draft-ietf-ippm-spatial-composition-13.txt   draft-ietf-ippm-spatial-composition-14.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: December 27, 2010 France Telecom Division R&D Expires: January 2, 2011 France Telecom Division R&D
June 25, 2010 July 1, 2010
Spatial Composition of Metrics Spatial Composition of Metrics
draft-ietf-ippm-spatial-composition-13 draft-ietf-ippm-spatial-composition-14
Abstract Abstract
This memo utilizes IP Performance Metrics that are applicable to both This memo utilizes IP Performance 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 complete path metric from the sub-path metrics with some accuracy
w.r.t. the actual metrics. This is called Spatial Composition in RFC w.r.t. the actual metrics. This is called Spatial Composition in RFC
2330. The memo refers to the Framework for Metric Composition, and 2330. The memo refers to the Framework for Metric Composition, and
provides background and motivation for combining metrics to derive provides background and motivation for combining metrics to derive
others. The descriptions of several composed metrics and statistics others. The descriptions of several composed metrics and statistics
skipping to change at page 1, line 47 skipping to change at page 1, line 47
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This Internet-Draft will expire on December 27, 2010. This Internet-Draft will expire on January 2, 2011.
Copyright Notice Copyright Notice
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Table of Contents Table of Contents
1. Contributors . . . . . . . . . . . . . . . . . . . . . . . . . 5 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 5
2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . 6 2. Scope and Application . . . . . . . . . . . . . . . . . . . . 6
3. Scope and Application . . . . . . . . . . . . . . . . . . . . 6 2.1. Scope of work . . . . . . . . . . . . . . . . . . . . . . 6
3.1. Scope of work . . . . . . . . . . . . . . . . . . . . . . 7 2.2. Application . . . . . . . . . . . . . . . . . . . . . . . 7
3.2. Application . . . . . . . . . . . . . . . . . . . . . . . 7 2.3. Incomplete Information . . . . . . . . . . . . . . . . . . 7
3.3. Incomplete Information . . . . . . . . . . . . . . . . . . 8 3. Common Specifications for Composed Metrics . . . . . . . . . . 7
4. Common Specifications for Composed Metrics . . . . . . . . . . 8 3.1. Name: Type-P . . . . . . . . . . . . . . . . . . . . . . . 8
4.1. Name: Type-P . . . . . . . . . . . . . . . . . . . . . . . 8 3.1.1. Metric Parameters . . . . . . . . . . . . . . . . . . 8
4.1.1. Metric Parameters . . . . . . . . . . . . . . . . . . 8 3.1.2. Definition and Metric Units . . . . . . . . . . . . . 9
4.1.2. Definition and Metric Units . . . . . . . . . . . . . 9 3.1.3. Discussion and other details . . . . . . . . . . . . . 9
4.1.3. Discussion and other details . . . . . . . . . . . . . 9 3.1.4. Statistic: . . . . . . . . . . . . . . . . . . . . . . 9
4.1.4. Statistic: . . . . . . . . . . . . . . . . . . . . . . 9 3.1.5. Composition Function . . . . . . . . . . . . . . . . . 9
4.1.5. Composition Function . . . . . . . . . . . . . . . . . 9 3.1.6. Statement of Conjecture and Assumptions . . . . . . . 9
4.1.6. Statement of Conjecture and Assumptions . . . . . . . 9 3.1.7. Justification of the Composition Function . . . . . . 9
4.1.7. Justification of the Composition Function . . . . . . 10 3.1.8. Sources of Deviation from the Ground Truth . . . . . . 10
4.1.8. Sources of Deviation from the Ground Truth . . . . . . 10 3.1.9. Specific cases where the conjecture might fail . . . . 11
4.1.9. Specific cases where the conjecture might fail . . . . 11 3.1.10. Application of Measurement Methodology . . . . . . . . 11
4.1.10. Application of Measurement Methodology . . . . . . . . 12 4. One-way Delay Composed Metrics and Statistics . . . . . . . . 12
5. One-way Delay Composed Metrics and Statistics . . . . . . . . 12 4.1. Name:
5.1. Name:
Type-P-Finite-One-way-Delay-Poisson/Periodic-Stream . . . 12 Type-P-Finite-One-way-Delay-Poisson/Periodic-Stream . . . 12
5.1.1. Metric Parameters . . . . . . . . . . . . . . . . . . 12 4.1.1. Metric Parameters . . . . . . . . . . . . . . . . . . 12
5.1.2. Definition and Metric Units . . . . . . . . . . . . . 12 4.1.2. Definition and Metric Units . . . . . . . . . . . . . 12
5.1.3. Discussion and other details . . . . . . . . . . . . . 13 4.1.3. Discussion and other details . . . . . . . . . . . . . 12
5.1.4. Statistic: . . . . . . . . . . . . . . . . . . . . . . 13 4.1.4. Statistic: . . . . . . . . . . . . . . . . . . . . . . 13
5.2. Name: Type-P-Finite-Composite-One-way-Delay-Mean . . . . . 13 4.2. Name: Type-P-Finite-Composite-One-way-Delay-Mean . . . . . 13
5.2.1. Metric Parameters . . . . . . . . . . . . . . . . . . 13 4.2.1. Metric Parameters . . . . . . . . . . . . . . . . . . 13
5.2.2. Definition and Metric Units of the Mean Statistic . . 13 4.2.2. Definition and Metric Units of the Mean Statistic . . 13
5.2.3. Discussion and other details . . . . . . . . . . . . . 14 4.2.3. Discussion and other details . . . . . . . . . . . . . 13
5.2.4. Statistic: . . . . . . . . . . . . . . . . . . . . . . 14 4.2.4. Statistic: . . . . . . . . . . . . . . . . . . . . . . 13
5.2.5. Composition Function: Sum of Means . . . . . . . . . . 14 4.2.5. Composition Function: Sum of Means . . . . . . . . . . 13
5.2.6. Statement of Conjecture and Assumptions . . . . . . . 14 4.2.6. Statement of Conjecture and Assumptions . . . . . . . 14
5.2.7. Justification of the Composition Function . . . . . . 15 4.2.7. Justification of the Composition Function . . . . . . 14
5.2.8. Sources of Deviation from the Ground Truth . . . . . . 15 4.2.8. Sources of Deviation from the Ground Truth . . . . . . 14
5.2.9. Specific cases where the conjecture might fail . . . . 15 4.2.9. Specific cases where the conjecture might fail . . . . 14
5.2.10. Application of Measurement Methodology . . . . . . . . 15 4.2.10. Application of Measurement Methodology . . . . . . . . 15
5.3. Name: Type-P-Finite-Composite-One-way-Delay-Minimum . . . 15 4.3. Name: Type-P-Finite-Composite-One-way-Delay-Minimum . . . 15
5.3.1. Metric Parameters . . . . . . . . . . . . . . . . . . 15 4.3.1. Metric Parameters . . . . . . . . . . . . . . . . . . 15
5.3.2. Definition and Metric Units of the Minimum 4.3.2. Definition and Metric Units of the Minimum
Statistic . . . . . . . . . . . . . . . . . . . . . . 15 Statistic . . . . . . . . . . . . . . . . . . . . . . 15
5.3.3. Discussion and other details . . . . . . . . . . . . . 16 4.3.3. Discussion and other details . . . . . . . . . . . . . 15
5.3.4. Statistic: . . . . . . . . . . . . . . . . . . . . . . 16 4.3.4. Statistic: . . . . . . . . . . . . . . . . . . . . . . 15
5.3.5. Composition Function: Sum of Minima . . . . . . . . . 16 4.3.5. Composition Function: Sum of Minima . . . . . . . . . 15
5.3.6. Statement of Conjecture and Assumptions . . . . . . . 16 4.3.6. Statement of Conjecture and Assumptions . . . . . . . 16
5.3.7. Justification of the Composition Function . . . . . . 17 4.3.7. Justification of the Composition Function . . . . . . 16
5.3.8. Sources of Deviation from the Ground Truth . . . . . . 17 4.3.8. Sources of Deviation from the Ground Truth . . . . . . 16
5.3.9. Specific cases where the conjecture might fail . . . . 17 4.3.9. Specific cases where the conjecture might fail . . . . 16
5.3.10. Application of Measurement Methodology . . . . . . . . 17 4.3.10. Application of Measurement Methodology . . . . . . . . 16
6. Loss Metrics and Statistics . . . . . . . . . . . . . . . . . 17 5. Loss Metrics and Statistics . . . . . . . . . . . . . . . . . 16
6.1. Type-P-Composite-One-way-Packet-Loss-Empirical-Probability 17 5.1. Type-P-Composite-One-way-Packet-Loss-Empirical-Probability 16
6.1.1. Metric Parameters: . . . . . . . . . . . . . . . . . . 17 5.1.1. Metric Parameters: . . . . . . . . . . . . . . . . . . 17
6.1.2. Definition and Metric Units . . . . . . . . . . . . . 17 5.1.2. Definition and Metric Units . . . . . . . . . . . . . 17
6.1.3. Discussion and other details . . . . . . . . . . . . . 17 5.1.3. Discussion and other details . . . . . . . . . . . . . 17
6.1.4. Statistic: 5.1.4. Statistic:
Type-P-One-way-Packet-Loss-Empirical-Probability . . . 18 Type-P-One-way-Packet-Loss-Empirical-Probability . . . 17
6.1.5. Composition Function: Composition of Empirical 5.1.5. Composition Function: Composition of Empirical
Probabilities . . . . . . . . . . . . . . . . . . . . 18 Probabilities . . . . . . . . . . . . . . . . . . . . 17
6.1.6. Statement of Conjecture and Assumptions . . . . . . . 18 5.1.6. Statement of Conjecture and Assumptions . . . . . . . 18
6.1.7. Justification of the Composition Function . . . . . . 18 5.1.7. Justification of the Composition Function . . . . . . 18
6.1.8. Sources of Deviation from the Ground Truth . . . . . . 19 5.1.8. Sources of Deviation from the Ground Truth . . . . . . 18
6.1.9. Specific cases where the conjecture might fail . . . . 19 5.1.9. Specific cases where the conjecture might fail . . . . 18
6.1.10. Application of Measurement Methodology . . . . . . . . 19 5.1.10. Application of Measurement Methodology . . . . . . . . 18
7. Delay Variation Metrics and Statistics . . . . . . . . . . . . 19 6. Delay Variation Metrics and Statistics . . . . . . . . . . . . 18
7.1. Name: Type-P-One-way-pdv-refmin-Poisson/Periodic-Stream . 19 6.1. Name: Type-P-One-way-pdv-refmin-Poisson/Periodic-Stream . 18
7.1.1. Metric Parameters: . . . . . . . . . . . . . . . . . . 19 6.1.1. Metric Parameters: . . . . . . . . . . . . . . . . . . 19
7.1.2. Definition and Metric Units . . . . . . . . . . . . . 20 6.1.2. Definition and Metric Units . . . . . . . . . . . . . 19
7.1.3. Discussion and other details . . . . . . . . . . . . . 20 6.1.3. Discussion and other details . . . . . . . . . . . . . 20
7.1.4. Statistics: Mean, Variance, Skewness, Quanitle . . . . 20 6.1.4. Statistics: Mean, Variance, Skewness, Quantile . . . . 20
7.1.5. Composition Functions: . . . . . . . . . . . . . . . . 21 6.1.5. Composition Functions: . . . . . . . . . . . . . . . . 21
7.1.6. Statement of Conjecture and Assumptions . . . . . . . 22 6.1.6. Statement of Conjecture and Assumptions . . . . . . . 22
7.1.7. Justification of the Composition Function . . . . . . 22 6.1.7. Justification of the Composition Function . . . . . . 22
7.1.8. Sources of Deviation from the Ground Truth . . . . . . 23 6.1.8. Sources of Deviation from the Ground Truth . . . . . . 22
7.1.9. Specific cases where the conjecture might fail . . . . 23 6.1.9. Specific cases where the conjecture might fail . . . . 22
7.1.10. Application of Measurement Methodology . . . . . . . . 23 6.1.10. Application of Measurement Methodology . . . . . . . . 23
8. Security Considerations . . . . . . . . . . . . . . . . . . . 23 7. Security Considerations . . . . . . . . . . . . . . . . . . . 23
8.1. Denial of Service Attacks . . . . . . . . . . . . . . . . 23 7.1. Denial of Service Attacks . . . . . . . . . . . . . . . . 23
8.2. User Data Confidentiality . . . . . . . . . . . . . . . . 23 7.2. User Data Confidentiality . . . . . . . . . . . . . . . . 23
8.3. Interference with the metrics . . . . . . . . . . . . . . 24 7.3. Interference with the metrics . . . . . . . . . . . . . . 23
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 24 8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 24
10. Acknowlegements . . . . . . . . . . . . . . . . . . . . . . . 24 9. Contributors and Acknowledgements . . . . . . . . . . . . . . 27
11. References . . . . . . . . . . . . . . . . . . . . . . . . . . 25 10. References . . . . . . . . . . . . . . . . . . . . . . . . . . 27
11.1. Normative References . . . . . . . . . . . . . . . . . . . 25 10.1. Normative References . . . . . . . . . . . . . . . . . . . 27
11.2. Informative References . . . . . . . . . . . . . . . . . . 25 10.2. Informative References . . . . . . . . . . . . . . . . . . 28
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 26 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 29
1. Contributors
Thus far, the following people have contributed useful ideas,
suggestions, or the text of sections that have been incorporated into
this memo:
- Phil Chimento <vze275m9@verizon.net>
- Reza Fardid <RFardid@cariden.com>
- Roman Krzanowski <roman.krzanowski@verizon.com>
- Maurizio Molina <maurizio.molina@dante.org.uk>
- Lei Liang <L.Liang@surrey.ac.uk>
- Dave Hoeflin <dhoeflin@att.com>
2. Introduction 1. Introduction
The IPPM framework [RFC2330] describes two forms of metric The IP Performance Metrics (IPPM) framework [RFC2330] describes two
composition, spatial and temporal. The new composition framework forms of metric composition, spatial and temporal. The composition
[RFC5835] expands and further qualifies these original forms into framework [RFC5835] expands and further qualifies these original
three categories. This memo describes Spatial Composition, one of forms into three categories. This memo describes Spatial
the categories of metrics under the umbrella of the composition Composition, one of the categories of metrics under the umbrella of
framework. the composition framework.
Spatial composition encompasses the definition of performance metrics Spatial composition encompasses the definition of performance metrics
that are applicable to a complete path, based on metrics collected on that are applicable to a complete path, based on metrics collected on
various sub-paths. various sub-paths.
The main purpose of this memo is to define the deterministic The main purpose of this memo is to define the deterministic
functions that yield the complete path metrics using metrics of the functions that yield the complete path metrics using metrics of the
sub-paths. The effectiveness of such metrics is dependent on their sub-paths. The effectiveness of such metrics is dependent on their
usefulness in analysis and applicability with practical measurement usefulness in analysis and applicability with practical measurement
methods. methods.
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Approximate functions between the sub-path and complete path metrics Approximate functions between the sub-path and complete path metrics
are useful, with knowledge of the circumstances where the are useful, with knowledge of the circumstances where the
relationships are/are not applicable. For example, we would not relationships are/are not applicable. For example, we would not
expect that delay singletons from each sub-path would sum to produce expect that delay singletons from each sub-path would sum to produce
an accurate estimate of a delay singleton for the complete path an accurate estimate of a delay singleton for the complete path
(unless all the delays were essentially constant - very unlikely). (unless all the delays were essentially constant - very unlikely).
However, other delay statistics (based on a reasonable sample size) However, other delay statistics (based on a reasonable sample size)
may have a sufficiently large set of circumstances where they are may have a sufficiently large set of circumstances where they are
applicable. applicable.
2.1. Motivation 1.1. Motivation
One-way metrics defined in other IPPM RFCs all assume that the One-way metrics defined in other RFCs (such as [RFC2680] and
measurement can be practically carried out between the source and the [RFC2680]) all assume that the measurement can be practically carried
destination of interest. Sometimes there are reasons that the out between the source and the destination of interest. Sometimes
measurement can not be executed from the source to the destination. there are reasons that the measurement cannot be executed from the
For instance, the measurement path may cross several independent source to the destination. For instance, the measurement path may
domains that have conflicting policies, measurement tools and cross several independent domains that have conflicting policies,
methods, and measurement time assignment. The solution then may be measurement tools and methods, and measurement time assignment. The
the composition of several sub-path measurements. This means each solution then may be the composition of several sub-path
domain performs the One-way measurement on a sub path between two measurements. This means each domain performs the One-way
nodes that are involved in the complete path following its own measurement on a sub path between two nodes that are involved in the
policy, using its own measurement tools and methods, and using its complete path following its own policy, using its own measurement
own measurement timing. Under the appropriate conditions, one can tools and methods, and using its own measurement timing. Under the
combine the sub-path One-way metric results to estimate the complete appropriate conditions, one can combine the sub-path One-way metric
path One-way measurement metric with some degree of accuracy. results to estimate the complete path One-way measurement metric with
some degree of accuracy.
3. Scope and Application 2. Scope and Application
3.1. Scope of work
For the primary IPPM metrics of Loss, Delay, and Delay Variation, 2.1. Scope of work
this memo gives a set of metrics that can be composed from the same
or similar sub-path metrics. This means that the composition For the primary IPPM metrics of Loss [RFC2680], Delay [RFC2680], and
function may utilize: Delay Variation [RFC3393], this memo gives a set of metrics that can
be composed from the same or similar sub-path metrics. This means
that the composition function may utilize:
o the same metric for each sub-path; o the same metric for each sub-path;
o multiple metrics for each sub-path (possibly one that is the same o multiple metrics for each sub-path (possibly one that is the same
as the complete path metric); as the complete path metric);
o a single sub-path metric that is different from the complete path o a single sub-path metric that is different from the complete path
metric; metric;
o different measurement techniques like active [RFC2330], [RFC3432] o different measurement techniques like active [RFC2330], [RFC3432]
and passive [RFC5474]. and passive [RFC5474].
We note a possibility: Using a complete path metric and all but one We note a possibility: Using a complete path metric and all but one
sub-path metric to infer the performance of the missing sub-path, sub-path metric to infer the performance of the missing sub-path,
especially when the "last" sub-path metric is missing. However, such especially when the "last" sub-path metric is missing. However, such
de-composition calculations, and the corresponding set of issues they de-composition calculations, and the corresponding set of issues they
raise, are beyond the scope of this memo. raise, are beyond the scope of this memo.
3.2. Application 2.2. Application
The new composition framework [RFC5835] requires the specification of The new composition framework [RFC5835] requires the specification of
the applicable circumstances for each metric. In particular, each the applicable circumstances for each metric. In particular, each
section addresses whether the metric: section addresses whether the metric:
Requires the same test packets to traverse all sub-paths, or may use Requires the same test packets to traverse all sub-paths, or may use
similar packets sent and collected separately in each sub-path. similar packets sent and collected separately in each sub-path.
Requires homogeneity of measurement methodologies, or can allow a Requires homogeneity of measurement methodologies, or can allow a
degree of flexibility (e.g., active or passive methods produce the degree of flexibility (e.g., active or passive methods produce the
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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.
3.3. Incomplete Information 2.3. Incomplete Information
In practice, when measurements cannot be initiated on a sub-path (and In practice, when measurements cannot be initiated on a sub-path (and
perhaps the measurement system gives up during the test interval), perhaps the measurement system gives up during the test interval),
then there will not be a value for the sub-path reported, and the then there will not be a value for the sub-path reported, and the
entire test result SHOULD be recorded as "undefined". This case entire test result SHOULD be recorded as "undefined". This case
should be distinguished from the case where the measurement system should be distinguished from the case where the measurement system
continued to send packets throughout the test interval, but all were continued to send packets throughout the test interval, but all were
declared lost. declared lost.
When a composed metric requires measurements from sub paths A, B, and When a composed metric requires measurements from sub paths A, B, and
C, and one or more of the sub-path results are undefined, then the C, and one or more of the sub-path results are undefined, then the
composed metric SHOULD also be recorded as undefined. composed metric SHOULD also be recorded as undefined.
4. Common Specifications for Composed Metrics 3. Common Specifications for Composed Metrics
To reduce the redundant information presented in the detailed metrics To reduce the redundant information presented in the detailed metrics
sections that follow, this section presents the specifications that sections that follow, this section presents the specifications that
are common to two or more metrics. The section is organized using are common to two or more metrics. The section is organized using
the same subsections as the individual metrics, to simplify the same subsections as the individual metrics, to simplify
comparisons. comparisons.
Also, the following index variables represent the following: Also, the following index variables represent the following:
o m = index for packets sent o m = index for packets sent
o n = index for packets received o n = index for packets received
o s = index for involved sub-paths o s = index for involved sub-paths
4.1. Name: Type-P 3.1. Name: Type-P
All metrics use the Type-P convention as described in [RFC2330]. The All metrics use the Type-P convention as described in [RFC2330]. The
rest of the name is unique to each metric. rest of the name is unique to each metric.
4.1.1. Metric Parameters 3.1.1. Metric Parameters
o Src, the IP address of a host o Src, the IP address of a host
o Dst, 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)
skipping to change at page 9, line 29 skipping to change at page 9, line 8
is not truncated. is not truncated.
o M, the total number of packets sent between T0 and Tf 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 o N, the total number of packets received at Dst (sent between T0
and Tf) and Tf)
o S, the number of sub-paths involved in the complete Src-Dst path o S, the number of sub-paths involved in the complete Src-Dst path
o Type-P, as defined in [RFC2330], which includes any field that may o Type-P, as defined in [RFC2330], which includes any field that may
affect a packet's treatment as it traverses network affect a packet's treatment as it traverses the network
4.1.2. Definition and Metric Units 3.1.2. Definition and Metric Units
This section is unique for every metric. This section is unique for every metric.
4.1.3. Discussion and other details 3.1.3. Discussion and other details
This section is unique for every metric. This section is unique for every metric.
4.1.4. Statistic: 3.1.4. Statistic:
This section is unique for every metric. This section is unique for every metric.
4.1.5. Composition Function 3.1.5. Composition Function
This section is unique for every metric. This section is unique for every metric.
4.1.6. Statement of Conjecture and Assumptions 3.1.6. Statement of Conjecture and Assumptions
This section is unique for each metric. This section is unique for each metric.
4.1.7. Justification of the Composition Function 3.1.7. Justification of the Composition Function
It is sometimes impractical to conduct active measurements between It is sometimes impractical to conduct active measurements between
every Src-Dst pair. Since the full mesh of N measurement points every Src-Dst pair. Since the full mesh of N measurement points
grows as N x N, the scope of measurement may be limited by testing grows as N x N, the scope of measurement may be limited by testing
resources. resources.
There may be varying limitations on active testing in different parts There may be varying limitations on active testing in different parts
of the network. For example, it may not be possible to collect the of the network. For example, it may not be possible to collect the
desired sample size in each test interval when access link speed is desired sample size in each test interval when access link speed is
limited, because of the potential for measurement traffic to degrade limited, because of the potential for measurement traffic to degrade
the user traffic performance. The conditions on a low-speed access the user traffic performance. The conditions on a low-speed access
link may be understood well-enough to permit use of a small sample 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- size/rate, while a larger sample size/rate may be used on other sub-
paths. paths.
Also, since measurement operations have a real monetary cost, there Also, since measurement operations have a real monetary cost, there
is value in re-using measurements where they are applicable, rather is value in re-using measurements where they are applicable, rather
than launching new measurements for every possible source-destination than launching new measurements for every possible source-destination
pair. pair.
4.1.8. Sources of Deviation from the Ground Truth 3.1.8. Sources of Deviation from the Ground Truth
4.1.8.1. Sub-path List Differs from Complete Path 3.1.8.1. Sub-path List Differs from Complete Path
The measurement packets, each having source and destination addresses The measurement packets, each having source and destination addresses
intended for collection at edges of the sub-path, may take a intended for collection at edges of the sub-path, may take a
different specific path through the network equipment and links when different specific path through the network equipment and links when
compared to packets with the source and destination addresses of the compared to packets with the source and destination addresses of the
complete path. Examples sources of parallel paths include Equal Cost complete path. Examples sources of parallel paths include Equal Cost
Multi-Path and parallel (or bundled) links. Therefore, the Multi-Path and parallel (or bundled) links. Therefore, the
performance estimated from the composition of sub-path measurements performance estimated from the composition of sub-path measurements
may differ from the performance experienced by packets on the may differ from the performance experienced by packets on the
complete path. Multiple measurements employing sufficient sub-path complete path. Multiple measurements employing sufficient sub-path
address pairs might produce bounds on the extent of this error. address pairs might produce bounds on the extent of this error.
We also note the possibility of re-routing during a measurement We also note the possibility of re-routing during a measurement
interval, as it may affect the correspondence between packets interval, as it may affect the correspondence between packets
traversing the complete path and the sub-paths that were "involved" traversing the complete path and the sub-paths that were "involved"
prior to the re-route. prior to the re-route.
4.1.8.2. Sub-path Contains Extra Network Elements 3.1.8.2. Sub-path Contains Extra Network Elements
Related to the case of an alternate path described above is the case Related to the case of an alternate path described above is the case
where elements in the measured path are unique to measurement system where elements in the measured path are unique to measurement system
connectivity. For example, a measurement system may use a dedicated connectivity. For example, a measurement system may use a dedicated
link to a LAN switch, and packets on the complete path do not link to a LAN switch, and packets on the complete path do not
traverse that link. The performance of such a dedicated link would traverse that link. The performance of such a dedicated link would
be measured continuously, and its contribution to the sub-path be measured continuously, and its contribution to the sub-path
metrics SHOULD be minimized as a source of error. metrics SHOULD be minimized as a source of error.
4.1.8.3. Sub-paths Have Incomplete Coverage 3.1.8.3. Sub-paths Have Incomplete Coverage
Measurements of sub-path performance may not cover all the network Measurements of sub-path performance may not cover all the network
elements on the complete path. For example, the network exchange elements on the complete path. For example, the network exchange
points might be excluded unless a cooperative measurement is points might be excluded unless a cooperative measurement is
conducted. In this example, test packets on the previous sub-path conducted. In this example, test packets on the previous sub-path
are received just before the exchange point and test packets on the are received just before the exchange point and test packets on the
next sub-path are injected just after the same exchange point. next sub-path are injected just after the same exchange point.
Clearly, the set of sub-path measurements SHOULD cover all critical Clearly, the set of sub-path measurements SHOULD cover all critical
network elements in the complete path. network elements in the complete path.
4.1.8.4. Absence of route 3.1.8.4. Absence of route
At a specific point in time, no viable route exists between the At a specific point in time, no viable route exists between the
complete path source and destination. The routes selected for one or complete path source and destination. The routes selected for one or
more sub-paths therefore differs from the complete path. more sub-paths therefore differs from the complete path.
Consequently, spatial composition may produce finite estimation of a Consequently, spatial composition may produce finite estimation of a
ground truth metric between a source and a destination, even when the ground truth metric between a source and a destination, even when the
route between them is undefined. route between them is undefined.
4.1.9. Specific cases where the conjecture might fail 3.1.9. Specific cases where the conjecture might fail
This section is unique for most metrics (see the metric-specific This section is unique for most metrics (see the metric-specific
sections). sections).
For delay-related metrics, One-way delay always depends on packet For delay-related metrics, One-way delay always depends on packet
size and link capacity, since it is measured in [RFC2679] from first size and link capacity, since it is measured in [RFC2679] from first
bit to last bit. If the size of an IP packet changes on route (due bit to last bit. If the size of an IP packet changes on route (due
to encapsulation), this can influence delay performance. However, to encapsulation), this can influence delay performance. However,
the main error source may be the additional processing associated the main error source may be the additional processing associated
with encapsulation and encryption/decryption if not experienced or with encapsulation and encryption/decryption if not experienced or
skipping to change at page 12, line 5 skipping to change at page 11, line 30
metrics require all fragments to arrive before proceeding, and metrics require all fragments to arrive before proceeding, and
fragmented complete path performance is likely to be different from fragmented complete path performance is likely to be different from
performance with non-fragmented packets and composed metrics based on performance with non-fragmented packets and composed metrics based on
non-fragmented sub-path measurements. non-fragmented sub-path measurements.
Highly manipulated routing can cause measurement error if not Highly manipulated routing can cause measurement error if not
expected and compensated. For example, policy-based MPLS routing expected and compensated. For example, policy-based MPLS routing
could modify the class of service for the sub-paths and complete could modify the class of service for the sub-paths and complete
path. path.
4.1.10. Application of Measurement Methodology 3.1.10. Application of Measurement Methodology
The methodology: The methodology:
SHOULD use similar packets sent and collected separately in each sub- SHOULD use similar packets sent and collected separately in each sub-
path, where "similar" in this case means that the Type-P contains as path, where "similar" in this case means that the Type-P contains as
many equal attributes as possible, while recognizing that there will many equal attributes as possible, while recognizing that there will
be differences. Note that Type-P includes stream characteristics be differences. Note that Type-P includes stream characteristics
(e.g., Poisson, Periodic). (e.g., Poisson, Periodic).
Allows a degree of flexibility regarding test stream generation Allows a degree of flexibility regarding test stream generation
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but the lack of control over the source, timing and correlation of but the lack of control over the source, timing and correlation of
passive measurements is much more challenging). passive measurements is much more challenging).
Poisson and/or Periodic streams are RECOMMENDED. Poisson and/or Periodic streams are RECOMMENDED.
Applies to both Inter-domain and Intra-domain composition. Applies to both Inter-domain and Intra-domain composition.
SHOULD have synchronized measurement time intervals in all sub-paths, SHOULD have synchronized measurement time intervals in all sub-paths,
but largely overlapping intervals MAY suffice. but largely overlapping intervals MAY suffice.
REQUIRES assumption of sub-path independence w.r.t. the metric being Assumption of sub-path independence w.r.t. the metric being defined/
defined/composed. composed is REQUIRED.
5. One-way Delay Composed Metrics and Statistics 4. One-way Delay Composed Metrics and Statistics
5.1. Name: Type-P-Finite-One-way-Delay-Poisson/Periodic-Stream 4.1. Name: Type-P-Finite-One-way-Delay-Poisson/Periodic-Stream
This metric is a necessary element of Delay Composition metrics, and This metric is a necessary element of Delay Composition metrics, and
its definition does not formally exist elsewhere in IPPM literature. its definition does not formally exist elsewhere in IPPM literature.
5.1.1. Metric Parameters 4.1.1. Metric Parameters
See the common parameters section above. See the common parameters section above.
5.1.2. Definition and Metric Units 4.1.2. 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
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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
The units of measure for this metric are time in seconds, expressed The units of measure for this metric are time in seconds, expressed
in sufficiently low resolution to convey meaningful quantitative in sufficiently low resolution to convey meaningful quantitative
information. For example, resolution of microseconds is usually information. For example, resolution of microseconds is usually
sufficient. sufficient.
5.1.3. Discussion and other details 4.1.3. 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] .
5.1.4. Statistic: 4.1.4. Statistic:
All statistics defined in [RFC2679] are applicable to the finite one- All statistics defined in [RFC2679] are applicable to the finite one-
way delay,and additional metrics are possible, such as the mean (see way delay,and additional metrics are possible, such as the mean (see
below). below).
5.2. Name: Type-P-Finite-Composite-One-way-Delay-Mean 4.2. Name: Type-P-Finite-Composite-One-way-Delay-Mean
This section describes a statistic based on the Type-P-Finite-One- This section describes a statistic based on the Type-P-Finite-One-
way-Delay-Poisson/Periodic-Stream metric. way-Delay-Poisson/Periodic-Stream metric.
5.2.1. Metric Parameters 4.2.1. Metric Parameters
See the common parameters section above. See the common parameters section above.
5.2.2. Definition and Metric Units of the Mean Statistic 4.2.2. Definition and Metric Units of the Mean Statistic
We define We define
Type-P-Finite-One-way-Delay-Mean = Type-P-Finite-One-way-Delay-Mean =
N N
--- ---
1 \ 1 \
MeanDelay = - * > (FiniteDelay [n]) MeanDelay = - * > (FiniteDelay [n])
N / N /
--- ---
n = 1 n = 1
where all packets n= 1 through N have finite singleton delays. where all packets n= 1 through N have finite singleton delays.
skipping to change at page 14, line 20 skipping to change at page 13, line 40
--- ---
n = 1 n = 1
where all packets n= 1 through N have finite singleton delays. where all packets n= 1 through N have finite singleton delays.
The units of measure for this metric are time in seconds, expressed The units of measure for this metric are time in seconds, expressed
in sufficiently fine resolution to convey meaningful quantitative in sufficiently fine resolution to convey meaningful quantitative
information. For example, resolution of microseconds is usually information. For example, resolution of microseconds is usually
sufficient. sufficient.
5.2.3. Discussion and other details 4.2.3. Discussion and other details
The Type-P-Finite-One-way-Delay-Mean metric requires the conditional The Type-P-Finite-One-way-Delay-Mean metric requires the conditional
delay distribution described in section 5.1. delay distribution described in section 5.1.
5.2.4. Statistic: 4.2.4. Statistic:
This metric, a mean, does not require additional statistics. This metric, a mean, does not require additional statistics.
5.2.5. Composition Function: Sum of Means 4.2.5. Composition Function: Sum of Means
The Type-P-Finite--Composite-One-way-Delay-Mean, or CompMeanDelay, The Type-P-Finite--Composite-One-way-Delay-Mean, or CompMeanDelay,
for the complete Source to Destination path can be calculated from for the complete Source to Destination path can be calculated from
sum of the Mean Delays of all its S constituent sub-paths. sum of the Mean Delays of all its S constituent sub-paths.
Then the Then the
Type-P-Finite-Composite-One-way-Delay-Mean = Type-P-Finite-Composite-One-way-Delay-Mean =
S S
--- ---
\ \
CompMeanDelay = > (MeanDelay [s]) CompMeanDelay = > (MeanDelay [s])
/ /
--- ---
s = 1 s = 1
where sub-paths s = 1 to S are involved in the complete path. where sub-paths s = 1 to S are involved in the complete path.
5.2.6. Statement of Conjecture and Assumptions 4.2.6. Statement of Conjecture and Assumptions
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
ground truth mean of the delay distribution (and the distributions ground truth mean of the delay distribution (and the distributions
themselves are sufficiently independent), such that the means may be themselves are sufficiently independent), such that the means may be
added to produce an estimate of the complete path mean delay. added to produce an estimate of the complete path mean delay.
It is assumed that the one-way delay distributions of the sub-paths It is assumed that the one-way delay distributions of the sub-paths
and the complete path are continuous. The mean of multi-modal and the complete path are continuous. The mean of multi-modal
distributions have the unfortunate property that such a value may distributions have the unfortunate property that such a value may
never occur. never occur.
5.2.7. Justification of the Composition Function 4.2.7. Justification of the Composition Function
See the common section. See the common section.
5.2.8. Sources of Deviation from the Ground Truth 4.2.8. Sources of Deviation from the Ground Truth
See the common section. See the common section.
5.2.9. Specific cases where the conjecture might fail 4.2.9. Specific cases where the conjecture might fail
If any of the sub-path distributions are multi-modal, then the If any of the sub-path distributions are multi-modal, then the
measured means may not be stable, and in this case the mean will not measured means may not be stable, and in this case the mean will not
be a particularly useful statistic when describing the delay be a particularly useful statistic when describing the delay
distribution of the complete path. distribution of the complete path.
The mean may not be sufficiently robust statistic to produce a The mean may not be a 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.
If a link contributing non-negligible delay is erroneously included If a link contributing non-negligible delay is erroneously included
or excluded, the composition will be in error. or excluded, the composition will be in error.
5.2.10. Application of Measurement Methodology 4.2.10. Application of Measurement Methodology
The requirements of the common section apply here as well. The requirements of the common section apply here as well.
5.3. Name: Type-P-Finite-Composite-One-way-Delay-Minimum 4.3. Name: Type-P-Finite-Composite-One-way-Delay-Minimum
This section describes is a statistic based on the Type-P-Finite-One- This section describes is a statistic based on the Type-P-Finite-One-
way-Delay-Poisson/Periodic-Stream metric, and the composed metric way-Delay-Poisson/Periodic-Stream metric, and the composed metric
based on that statistic. based on that statistic.
5.3.1. Metric Parameters 4.3.1. Metric Parameters
See the common parameters section above. See the common parameters section above.
5.3.2. Definition and Metric Units of the Minimum Statistic 4.3.2. Definition and Metric Units of the Minimum Statistic
We define We define
Type-P-Finite-One-way-Delay-Minimum = Type-P-Finite-One-way-Delay-Minimum =
= MinDelay = (FiniteDelay [j]) = MinDelay = (FiniteDelay [j])
such that for some index, j, where 1<= j <= N such that for some index, j, where 1<= j <= N
FiniteDelay[j] <= FiniteDelay[n] for all n FiniteDelay[j] <= FiniteDelay[n] for all n
where all packets n = 1 through N have finite singleton delays. where all packets n = 1 through N have finite singleton delays.
The units of measure for this metric are time in seconds, expressed The units of measure for this metric are time in seconds, expressed
in sufficiently fine resolution to convey meaningful quantitative in sufficiently fine resolution to convey meaningful quantitative
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such that for some index, j, where 1<= j <= N such that for some index, j, where 1<= j <= N
FiniteDelay[j] <= FiniteDelay[n] for all n FiniteDelay[j] <= FiniteDelay[n] for all n
where all packets n = 1 through N have finite singleton delays. where all packets n = 1 through N have finite singleton delays.
The units of measure for this metric are time in seconds, expressed The units of measure for this metric are time in seconds, expressed
in sufficiently fine resolution to convey meaningful quantitative in sufficiently fine resolution to convey meaningful quantitative
information. For example, resolution of microseconds is usually information. For example, resolution of microseconds is usually
sufficient. sufficient.
5.3.3. Discussion and other details 4.3.3. Discussion and other details
The Type-P-Finite-One-way-Delay-Minimum metric requires the The Type-P-Finite-One-way-Delay-Minimum metric requires the
conditional delay distribution described in section 5.1.3. conditional delay distribution described in section 5.1.3.
5.3.4. Statistic: 4.3.4. Statistic:
This metric, a minimum, does not require additional statistics. This metric, a minimum, does not require additional statistics.
5.3.5. Composition Function: Sum of Minima 4.3.5. Composition Function: Sum of Minima
The Type-P-Finite--Composite-One-way-Delay-Minimum, or CompMinDelay, The Type-P-Finite--Composite-One-way-Delay-Minimum, or CompMinDelay,
for the complete Source to Destination path can be calculated from for the complete Source to Destination path can be calculated from
sum of the Minimum Delays of all its S constituent sub-paths. sum of the Minimum Delays of all its S constituent sub-paths.
Then the Then the
Type-P-Finite-Composite-One-way-Delay-Minimum = Type-P-Finite-Composite-One-way-Delay-Minimum =
S S
--- ---
\ \
CompMinDelay = > (MinDelay [s]) CompMinDelay = > (MinDelay [s])
/ /
--- ---
s = 1 s = 1
5.3.6. Statement of Conjecture and Assumptions 4.3.6. Statement of Conjecture and Assumptions
The minimum of a sufficiently large stream of packets measured on The minimum of a sufficiently large stream of packets measured on
each sub-path during the interval [T, Tf] will be representative of each sub-path during the interval [T, Tf] will be representative of
the ground truth minimum of the delay distribution (and the the ground truth minimum of the delay distribution (and the
distributions themselves are sufficiently independent), such that the distributions themselves are sufficiently independent), such that the
minima may be added to produce an estimate of the complete path minima may be added to produce an estimate of the complete path
minimum delay. minimum delay.
It is assumed that the one-way delay distributions of the sub-paths It is assumed that the one-way delay distributions of the sub-paths
and the complete path are continuous. and the complete path are continuous.
5.3.7. Justification of the Composition Function 4.3.7. Justification of the Composition Function
See the common section. See the common section.
5.3.8. Sources of Deviation from the Ground Truth 4.3.8. Sources of Deviation from the Ground Truth
See the common section. See the common section.
5.3.9. Specific cases where the conjecture might fail 4.3.9. Specific cases where the conjecture might fail
If the routing on any of the sub-paths is not stable, then the If the routing on any of the sub-paths is not stable, then the
measured minimum may not be stable. In this case the composite measured minimum may not be stable. In this case the composite
minimum would tend to produce an estimate for the complete path that minimum would tend to produce an estimate for the complete path that
may be too low for the current path. may be too low for the current path.
5.3.10. Application of Measurement Methodology 4.3.10. Application of Measurement Methodology
The requirements of the common section apply here as well. The requirements of the common section apply here as well.
6. Loss Metrics and Statistics 5. Loss Metrics and Statistics
6.1. Type-P-Composite-One-way-Packet-Loss-Empirical-Probability
6.1.1. Metric Parameters: 5.1. Type-P-Composite-One-way-Packet-Loss-Empirical-Probability
5.1.1. Metric Parameters:
Same as section 4.1.1. Same as section 4.1.1.
6.1.2. Definition and Metric Units 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-
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.
6.1.3. Discussion and other details 5.1.3. Discussion and other details
6.1.4. Statistic: Type-P-One-way-Packet-Loss-Empirical-Probability
5.1.4. Statistic: Type-P-One-way-Packet-Loss-Empirical-Probability
Given the stream parameter M, the number of packets sent, we can Given the stream parameter M, the number of packets sent, we can
define the Empirical Probability of Loss Statistic (Ep), consistent define the Empirical Probability of Loss Statistic (Ep), consistent
with Average Loss in [RFC2680], as follows: with Average Loss in [RFC2680], as follows:
Type-P-One-way-Packet-Loss-Empirical-Probability = Type-P-One-way-Packet-Loss-Empirical-Probability =
M M
--- ---
1 \ 1 \
Ep = - * > (L[m]) Ep = - * > (L[m])
M / M /
--- ---
m = 1 m = 1
where all packets m = 1 through M have a value for L. where all packets m = 1 through M have a value for L.
6.1.5. Composition Function: Composition of Empirical Probabilities 5.1.5. 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-Composite-One-way-Packet-Loss-Empirical-Probability = Type-P-Composite-One-way-Packet-Loss-Empirical-Probability =
CompEp = 1 - {(1 - Ep1) x (1 - Ep2) x (1 - Ep3) x ... x (1 - EpS)} CompEp = 1 - {(1 - Ep1) x (1 - Ep2) x (1 - Ep3) x ... x (1 - EpS)}
If any Eps is undefined in a particular measurement interval, If any Eps is undefined in a particular measurement interval,
possibly because a measurement system failed to report a value, then possibly because a measurement system failed to report a value, then
any CompEp that uses sub-path s for that measurement interval is any CompEp that uses sub-path s for that measurement interval is
undefined. undefined.
6.1.6. Statement of Conjecture and Assumptions 5.1.6. Statement of Conjecture and Assumptions
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 ground truth empirical loss Tf] will be representative of the ground truth empirical loss
probability (and the probabilities themselves are sufficiently probability (and the probabilities themselves are sufficiently
independent), such that the sub-path probabilities may be combined to independent), such that the sub-path probabilities may be combined to
produce an estimate of the complete path empirical loss probability. produce an estimate of the complete path empirical loss probability.
6.1.7. Justification of the Composition Function 5.1.7. Justification of the Composition Function
See the common section. See the common section.
6.1.8. Sources of Deviation from the Ground Truth 5.1.8. Sources of Deviation from the Ground Truth
See the common section. See the common section.
6.1.9. Specific cases where the conjecture might fail 5.1.9. 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 catastrophic event like a fire in a physical route, then a single catastrophic event like a fire in a
tunnel could cause an outage or congestion on remaining paths in tunnel could cause an outage or congestion on remaining paths in
multiple networks. Here it is important to ensure that measurements multiple networks. Here it is important to ensure that measurements
before the event and after the event are not combined to estimate the before the event and after the event are not combined to estimate the
composite performance. composite 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 borne worm, loss due to queue overflow in one network may help
network to carry its traffic without loss. another network to carry its traffic without loss.
6.1.10. Application of Measurement Methodology 5.1.10. Application of Measurement Methodology
See the common section. See the common section.
7. Delay Variation Metrics and Statistics 6. Delay Variation Metrics and Statistics
7.1. Name: Type-P-One-way-pdv-refmin-Poisson/Periodic-Stream 6.1. Name: Type-P-One-way-pdv-refmin-Poisson/Periodic-Stream
This packet delay variation (PDV) metric is a necessary element of This packet delay variation (PDV) metric is a necessary element of
Composed Delay Variation metrics, and its definition does not Composed Delay Variation metrics, and its definition does not
formally exist elsewhere in IPPM literature. formally exist elsewhere in IPPM literature.
7.1.1. Metric Parameters: 6.1.1. 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)
(measurement point at the source) (measurement point at the source)
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 20, line 20 skipping to change at page 19, line 36
multiple packets have equal minimum Type-P-Finite-One-way-Delay multiple packets have equal minimum Type-P-Finite-One-way-Delay
values, then the value for the earliest arriving packet SHOULD be values, then the value for the earliest arriving packet SHOULD be
used. used.
o MinDelay, the Type-P-Finite-One-way-Delay value for F(min_delay o MinDelay, the Type-P-Finite-One-way-Delay value for F(min_delay
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(current packet) criteria. F(current packet) criteria.
7.1.2. Definition and Metric Units 6.1.2. Definition and Metric Units
Using the definition above in section 5.1.2, we obtain the value of Using the definition above in section 5.1.2, we obtain the value of
Type-P-Finite-One-way-Delay-Poisson/Periodic-Stream[n], the singleton Type-P-Finite-One-way-Delay-Poisson/Periodic-Stream[n], 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[n] that meets the F(first packet) criteria given For each packet[n] that meets the F(first packet) criteria given
above: Type-P-One-way-pdv-refmin-Poisson/Periodic-Stream[n] = above: Type-P-One-way-pdv-refmin-Poisson/Periodic-Stream[n] =
PDV[n] = FiniteDelay[n] - MinDelay PDV[n] = FiniteDelay[n] - MinDelay
where PDV[i] is in units of time in seconds, expressed in where PDV[i] is in units of time in seconds, expressed in
sufficiently fine resolution to convey meaningful quantitative sufficiently fine resolution to convey meaningful quantitative
information. For example, resolution of microseconds is usually information. For example, resolution of microseconds is usually
sufficient. sufficient.
7.1.3. Discussion and other details 6.1.3. 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].
7.1.4. Statistics: Mean, Variance, Skewness, Quanitle 6.1.4. Statistics: Mean, Variance, Skewness, Quantile
We define the mean PDV as follows (where all packets n = 1 through N We define the mean PDV as follows (where all packets n = 1 through N
have a value for PDV[n]): have a value for PDV[n]):
Type-P-One-way-pdv-refmin-Mean = MeanPDV = Type-P-One-way-pdv-refmin-Mean = MeanPDV =
N N
--- ---
1 \ 1 \
- * > (PDV[n]) - * > (PDV[n])
N / N /
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--- ---
n = 1 n = 1
----------------------------------- -----------------------------------
/ \ / \
| ( 3/2 ) | | ( 3/2 ) |
\ (N - 1) * VarPDV / \ (N - 1) * VarPDV /
We define the Quantile of the PDVRefMin sample as the value where the We define the Quantile of the PDVRefMin sample as the value where the
specified fraction of singletons is less than the given value. specified fraction of singletons is less than the given value.
7.1.5. Composition Functions: 6.1.5. Composition Functions:
This section gives two alternative composition functions. The This section gives two alternative composition functions. The
objective is to estimate a quantile of the complete path delay objective is to estimate a quantile of the complete path delay
variation distribution. The composed quantile will be estimated variation distribution. The composed quantile will be estimated
using information from the sub-path delay variation distributions. using information from the sub-path delay variation distributions.
7.1.5.1. Approximate Convolution 6.1.5.1. Approximate Convolution
The Type-P-Finite-One-way-Delay-Poisson/Periodic-Stream samples from The Type-P-Finite-One-way-Delay-Poisson/Periodic-Stream samples from
each sub-path are summarized as a histogram with 1 ms bins each sub-path are summarized as a histogram with 1 ms bins
representing the one-way delay distribution. representing the one-way delay distribution.
From [Stats], the distribution of the sum of independent random From [Stats], the distribution of the sum of independent random
variables can be derived using the relation: variables can be derived using the relation:
Type-P-Composite-One-way-pdv-refmin-quantile-a = Type-P-Composite-One-way-pdv-refmin-quantile-a =
/ / / /
skipping to change at page 22, line 28 skipping to change at page 22, line 5
z y z y
where X, Y, and Z are random variables representing the delay where X, Y, and Z are random variables representing the delay
variation distributions of the sub-paths of the complete path (in variation distributions of the sub-paths of the complete path (in
this case, there are three sub-paths), and a is the quantile of this case, there are three sub-paths), and a is the quantile of
interest. Note dy and dz indicate partial integration here.This interest. Note dy and dz indicate partial integration here.This
relation can be used to compose a quantile of interest for the relation can be used to compose a quantile of interest for the
complete path from the sub-path delay distributions. The histograms complete path from the sub-path delay distributions. The histograms
with 1 ms bins are discrete approximations of the delay with 1 ms bins are discrete approximations of the delay
distributions. distributions.
7.1.5.2. Normal Power Approximation 6.1.5.2. Normal Power Approximation
Type-P-One-way-Composite-pdv-refmin-NPA for the complete Source to Type-P-One-way-Composite-pdv-refmin-NPA for the complete Source to
Destination path can be calculated by combining statistics of all the Destination path can be calculated by combining statistics of all the
constituent sub-paths in the process described in [Y.1541] clause 8 constituent sub-paths in the process described in [Y.1541] clause 8
and Appendix X. and Appendix X.
7.1.6. Statement of Conjecture and Assumptions 6.1.6. Statement of Conjecture and Assumptions
The delay distribution of a sufficiently large stream of packets The delay distribution of a sufficiently large stream of packets
measured on each sub-path during the interval [T, Tf] will be measured on each sub-path during the interval [T, Tf] will be
sufficiently stationary and the sub-path distributions themselves are sufficiently stationary and the sub-path distributions themselves are
sufficiently independent, so that summary information describing the sufficiently independent, so that summary information describing the
sub-path distributions can be combined to estimate the delay sub-path distributions can be combined to estimate the delay
distribution of complete path. distribution of complete path.
It is assumed that the one-way delay distributions of the sub-paths It is assumed that the one-way delay distributions of the sub-paths
and the complete path are continuous. and the complete path are continuous.
7.1.7. Justification of the Composition Function 6.1.7. Justification of the Composition Function
See the common section. See the common section.
7.1.8. Sources of Deviation from the Ground Truth 6.1.8. Sources of Deviation from the Ground Truth
In addition to the common deviations, a few additional sources exist In addition to the common deviations, a few additional sources exist
here. For one, very tight distributions with range on the order of a here. For one, very tight distributions with range on the order of a
few milliseconds are not accurately represented by a histogram with 1 few milliseconds are not accurately represented by a histogram with 1
ms bins. This size was chosen assuming an implicit requirement on ms bins. This size was chosen assuming an implicit requirement on
accuracy: errors of a few milliseconds are acceptable when assessing accuracy: errors of a few milliseconds are acceptable when assessing
a composed distribution quantile. a composed distribution quantile.
Also, summary statistics cannot describe the subtleties of an Also, summary statistics cannot describe the subtleties of an
empirical distribution exactly, especially when the distribution is empirical distribution exactly, especially when the distribution is
very different from a classical form. Any procedure that uses these very different from a classical form. Any procedure that uses these
statistics alone may incur error. statistics alone may incur error.
7.1.9. Specific cases where the conjecture might fail 6.1.9. Specific cases where the conjecture might fail
If the delay distributions of the sub-paths are somehow correlated, If the delay distributions of the sub-paths are somehow correlated,
then neither of these composition functions will be reliable then neither of these composition functions will be reliable
estimators of the complete path distribution. estimators of the complete path distribution.
In practice, sub-path delay distributions with extreme outliers have In practice, sub-path delay distributions with extreme outliers have
increased the error of the composed metric estimate. increased the error of the composed metric estimate.
7.1.10. Application of Measurement Methodology 6.1.10. Application of Measurement Methodology
See the common section. See the common section.
8. Security Considerations 7. Security Considerations
8.1. Denial of Service Attacks 7.1. 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 the
destination and/or the intervening network(s). destination and/or the intervening network(s).
Administrators of source, destination, and the intervening network(s) Administrators of source, destination, and the intervening network(s)
should establish bilateral or multi-lateral agreements regarding the should establish bilateral or multi-lateral agreements regarding the
timing, size, and frequency of collection of sample metrics. Use of timing, size, and frequency of collection of sample metrics. Use of
this method in excess of the terms agreed between the participants this method in excess of the terms agreed between the participants
may be cause for immediate rejection or discard of packets or other may be cause for immediate rejection or discard of packets or other
escalation procedures defined between the affected parties. escalation procedures defined between the affected parties.
8.2. User Data Confidentiality 7.2. User Data Confidentiality
Active use of this method generates packets for a sample, rather than Active use of this method generates packets for a sample, rather than
taking samples based on user data, and does not threaten user data taking samples based on user data, and does not threaten user data
confidentiality. Passive measurement must restrict attention to the confidentiality. Passive measurement must restrict attention to the
headers of interest. Since user payloads may be temporarily stored headers of interest. Since user payloads may be temporarily stored
for length analysis, suitable precautions MUST be taken to keep this for length analysis, suitable precautions MUST be taken to keep this
information safe and confidential. In most cases, a hashing function information safe and confidential. In most cases, a hashing function
will produce a value suitable for payload comparisons. will produce a value suitable for payload comparisons.
8.3. Interference with the metrics 7.3. Interference with the metrics
It may be possible to identify that a certain packet or stream of It may be possible to identify that a certain packet or stream of
packets is part of a sample. With that knowledge at the destination packets is part of a sample. With that knowledge at the destination
and/or the intervening networks, it is possible to change the and/or the intervening networks, it is possible to change the
processing of the packets (e.g. increasing or decreasing delay) that processing of the packets (e.g. increasing or decreasing delay) that
may distort the measured performance. It may also be possible to may distort the measured performance. It may also be possible to
generate additional packets that appear to be part of the sample generate additional packets that appear to be part of the sample
metric. These additional packets are likely to perturb the results metric. These additional packets are likely to perturb the results
of the sample measurement. of the sample measurement.
To discourage the kind of interference mentioned above, packet To discourage the kind of interference mentioned above, packet
interference checks, such as cryptographic hash, may be used. interference checks, such as cryptographic hash, may be used.
9. IANA Considerations 8. IANA Considerations
Metrics defined in IETF are typically registered in the IANA IPPM Metrics defined in IETF are typically registered in the IANA IPPM
METRICS REGISTRY as described in initial version of the registry METRICS REGISTRY as described in initial version of the registry
[RFC4148]. However, areas for improvement of this registry have been [RFC4148].
identified, and the registry structure has to be revisited when there
is consensus to do so.
Therefore, the metrics in this draft may be considered for IANA is asked to register the following metrics in the IANA-IPPM-
registration in the future, and no IANA Action is requested at this METRICS-REGISTRY-MIB:
time.
10. Acknowlegements ietfFiniteOneWayDelayStream OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Finite-One-way-Delay-Stream"
REFERENCE
"Reference "RFCyyyy, section 5.1."
-- RFC Ed.: replace yyyy with actual RFC number & remove this
note
::= { ianaIppmMetrics nn } -- IANA assigns nn
ietfFiniteOneWayDelayMean OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Finite-One-way-Delay-Mean"
REFERENCE
"Reference "RFCyyyy, section 5.2."
-- RFC Ed.: replace yyyy with actual RFC number & remove this
note
::= { ianaIppmMetrics nn } -- IANA assigns nn
ietfCompositeOneWayDelayMean OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Finite-Composite-One-way-Delay-Mean"
REFERENCE
"Reference "RFCyyyy, section 5.2.5."
-- RFC Ed.: replace yyyy with actual RFC number & remove this
note
::= { ianaIppmMetrics nn } -- IANA assigns nn
ietfFiniteOneWayDelayMinimum OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Finite-One-way-Delay-Minimum"
REFERENCE
"Reference "RFCyyyy, section 5.3.2."
-- RFC Ed.: replace yyyy with actual RFC number & remove this
note
::= { ianaIppmMetrics nn } -- IANA assigns nn
ietfCompositeOneWayDelayMinimum OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Finite-Composite-One-way-Delay-Minimum"
REFERENCE
"Reference "RFCyyyy, section 5.3.5."
-- RFC Ed.: replace yyyy with actual RFC number & remove this
note
::= { ianaIppmMetrics nn } -- IANA assigns nn
ietfOneWayPktLossEmpiricProb OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-Packet-Loss-Empirical-Probability"
REFERENCE
"Reference "RFCyyyy, section 6.1.4."
-- RFC Ed.: replace yyyy with actual RFC number & remove this
note
::= { ianaIppmMetrics nn } -- IANA assigns nn
ietfCompositeOneWayPktLossEmpiricProb OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Composite-One-way-Packet-Loss-Empirical-Probability"
REFERENCE
"Reference "RFCyyyy, section 6.1.5."
-- RFC Ed.: replace yyyy with actual RFC number & remove this
note
::= { ianaIppmMetrics nn } -- IANA assigns nn
ietfOneWayPdvRefminStream OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-pdv-refmin-Stream"
REFERENCE
"Reference "RFCyyyy, section 7.1."
-- RFC Ed.: replace yyyy with actual RFC number & remove this
note
::= { ianaIppmMetrics nn } -- IANA assigns nn
ietfOneWayPdvRefminMean OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-pdv-refmin-Mean"
REFERENCE
"Reference "RFCyyyy, section 7.1.4."
-- RFC Ed.: replace yyyy with actual RFC number & remove this
note
::= { ianaIppmMetrics nn } -- IANA assigns nn
ietfOneWayPdvRefminVariance OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-pdv-refmin-Variance"
REFERENCE
"Reference "RFCyyyy, section 7.1.4."
-- RFC Ed.: replace yyyy with actual RFC number & remove this
note
::= { ianaIppmMetrics nn } -- IANA assigns nn
ietfOneWayPdvRefminSkewness OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-pdv-refmin-Skewness"
REFERENCE
"Reference "RFCyyyy, section 7.1.4."
-- RFC Ed.: replace yyyy with actual RFC number & remove this
note
::= { ianaIppmMetrics nn } -- IANA assigns nn
ietfCompositeOneWayPdvRefminQtil OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Composite-One-way-pdv-refmin-quantile-a"
REFERENCE
"Reference "RFCyyyy, section 7.1.5.1."
-- RFC Ed.: replace yyyy with actual RFC number & remove this
note
::= { ianaIppmMetrics nn } -- IANA assigns nn
ietfCompositeOneWayPdvRefminNPA OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-Composite-pdv-refmin-NPA"
REFERENCE
"Reference "RFCyyyy, section 7.1.5.2."
-- RFC Ed.: replace yyyy with actual RFC number & remove this
note
::= { ianaIppmMetrics nn } -- IANA assigns nn
9. Contributors and Acknowledgements
The following people have contributed useful ideas, suggestions, or
the text of sections that have been incorporated into this memo:
- Phil Chimento <vze275m9@verizon.net>
- Reza Fardid <RFardid@cariden.com>
- Roman Krzanowski <roman.krzanowski@verizon.com>
- Maurizio Molina <maurizio.molina@dante.org.uk>
- Lei Liang <L.Liang@surrey.ac.uk>
- Dave Hoeflin <dhoeflin@att.com>
A long time ago, in a galaxy far, far away (Minneapolis), Will Leland A long time ago, in a galaxy far, far away (Minneapolis), Will Leland
suggested the simple and elegant Type-P-Finite-One-way-Delay concept. suggested the simple and elegant Type-P-Finite-One-way-Delay concept.
Thanks Will. Thanks Will.
Yaakov Stein and Donald McLachlan also provided useful comments along Yaakov Stein and Donald McLachlan also provided useful comments along
the way. the way.
11. References 10. References
11.1. Normative References
10.1. Normative References
[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 25, line 33 skipping to change at page 28, line 29
[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.
[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.
[RFC5835] Morton, A. and S. Van den Berghe, "Framework for Metric [RFC5835] Morton, A. and S. Van den Berghe, "Framework for Metric
Composition", RFC 5835, April 2010. Composition", RFC 5835, April 2010.
11.2. Informative References 10.2. Informative References
[RFC5474] Duffield, N., Chiou, D., Claise, B., Greenberg, A., [RFC5474] Duffield, N., Chiou, D., Claise, B., Greenberg, A.,
Grossglauser, M., and J. Rexford, "A Framework for Packet Grossglauser, M., and J. Rexford, "A Framework for Packet
Selection and Reporting", RFC 5474, March 2009. Selection and Reporting", RFC 5474, March 2009.
[RFC5644] Stephan, E., Liang, L., and A. Morton, "IP Performance [RFC5644] Stephan, E., Liang, L., and A. Morton, "IP Performance
Metrics (IPPM): Spatial and Multicast", RFC 5644, Metrics (IPPM): Spatial and Multicast", RFC 5644,
October 2009. October 2009.
[Stats] McGraw-Hill NY NY, "Introduction to the Theory of [Stats] McGraw-Hill NY NY, "Introduction to the Theory of
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