draft-ietf-ippm-multimetrics-08.txt   draft-ietf-ippm-multimetrics-09.txt 
Network Working Group E. Stephan Network Working Group E. Stephan
Internet-Draft France Telecom Internet-Draft France Telecom
Intended status: Standards Track L. Liang Intended status: Standards Track L. Liang
Expires: April 3, 2009 University of Surrey Expires: April 18, 2009 University of Surrey
A. Morton A. Morton
AT&T Labs AT&T Labs
September 30, 2008 October 15, 2008
IP Performance Metrics (IPPM) for spatial and multicast IP Performance Metrics (IPPM) for spatial and multicast
draft-ietf-ippm-multimetrics-08 draft-ietf-ippm-multimetrics-09
Status of this Memo Status of this Memo
By submitting this Internet-Draft, each author represents that any By submitting this Internet-Draft, each author represents that any
applicable patent or other IPR claims of which he or she is aware applicable patent or other IPR claims of which he or she is aware
have been or will be disclosed, and any of which he or she becomes have been or will be disclosed, and any of which he or she becomes
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Internet-Drafts are working documents of the Internet Engineering Internet-Drafts are working documents of the Internet Engineering
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This Internet-Draft will expire on April 3, 2009. This Internet-Draft will expire on April 18, 2009.
Abstract Abstract
The IETF has standardized IP Performance Metrics (IPPM) for measuring The IETF has standardized IP Performance Metrics (IPPM) for measuring
end-to-end performance between two points. This memo defines two new end-to-end performance between two points. This memo defines two new
categories of metrics that extend the coverage to multiple categories of metrics that extend the coverage to multiple
measurement points. It defines spatial metrics for measuring the measurement points. It defines spatial metrics for measuring the
performance of segments of a source to destination path, and metrics performance of segments of a source to destination path, and metrics
for measuring the performance between a source and many destinations for measuring the performance between a source and many destinations
in multiparty communications (e.g., a multicast tree). in multiparty communications (e.g., a multicast tree).
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`. ,-. `. ,-.
`. ,' `...... 1 `. ,' `...... 1
`. ; : `. ; :
`. ; : `. ; :
; :... 2 ; :... 2
| | | |
: ; : ;
: ;.... 3 : ;.... 3
: ; : ;
`. ,' `. ,'
`-'....... N `-'....... I
Figure 1: One-to-group points of interest Figure 1: One-to-group points of interest
A candidate point of interest for spatial metrics is a host from the A candidate point of interest for spatial metrics is a host from the
set of hosts involved in the delivery of the packets from source to set of hosts involved in the delivery of the packets from source to
destination. destination.
Src ------. Hosts Src ------. Hosts
\ \
`---X ... 1 `---X ... 1
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x x
/ /
.---------X .... 2 .---------X .... 2
/ /
x x
\ \
`---X .... 3 `---X .... 3
\ \
\ \
\ \
X .... N X .... J
\ \
\ \
\ \
`---- Dst `---- Dst
Note: 'x' are nodes which are not points of interest Note: 'x' are nodes which are not points of interest
Figure 2: Spatial points of interest Figure 2: Spatial points of interest
2.8. Reference point 2.8. Reference point
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o Each Hi extracts the timestamp T from the packet; o Each Hi extracts the timestamp T from the packet;
o Each Hi computes the one-way-delay from Src to Hi: dTi = Ti - T; o Each Hi computes the one-way-delay from Src to Hi: dTi = Ti - T;
o The reference point gathers the result of each Hi and arranges o The reference point gathers the result of each Hi and arranges
them according to the path ordering information received to build them according to the path ordering information received to build
the type-P spatial one-way vector (e.g. Type-P-Spatial-One-way- the type-P spatial one-way vector (e.g. Type-P-Spatial-One-way-
Delay-Vector metric <T, dT1, dT2,..., dTn, dT>) over the path Delay-Vector metric <T, dT1, dT2,..., dTn, dT>) over the path
<Src, H1, H2,..., Hn, Dst> at time T. <Src, H1, H2,..., Hn, Dst> at time T.
5.4.1. Packet Loss Detection 5.4.1. Packet Loss Detection
In an pure end-to-end measurement, packet losses are detected by the In a pure end-to-end measurement, packet losses are detected by the
receiver only. A packet is lost when Type-P-One-way-Delay is receiver only. A packet is lost when Type-P-One-way-Delay is
undefined or very large (See section 2.4 ans 2.5 of [RFC2680] and undefined or very large (See section 2.4 ans 2.5 of [RFC2680] and
section 3.5 of [RFC2680]). A packet is deemed lost by the receiver section 3.5 of [RFC2680]). A packet is deemed lost by the receiver
after a duration which starts at the time the packet is sent. This after a duration which starts at the time the packet is sent. This
timeout value is chosen by a measurement process; It determines the timeout value is chosen by a measurement process. It determines the
threshold between recording a long packet transfer time as a finite threshold between recording a long packet transfer time as a finite
value or an undefined value. value or an undefined value.
In a spatial measurement, packet losses may be detected at several In a spatial measurement, packet losses may be detected at several
measurement collection points. Depending on the consistency of the measurement collection points. Depending on the consistency of the
packet loss detections among the points of interest, a packet may be packet loss detections among the points of interest, a packet may be
considered as lost at one point despite having a finite delay at considered as lost at one point despite having a finite delay at
another one, or may be observed by the last measurement collection another one, or may be observed by the last measurement collection
point of the path but considered lost by Dst. point of the path but considered lost by Dst.
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information but also information on "relative performance". The information but also information on "relative performance". The
relative performance means the difference between absolute relative performance means the difference between absolute
performance of all users. Directly using the one-way metrics cannot performance of all users. Directly using the one-way metrics cannot
present the relative performance situation. However, if we use the present the relative performance situation. However, if we use the
variations of all users one-way parameters, we can have new metrics variations of all users one-way parameters, we can have new metrics
to measure the difference of the absolute performance and hence to measure the difference of the absolute performance and hence
provide the threshold value of relative performance that a multiparty provide the threshold value of relative performance that a multiparty
service might demand. A very good example of the high relative service might demand. A very good example of the high relative
performance requirement is the online gaming. A very light performance requirement is the online gaming. A very light
difference in delay might result in failure in the game. We have to difference in delay might result in failure in the game. We have to
use multicast specific statistic metrics to define exactly how small use multicast specific statistic metrics to define the relative delay
the relative delay the online gaming requires. There are many other required by online gaming. There are many other services, e.g.
services, e.g. online biding, online stock market, etc., that require online biding, online stock market, etc., that require multicast
multicast metrics in order to evaluate the network against their metrics in order to evaluate the network against their requirements.
requirements. Therefore, we can see the importance of new, multicast Therefore, we can see the importance of new, multicast specific,
specific, statistic metrics to feed this need. statistic metrics to feed this need.
We might also use some one-to-group statistic conceptions to present We might also use some one-to-group statistic conceptions to present
and report the group performance and relative performance to save the and report the group performance and relative performance to save the
report transmission bandwidth. Statistics have been defined for One- report transmission bandwidth. Statistics have been defined for One-
way metrics in corresponding RFCs. They provide the foundation of way metrics in corresponding RFCs. They provide the foundation of
definition for performance statistics. For instance, there are definition for performance statistics. For instance, there are
definitions for minimum and maximum One-way delay in [RFC2679]. definitions for minimum and maximum One-way delay in [RFC2679].
However, there is a dramatic difference between the statistics for However, there is a dramatic difference between the statistics for
one-to-one communications and for one-to-many communications. The one-to-one communications and for one-to-many communications. The
former one only has statistics over the time dimension while the former one only has statistics over the time dimension while the
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dimension. For instance, a TV broadcast service provider had the dimension. For instance, a TV broadcast service provider had the
performance Matrix M and calculated the One-way delay mean over the performance Matrix M and calculated the One-way delay mean over the
time dimension to obtain a delay Vector as {V1,V2,..., VN}. He then time dimension to obtain a delay Vector as {V1,V2,..., VN}. He then
calculated the mean of all the elements in the Vector to see what calculated the mean of all the elements in the Vector to see what
level of delay he has served to all N users. This new delay mean level of delay he has served to all N users. This new delay mean
gives information on how good the service has been delivered to a gives information on how good the service has been delivered to a
group of users during a sampling interval in terms of delay. It group of users during a sampling interval in terms of delay. It
needs twice calculation to have this statistic over both time and needs twice calculation to have this statistic over both time and
space dimensions. We name this kind of statistics 2-level statistics space dimensions. We name this kind of statistics 2-level statistics
to distinct with those 1-level statistics calculated over either to distinct with those 1-level statistics calculated over either
space or time dimension. It can be easily prove that no matter over space or time dimension. It can be easily proven that no matter over
which dimension a 2-level statistic is calculated first, the results which dimension a 2-level statistic is calculated first, the results
are the same. I.e. one can calculate the 2-level delay mean using are the same. I.e. one can calculate the 2-level delay mean using
the Matrix M by having the 1-level delay mean over the time dimension the Matrix M by having the 1-level delay mean over the time dimension
first and then calculate the mean of the obtained vector to find out first and then calculate the mean of the obtained vector to find out
the 2-level delay mean. Or, he can do the 1-level statistic the 2-level delay mean. Or, he can do the 1-level statistic
calculation over the space dimension first and then have the 2-level calculation over the space dimension first and then have the 2-level
delay mean. Both two results will be exactly the same. Therefore, delay mean. Both two results will be exactly the same. Therefore,
when define a 2-level statistic, there is no need to specify in which when defining a 2-level statistic there is no need to specify the
procedure the calculation should follow. order in which the calculation is executed.
Many statistics can be defined for the proposed one-to-group metrics Many statistics can be defined for the proposed one-to-group metrics
over either the space dimension or the time dimension or both. This over either the space dimension or the time dimension or both. This
memo treats the case where a stream of packets from the Source memo treats the case where a stream of packets from the Source
results in a sample at each of the Receivers in the Group, and these results in a sample at each of the Receivers in the Group, and these
samples are each summarized with the usual statistics employed in samples are each summarized with the usual statistics employed in
one-to-one communication. New statistic definitions are presented, one-to-one communication. New statistic definitions are presented,
which summarize the one-to-one statistics over all the Receivers in which summarize the one-to-one statistics over all the Receivers in
the Group. the Group.
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This section defines the overall one-way delay statistics for a This section defines the overall one-way delay statistics for a
receiver and for an entire group as illustrated by the matrix below. receiver and for an entire group as illustrated by the matrix below.
Recv /----------- Sample -------------\ Stats Group Stat Recv /----------- Sample -------------\ Stats Group Stat
1 R1dT1 R1dT2 R1dT3 ... R1dTk R1MD \ 1 R1dT1 R1dT2 R1dT3 ... R1dTk R1MD \
| |
2 R2dT1 R2dT2 R2dT3 ... R2dTk R2MD | 2 R2dT1 R2dT2 R2dT3 ... R2dTk R2MD |
| |
3 R3dT1 R3dT2 R3dT3 ... R3dTk R2MD > Group delay 3 R3dT1 R3dT2 R3dT3 ... R3dTk R3MD > Group delay
. | . |
. | . |
. | . |
n RndT1 RndT2 RndT3 ... RndTk RnMD / n RndT1 RndT2 RndT3 ... RndTk RnMD /
Receiver-n Receiver-n
delay delay
Figure 5: One-to-group Mean Delay Figure 5: One-to-group Mean Delay
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a single reference point with connectivity to all the points of a single reference point with connectivity to all the points of
interest. In this case, the number of points of interest determines interest. In this case, the number of points of interest determines
both storage capacity and packet transfer capacity of the host acting both storage capacity and packet transfer capacity of the host acting
as the reference point. However, both the storage and transfer as the reference point. However, both the storage and transfer
capacity can be reduced if the points of interest are capable of capacity can be reduced if the points of interest are capable of
computing the summary statistics that describe each measurement computing the summary statistics that describe each measurement
interval. This is consistent with many operational monitoring interval. This is consistent with many operational monitoring
architectures today, where even the individual singletons may not be architectures today, where even the individual singletons may not be
stored at each point of interest. stored at each point of interest.
In recognition of the likely need to minimize form of the results for In recognition of the likely need to minimize the form of the results
storage and communication, the Group metrics above have been for storage and communication, the Group metrics above have been
constructed to allow some computations on a per-Receiver basis. This constructed to allow some computations on a per-Receiver basis. This
means that each Receiver's statistics would normally have an equal means that each Receiver's statistics would normally have an equal
weight with all other Receivers in the Group (regardless of the weight with all other Receivers in the Group (regardless of the
number of packets received). number of packets received).
9.1. Computation methods 9.1. Computation methods
The scalability issue can be raised when there are thousands of The scalability issue can be raised when there are thousands of
points of interest in a group who are trying to send back the points of interest in a group who are trying to send back the
measurement results to the reference point for further processing and measurement results to the reference point for further processing and
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