draft-ietf-ippm-spatial-composition-02.txt   draft-ietf-ippm-spatial-composition-03.txt 
Network Working Group A. Morton Network Working Group A. Morton
Internet-Draft AT&T Labs Internet-Draft AT&T Labs
Intended status: Standards Track E. Stephan Intended status: Standards Track E. Stephan
Expires: April 25, 2007 France Telecom Division R&D Expires: September 16, 2007 France Telecom Division R&D
October 22, 2006 March 15, 2007
Spatial Composition of Metrics Spatial Composition of Metrics
draft-ietf-ippm-spatial-composition-02 draft-ietf-ippm-spatial-composition-03
Status of this Memo Status of this Memo
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Copyright Notice Copyright Notice
Copyright (C) The Internet Society (2006). Copyright (C) The IETF Trust (2007).
Abstract Abstract
This memo utilizes IPPM metrics that are applicable to both complete This memo utilizes IPPM metrics that are applicable to both complete
paths and sub-paths, and defines relationships to compose a complete paths and sub-paths, and defines relationships to compose a complete
path metric from the sub-path metrics with some accuracy w.r.t. the path metric from the sub-path metrics with some accuracy w.r.t. the
actual metrics. This is called Spatial Composition in RFC 2330. The actual metrics. This is called Spatial Composition in RFC 2330. The
memo refers to the Framework for Metric Composition, and provides memo refers to the Framework for Metric Composition, and provides
background and motivation for combining metrics to derive others. background and motivation for combining metrics to derive others.
The descriptions of several composed metrics and statistics follow. The descriptions of several composed metrics and statistics follow.
skipping to change at page 2, line 22 skipping to change at page 2, line 22
equal to" and ">=" as "greater than or equal to". equal to" and ">=" as "greater than or equal to".
Table of Contents Table of Contents
1. Contributors . . . . . . . . . . . . . . . . . . . . . . . . . 4 1. Contributors . . . . . . . . . . . . . . . . . . . . . . . . . 4
2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 4 2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . 5
3. Scope and Application . . . . . . . . . . . . . . . . . . . . 5 3. Scope and Application . . . . . . . . . . . . . . . . . . . . 5
3.1. Scope of work . . . . . . . . . . . . . . . . . . . . . . 6 3.1. Scope of work . . . . . . . . . . . . . . . . . . . . . . 6
3.2. Application . . . . . . . . . . . . . . . . . . . . . . . 6 3.2. Application . . . . . . . . . . . . . . . . . . . . . . . 6
4. One-way Delay Composed Metrics and Statistics . . . . . . . . 7 3.3. Incomplete Information . . . . . . . . . . . . . . . . . . 7
4.1. Name: 4. Common Specifications for Composed Metrics . . . . . . . . . . 7
Type-P-Finite-One-way-Delay-Poisson/Periodic-Stream . . . 7 4.1. Name: Type-P . . . . . . . . . . . . . . . . . . . . . . . 7
4.1.1. Metric Parameters: . . . . . . . . . . . . . . . . . . 7 4.1.1. Metric Parameters . . . . . . . . . . . . . . . . . . 7
4.1.2. Definition and Metric Units . . . . . . . . . . . . . 7 4.1.2. Definition and Metric Units . . . . . . . . . . . . . 8
4.1.3. Discussion and other details . . . . . . . . . . . . . 8 4.1.3. Discussion and other details . . . . . . . . . . . . . 8
4.1.4. Mean Statistic . . . . . . . . . . . . . . . . . . . . 8 4.1.4. Statistic: . . . . . . . . . . . . . . . . . . . . . . 8
4.1.5. Composition Function: Sum of Means . . . . . . . . . . 8 4.1.5. Composition Function: Sum of Means . . . . . . . . . . 8
4.1.6. Statement of Conjecture . . . . . . . . . . . . . . . 9 4.1.6. Statement of Conjecture . . . . . . . . . . . . . . . 8
4.1.7. Justification of the Composition Function . . . . . . 9 4.1.7. Justification of the Composition Function . . . . . . 8
4.1.8. Sources of Deviation from the Ground Truth . . . . . . 9 4.1.8. Sources of Deviation from the Ground Truth . . . . . . 9
4.1.9. Specific cases where the conjecture might fail . . . . 9 4.1.9. Specific cases where the conjecture might fail . . . . 9
4.1.10. Application of Measurement Methodology . . . . . . . . 10 4.1.10. Application of Measurement Methodology . . . . . . . . 9
5. Loss Metrics and Statistics . . . . . . . . . . . . . . . . . 10 5. One-way Delay Composed Metrics and Statistics . . . . . . . . 9
5.1. Name: 5.1. Name:
Type-P-One-way-Packet-Loss-Poisson/Periodic-Stream . . . . 10 Type-P-Finite-One-way-Delay-Poisson/Periodic-Stream . . . 10
5.1.1. Metric Parameters: . . . . . . . . . . . . . . . . . . 10 5.1.1. Metric Parameters . . . . . . . . . . . . . . . . . . 10
5.1.2. Definition and Metric Units . . . . . . . . . . . . . 10 5.1.2. Definition and Metric Units . . . . . . . . . . . . . 10
5.1.3. Discussion and other details . . . . . . . . . . . . . 11 5.1.3. Discussion and other details . . . . . . . . . . . . . 10
5.1.4. Statistic: 5.1.4. Mean Statistic . . . . . . . . . . . . . . . . . . . . 10
Type-P-One-way-Packet-Loss-Empirical-Probability . . . 11 5.1.5. Composition Function: Sum of Means . . . . . . . . . . 11
5.1.5. Composition Function: Composition of Empirical
Probabilities . . . . . . . . . . . . . . . . . . . . 11
5.1.6. Statement of Conjecture . . . . . . . . . . . . . . . 11 5.1.6. Statement of Conjecture . . . . . . . . . . . . . . . 11
5.1.7. Justification of the Composition Function . . . . . . 11 5.1.7. Justification of the Composition Function . . . . . . 11
5.1.8. Sources of Deviation from the Ground Truth . . . . . . 12 5.1.8. Sources of Deviation from the Ground Truth . . . . . . 11
5.1.9. Specific cases where the conjecture might fail . . . . 12 5.1.9. Specific cases where the conjecture might fail . . . . 11
5.1.10. Application of Measurement Methodology . . . . . . . . 12 5.1.10. Application of Measurement Methodology . . . . . . . . 12
6. Delay Variation Metrics and Statistics . . . . . . . . . . . . 13 6. Loss Metrics and Statistics . . . . . . . . . . . . . . . . . 12
6.1. Name: 6.1. Name:
Type-P-One-way-Packet-Loss-Poisson/Periodic-Stream . . . . 12
Type-P-One-way-ipdv-refmin-Poisson/Periodic-Stream . . . . 13 6.1.1. Metric Parameters: . . . . . . . . . . . . . . . . . . 12
6.1.1. Metric Parameters: . . . . . . . . . . . . . . . . . . 13 6.1.2. Definition and Metric Units . . . . . . . . . . . . . 12
6.1.2. Definition and Metric Units . . . . . . . . . . . . . 14 6.1.3. Discussion and other details . . . . . . . . . . . . . 12
6.1.3. Discussion and other details . . . . . . . . . . . . . 14 6.1.4. Statistic:
6.1.4. Statistics: Mean, Variance, Skewness, Quanitle . . . . 14 Type-P-One-way-Packet-Loss-Empirical-Probability . . . 12
6.1.5. Composition Functions: . . . . . . . . . . . . . . . . 15 6.1.5. Composition Function: Composition of Empirical
6.1.6. Statement of Conjecture . . . . . . . . . . . . . . . 15 Probabilities . . . . . . . . . . . . . . . . . . . . 13
6.1.7. Justification of the Composition Function . . . . . . 15 6.1.6. Statement of Conjecture . . . . . . . . . . . . . . . 13
6.1.8. Sources of Deviation from the Ground Truth . . . . . . 15 6.1.7. Justification of the Composition Function . . . . . . 13
6.1.9. Specific cases where the conjecture might fail . . . . 15 6.1.8. Sources of Deviation from the Ground Truth . . . . . . 13
6.1.10. Application of Measurement Methodology . . . . . . . . 15 6.1.9. Specific cases where the conjecture might fail . . . . 13
7. Other Metrics and Statistics: One-way Combined Metric . . . . 16 6.1.10. Application of Measurement Methodology . . . . . . . . 14
8. Security Considerations . . . . . . . . . . . . . . . . . . . 16 7. Delay Variation Metrics and Statistics . . . . . . . . . . . . 14
8.1. Denial of Service Attacks . . . . . . . . . . . . . . . . 16 7.1. Name:
8.2. User Data Confidentiality . . . . . . . . . . . . . . . . 16 Type-P-One-way-ipdv-refmin-Poisson/Periodic-Stream . . . . 14
8.3. Interference with the metrics . . . . . . . . . . . . . . 16 7.1.1. Metric Parameters: . . . . . . . . . . . . . . . . . . 14
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 16 7.1.2. Definition and Metric Units . . . . . . . . . . . . . 15
10. Issues (Open and Closed) . . . . . . . . . . . . . . . . . . . 17 7.1.3. Discussion and other details . . . . . . . . . . . . . 15
11. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 18 7.1.4. Statistics: Mean, Variance, Skewness, Quanitle . . . . 15
12. References . . . . . . . . . . . . . . . . . . . . . . . . . . 18 7.1.5. Composition Functions: . . . . . . . . . . . . . . . . 16
12.1. Normative References . . . . . . . . . . . . . . . . . . . 18 7.1.6. Statement of Conjecture . . . . . . . . . . . . . . . 17
12.2. Informative References . . . . . . . . . . . . . . . . . . 19 7.1.7. Justification of the Composition Function . . . . . . 17
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 19 7.1.8. Sources of Deviation from the Ground Truth . . . . . . 17
Intellectual Property and Copyright Statements . . . . . . . . . . 20 7.1.9. Specific cases where the conjecture might fail . . . . 18
7.1.10. Application of Measurement Methodology . . . . . . . . 18
8. Security Considerations . . . . . . . . . . . . . . . . . . . 18
8.1. Denial of Service Attacks . . . . . . . . . . . . . . . . 18
8.2. User Data Confidentiality . . . . . . . . . . . . . . . . 18
8.3. Interference with the metrics . . . . . . . . . . . . . . 18
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 19
10. Issues (Open and Closed) . . . . . . . . . . . . . . . . . . . 19
11. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 20
12. References . . . . . . . . . . . . . . . . . . . . . . . . . . 20
12.1. Normative References . . . . . . . . . . . . . . . . . . . 20
12.2. Informative References . . . . . . . . . . . . . . . . . . 21
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 21
Intellectual Property and Copyright Statements . . . . . . . . . . 23
1. Contributors 1. Contributors
Thus far, the following people have contributed useful ideas, Thus far, the following people have contributed useful ideas,
suggestions, or the text of sections that have been incorporated into suggestions, or the text of sections that have been incorporated into
this memo: this memo:
- Phil Chimento <vze275m9@verizon.net> - Phil Chimento <vze275m9@verizon.net>
- Reza Fardid <RFardid@Covad.COM> - Reza Fardid <RFardid@Covad.COM>
skipping to change at page 4, line 25 skipping to change at page 4, line 25
- Roman Krzanowski <roman.krzanowski@verizon.com> - Roman Krzanowski <roman.krzanowski@verizon.com>
- Maurizio Molina <maurizio.molina@dante.org.uk> - Maurizio Molina <maurizio.molina@dante.org.uk>
- Al Morton <acmorton@att.com> - Al Morton <acmorton@att.com>
- Emile Stephan <emile.stephan@francetelecom.com> - Emile Stephan <emile.stephan@francetelecom.com>
- Lei Liang <L.Liang@surrey.ac.uk> - Lei Liang <L.Liang@surrey.ac.uk>
- Dave Hoeflin <dhoeflin@att.com>
2. Introduction 2. Introduction
The IPPM framework [RFC2330] describes two forms of metric The IPPM framework [RFC2330] describes two forms of metric
composition, spatial and temporal. The new composition framework composition, spatial and temporal. The new composition framework
[I-D.ietf-ippm-framework-compagg] expands and further qualifies these [I-D.ietf-ippm-framework-compagg] expands and further qualifies these
original forms into three categories. This memo describes Spatial original forms into three categories. This memo describes Spatial
Composition, one of the categories of metrics under the umbrella of Composition, one of the categories of metrics under the umbrella of
the composition framework. the composition framework.
Spatial composition encompasses the definition of performance metrics Spatial composition encompasses the definition of performance metrics
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operator's domain, or is applicable to Inter-domain composition. operator's domain, or is applicable to Inter-domain composition.
Requires synchronized measurement time intervals in all sub-paths, or Requires synchronized measurement time intervals in all sub-paths, or
largely overlapping, or no timing requirements. largely overlapping, or no timing requirements.
Requires assumption of sub-path independence w.r.t. the metric being Requires assumption of sub-path independence w.r.t. the metric being
defined/composed, or other assumptions. defined/composed, or other assumptions.
Has known sources of inaccuracy/error, and identifies the sources. Has known sources of inaccuracy/error, and identifies the sources.
4. One-way Delay Composed Metrics and Statistics 3.3. Incomplete Information
4.1. Name: Type-P-Finite-One-way-Delay-Poisson/Periodic-Stream In practice, when measurements cannot be initiated on a sub-path (and
perhaps the measurement system gives up during the test interval),
then there will not be a value for the sub-path reported, and the
result SHOULD be recorded as "undefined". This case should be
distinguished from the case where the measurement system continued to
send packets throughout the test interval, but all were declared
lost.
This metric is a necessary element of Delay Composition metrics, and When a composed metric requires measurements from sub paths A, B, and
its definition does not formally exist elsewhere in IPPM literature. C, and one or more of the sub-path results are undefined, then the
composed metric SHOULD also be recorded as undefined.
4.1.1. Metric Parameters: 4. Common Specifications for Composed Metrics
o Src, the IP address of a host + Dst, the IP address of a host To reduce the redundant information presented in the detailed metrics
sections that follow, this section presents the specifications that
are common to two or more metrics. The section is organized using
the same subsections as the individual metrics, to simplify
comparisons.
4.1. Name: Type-P
All metrics use the Type-P convention as described in [RFC2330]. The
rest of the name is unique to each metric.
4.1.1. Metric Parameters
o Src, the IP address of a host
o Dst, the IP address of a host
o T, a time (start of test interval) o T, a time (start of test interval)
o Tf, a time (end of test interval) o Tf, a time (end of test interval)
o lambda, a rate in reciprocal seconds (for Poisson Streams) o lambda, a rate in reciprocal seconds (for Poisson Streams)
o incT, the nominal duration of inter-packet interval, first bit to o incT, the nominal duration of inter-packet interval, first bit to
first bit (for Periodic Streams) first bit (for Periodic Streams)
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o TstampSrc, the wire time of the packet as measured at MP(Src) o TstampSrc, the wire time of the packet as measured at MP(Src)
o TstampDst, the wire time of the packet as measured at MP(Dst), o TstampDst, the wire time of the packet as measured at MP(Dst),
assigned to packets that arrive within a "reasonable" time. assigned to packets that arrive within a "reasonable" time.
o Tmax, a maximum waiting time for packets at the destination, set o Tmax, a maximum waiting time for packets at the destination, set
sufficiently long to disambiguate packets with long delays from sufficiently long to disambiguate packets with long delays from
packets that are discarded (lost), thus the distribution of delay packets that are discarded (lost), thus the distribution of delay
is not truncated. is not truncated.
o M, the total number of packets sent between T0 and Tf
o N, the total number of packets received at Dst (sent between T0
and Tf)
o S, the number of sub-paths involved in the complete Src-Dst path
4.1.2. Definition and Metric Units 4.1.2. Definition and Metric Units
This section is unique for every metric.
4.1.3. Discussion and other details
This section is unique for every metric.
4.1.4. Statistic:
This section is unique for every metric.
4.1.5. Composition Function: Sum of Means
This section is unique for every metric.
4.1.6. Statement of Conjecture
This section is unique for each metric.
4.1.7. Justification of the Composition Function
It is sometimes impractical to conduct active measurements between
every Src-Dst pair. For example, it may not be possible to collect
the desired sample size in each test interval when access link speed
is limited, because of the potential for measurement traffic to
degrade the user traffic performance. The conditions on a low-speed
access link may be understood well-enough to permit use of a small
sample size/rate, while a larger sample size/rate may be used on
other sub-paths.
Also, since measurement operations have a real monetary cost, there
is value in re-using measurements where they are applicable, rather
than launching new measurements for every possible source-destination
pair.
4.1.8. Sources of Deviation from the Ground Truth
The measurement packets, each having source and destination addresses
intended for collection at edges of the sub-path, may take a
different specific path through the network equipment and parallel
exchanges than packets with the source and destination addresses of
the complete path. Therefore, the sub-path measurements may differ
from the performance experienced by packets on the complete path.
Multiple measurements employing sufficient sub-path address pairs
might produce bounds on the extent of this error.
others...
4.1.9. Specific cases where the conjecture might fail
This section is unique for each metric.
4.1.10. Application of Measurement Methodology
The methodology:
SHOULD use similar packets sent and collected separately in each sub-
path.
Allows a degree of flexibility (e.g., active or passive methods can
produce the "same" metric, but timing and correlation of passive
measurements is much more challenging).
Poisson and/or Periodic streams are RECOMMENDED.
Applicable to both Inter-domain and Intra-domain composition.
SHOULD have synchronized measurement time intervals in all sub-paths,
but largely overlapping intervals MAY suffice.
REQUIRES assumption of sub-path independence w.r.t. the metric being
defined/composed.
5. One-way Delay Composed Metrics and Statistics
5.1. Name: Type-P-Finite-One-way-Delay-Poisson/Periodic-Stream
This metric is a necessary element of Delay Composition metrics, and
its definition does not formally exist elsewhere in IPPM literature.
5.1.1. Metric Parameters
See the common parameters section above.
5.1.2. Definition and Metric Units
Using the parameters above, we obtain the value of Type-P-One-way- Using the parameters above, we obtain the value of Type-P-One-way-
Delay singleton as per [RFC2679]. Delay singleton as per [RFC2679].
For each packet [i] that has a finite One-way Delay (in other words, For each packet [i] that has a finite One-way Delay (in other words,
excluding packets which have undefined one-way delay): excluding packets which have undefined one-way delay):
Type-P-Finite-One-way-Delay-Poisson/Periodic-Stream[i] = Type-P-Finite-One-way-Delay-Poisson/Periodic-Stream[i] =
FiniteDelay[i] = TstampDst - TstampSrc FiniteDelay[i] = TstampDst - TstampSrc
4.1.3. Discussion and other details 5.1.3. Discussion and other details
The "Type-P-Finite-One-way-Delay" metric permits calculation of the The "Type-P-Finite-One-way-Delay" metric permits calculation of the
sample mean statistic. This resolves the problem of including lost sample mean statistic. This resolves the problem of including lost
packets in the sample (whose delay is undefined), and the issue with packets in the sample (whose delay is undefined), and the issue with
the informal assignment of infinite delay to lost packets (practical the informal assignment of infinite delay to lost packets (practical
systems can only assign some very large value). systems can only assign some very large value).
The Finite-One-way-Delay approach handles the problem of lost packets The Finite-One-way-Delay approach handles the problem of lost packets
by reducing the event space. We consider conditional statistics, and by reducing the event space. We consider conditional statistics, and
estimate the mean one-way delay conditioned on the event that all estimate the mean one-way delay conditioned on the event that all
packets in the sample arrive at the destination (within the specified packets in the sample arrive at the destination (within the specified
waiting time, Tmax). This offers a way to make some valid statements waiting time, Tmax). This offers a way to make some valid statements
about one-way delay, and at the same time avoiding events with about one-way delay, and at the same time avoiding events with
undefined outcomes. This approach is derived from the treatment of undefined outcomes. This approach is derived from the treatment of
lost packets in [RFC3393], and is similar to [Y.1540] . lost packets in [RFC3393], and is similar to [Y.1540] .
4.1.4. Mean Statistic 5.1.4. Mean Statistic
We add the following parameter:
o N, the total number of packets received at Dst (sent between T0
and Tf)
and define
We define
Type-P-Finite-One-way-Delay-Mean = Type-P-Finite-One-way-Delay-Mean =
N N
--- ---
1 \ 1 \
- * > (FiniteDelay [i]) - * > (FiniteDelay [i])
N / N /
--- ---
i = 1 i = 1
where all packets i= 1 through N have finite singleton delays. where all packets i= 1 through N have finite singleton delays.
4.1.5. Composition Function: Sum of Means 5.1.5. Composition Function: Sum of Means
The Type-P-Finite--Composite-One-way-Delay-Mean, or CompMeanDelay for The Type-P-Finite--Composite-One-way-Delay-Mean, or CompMeanDelay for
the complete Source to Destination path can be calculated from sum of the complete Source to Destination path can be calculated from sum of
the Mean Delays of all its S constituent sub-paths. the Mean Delays of all its S constituent sub-paths.
o S, the number of sub-paths involved in the complete Src-Dst path.
Then the Then the
Type-P-Finite-Composite-One-way-Delay-Mean = Type-P-Finite-Composite-One-way-Delay-Mean =
CompMeanDelay = (1/S)Sum(from i=1 to S, MeanDelay[i]) S
---
\
CompMeanDelay = > (MeanDelay [i])
/
---
i = 1
4.1.6. Statement of Conjecture 5.1.6. Statement of Conjecture
The mean of a sufficiently large stream of packets measured on each The mean of a sufficiently large stream of packets measured on each
sub-path during the interval [T, Tf] will be representative of the sub-path during the interval [T, Tf] will be representative of the
true mean of the delay distribution (and the distributions themselves true mean of the delay distribution (and the distributions themselves
are sufficiently independent), such that the means may be added to are sufficiently independent), such that the means may be added to
produce an estimate of the complete path mean delay. produce an estimate of the complete path mean delay.
4.1.7. Justification of the Composition Function 5.1.7. Justification of the Composition Function
It is sometimes impractical to conduct active measurements between
every Src-Dst pair. For example, it may not be possible to collect
the desired sample size in each test interval when access link speed
is limited, because of the potential for measurement traffic to
degrade the user traffic performance. The conditions on a low-speed
access link may be understood well-enough to permit use of a small
sample size/rate, while a larger sample size/rate may be used on
other sub-paths.
Also, since measurement operations have a real monetary cost, there
is value in re-using measurements where they are applicable, rather
than launching new measurements for every possible source-destination
pair.
4.1.8. Sources of Deviation from the Ground Truth See the common section.
The measurement packets, each having source and destination addresses 5.1.8. Sources of Deviation from the Ground Truth
intended for collection at edges of the sub-path, may take a
different specific path through the network equipment and parallel
exchanges than packets with the source and destination addresses of
the complete path. Therefore, the sub-path measurements may differ
from the performance experienced by packets on the complete path.
Multiple measurements employing sufficient sub-path address pairs
might produce bounds on the extent of this error.
others... See the common section.
4.1.9. Specific cases where the conjecture might fail 5.1.9. Specific cases where the conjecture might fail
If any of the sub-path distributions are bimodal, then the measured If any of the sub-path distributions are bimodal, then the measured
means may not be stable, and in this case the mean will not be a means may not be stable, and in this case the mean will not be a
particularly useful statistic when describing the delay distribution particularly useful statistic when describing the delay distribution
of the complete path. of the complete path.
The mean may not be sufficiently robust statistic to produce a The mean may not be sufficiently robust statistic to produce a
reliable estimate, or to be useful even if it can be measured. reliable estimate, or to be useful even if it can be measured.
others... others...
4.1.10. Application of Measurement Methodology 5.1.10. Application of Measurement Methodology
The methodology:
SHOULD use similar packets sent and collected separately in each sub-
path.
Allows a degree of flexibility (e.g., active or passive methods can
produce the "same" metric, but timing and correlation of passive
measurements is much more challenging).
Poisson and/or Periodic streams are RECOMMENDED.
Applicable to both Inter-domain and Intra-domain composition.
SHOULD have synchronized measurement time intervals in all sub-paths,
but largely overlapping intervals MAY suffice.
REQUIRES assumption of sub-path independence w.r.t. the metric being The requirements of the common section apply here as well.
defined/composed.
5. Loss Metrics and Statistics 6. Loss Metrics and Statistics
5.1. Name: Type-P-One-way-Packet-Loss-Poisson/Periodic-Stream 6.1. Name: Type-P-One-way-Packet-Loss-Poisson/Periodic-Stream
5.1.1. Metric Parameters: 6.1.1. Metric Parameters:
Same as section 4.1.1. Same as section 4.1.1.
5.1.2. Definition and Metric Units 6.1.2. Definition and Metric Units
Using the parameters above, we obtain the value of Type-P-One-way- Using the parameters above, we obtain the value of Type-P-One-way-
Packet-Loss singleton and stream as per [RFC2680]. Packet-Loss singleton and stream as per [RFC2680].
We obtain a sequence of pairs with elements as follows: We obtain a sequence of pairs with elements as follows:
o TstampSrc, as above o TstampSrc, as above
o L, either zero or one, where L=1 indicates loss and L=0 indicates o L, either zero or one, where L=1 indicates loss and L=0 indicates
arrival at the destination within TstampSrc + Tmax. arrival at the destination within TstampSrc + Tmax.
5.1.3. Discussion and other details 6.1.3. Discussion and other details
5.1.4. Statistic: Type-P-One-way-Packet-Loss-Empirical-Probability
Given the following stream parameter
o M, the total number of packets sent between T0 and Tf 6.1.4. Statistic: Type-P-One-way-Packet-Loss-Empirical-Probability
We can define the Empirical Probability of Loss Statistic (Ep), Given the stream parameter M, the number of packets sent, we can
consistent with Average Loss in [RFC2680], as follows: define the Empirical Probability of Loss Statistic (Ep), consistent
with Average Loss in [RFC2680], as follows:
Type-P-One-way-Packet-Loss-Empirical-Probability = Type-P-One-way-Packet-Loss-Empirical-Probability =
M
Ep = (1/M)Sum(from i=1 to M, L[i]) ---
1 \
Ep = - * > (L[i])
M /
---
i = 1
where all packets i= 1 through M have a value for L. where all packets i= 1 through M have a value for L.
5.1.5. Composition Function: Composition of Empirical Probabilities 6.1.5. Composition Function: Composition of Empirical Probabilities
The Type-P-One-way-Composite-Packet-Loss-Empirical-Probability, or The Type-P-One-way-Composite-Packet-Loss-Empirical-Probability, or
CompEp for the complete Source to Destination path can be calculated CompEp for the complete Source to Destination path can be calculated
by combining Ep of all its constituent sub-paths (Ep1, Ep2, Ep3, ... by combining Ep of all its constituent sub-paths (Ep1, Ep2, Ep3, ...
Epn) as Epn) as
Type-P-One-way-Composite-Packet-Loss-Empirical-Probability = CompEp = Type-P-One-way-Composite-Packet-Loss-Empirical-Probability =
1 - {(1 - Ep1) x (1 - Ep2) x (1 - Ep3) x ... x (1 - Epn)} CompEp = 1 ? {(1 - Ep1) x (1 ? Ep2) x (1 ? Ep3) x ... x (1 ? Epn)}
5.1.6. Statement of Conjecture If any EpN is undefined in a particular measurement interval,
possibly because a measurement system failed to report a value, then
any CompEp that uses sub-path N for that measurement interval is
undefined.
6.1.6. Statement of Conjecture
The empirical probability of loss calculated on a sufficiently large The empirical probability of loss calculated on a sufficiently large
stream of packets measured on each sub-path during the interval [T, stream of packets measured on each sub-path during the interval [T,
Tf] will be representative of the true loss probability (and the Tf] will be representative of the true loss probability (and the
probabilities themselves are sufficiently independent), such that the probabilities themselves are sufficiently independent), such that the
sub-path probabilities may be combined to produce an estimate of the sub-path probabilities may be combined to produce an estimate of the
complete path loss probability. complete path loss probability.
5.1.7. Justification of the Composition Function 6.1.7. Justification of the Composition Function
It is sometimes impractical to conduct active measurements between
every Src-Dst pair. For example, it may not be possible to collect
the desired sample size in each test interval when access link speed
is limited, because of the potential for measurement traffic to
degrade the user traffic performance. The conditions on a low-speed
access link may be understood well-enough to permit use of a small
sample size/rate, while a larger sample size/rate may be used on
other sub-paths.
Also, since measurement operations have a real monetary cost, there
is value in re-using measurements where they are applicable, rather
than launching new measurements for every possible source-destination
pair.
5.1.8. Sources of Deviation from the Ground Truth See the common section.
The measurement packets, each having source and destination addresses 6.1.8. Sources of Deviation from the Ground Truth
intended for collection at edges of the sub-path, may take a
different specific path through the network equipment and parallel
exchanges than packets with the source and destination addresses of
the complete path. Therefore, the sub-path measurements may differ
from the performance experienced by packets on the complete path.
Multiple measurements employing sufficient sub-path address pairs
might produce bounds on the extent of this error.
others... See the common section.
5.1.9. Specific cases where the conjecture might fail 6.1.9. Specific cases where the conjecture might fail
A concern for loss measurements combined in this way is that root A concern for loss measurements combined in this way is that root
causes may be correlated to some degree. causes may be correlated to some degree.
For example, if the links of different networks follow the same For example, if the links of different networks follow the same
physical route, then a single event like a tunnel fire could cause an physical route, then a single event like a tunnel fire could cause an
outage or congestion on remaining paths in multiple networks. Here outage or congestion on remaining paths in multiple networks. Here
it is important to ensure that measurements before the event and it is important to ensure that measurements before the event and
after the event are not combined to estimate the composite after the event are not combined to estimate the composite
performance. performance.
Or, when traffic volumes rise due to the rapid spread of an email- Or, when traffic volumes rise due to the rapid spread of an email-
born worm, loss due to queue overflow in one network may help another born worm, loss due to queue overflow in one network may help another
network to carry its traffic without loss. network to carry its traffic without loss.
others... others...
5.1.10. Application of Measurement Methodology 6.1.10. Application of Measurement Methodology
The methodology:
SHOULD use similar packets sent and collected separately in each sub-
path.
Allows a degree of flexibility (e.g., active or passive methods can
produce the "same" metric, but timing and correlation of passive
measurements is much more challenging).
Poisson and/or Periodic streams are RECOMMENDED.
Applicable to both Inter-domain and Intra-domain composition.
SHOULD have synchronized measurement time intervals in all sub-paths,
but largely overlapping intervals MAY suffice.
REQUIRES assumption of sub-path independence w.r.t. the metric being See the common section.
defined/composed.
6. Delay Variation Metrics and Statistics 7. Delay Variation Metrics and Statistics
6.1. Name: Type-P-One-way-ipdv-refmin-Poisson/Periodic-Stream 7.1. Name: Type-P-One-way-ipdv-refmin-Poisson/Periodic-Stream
This metric is a necessary element of Composed Delay Variation This metric is a necessary element of Composed Delay Variation
metrics, and its definition does not formally exist elsewhere in IPPM metrics, and its definition does not formally exist elsewhere in IPPM
literature. literature.
6.1.1. Metric Parameters: 7.1.1. Metric Parameters:
In addition to the parameters of section 4.1.1: In addition to the parameters of section 4.1.1:
o TstampSrc[i], the wire time of packet[i] as measured at MP(Src) o TstampSrc[i], the wire time of packet[i] as measured at MP(Src)
o TstampDst[i], the wire time of packet[i] as measured at MP(Dst), o TstampDst[i], the wire time of packet[i] as measured at MP(Dst),
assigned to packets that arrive within a "reasonable" time. assigned to packets that arrive within a "reasonable" time.
o B, a packet length in bits o B, a packet length in bits
skipping to change at page 14, line 5 skipping to change at page 15, line 11
addition to the criteria for F(first packet). If multiple packets addition to the criteria for F(first packet). If multiple packets
have equal minimum Type-P-Finite-One-way-Delay values, then the have equal minimum Type-P-Finite-One-way-Delay values, then the
value for the earliest arriving packet SHOULD be used. value for the earliest arriving packet SHOULD be used.
o MinDelay, the Type-P-Finite-One-way-Delay value for F(second o MinDelay, the Type-P-Finite-One-way-Delay value for F(second
packet) given above. packet) given above.
o N, the number of packets received at the Destination meeting the o N, the number of packets received at the Destination meeting the
F(first packet) criteria. F(first packet) criteria.
6.1.2. Definition and Metric Units 7.1.2. Definition and Metric Units
Using the definition above in section 4.1.2, we obtain the value of Using the definition above in section 4.1.2, we obtain the value of
Type-P-Finite-One-way-Delay-Poisson/Periodic-Stream[i], the singleton Type-P-Finite-One-way-Delay-Poisson/Periodic-Stream[i], the singleton
for each packet[i] in the stream (a.k.a. FiniteDelay[i]). for each packet[i] in the stream (a.k.a. FiniteDelay[i]).
For each packet[i] that meets the F(first packet) criteria given For each packet[i] that meets the F(first packet) criteria given
above: Type-P-One-way-ipdv-refmin-Poisson/Periodic-Stream[i] = above: Type-P-One-way-ipdv-refmin-Poisson/Periodic-Stream[i] =
IPDVRefMin[i] = FiniteDelay[i] - MinDelay IPDVRefMin[i] = FiniteDelay[i] - MinDelay
where IPDVRefMin[i] is in units of time (seconds, milliseconds). where IPDVRefMin[i] is in units of time (seconds, milliseconds).
6.1.3. Discussion and other details 7.1.3. Discussion and other details
This metric produces a sample of delay variation normalized to the This metric produces a sample of delay variation normalized to the
minimum delay of the sample. The resulting delay variation minimum delay of the sample. The resulting delay variation
distribution is independent of the sending sequence (although distribution is independent of the sending sequence (although
specific FiniteDelay values within the distribution may be specific FiniteDelay values within the distribution may be
correlated, depending on various stream parameters such as packet correlated, depending on various stream parameters such as packet
spacing). This metric is equivalent to the IP Packet Delay Variation spacing). This metric is equivalent to the IP Packet Delay Variation
parameter defined in [Y.1540]. parameter defined in [Y.1540].
6.1.4. Statistics: Mean, Variance, Skewness, Quanitle 7.1.4. Statistics: Mean, Variance, Skewness, Quanitle
We define the mean IPDVRefMin as follows (where all packets i= 1 We define the mean IPDVRefMin as follows (where all packets i= 1
through N have a value for IPDVRefMin): through N have a value for IPDVRefMin):
Type-P-One-way-ipdv-refmin-Mean = MeanIPDVRefMin = Type-P-One-way-ipdv-refmin-Mean = MeanIPDVRefMin =
N N
--- ---
1 \ 1 \
- * > (IPDVRefMin [i]) - * > (IPDVRefMin [i])
N / N /
skipping to change at page 15, line 32 skipping to change at page 16, line 32
--- ---
i = 1 i = 1
------------------------------------------- -------------------------------------------
/ \ / \
| ( 3/2 ) | | ( 3/2 ) |
\ (N - 1) * VarIPDVRefMin / \ (N - 1) * VarIPDVRefMin /
We define the Quantile of the IPDVRefMin sample as the value where We define the Quantile of the IPDVRefMin sample as the value where
the specified fraction of points is less than the given value. the specified fraction of points is less than the given value.
6.1.5. Composition Functions: 7.1.5. Composition Functions:
The Type-P-One-way-Composite-ipdv-refmin-<something> for the complete This section gives two alternative composition functions. The
objective is to estimate a quantile of the complete path delay
variation distribution. The composed quantile will be estimated
using information from the sub-path delay variation distributions.
7.1.5.1. Approximate Convolution
The Type-P-One-way-Delay-Poisson/Periodic-Stream samples from each
sub-path are summarized as a histogram with 1 ms bins representing
the one-way delay distribution.
From [TBP], the distribution of the sum of independent random
variables can be derived using the relation:
Type-P-One-way-Composite-ipdv-refmin-quantile-a =
/ /
P(X + Y + Z <= a) = | | P(X <= a-y-z) * P(Y = y) * P(Z = z) dy dz
/ /
z y
where X, Y, and Z are random variables representing the delay
variation distributions of the sub-paths of the complete path, and a
is the quantile of interest. Note dy and dz indicate partial
integration here.This relation can be used to compose a quantile of
interest for the complete path from the sub-path delay distributions.
The histograms with 1 ms bins are discrete approximations of the
delay distributions.
7.1.5.2. new section
Type-P-One-way-Composite-ipdv-refmin-<something> for the complete
Source to Destination path can be calculated by combining statistics Source to Destination path can be calculated by combining statistics
of all the constituent sub-paths in the following process: of all the constituent sub-paths in the following process:
< see [Y.1541] > < see [Y.1541] section 8 >
6.1.6. Statement of Conjecture 7.1.6. Statement of Conjecture
6.1.7. Justification of the Composition Function The delay distribution of a sufficiently large stream of packets
measured on each sub-path during the interval [T, Tf] will be
sufficiently stationary and the sub-path distributions themselves are
sufficiently independent, so that summary information describing the
sub-path distributions can be combined to estimate the delay
distribution of complete path.
6.1.8. Sources of Deviation from the Ground Truth 7.1.7. Justification of the Composition Function
6.1.9. Specific cases where the conjecture might fail See the common section.
6.1.10. Application of Measurement Methodology 7.1.8. Sources of Deviation from the Ground Truth
7. Other Metrics and Statistics: One-way Combined Metric In addition to the common deviations, the a few additional sources
exist here. For one, very tight distributions with range on the
order of a few milliseconds are not accurately represented by a
histogram with 1 ms bins. This size was chosen assuming an implicit
requirement on accuracy: errors of a few milliseconds are acceptable
when assessing a composed distribution quantile.
Also, summary statistics cannot describe the subtleties of an
empirical distribution exactly, especially when the distribution is
very different from a classical form. Any procedure that uses these
statistics alone may incur error.
7.1.9. Specific cases where the conjecture might fail
If the delay distributions of the sub-paths are somehow correlated,
then neither of these composition functions will be reliable
estimators of the complete path distribution.
In practice, sub-path delay distributions with extreme outliers have
increased the error of the composed metric estimate.
7.1.10. Application of Measurement Methodology
See the common section.
8. Security Considerations 8. Security Considerations
8.1. Denial of Service Attacks 8.1. Denial of Service Attacks
This metric requires a stream of packets sent from one host (source) This metric requires a stream of packets sent from one host (source)
to another host (destination) through intervening networks. This to another host (destination) through intervening networks. This
method could be abused for denial of service attacks directed at method could be abused for denial of service attacks directed at
destination and/or the intervening network(s). destination and/or the intervening network(s).
skipping to change at page 17, line 32 skipping to change at page 19, line 42
measurement to investigate the performance of different part of the measurement to investigate the performance of different part of the
network. network.
Editor's Questions for clarification: What additional information Editor's Questions for clarification: What additional information
would be provided to the decomposition process, beyond the would be provided to the decomposition process, beyond the
measurement of the complete path? measurement of the complete path?
Is the decomposition described above intended to estimate a metric Is the decomposition described above intended to estimate a metric
for some/all disjoint sub-paths involved in the complete path? for some/all disjoint sub-paths involved in the complete path?
>>>>>>>>>>>>>>>>>>RESOLUTION: treat this topic in a seperate memo >>>>>>>>>>>>>>>>>>RESOLUTION: treat this topic in a separate memo
>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>Issue >>>>>>>>>>>>>>>>>>>Issue
Section 7 defines a new type of metric, a "combination" of metrics Section 7 defines a new type of metric, a "combination" of metrics
for one-way delay and packet loss. The purpose of this metric is to for one-way delay and packet loss. The purpose of this metric is to
link these two primary metrics in a convenient way. link these two primary metrics in a convenient way.
Readers are asked to comment on the efficiency of the combination Readers are asked to comment on the efficiency of the combination
skipping to change at page 18, line 23 skipping to change at page 20, line 33
>>>>>>>>>>>>>>>>RESOLUTION: No and Yes. >>>>>>>>>>>>>>>>RESOLUTION: No and Yes.
11. Acknowledgements 11. Acknowledgements
12. References 12. References
12.1. Normative References 12.1. Normative References
[I-D.ietf-ippm-framework-compagg] [I-D.ietf-ippm-framework-compagg]
Morton, A. and S. Berghe, "Framework for Metric Morton, A. and S. Berghe, "Framework for Metric
Composition", draft-ietf-ippm-framework-compagg-01 (work Composition", draft-ietf-ippm-framework-compagg-03 (work
in progress), June 2006. in progress), March 2007.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997. Requirement Levels", BCP 14, RFC 2119, March 1997.
[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.
[RFC2679] Almes, G., Kalidindi, S., and M. Zekauskas, "A One-way [RFC2679] Almes, G., Kalidindi, S., and M. Zekauskas, "A One-way
Delay Metric for IPPM", RFC 2679, September 1999. Delay Metric for IPPM", RFC 2679, September 1999.
skipping to change at page 19, line 31 skipping to change at page 22, line 4
Al Morton Al Morton
AT&T Labs AT&T Labs
200 Laurel Avenue South 200 Laurel Avenue South
Middletown,, NJ 07748 Middletown,, NJ 07748
USA USA
Phone: +1 732 420 1571 Phone: +1 732 420 1571
Fax: +1 732 368 1192 Fax: +1 732 368 1192
Email: acmorton@att.com Email: acmorton@att.com
URI: http://home.comcast.net/~acmacm/ URI: http://home.comcast.net/~acmacm/
Emile Stephan Emile Stephan
France Telecom Division R&D France Telecom Division R&D
2 avenue Pierre Marzin 2 avenue Pierre Marzin
Lannion, F-22307 Lannion, F-22307
France France
Phone: Phone:
Fax: +33 2 96 05 18 52 Fax: +33 2 96 05 18 52
Email: emile.stephan@francetelecom.com Email: emile.stephan@orange-ftgroup.com
URI: URI:
Full Copyright Statement Full Copyright Statement
Copyright (C) The Internet Society (2006). Copyright (C) The IETF Trust (2007).
This document is subject to the rights, licenses and restrictions This document is subject to the rights, licenses and restrictions
contained in BCP 78, and except as set forth therein, the authors contained in BCP 78, and except as set forth therein, the authors
retain all their rights. retain all their rights.
This document and the information contained herein are provided on an This document and the information contained herein are provided on an
"AS IS" basis and THE CONTRIBUTOR, THE ORGANIZATION HE/SHE REPRESENTS "AS IS" basis and THE CONTRIBUTOR, THE ORGANIZATION HE/SHE REPRESENTS
OR IS SPONSORED BY (IF ANY), THE INTERNET SOCIETY AND THE INTERNET OR IS SPONSORED BY (IF ANY), THE INTERNET SOCIETY, THE IETF TRUST AND
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WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE.
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Intellectual Property Rights or other rights that might be claimed to Intellectual Property Rights or other rights that might be claimed to
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