriodLengths
This statistic is a sequence of pairs , with the
"loss period" entry ranging from 1  TypePOneWayLossPeriodTotal.
Thus the total number of pairs in this statistic equals
TypePOneWayLossPeriodTotal. In each pair, the "length" is
obtained by counting the number of pairs, , in the
metric TypePOneWayLossPeriodStream which have first entry equal
to "loss period."
Thus, this statistic represents the number of packets lost in each
loss period.
+Since this statistic represents the number of packets lost in each
+loss period, it is an indicator of burstiness of each loss period. In
+conjunction with lossperiodtotal statistic, this statistic is generally
+useful in observing which loss periods are potentially more influential
+than others from a quality perspective.
5.4 TypePOneWayInterLossPeriodLengths
This statistic measures distance between successive loss periods. It
takes the form of a set of pairs
, with the
"loss period" entry ranging from 1  TypePOneWayLossPeriodTotal,
and "interlossperiodlength" is the loss distance between the last
packet considered lost in "loss period" 'i1', and the first packet
considered lost in "loss period" 'i', where 'i' ranges from 2 to
TypePOneWayLossPeriodTotal. The "interlossperiodlength"
associated with the first "loss period" is defined to be zero. This
statistic allows one to consider, for example, two loss periods each
+associated with the first "loss period" is defined to be zero.
+
+This statistic allows one to consider, for example, two loss periods each
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

is deemed useful.
+is deemed useful. When the InterLossPeriodLength between two bursty
+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
We continue with the same example as in Section 4.4.3. The three
statistics defined above will have the following values.
+ Let delta = 2.
In TypePOneWayLossDistanceStream
{<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,
@@ 405,21 +416,21 @@
[Bolot] J.C. Bolot and A. vega Garcia, "The case for FECbased
error control for Packet Audio in the Internet", ACM Multimedia
Systems, 1997.
[Borella] M. S. Borella, D. Swider, S. Uludag, and G. B. Brewster,
"Internet Packet Loss: Measurement and Implications for EndtoEnd
QoS," Proceedings, International Conference on Parallel Processing,
August 1998.
[Handley] M. Handley, "An examination of MBONE performance",
 Technical Report, USC/ISI, ISI/RR97450, January 1997
+ Technical Report, USC/ISI, ISI/RR97450, July 1997
[RK97] R. Koodli, "Scheduling Support for Multitier Quality of
Service in Continuous Media Applications", PhD dissertation,
Electrical and Computer Engineering Department, University of
Massachusetts, Amherst, MA 01003.
[Padhye1] J. Padhye, V. Firoiu, J. Kurose and D. Towsley, "Modeling
TCP throughput: a simple model and its empirical validation", in
Proceedings of SIGCOMM'98, 1998.