draft-ietf-ippm-framework-compagg-00.txt   draft-ietf-ippm-framework-compagg-01.txt 
Network Working Group A. Morton, Ed. Network Working Group A. Morton, Ed.
Internet-Draft AT&T Labs Internet-Draft AT&T Labs
Expires: August 28, 2006 S. Van den Berghe, Ed. Expires: December 26, 2006 S. Van den Berghe, Ed.
Ghent University - IBBT Ghent University - IBBT
February 24, 2006 June 24, 2006
Framework for Metric Composition Framework for Metric Composition
draft-ietf-ippm-framework-compagg-00 draft-ietf-ippm-framework-compagg-01
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Copyright Notice Copyright Notice
Copyright (C) The Internet Society (2006). Copyright (C) The Internet Society (2006).
Abstract Abstract
This memo describes a framework for composing and aggregating metrics This memo describes a framework for composing and aggregating metrics
(both in time and in space) defined by RFC 2330 and developed by the (both in time and in space) defined by RFC 2330 and developed by the
IPPM working group. The framework describes the generic composition IPPM working group. The framework describes the generic composition
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Requirements Language Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119]. document are to be interpreted as described in RFC 2119 [RFC2119].
Table of Contents Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Purpose and Scope . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1. Reducing Measurement Overhead . . . . . . . . . . . . 3
3. Description of Metric Types . . . . . . . . . . . . . . . . . 4 1.1.2. Measurement Re-use . . . . . . . . . . . . . . . . . . 4
3.1. Time Aggregation Description . . . . . . . . . . . . . . . 4 1.1.3. Data Reduction and Consolidation . . . . . . . . . . . 4
3.2. Spatial Aggregation Description . . . . . . . . . . . . . 5 1.1.4. Implications on Measurement Design and Reporting . . . 5
3.3. Spatial Composition Description . . . . . . . . . . . . . 5 2. Purpose and Scope . . . . . . . . . . . . . . . . . . . . . . 5
3.4. Help Metrics . . . . . . . . . . . . . . . . . . . . . . . 6 3. Description of Metric Types . . . . . . . . . . . . . . . . . 5
3.5. Higher Order Composition . . . . . . . . . . . . . . . . . 6 3.1. Temporal Aggregation Description . . . . . . . . . . . . . 5
4. Requirements for Composed Metrics . . . . . . . . . . . . . . 6 3.2. Spatial Aggregation Description . . . . . . . . . . . . . 6
5. Guidelines for Defining Composed Metrics . . . . . . . . . . . 7 3.3. Spatial Composition Description . . . . . . . . . . . . . 7
5.1. Ground Truth: Comparison with other IPPM Metrics . . . . . 7 3.4. Help Metrics . . . . . . . . . . . . . . . . . . . . . . . 7
5.2. Deviation from the Ground Truth . . . . . . . . . . . . . 9 3.5. Higher Order Composition . . . . . . . . . . . . . . . . . 7
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 10 4. Requirements for Composed Metrics . . . . . . . . . . . . . . 7
7. Security Considerations . . . . . . . . . . . . . . . . . . . 10 5. Guidelines for Defining Composed Metrics . . . . . . . . . . . 9
8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 10 5.1. Ground Truth: Comparison with other IPPM Metrics . . . . . 9
9. References . . . . . . . . . . . . . . . . . . . . . . . . . . 10 5.2. Deviation from the Ground Truth . . . . . . . . . . . . . 11
9.1. Normative References . . . . . . . . . . . . . . . . . . . 10 6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 12
9.2. Informative References . . . . . . . . . . . . . . . . . . 11 7. Security Considerations . . . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 12 8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 12
Intellectual Property and Copyright Statements . . . . . . . . . . 13 9. References . . . . . . . . . . . . . . . . . . . . . . . . . . 12
9.1. Normative References . . . . . . . . . . . . . . . . . . . 12
9.2. Informative References . . . . . . . . . . . . . . . . . . 13
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 14
Intellectual Property and Copyright Statements . . . . . . . . . . 15
1. Introduction 1. Introduction
The IPPM framework RFC 2330 [RFC2330] describes two forms of metric The IPPM framework RFC 2330 [RFC2330] describes two forms of metric
composition, spatial and temporal. Also, the text suggests that the composition, spatial and temporal. Also, the text suggests that the
concepts of the analytical framework (or A-frame) would help to concepts of the analytical framework (or A-frame) would help to
develop useful relationships to derive the composed metrics from real develop useful relationships to derive the composed metrics from real
metrics. The effectiveness of composed metrics is dependent on their metrics. The effectiveness of composed metrics is dependent on their
usefulness in analysis and applicability to practical measurement usefulness in analysis and applicability to practical measurement
circumstances. circumstances.
This memo expands on the notion of composition, and provides a This memo expands on the notion of composition, and provides a
detailed framework for several classes of metrics that were mentioned detailed framework for several classes of metrics that were mentioned
in the original IPPM framework. The classes include temporal in the original IPPM framework. The classes include temporal
aggregation, spatial aggregation, and spatial composition. aggregation, spatial aggregation, and spatial composition.
1.1. Motivation 1.1. Motivation
The deployment of a measurement infrastructure and the collection of Network operators have deployed measurement systems to serve many
elementary measurements are not enough to understand and keep under purposes, including performance monitoring, maintenance support,
control the network's behaviour. Network measurements need in network engineering, and customer reporting. The collection of
general to be post-processed to be useful for the several tasks of elementary measurements alone is not enough to understand a network's
network engineering and management. The first step of this post behaviour. In general, measurements need to be post-processed to
processing is often a process of "composition" of single measurements present the most relevant information for each purpose. The first
or measurement sets into other ones. The reasons for doing so are step is often a process of "composition" of single measurements or
briefly introduced here. measurement sets into other forms. Composition and aggregation
present several more post-processing opportunites to the network
operator, and we describe the key motivations below.
A first reason, mainly applicable to network engineering, is 1.1.1. Reducing Measurement Overhead
scaleability. Due to the number of network elements in large
networks, it is impossible to perform measurements from each element
to all others. It is necessary to select a set of links of special
interest and to perform the measurements there. Examples for this
are active measurements of one-way delay, jitter, and loss.
Another reason may be data reduction (opposite need with respect to A network's measurement possibilities scale upward with the square of
the previous one, where more data is generated). This is of interest the number of nodes. But each measurement implies overhead, in terms
for network planners and managers. Let us assume that there is of the storage for the results, the traffic on the network (assuming
network domain in which delay measurements are performed among a active methods), and the OA&M for the measurement system itself. In
subset of its elements. A network manager might ask whether there is a large network, it is impossible to perform measurements from each
a problem with the network delay in general. Therefore, it would be node to all others.
desirable to obtain a single value giving an indication of the
general network delay. This value has to be calculated from the
elementary delay measurements, but it not obvious how: for example,
it does not seem to be reasonable to average all delay measurements,
as some links (e.g. having a higher bandwidth or more important
customers) might be considered more important than others.
Moreover, metric manipulation (or "composition") can be helpful to An individual network operator should be able to organize their
provide, from raw measurement data, some tangible, well-understood measurement paths along the lines of physical topology, or routing
and agreed upon information about the services guarantees provided by areas/Autonomous Systems, and thus minimize dependencies and overlap
a network. Such information can be used in the SLA/SLS contracts between different measurement paths. This way, the sheer number of
among a Provider and its Customers Finally, another important reason measurements can be reduced, as long as the operator has a set of
for composing measurements is to perform trend analysis. For doing methods to estimate performance between any particular nodes when
so, a single value for an hour, a day or, a month is computed from needed.
the basic measurements which are scheduled e.g. every five minutes.
In doing so, trends can be more easily witnessed, like an increasing Composition and aggregation play a key role when the path of interest
usage of a backbone link which might require the installation of spans multiple networks, and where each operator conducts their own
alternative links or the rerouting of some network flows. measurements. Here, the complete path performance may be estimated
from measurements on the component parts.
Operators that take advantage of the composition and aggregation
methods recognize that the estimates may exhibit some additional
error beyond that inherent in the measurements themselves, and so
they are making a trade-off to achieve reasonable measurement system
overhead.
1.1.2. Measurement Re-use
There are many different measurement users, each bringing specific
requirements for the reporting timescale. Network managers and
maintenance forces prefer to see results presented very rapidly, to
detect problems quickly or see if their action has corrected a
problem. On the other hand, network capacity planners and even
network users sometimes prefer a long-term view of performance, for
example to check trends. How can one set of measurements serve both
needs?
The answer lies in temporal aggregation, where the short-term
measurements needed by the operations community are combined to
estimate a longer-term result for others. Also, problems with the
measurement system itself may be isolated to one or more of the
short-term measurements, rather than possibly invalidating an entire
long-term measurement if the problem was undetected.
1.1.3. Data Reduction and Consolidation
Another motivation is data reduction. Assume there is a network
domain in which delay measurements are performed among a subset of
its nodes. A network manager might ask whether there is a problem
with the network delay in general. It would be desirable to obtain a
single value that gives an indication of the overall network delay.
Spatial aggregation methods would address this need, and can produce
the desired "single figure of merit" asked for, one that may also be
useful in trend analysis.
The overall value would be calculated from the elementary delay
measurements, but it not obvious how: for example, it may not to be
reasonable to average all delay measurements, as some paths (e.g.
having a higher bandwidth or more important customers) might be
considered more critical than others.
Metric composition can help to provide, from raw measurement data,
some tangible, well-understood and agreed upon information about the
service guarantees provided by a network. Such information can be
used in the Service Level Agreement/Service Level Specification (SLA/
SLS) contracts between a service provider and its customers.
1.1.4. Implications on Measurement Design and Reporting
If a network operator can anticipate needing to aggregate or compose
overall metrics in the future, it is more efficient to start by
considering the tenants of these methods in the measurement design/
sampling plan, and reporting the results. The Summary Statistics of
certain metrics are more conducive to composition than others. This
figures prominently in the design of measurements and the results
reports.
2. Purpose and Scope 2. Purpose and Scope
The purpose of this memo is provide a common framework for the The purpose of this memo is provide a common framework for the
various classes of metrics based on composition of primary metrics. various classes of metrics based on composition of primary metrics.
The scope is limited to the definitions of metrics that are composed The scope is limited to the definitions of metrics that are composed
from primary metrics using a deterministic relationship. Key from primary metrics using a deterministic function. Key information
information about each metric, such as its assumptions under which about each metric, such as its assumptions under which the
the relationship holds, and possible sources of error/circumstances relationship holds, and possible sources of error/circumstances where
where the composition may fail, are included. the composition may fail, are included.
At this time, the scope of effort is limited to the metrics for
packet loss, delay, and delay variation. Composition of packet
reordering metrics is considered a research topic, and beyond the
scope at the time this memo was prepared.
This memo will retain the terminology of the IPPM Framework as much This memo will retain the terminology of the IPPM Framework as much
as possible, but will extend the terminology when necessary. as possible, but will extend the terminology when necessary.
3. Description of Metric Types 3. Description of Metric Types
This section defines the various classes of Composition. There are This section defines the various classes of Composition. There are
two classes more accurately referred to as aggregation over time and two classes more accurately referred to as aggregation over time and
space, and the third is simply composition in space. space, and the third is simply composition in space.
3.1. Time Aggregation Description 3.1. Temporal Aggregation Description
Firstly, aggregation in time is defined as the composition of metrics Aggregation in time is defined as the composition of metrics with the
with the same type and scope obtained in different time instants or same type and scope obtained in different time instants or time
time windows. For example, starting from a time series of One-Way windows. For example, starting from a time series of One-Way Delay
Delay measurements on a certain network path obtained in 5-minute measurements on a certain network path obtained in 5-minute periods
periods and averaging groups of 12 consecutive values, a time series and averaging groups of 12 consecutive values, we obtain a time
measurement with a coarser resolution. The main reason for doing series measurement with a coarser resolution (60 minutes). The main
time aggregation is to reduce the amount of data that has to be reason for doing time aggregation is to reduce the amount of data
stored, and make the visualization/spotting of regular cycles and/or that has to be stored, and make the visualization/spotting of regular
growing or decreasing trends easier. Another useful application is cycles and/or growing or decreasing trends easier. Another useful
to detect anomalies or abnormal changes in the network application is to detect anomalies or abnormal changes in the network
characteristics. characteristics.
Note that in RFC 2330, the term temporal composition is introduced, In RFC 2330, the term "temporal composition" is introduced and
but with a different meaning than the one given here to aggregation differs from temporal aggregation in that it refers to methodologies
in time. The temporal composition considered there refers to to predict future metrics on the basis of past observations,
methodologies to predict future metrics on the basis of past exploiting the time correlation that certain metrics can exhibit. We
observations, exploiting the time correlation that certain metrics do not consider this type of composition here.
can exhibit. We do not consider this type of composition here.
>>>>>>>>Comment: Why no forecasting? This was apparently a limit on
the Geant2 project, but may not apply here.
3.2. Spatial Aggregation Description 3.2. Spatial Aggregation Description
Aggregation in space is defined as the composition of metrics of the Aggregation in space is defined as the combination of metrics of the
same type but with different scope. This composition may involve same type and different scope, in order to estimate the overall
weighing the contributions of the several input metrics. For performance of a larger domain. This combination may involve
example, if we want to compose together the average OWD of the weighing the contributions of the input metrics.
several Origin- Destination pairs of a network domain (thus where the
inputs are already "statistics" metrics) it makes sense to weight Suppose we want to compose the average One-Way-Delay (OWD)
each metric according to the traffic carried on the relative OD pair: experienced by flows traversing all the Origin-Destination (OD) pairs
OWD_sum=f1*OWD_1+f2*OWD_2+...+fn*OWD_n where fi=load_OD_i/total_load. of a network domain (where the inputs are already metric
Another example of metric that could be "aggregated in space", is the "statistics"). Since we wish to include the effect of the traffic
maximum edge-to-edge delay across a single domain. Assume that a matrix on the result, it makes sense to weight each metric according
Service Provider wants to advertise the maximum delay that transit to the traffic carried on the corresponding OD pair:
traffic will experience while passing through his/her domain. As
there are multiple edge-to-edge paths across a domain, shown with OWD_sum=f1*OWD_1+f2*OWD_2+...+fn*OWD_n
different coloured arrows in the following figure, the Service
Provider has to either advertise a list of delays each of them where fi=load_OD_i/total_load.
corresponding to a specific edge-to-edge path, or advertise a maximum
value. The latter approach is more scalable and simplifies the A simple average OWD across all network OD pairs would not use the
advertisement of measurement information via interdomain protocols, traffic weighting.
such as BGP. Similar operations can be provided to other metrics,
e.g. "maximum edge-to-edge packet loss", etc. We suggest that space Another example metric that is "aggregated in space", is the maximum
aggregation is generally useful to obtain a summary view of the edge-to-edge delay across a single domain. Assume that a Service
behaviour of large network portions, or in general of coarser Provider wants to advertise the maximum delay that transit traffic
aggregates. The metric collection time instant, i.e. the metric will experience while passing through his/her domain. There can be
collection time window of measured metrics is not considered in space multiple edge-to-edge paths across a domain, and the Service Provider
aggregation. We assume that either it is consistent for all the chooses either to publish a list of delays (each corresponding to a
composed metrics, e.g. compose a set of average delays all referred specific edge-to-edge path), or publish a single maximum value. The
to the same time window, or the time window of each composed metric latter approach simplifies the publication of measurement
does not affect aggregated metric. information, and may be sufficient for some purposes. Similar
operations can be provided to other metrics, e.g. "maximum edge-to-
edge packet loss", etc.
We suggest that space aggregation is generally useful to obtain a
summary view of the behaviour of large network portions, or in
general of coarser aggregates. The metric collection time instant,
i.e. the metric collection time window of measured metrics is not
considered in space aggregation. We assume that either it is
consistent for all the composed metrics, e.g. compose a set of
average delays all referred to the same time window, or the time
window of each composed metric does not affect aggregated metric.
3.3. Spatial Composition Description 3.3. Spatial Composition Description
The concatenation in space is defined as the composition of metrics Concatenation in space is defined as the composition of metrics of
of same type and different (physical and non-overlapping) spatial same type and (ideally) different spatial scope, so that the
scope, so that the resulting metric is representative of what the resulting metric is representative of what the metric would be if
metric would be if directly obtained with a direct measurement over obtained with a direct measurement over the sequence of the several
the sequence of the several spatial scopes. An example is the sum of spatial scopes. An example is the sum of OWDs of different edge-to-
OWDs of different edge-to- edge domain's delays, where the edge domain's delays, where the intermediate edge points are close to
intermediate edge points are close to each other or happen to be the each other or happen to be the same. In this way, we can for example
same. In this way, we can for example estimate OWD_AC starting from estimate OWD_AC starting from the knowledge of OWD_AB and OWD_BC.
the knowledge of OWD_AB and OWD_BC. Note that there may be small gaps in measurement coverage, likewise
there may be small overlaps (e.g., the link where test equipment
connects to the network).
Different from aggregation in space, all path's portions contribute One key difference from examples of aggregation in space is that all
equally to the composed metric, independent of the traffic load sub-paths contribute equally to the composed metric, independent of
present. the traffic load present.
3.4. Help Metrics 3.4. Help Metrics
Finally, note that in practice there is often the need of extracting Finally, note that in practice there is often the need of extracting
a new metric making some computation over one or more metrics with a new metric making some computation over one or more metrics with
the same spatial and time scope. For example, the composed metric the same spatial and time scope. For example, the composed metric
rtt_sample_variance may be composed from two different metrics: the rtt_sample_variance may be composed from two different metrics: the
help metric rtt_square_sum and the statistical metric rtt_sum. This help metric rtt_square_sum and the statistical metric rtt_sum. This
operation is however more a simple calculation and not an aggregation operation is however more a simple calculation and not an aggregation
or a concatenation, and we'll not investigate it further in this or a concatenation, and we'll not investigate it further in this
document. memo.
3.5. Higher Order Composition 3.5. Higher Order Composition
Composed metrics might themselves be subject to further concatenation Composed metrics might themselves be subject to further steps of
or aggregation. An example would be a maximal domain obtained composition or aggregation. An example would be a the delay of a
through the spatial composition of end-to-end delays, that are maximal domain obtained through the spatial composition of several
themselves obtained through spatial composition. All requirements composed end-to-end delays (obtained through spatial composition).
for first order composition metrics apply to higher order All requirements for first order composition metrics apply to higher
composition. order composition.
4. Requirements for Composed Metrics >>>>> Comment Response: are more examples needed here?
4. Requirements for Composed Metrics
The definitions for all composed metrics MUST include sections to The definitions for all composed metrics MUST include sections to
treat the following topics. treat the following topics.
The description of each metric will clearly state: The description of each metric will clearly state:
1. the definition (and statistic, where appropriate); 1. the definition (and statistic, where appropriate);
2. the composition or aggregation relationship; 2. the composition or aggregation relationship;
3. the specific conjecture on which the relationship is based; 3. the specific conjecture on which the relationship is based;
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5. Guidelines for Defining Composed Metrics 5. Guidelines for Defining Composed Metrics
5.1. Ground Truth: Comparison with other IPPM Metrics 5.1. Ground Truth: Comparison with other IPPM Metrics
Figure 1 illustrates the process to derive a metric using spatial Figure 1 illustrates the process to derive a metric using spatial
composition, and compares the composed metric to other IPPM metrics. composition, and compares the composed metric to other IPPM metrics.
Metrics <M1, M2, M3> describe the performance of sub-paths between Metrics <M1, M2, M3> describe the performance of sub-paths between
the Source and Destination of interest during time interval <T, Tf>. the Source and Destination of interest during time interval <T, Tf>.
These metrics are the inputs for a Composition Relationship that These metrics are the inputs for a Composition Function that produces
produces a Composed Metric. a Composed Metric.
Sub-Path Metrics Sub-Path Metrics
++ M1 ++ ++ M2 ++ ++ M3 ++ ++ M1 ++ ++ M2 ++ ++ M3 ++
Src ||.......|| ||.......|| ||.......|| Dst Src ||.......|| ||.......|| ||.......|| Dst
++ `. ++ ++ | ++ ++ .' ++ ++ `. ++ ++ | ++ ++ .' ++
`. | .-' `. | .-'
`-. | .' `-. | .'
`._..|.._.' `._..|.._.'
,-' `-. ,-' `-.
,' `. ,' `.
| Composition | | Composition |
\ Relationship ' \ Function '
`._ _,' `._ _,'
`--.....--' `--.....--'
| |
++ | ++ ++ | ++
Src ||...............................|| Dst Src ||...............................|| Dst
++ Composed Metric ++ ++ Composed Metric ++
++ Complete Path Metric ++ ++ Complete Path Metric ++
Src ||...............................|| Dst Src ||...............................|| Dst
++ ++ ++ ++
skipping to change at page 10, line 35 skipping to change at page 12, line 35
Note to RFC Editor: this section may be removed on publication as an Note to RFC Editor: this section may be removed on publication as an
RFC. RFC.
7. Security Considerations 7. Security Considerations
8. Acknowledgements 8. Acknowledgements
The authors would like to thank Maurizio Molina, Andy Van Maele, The authors would like to thank Maurizio Molina, Andy Van Maele,
Andreas Haneman, Igor Velimirovic, Andreas Solberg, Athanassios Andreas Haneman, Igor Velimirovic, Andreas Solberg, Athanassios
Liakopulos, David Schitz, Nicolas Simar and the Geant2 Project. We Liakopulos, David Schitz, Nicolas Simar and the Geant2 Project. We
also acknowledge comments and suggestions from Emile Stephan and Lei also acknowledge comments and suggestions from Phil Chimento, Emile
Liang. Stephan and Lei Liang.
9. References 9. References
9.1. Normative References 9.1. Normative References
[I-D.ietf-ippm-multimetrics] [I-D.ietf-ippm-multimetrics]
Stephan, E., "IP Performance Metrics (IPPM) for spatial Stephan, E., "IP Performance Metrics (IPPM) for spatial
and multicast", draft-ietf-ippm-multimetrics-00 (work in and multicast", draft-ietf-ippm-multimetrics-00 (work in
progress), January 2006. progress), January 2006.
[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,
"Framework for IP Performance Metrics", RFC 2330,
May 1998.
9.2. Informative References 9.2. Informative References
Authors' Addresses Authors' Addresses
Al Morton (editor) Al Morton (editor)
AT&T Labs AT&T Labs
200 Laurel Avenue South 200 Laurel Avenue South
Middletown,, NJ 07748 Middletown,, NJ 07748
USA USA
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