draft-ietf-ippm-multimetrics-09.txt   draft-ietf-ippm-multimetrics-10.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 18, 2009 University of Surrey Expires: October 24, 2009 University of Surrey
A. Morton A. Morton
AT&T Labs AT&T Labs
October 15, 2008 April 22, 2009
IP Performance Metrics (IPPM) for spatial and multicast IP Performance Metrics (IPPM) for spatial and multicast
draft-ietf-ippm-multimetrics-09 draft-ietf-ippm-multimetrics-10
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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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8. One-to-group Sample Statistics . . . . . . . . . . . . . . . . 26 8. One-to-group Sample Statistics . . . . . . . . . . . . . . . . 26
9. Measurement Methods: Scalability and Reporting . . . . . . . . 36 9. Measurement Methods: Scalability and Reporting . . . . . . . . 36
10. Manageability Considerations . . . . . . . . . . . . . . . . . 39 10. Manageability Considerations . . . . . . . . . . . . . . . . . 39
11. Security Considerations . . . . . . . . . . . . . . . . . . . 44 11. Security Considerations . . . . . . . . . . . . . . . . . . . 44
12. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 45 12. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 45
13. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 45 13. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 45
14. References . . . . . . . . . . . . . . . . . . . . . . . . . . 49 14. References . . . . . . . . . . . . . . . . . . . . . . . . . . 49
14.1. Normative References . . . . . . . . . . . . . . . . . . 49 14.1. Normative References . . . . . . . . . . . . . . . . . . 49
14.2. Informative References . . . . . . . . . . . . . . . . . 50 14.2. Informative References . . . . . . . . . . . . . . . . . 50
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 50 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 50
Intellectual Property and Copyright Statements . . . . . . . . . . 51
1. Introduction and Scope 1. Introduction and Scope
IETF has standardized IP Performance Metrics (IPPM) for measuring 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).
skipping to change at page 3, line 27 skipping to change at page 3, line 27
Spatial metrics measure the performance of each segment along a path. Spatial metrics measure the performance of each segment along a path.
One-to-group metrics measure the performance for a group of users. One-to-group metrics measure the performance for a group of users.
These metrics are derived from one-way end-to-end metrics, all of These metrics are derived from one-way end-to-end metrics, all of
which follow the IPPM framework [RFC2330]. which follow the IPPM framework [RFC2330].
This memo is organized as follows: Section 2 introduces new terms This memo is organized as follows: Section 2 introduces new terms
that extend the original IPPM framework [RFC2330]. Section 3 that extend the original IPPM framework [RFC2330]. Section 3
motivates each metric category and briefly introduces the new motivates each metric category and briefly introduces the new
metrics. Sections 4 through 7 develop each category of metrics with metrics. Sections 4 through 7 develop each category of metrics with
definitions and statistics. Then the memo discusses the impact of definitions and statistics. Then the memo discusses the impact of
the measurement methods on the scaleability and proposes an the measurement methods on the scalability and proposes an
information model for reporting the measurements. Finally, the memo information model for reporting the measurements. Finally, the memo
discusses security aspects related to measurement and registers the discusses security aspects related to measurement and registers the
metrics in the IANA IP Performance Metrics Registry [RFC4148]. metrics in the IANA IP Performance Metrics Registry [RFC4148].
The scope of this memo is limited to metrics using a single source The scope of this memo is limited to metrics using a single source
packet or stream, and observations of corresponding packets along the packet or stream, and observations of corresponding packets along the
path (spatial), at one or more destinations (one-to-group), or both. path (spatial), at one or more destinations (one-to-group), or both.
Note that all the metrics defined herein are based on observations of Note that all the metrics defined herein are based on observations of
packets dedicated to testing, a process which is called active packets dedicated to testing, a process which is called active
measurement. Passive measurement (for example, a spatial metric measurement. Passive measurement (for example, a spatial metric
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The one-to-group metrics defined above are directly achieved by The one-to-group metrics defined above are directly achieved by
collecting relevant unicast one-way metrics measurements results and collecting relevant unicast one-way metrics measurements results and
by gathering them per group of receivers. They produce network by gathering them per group of receivers. They produce network
performance information which guides engineers toward potential performance information which guides engineers toward potential
problems which may have happened on any branch of a multicast routing problems which may have happened on any branch of a multicast routing
tree. tree.
The results of these metrics are not directly usable to present the The results of these metrics are not directly usable to present the
performance of a group because each result is made of a huge number performance of a group because each result is made of a huge number
of singletons which are difficult to read and analyze. As an of singletons which are difficult to read and analyze. As an
example, delay are not comparable because the distance between example, delays are not comparable because the distance between
receiver and sender differs. Furthermore they don't capture relative receiver and sender differs. Furthermore they don't capture relative
performance situation a multiparty communication. performance situation a multiparty communication.
From the performance point of view, the multiparty communication From the performance point of view, the multiparty communication
services not only require the support of absolute performance services not only require the support of absolute performance
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 online gaming. A very small difference in
difference in delay might result in failure in the game. We have to delay might result in failure in the game. We have to use multicast
use multicast specific statistic metrics to define the relative delay specific statistic metrics to define the relative delay required by
required by online gaming. There are many other services, e.g. online gaming. There are many other services, e.g. online biding,
online biding, online stock market, etc., that require multicast online stock market, etc., that require multicast metrics in order to
metrics in order to evaluate the network against their requirements. evaluate the network against their requirements. Therefore, we can
Therefore, we can see the importance of new, multicast specific, see the importance of new, multicast specific, statistic metrics to
statistic metrics to feed this need. 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
skipping to change at page 29, line 4 skipping to change at page 29, line 4
rather than sending every one-way singleton it observed. As long as rather than sending every one-way singleton it observed. As long as
an appropriate time interval is decided, appropriate statistics can an appropriate time interval is decided, appropriate statistics can
represent the performance in a certain accurate scale. How to decide represent the performance in a certain accurate scale. How to decide
the time interval and how to bootstrap all points of interest and the the time interval and how to bootstrap all points of interest and the
reference point depend on applications. For instance, applications reference point depend on applications. For instance, applications
with lower transmission rate can have the time interval longer and with lower transmission rate can have the time interval longer and
ones with higher transmission rate can have the time interval ones with higher transmission rate can have the time interval
shorter. However, this is out of the scope of this memo. shorter. However, this is out of the scope of this memo.
Moreover, after knowing the statistics over the time dimension, one Moreover, after knowing the statistics over the time dimension, one
might want to know how this statistics distributed over the space might want to know how these statistics are distributed over the
dimension. For instance, a TV broadcast service provider had the space dimension. For instance, a TV broadcast service provider had
performance Matrix M and calculated the One-way delay mean over the the performance Matrix M and calculated the One-way delay mean over
time dimension to obtain a delay Vector as {V1,V2,..., VN}. He then the time dimension to obtain a delay Vector as {V1,V2,..., VN}. He
calculated the mean of all the elements in the Vector to see what then calculated the mean of all the elements in the Vector to see
level of delay he has served to all N users. This new delay mean what level of delay he has served to all N users. This new delay
gives information on how good the service has been delivered to a mean gives information on how good the service has been delivered to
group of users during a sampling interval in terms of delay. It a group of users during a sampling interval in terms of delay. It
needs twice calculation to have this statistic over both time and requires twice as much calculation to have this statistic over both
space dimensions. We name this kind of statistics 2-level statistics time and space dimensions. This kind of statistics is referred to as
to distinct with those 1-level statistics calculated over either 2-level statistics to distinguish them from 1-level statistics
space or time dimension. It can be easily proven that no matter over calculated over either space or time dimension. It can be easily
which dimension a 2-level statistic is calculated first, the results proven that no matter over which dimension a 2-level statistic is
are the same. I.e. one can calculate the 2-level delay mean using calculated first, the results are the same. I.e. one can calculate
the Matrix M by having the 1-level delay mean over the time dimension the 2-level delay mean using the Matrix M by having the 1-level delay
first and then calculate the mean of the obtained vector to find out mean over the time dimension first and then calculate the mean of the
the 2-level delay mean. Or, he can do the 1-level statistic obtained vector to find out the 2-level delay mean. Or, he can do
calculation over the space dimension first and then have the 2-level the 1-level statistic calculation over the space dimension first and
delay mean. Both two results will be exactly the same. Therefore, then have the 2-level delay mean. Both two results will be exactly
when defining a 2-level statistic there is no need to specify the the same. Therefore, when defining a 2-level statistic there is no
order in which the calculation is executed. need to specify the 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.
8.1. Discussion on the Impact of packet loss on statistics 8.1. Discussion on the Impact of packet loss on statistics
The packet loss does have effects on one-way metrics and their The packet loss does have effects on one-way metrics and their
statistics. For example, the lost packet can result in an infinite statistics. For example, a lost packet can result in an infinite
one-way delay. It is easy to handle the problem by simply ignoring one-way delay. It is easy to handle the problem by simply ignoring
the infinite value in the metrics and in the calculation of the the infinite value in the metrics and in the calculation of the
corresponding statistics. However, the packet loss has so strong corresponding statistics. However, the packet loss has such a strong
impact on the statistics calculation for the one-to-group metrics impact on the statistics calculation for the one-to-group metrics
that it can not be solved by the same method used for one-way that it can not be solved by the same method used for one-way
metrics. This is due to the complexity of building a matrix, which metrics. This is due to the complexity of building a matrix, which
is needed for calculation of the statistics proposed in this memo. is needed for calculation of the statistics proposed in this memo.
The situation is that measurement results obtained by different end The situation is that measurement results obtained by different end
users might have different packet loss pattern. For example, for users might have different packet loss pattern. For example, for
User1, packet A was observed lost. And for User2, packet A was User1, packet A was observed lost. And for User2, packet A was
successfully received but packet B was lost. If the method to successfully received but packet B was lost. If the method to
overcome the packet loss for one-way metrics is applied, the two overcome the packet loss for one-way metrics is applied, the two
singleton sets reported by User1 and User2 will be different in terms singleton sets reported by User1 and User2 will be different in terms
of the transmitted packets. Moreover, if User1 and User2 have of the transmitted packets. Moreover, if User1 and User2 have
different number of lost packets, the size of the results will be different number of lost packets, the size of the results will be
different. Therefore, for the centralized calculation, the reference different. Therefore, for the centralized calculation, the reference
point will not be able to use these two results to build up the group point will not be able to use these two results to build up the group
Matrix and can not calculate the statistics. In an extreme Matrix and can not calculate the statistics. The extreme situation
situation, no single packet arrives all users in the measurement and being the case when no packets arrive at any user. One of the
the Matrix will be empty. One of the possible solutions is to possible solutions is to replace the infinite/undefined delay value
replace the infinite/undefined delay value by the average of the two by the average of the two adjacent values. For example, if the
adjacent values. For example, if the result reported by user1 is { result reported by user1 is { R1dT1 R1dT2 R1dT3 ... R1dTK-1 UNDEF
R1dT1 R1dT2 R1dT3 ... R1dTK-1 UNDEF R1dTK+1... R1DM } where "UNDEF" R1dTK+1... R1DM } where "UNDEF" is an undefined value, the reference
is an undefined value, the reference point can replace it by R1dTK = point can replace it by R1dTK = {(R1dTK-1)+( R1dTK+1)}/2. Therefore,
{(R1dTK-1)+( R1dTK+1)}/2. Therefore, this result can be used to this result can be used to build up the group Matrix with an
build up the group Matrix with an estimated value R1dTK. There are estimated value R1dTK. There are other possible solutions such as
other possible solutions such as using the overall mean of the whole using the overall mean of the whole result to replace the infinite/
result to replace the infinite/undefined value, and so on. However undefined value, and so on. However this is out of the scope of this
this is out of the scope of this memo. memo.
For the distributed calculation, the reported statistics might have For the distributed calculation, the reported statistics might have
different "weight" to present the group performance, which is different "weight" to present the group performance, which is
especially true for delay and ipdv relevant metrics. For example, especially true for delay and ipdv relevant metrics. For example,
User1 calculates the Type-P-Finite-One-way-Delay-Mean R1DM as shown User1 calculates the Type-P-Finite-One-way-Delay-Mean R1DM as shown
in Figure. 8 without any packet loss and User2 calculates the R2DM in Figure. 8 without any packet loss and User2 calculates the R2DM
with N-2 packet loss. The R1DM and R2DM should not be treated with with N-2 packet loss. The R1DM and R2DM should not be treated with
equal weight because R2DM was calculated only based on 2 delay values equal weight because R2DM was calculated only based on 2 delay values
in the whole sample interval. One possible solution is to use a in the whole sample interval. One possible solution is to use a
weight factor to mark every statistic value sent by users and use weight factor to mark every statistic value sent by users and use
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distributed statistic calculation method. The sample should include distributed statistic calculation method. The sample should include
all metrics parameters, the values and the corresponding sequence all metrics parameters, the values and the corresponding sequence
numbers. The transmission of the whole sample can cost much more numbers. The transmission of the whole sample can cost much more
bandwidth than the transmission of the statistics that should include bandwidth than the transmission of the statistics that should include
all statistic parameters specified by policies and the additional all statistic parameters specified by policies and the additional
information about the whole sample, such as the size of the sample, information about the whole sample, such as the size of the sample,
the group address, the address of the point of interest, the ID of the group address, the address of the point of interest, the ID of
the sample session, and so on. Apparently, the centralized the sample session, and so on. Apparently, the centralized
calculation method can require much more bandwidth than the calculation method can require much more bandwidth than the
distributed calculation method when the sample size is big. This is distributed calculation method when the sample size is big. This is
especially true when the measurement has huge number of the points of especially true when the measurement has a very large number of the
interest. It can lead to a scalability issue at the reference point points of interest. It can lead to a scalability issue at the
by over load the network resources. The distributed calculation reference point by overloading the network resources.
method can save much more bandwidth and release the pressure of the
scalability issue at the reference point side. However, it can The distributed calculation method can save much more bandwidth and
result in the lack of information because not all measured singletons mitigate issues arising from scalability at the reference point side.
are obtained for building up the group matrix. The performance over
time can be hidden from the analysis. For example, the loss pattern However, it may result in a lost of information. As all measured
can be missed by simply accepting the loss ratio as well as the delay singletons are not available for building up the group matrix, the
pattern. This tradeoff between the bandwidth consuming and the real performance over time can be hidden from the result. For
information acquiring has to be taken into account when design the example, the loss pattern can be missed by simply accepting the loss
measurement campaign to optimize the measurement results delivery. ratio. This tradeoff between bandwidth consumption and information
The possible solution could be to transit the statistic parameters to acquisition has to be taken into account when designing the
measurement approach.
One possible solution could be to transit the statistic parameters to
the reference point first to obtain the general information of the the reference point first to obtain the general information of the
group performance. If the detail results are required, the reference group performance. If detailed results are required, the reference
point should send the requests to the points of interest, which could point should send the requests to the points of interest, which could
be particular ones or the whole group. This procedure can happen in be particular ones or the whole group. This procedure can happen in
the off peak time and can be well scheduled to avoid delivery of too the off peak time and can be well scheduled to avoid delivery of too
many points of interest at the same time. Compression techniques can many points of interest at the same time. Compression techniques can
also be used to minimize the bandwidth required by the transmission. also be used to minimize the bandwidth required by the transmission.
This could be a measurement protocol to report the measurement This could be a measurement protocol to report the measurement
results. However, this is out of the scope of this memo. results. However, this is out of the scope of this memo.
9.2. Measurement 9.2. Measurement
To prevent any bias in the result, the configuration of a one-to-many To prevent any bias in the result, the configuration of a one-to-many
measure must take in consideration that implicitly more packets will measure must take in consideration that intrically more packets will
to be routed than send and selects a test packets rate that will not to be routed than sent (copies of a packet sent are expected to
impact the network performance. arrive at many destination points) and selects a test packets rate
that will not impact the network performance.
9.3. Effect of Time and Space Aggregation Order on Stats 9.3. Effect of Time and Space Aggregation Order on Stats
This section presents the impact of the aggregation order on the This section presents the impact of the aggregation order on the
scalability of the reporting and of the computation. It makes the scalability of the reporting and of the computation. It makes the
hypothesis that receivers are not co-located and that results are hypothesis that receivers are not co-located and that results are
gathered in a point of reference for further usages. gathered in a point of reference for further usages.
Multimetrics samples are represented in a matrix as illustrated below Multimetrics samples are represented in a matrix as illustrated below
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n RnS1 RnS2 RnS3 ... RnSk / n RnS1 RnS2 RnS3 ... RnSk /
S1M S2M S3M ... SnM Stats over space S1M S2M S3M ... SnM Stats over space
\------------- ------------/ \------------- ------------/
\/ \/
Stat over space and time Stat over space and time
Figure 13: Impact of space aggregation on multimetrics Stat Figure 13: Impact of space aggregation on multimetrics Stat
2 methods are available to compute statistics on a matrix: Two methods are available to compute statistics on a matrix:
o Method 1: The statistic metric is computed over time and then over o Method 1: The statistic metric is computed over time and then over
space; space;
o Method 2: The statistic metric is computed over space and then o Method 2: The statistic metric is computed over space and then
over time. over time.
These 2 methods differ only by the order of the aggregation. The These 2 methods differ only by the order of the aggregation. The
order does not impact the computation resources required. It does order does not impact the computation resources required. It does
not change the value of the result. However, it impacts severely the not change the value of the result. However, it impacts severely the
minimal volume of data to report: minimal volume of data to report:
o Method 1: Each point of interest computes periodically statistics o Method 1: Each point of interest computes periodically statistics
over time to lower the volume of data to report. They are over time to lower the volume of data to report. They are
reported to the reference point for computing the stat over space. reported to the reference point for for subsequent computations
This volume no longer depends on the number of samples. It is over the spatial dimension. This volume no longer depends on the
only proportional to the computation period; number of samples. It is only proportional to the computation
period;
o Method 2: The volume of data to report is proportional to the o Method 2: The volume of data to report is proportional to the
number of samples. Each sample, RiSi, must be reported to the number of samples. Each sample, RiSi, must be reported to the
reference point for computing statistic over space and statistic reference point for computing statistic over space and statistic
over time. The volume increases with the number of samples. It over time. The volume increases with the number of samples. It
is proportional to the number of test packets; is proportional to the number of test packets;
Method 2 has severe drawbacks in terms of security and dimensioning: Method 2 has severe drawbacks in terms of security and dimensioning:
o Increasing the rate of the test packets may result in a Denial of o Increasing the rate of the test packets may result in a Denial of
Service toward the points of reference; Service toward the points of reference;
o The dimensioning of a measurement system is quite impossible to o The dimensioning of a measurement system is quite impossible to
validate because any increase of the rate of the test packets will validate because any increase of the rate of the test packets will
increase the bandwidth requested to collect the raw results. increase the bandwidth requested to collect the raw results.
The computation period over time period (commonly named aggregation The computation period over time period (commonly named aggregation
period) provides the reporting side with a control of various period) provides the reporting side with a control of various
collecting aspects such as bandwidth, computation and storage collecting aspects such as bandwidth, computation and storage
capacities. So this draft defines metrics based on method 1. capacities. So this draft defines metrics based on method 1.
9.3.1. Impact on spatial statistics 9.3.1. Impact on spatial statistics
2 methods are available to compute spatial statistics: Two methods are available to compute spatial statistics:
o Method 1: spatial segment metrics and statistics are preferably o Method 1: spatial segment metrics and statistics are preferably
computed over time by each points of interest; computed over time for each points of interest;
o Method 2: Vectors metrics are intrinsically instantaneous space o Method 2: Vectors metrics are intrinsically instantaneous space
metrics which must be reported using method2 whenever metrics which must be reported using Method2 whenever
instantaneous metrics information is needed. instantaneous metrics information is needed.
9.3.2. Impact on one-to-group statistics 9.3.2. Impact on one-to-group statistics
2 methods are available to compute group statistics: Two methods are available to compute group statistics:
o Method1: Figure 5 and Figure 8 illustrate the method chosen: the o Method1: Figure 5 and Figure 8 illustrate the method chosen: the
one-to-one statistic is computed per interval of time before the one-to-one statistic is computed per interval of time before the
computation of the mean over the group of receivers; computation of the mean over the group of receivers;
o Method2: Figure 13 presents the second one, metric is computed o Method2: Figure 13 presents the second one, metric is computed
over space and then over time. over space and then over time.
10. Manageability Considerations 10. Manageability Considerations
Usually IPPM WG documents defines each metric reporting within its Usually IPPM WG documents defines each metric reporting within its
definition. This document defines the reporting of all the metrics definition. This document defines the reporting of all the metrics
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10.2. Reporting One-to-group metric 10.2. Reporting One-to-group metric
All reporting rules described in [RFC2679] and [RFC2680] apply to the All reporting rules described in [RFC2679] and [RFC2680] apply to the
corresponding One-to-group metrics. Following are specific corresponding One-to-group metrics. Following are specific
parameters that should be reported. parameters that should be reported.
10.2.1. Path 10.2.1. Path
As suggested by the [RFC2679] and [RFC2680] , the path traversed by As suggested by the [RFC2679] and [RFC2680] , the path traversed by
the packet SHOULD be reported, if possible. For One-to-group the packet SHOULD be reported, if possible. For One-to-group
metrics, there is a path tree SHOULD be reported rather than A path. metrics, the path tree between the source and the destinations or the
This is even more impractical. If, by anyway, partial information is set of paths between the source and each destination SHOULD be
available to report, it might not be as valuable as it is in the one- reported.
to-one case because the incomplete path might be difficult to
identify its position in the path tree. For example, how many points Path tree might not be as valuable as individual paths because an
of interest are reached by the packet travelled through this incomplete path might be difficult to identify in the path tree. For
incomplete path? example, how many points of interest are reached by a packet
travelling along an incomplete path?
10.2.2. Group size 10.2.2. Group size
The group size should be reported as one of the critical management The group size should be reported as one of the critical management
parameters. Unlike the spatial metrics, there is no need of order of parameters. One-to-group metrics, unlike spatial metrics, don't
points of interests. require the ordering of the points of interests because group members
receive the packets in parallel.
10.2.3. Timestamping bias 10.2.3. Timestamping bias
It is the same as described in section 10.1.3. It is the same as described in section 10.1.3.
10.2.4. Reporting One-to-group One-way Delay 10.2.4. Reporting One-to-group One-way Delay
It is the same as described in section 10.1.4. It is the same as described in section 10.1.4.
10.2.5. Measurement method 10.2.5. Measurement method
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IANA assigns each metric defined by the IPPM WG with a unique IANA assigns each metric defined by the IPPM WG with a unique
identifier as per [RFC4148] in the IANA-IPPM-METRICS-REGISTRY-MIB. identifier as per [RFC4148] in the IANA-IPPM-METRICS-REGISTRY-MIB.
10.4. Information model 10.4. Information model
This section presents the elements of information and the usage of This section presents the elements of information and the usage of
the information reported for network performance analysis. It is out the information reported for network performance analysis. It is out
of the scope of this section to define how the information is of the scope of this section to define how the information is
reported. reported.
The information model is build with pieces of information introduced The information model is built with pieces of information introduced
and explained in one-way delay definitions [RFC2679], in packet loss and explained in one-way delay definitions [RFC2679], in packet loss
definitions [RFC2680] and in IPDV definitions of [RFC3393] and definitions [RFC2680] and in IPDV definitions of [RFC3393] and
[RFC3432]. It includes not only information given by "Reporting the [RFC3432]. It includes not only information given by "Reporting the
metric" sections but by sections "Methodology" and "Errors and metric" sections but by sections "Methodology" and "Errors and
Uncertainties" sections. Uncertainties".
Following are the elements of information taken from end-to-end Following are the elements of information taken from end-to-end
definitions referred in this memo and from spatial and multicast metrics definitions referred in this memo and from spatial and
metrics it defines: multicast metrics it defines:
o Packet_type, The Type-P of test packets (Type-P); o Packet_type, The Type-P of test packets (Type-P);
o Packet_length, a packet length in bits (L); o Packet_length, a packet length in bits (L);
o Src_host, the IP address of the sender; o Src_host, the IP address of the sender;
o Dst_host, the IP address of the receiver; o Dst_host, the IP address of the receiver;
o Hosts_serie: <H1, H2,..., Hn>, a list of points of interest; o Hosts_serie: <H1, H2,..., Hn>, a list of points of interest;
o Loss_threshold: The threshold of infinite delay; o Loss_threshold: The threshold of infinite delay;
o Systematic_error: constant delay between wire time and o Systematic_error: constant delay between wire time and
timestamping; timestamping;
o Calibration_error: maximal uncertainty; o Calibration_error: maximal uncertainty;
skipping to change at page 44, line 36 skipping to change at page 44, line 36
function used to detect the packets may lead to a DoS attack toward function used to detect the packets may lead to a DoS attack toward
the point of reference. the point of reference.
11.2. One-to-group metrics 11.2. One-to-group metrics
Reporting of measurement results from a huge number of probes may Reporting of measurement results from a huge number of probes may
overload reference point resources (network, network interfaces, overload reference point resources (network, network interfaces,
computation capacities ...). computation capacities ...).
The configuration of a measurement must take in consideration that The configuration of a measurement must take in consideration that
implicitly more packets will to be routed than send and selects a implicitly more packets will be routed than sent and selects a test
test packets rate accordingly. Collecting statistics from a huge packets rate accordingly. Collecting statistics from a huge number
number of probes may overload any combination of the network where of probes may overload any combination of the network where the
the measurement controller is attached to, measurement controller measurement controller is attached to, measurement controller network
network interfaces and measurement controller computation capacities. interfaces and measurement controller computation capacities.
One-to-group metrics measurement should consider using source One-to-group metrics measurement should consider using source
authentication protocols, standardized in the MSEC group, to avoid authentication protocols, standardized in the MSEC group, to avoid
fraud packet in the sampling interval. The test packet rate could be fraud packet in the sampling interval. The test packet rate could be
negotiated before any measurement session to avoid deny of service negotiated before any measurement session to avoid deny of service
attacks. attacks.
12. Acknowledgments 12. Acknowledgments
Lei would like to acknowledge Prof. Zhili Sun from CCSR, University Lei would like to acknowledge Prof. Zhili Sun from CCSR, University
skipping to change at page 50, line 9 skipping to change at page 50, line 9
Metric for IP Performance Metrics (IPPM)", RFC 3393, Metric for IP Performance Metrics (IPPM)", RFC 3393,
November 2002. November 2002.
[RFC4148] Stephan, E., "IP Performance Metrics (IPPM) Metrics [RFC4148] Stephan, E., "IP Performance Metrics (IPPM) Metrics
Registry", BCP 108, RFC 4148, August 2005. Registry", BCP 108, RFC 4148, August 2005.
14.2. Informative References 14.2. Informative References
[I-D.ietf-ippm-spatial-composition] [I-D.ietf-ippm-spatial-composition]
Morton, A. and E. Stephan, "Spatial Composition of Morton, A. and E. Stephan, "Spatial Composition of
Metrics", draft-ietf-ippm-spatial-composition-07 (work in Metrics", draft-ietf-ippm-spatial-composition-08 (work in
progress), July 2008. progress), March 2009.
[RFC2330] Paxson, V., Almes, G., Mahdavi, J., and M. Mathis, [RFC2330] Paxson, V., Almes, G., Mahdavi, J., and M. Mathis,
"Framework for IP Performance Metrics", RFC 2330, "Framework for IP Performance Metrics", RFC 2330,
May 1998. May 1998.
[RFC3432] Raisanen, V., Grotefeld, G., and A. Morton, "Network [RFC3432] Raisanen, V., Grotefeld, G., and A. Morton, "Network
performance measurement with periodic streams", RFC 3432, performance measurement with periodic streams", RFC 3432,
November 2002. November 2002.
Authors' Addresses Authors' Addresses
skipping to change at page 51, line 4 skipping to change at line 2308
Fax: +44 1483 683641 Fax: +44 1483 683641
Email: L.Liang@surrey.ac.uk Email: L.Liang@surrey.ac.uk
Al Morton Al Morton
200 Laurel Ave. South 200 Laurel Ave. South
Middletown, NJ 07748 Middletown, NJ 07748
USA USA
Phone: +1 732 420 1571 Phone: +1 732 420 1571
Email: acmorton@att.com Email: acmorton@att.com
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