Internet Engineering Task Force R. Geib, Ed.
Internet-Draft Deutsche Telekom
Intended status: Informational R. Fardid
Expires: January 7, 2010 Covad Communications
July 6, 2009
IPPM standard compliance testing
draft-geib-ippm-metrictest-00
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Abstract
This document specifies tests to determine if multiple, independent,
and interoperable implementations of a metrics specification document
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are at hand so that the metrics specification can be advanced to an
Internet standard. Results of different IPPM implementations can be
compared if they measure under the same underlying network
conditions. Results are compared using state of the art statistical
methods.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Requirements Language . . . . . . . . . . . . . . . . . . 4
2. Basic idea . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3. Verification of equivalence by statistic measurements . . . . 5
4. Recommended Metric Verification Measurement Process . . . . . 12
5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 14
6. Contributors . . . . . . . . . . . . . . . . . . . . . . . . . 14
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 15
8. Security Considerations . . . . . . . . . . . . . . . . . . . 15
9. References . . . . . . . . . . . . . . . . . . . . . . . . . . 15
9.1. Normative References . . . . . . . . . . . . . . . . . . . 15
9.2. Informative References . . . . . . . . . . . . . . . . . . 15
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 16
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1. Introduction
Draft bradner-metrictest [bradner-metrictest] states:
The Internet Standards Process RFC2026 [RFC2026] requires that for a
IETF specification to advance beyond the Proposed Standard level, at
least two genetically unrelated implementations must be shown to
interoperate correctly with all features and options. There are two
distinct reasons for this requirement.
In the case of a protocol specification, the notion of
"interoperability" is reasonably intuitive - the implementations must
successfully "talk to each other", while exercising all features and
options.
In the case of a specification for a performance metric, network
latency for example, exactly what constitutes "interoperation" is
less obvious. The IESG didn't yet decide how to judge "metric
specification interoperability" in the context of the IETF Standards
Process and this new draft suggests a methodology which (hopefully)
is suitable for IPPM metrics. General applicability of the methods
proposed in the following should however not be excluded.
A metric specification describes a method of testing and a way to
report the results of this testing. One example of such a metric
would be a way to test and report the latency that data packets would
incur while being sent from one network location to another.
Since implementations of testing metrics are by their nature stand-
alone and do not interact with each other, the level of the
interoperability called for in the IETF standards process cannot be
simply determined by seeing that the implementations interact
properly. Instead, verifying equivalence by proofing that different
implementations verifiably give statistically equivalent results
Verifiable equivalence may take the place of interoperability.
This document defines the process of verifying equivalence by using a
specified test set up to create the required separate data sets
(which may be seen as samples taken from the same underlying
distribution) and then apply state of the art statistical methods to
verify equivalence of the results. To illustrate application of the
process defined her, validating compliance with RFC2679 [RFC2679] is
picked as an example. While test set ups may vary with the metrics
to be validated, the statistical methods will not. Documents
defining test setups to validate other metrics should be created by
the IPPM WG, once the process proposed here has been agreed upon.
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1.1. Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
2. Basic idea
Two different IPPM implementations are expected to measure
statistically equivalent results, if they both measure a metric under
the same networking conditions. Formulating the measurement in
statistical terms: separate samples are collected (by separate metric
implementations) from the same underlying statistical process (the
same network conditions). The "statistical hypothesis" to be tested
is the expectation, that both samples expose statistically equivalent
properties. This requires careful test design:
o The error induced by the sample size must be small enough to
minimize its influence on the test result. This may have to be
respected, especially if two implementations measure with
different average probing rates.
o If time series are compared, the implementation with the lowest
probing frequency determines the smallest temporal interval for
which results can be compared.
o Every comparison must be repeated several times based on different
measurement data to avoid random indications of compatibility (or
the lack of it).
o The measurement test set up must be self-consistent to the largest
possible extent. This means, network conditions, paths and IPPM
metric implementations SHOULD be identical for the compared
implementations to the largest possible degree to minimize the
influence of the test and measurement set up on the result. This
includes e.g. aspects of the stability and non-ambiguity of routes
taken by the measurement packets. See RFC 2330 for a discussion
on self-consistency RFC 2330 [RFC2330].
State of the art statistical methods are proposed for a comparison of
measurement results in the hope that user friendly tools required to
perform the necessary statistical analysis are easily accessible.
[editor: this sentence may be reworded or deleted, if the expectation
doesn't hold].
Let's assume a one way delay measurement comparison between system A,
probing with a frequency of 2 probes per second and system B probing
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at a rate of 2 probes every 3 minutes. To ensure reasonable
confidence in results, sample metrics are calculated from at least 5
singletons per compared time interval. This means, sample delay
values are calculated for each system for identical 6 minute
intervals for the whole test duration. Per 6 minute interval, the
sample metric is calculated from 720 singletons for system A and from
6 singletons for system B). Note, that if outliers are not filtered,
moving averages are an option for an evaluation too. The minimum
move of an averaging interval is three minutes in our example.
The test set up for the delay measurement is chosen to minimize
errors by locating one system of each implementation at the same end
of two separate sites, between which delay is measured for the metric
test. Both measurement sites are connected by one IPSEC tunnel, so
that all measurement packets cross the Internet with the same IP
addresses. Both measurement systems measure simultaneously and the
local links are dimensioned to avoid congestion caused by the probing
traffic itself.
The measured delay values are reported with a resolution above the
measurement error and above the synchronisation error. This is done
to avoid comparing these errors between two different metric
implementations instead of comparing the IPPM metric implementation
itself.
The overall duration of the test is chosen so that more than 1000 six
minute measurement intervals are collected. The amount of data
collected allows separate comparisons for e.g. 200 consecutive 6
minute intervals. intervals, during which routes were instable, are
discarded prior to evaluation.
3. Verification of equivalence by statistic measurements
Following the definition of statistical precision [Precision], a
measurement process can be characterised by two properties:
o Accuracy, which is the degree of conformity of a measured quantity
to its actual (true) value.
o Precision, also called reproducibility or repeatability, the
degree to which repeated measurements show the same or similar
results.
Figure 1 further clarifies the difference between accuracy and
precision of a measurement.
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Probability ^
Density |
| Reference value Measured Value
| | |
| |<---Accuracy---->|
| | _|_
| | / | \
| | / | \
| | / | \
| | / | \
| | / | \
| | / | \
Measured | | /<- Precision ->\
Value -|---------|-----------------|---------->
|
Measurement accuracy and precision [Precision].
Figure 1
The Framework for IP Performance Metrics (RFC 2330, [RFC2330])
expects that a "methodology for a metric should have the property
that it is repeatable: if the methodology is used multiple times
under identical conditions, it should result in consistent
measurements." This means, an IPPM implementation is expected to
measure a metric with high precision.
Further, RFC2330 expects that a "a methodology for a given metric
exhibits continuity if, for small variations in conditions, it
results in small variations in the resulting measurements. Slightly
more precisely, for every positive epsilon, there exists a positive
delta, such that if two sets of conditions are within delta of each
other, then the resulting measurements will be within epsilon of each
other." A small variation in conditions in the context of a metric
comparison can be seen as two implementations measuring the same
metric along the same path.
Two guidelines for an IPPM conformant metric implementation can be
taken from these principles:
o A single IPPM conformant implementation MUST under otherwise
identical network conditions produce highly precise results for
repeated measurements of the same metric.
o Two different implementations measuring the same IPPM metric MUST
produce results with a rather limited difference if measuring
under to the largest extent possible identical network conditions.
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In a metric test, both conditions must hold, meaning that repeated
tests of two implementations MUST produce precise results for all
repetition intervals.
A suitable statistical test and and a level of confidence to define
whether differences are rather limited and whether a measurement is
highly precise are specified below.
RFC 2330 prefers the "empirical distribution function" EDF to
describe collections of measurements. RFC 2330 uses the EDF to test
goodness of fit of an IPPM flow's inter packet spacing to a Poisson
process. To do that, RFC 2330 uses the Anderson-Darling test with a
5% significance. RFC 2330 further determines, that "unless otherwise
stated, IPPM goodness-of-fit tests are done using 5% significance."
The principles suggested by RFC 2330 are applied to compare the
implementation of IPPM metrics as follows:
o The empirical distribution function of the singletons or samples
resulting from the measurement of a particular metric is forming
the basis of a comparison of two IPPM implementations. Note that
a parametric description of this distribution is not required.
o The hypothesis to be validated by an IPPM metric test is that two
implementations of an IPPM metric draw probes from the same
underlying distribution. The hypothesis is true, if samples of
two tested metric implementations follow the same distribution by
a significance of 95%. Note that the distribution function from
which the probes are drawn itself is irrelevant.
o The samples taken by two implementations to be tested are compared
by an Anderson-Darling k sample test. The Anderson-Darling k
sample test is the generalization of the classical Anderson-
Darling goodness of fit test, and it is used to test the
hypothesis that k independent samples belong to the same
population without specifying their common distribution function.
[Editor: I couldn't find a complete documentation of that test on
the web by a fast search, but a reference to a publication is
there and code seems to be available too. Other tests which are
documented in Wikipedia for that purpose are Kolmogorov-Smirnov
and Chi-Square. it is proposed to make Anderson Darling k sample
obligatory/a MUST if code can be appended to this draft. If not,
Anderson Darling k sample is recommended and Kolmogorov-Smirnov or
Chi Square are optional].
Getting back to the chosen example delay measurement, the captured
delays may have been captured singletons ranging from an absolute
minimum Delay Dmin to values Dmin + 5 ms. To compare distributions,
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the set of singletons of a chosen evaluation interval (e.g. the data
of one of the five 1800 minute capture sequences, see above) is
sorted for the frequency of singletons per Dmin + N * 0.5 ms (n = 1,
2, ...). After that, a comparison of the two probe sets with any of
the mentioned tests may be applied.
While constructing the example, some additional rules to calculate
and compare samples have been respected. The following two rules are
of importance for the IPPM metric tests:
o To compare different probes of a common underlying distribution in
terms of metrics characterising a communication network requires
to respect the temporal nature for which the assumption of common
underlying distribution may hold. Any singletons or samples to be
compared MUST be captured within the same time interval.
o Whenever sample metrics, samples of singletons or rates are used
to characterise measured metrics of a time-interval, at least 5
events of a relevant metric MUST be present to ensure a minimum
confidence into the reported value (see Wikipedia on confidence
[Rule of thumb]). Note that this criterion is to be respected
e.g. when comparing packet loss metrics. Any packet loss
measurement interval to be compared with the results of another
implementation needs to contain at least five lost packets to have
a minimum confidence that these losses didn't happen randomly.
o The minimum number of singletons or samples to be compared by an
Anderson-Darling test is 100 per tested metric implementation.
Note that the Anderson-Darling test detects small differences in
distributions fairly well and will fail for high number of
compared results (RFC2330 mentions an example with 8192
measurements to guarantee a failure of an Anderson-Darling test).
Comparing "Accuracy" of IPPM implementations based on averages and
variations may require prior checks for the absence of long range
dependency within the compared measurements. Large outliers as
typically occurring in the case of long range dependency, can have a
serious impact on mean values. The median or percentiles may be more
robust measures on which to compare the accuracy of different IPPM
implementations. An idea may be to consider data up to a certain
percentile, calculate the mean for data up to this percentile and
then compare the means of the two implementations. This could be
repeated for different percentiles. If long range dependencies
impact is limited to large outliers, the method may work for lower
percentiles. Whether this makes sense must be confirmed by a
statistician, so this attempt requires further study.
IPPM metrics are captured by time series. Time series can be checked
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for correlation. There are two expectations on statistical time
series properties which should be met by separate measurements
probing the same underlying network performance distribution:
o The Autocorrelation indicates, whether there are any repeating
patterns within a time series. For the purpose of this document,
it does not matter whether there is autocorrelation in a
measurement. It is however expected, that two measurements expose
the same autocorrelation on identical "lag" intervals. If
calculable, the autocorrelation lies within an interval [-1;1],
(see Wikipedia on autocorrelation [Autocorrelation]).
o The correlation coefficient "indicates the strength of a linear
relationship between two random variables." The two random
variables in the case of this document are the measurement time
series of the IPPM implementations to be compared. The
expectation is, that both are strongly correlated and the
resulting correlation coefficient is close to 1, (see Wikipedia on
correlation [Correlation]).
A metric test can derive additional statistics from time series
analysis. Further, formulation of a test hypothesis is possible for
autocorrelation and the correlation coefficient. It is however not
clear, whether an appropriate statistical test to validate the
hypothesis by 95% significance exists. Applicability of time series
analysis for a metric test requires further input from statisticians.
In the absence of any metric test on time series, any test result
SHOULD provide the autocorrelation of the compared metrics time
series by lags from 1 to 10. In addition, the value of the
correlation coefficient SHOULD be provided. Autocorrelation and
Correlation coefficient are expected to be rather close to the value
1.
As mentioned earlier, the time series analysis requires application
of identical time intervals to allow a comparison. In our delay
example, single sample delay metric values are calculated for 9
minute intervals. If 200 consecutive sample delay metrics with the
same start and end interval are available for each implementation,
autocorrelation can be calculated for different n * 9 minute lags.
The autocorrelation calculated for the time series of each
implementation should be very close to the autocorrelation of the
other implementation for the same time lag. Further, the correlation
coefficient for both time series should be close to 1.
The way to prove that two IPPM metric measurements provide compatible
results then could be performed stepwise:
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o First prove that the two compared implementations have the same
precision by comparing statistics of the distribution of
singletons (or samples) of a metric by comparing the EDF of the
samples captured by the two implementations.
o Second indicate that two compared implementations produce strongly
correlated time series of which each one individually has the same
autocorrelation as the other one.
Clock synchronization effects require special attention. Accuracy of
one-way active delay measurements for any metrics implementation
depends on clock synchronization between the source and destination
of tests. Ideally, one-way active delay measurement (RFC 2679,
[RFC2679]) test endpoints either have direct access to independent
GPS or CDMA-based time sources or indirect access to nearby NTP
primary (stratum 1) time sources, equipped with GPS receivers.
Access to these time sources may not be available at all test
locations associated with different Internet paths, for a variety of
reasons out of scope of this document.
When secondary (stratum 2 and above) time sources are used with NTP
running acrossthe same network, whose metrics are subject to
comparative implementation tests, network impairments can affect
clock synchronization, distort sample one-way values and their
interval statistics. It is RECOMMENDED to discard sample one-way
delay values for any implementation, when one of the following
reliability conditions is met:
o Delay is measured and is finite in one direction, but not the
other.
o Absolute value of the difference between the sum of one-way
measurements in both directions and round-trip measurement is
greater than X% of the latter value.
Examination of the second condition requires RTT measurement for
reference, e.g., based on TWAMP (RFC5357, RFC 5357 [RFC5357]), in
conjunction with one-way delay measurement.
Specification of X% to strike a balance between identification of
unreliable one-way delay samples and misidentification of reliable
samples under a wide range of Internet path RTTs probably requires
further study.
An IPPM compliant metric implementation whose measurement requires
synchonized clocks is however expected to provide precise measurement
results. Any IPPM metric implementation MUST be of a precision of 1
ms (+/- 500 us) with a confidence of 95% if the metric is captured
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along an Internet path which is stable and not congested during a
measurement duration of an hour or more. [Editor: this latter
definition may avoid NTP (stratum 2 or worse) synchonized IPPM
implementations from becoming IPPM compliant. However internal PC
clock synched implementations can't be rejected that way. Ideas on
criteria to deal with the latter are welcome. May drift be one, as
GPS synched implementations shouldn't have one or the same on origin
and destination, respectively].
Metric tests should be executed under conditions which are identical
to the largest possible or necessary extent. As "identical network
conditions" are fundamental to the nethodology proposed by this
document, more input and a thorough discussion is needed to define
these. Some thoughts are:
o In a laboratory environment, NTP synchronisation may have a less
serious impact. In a real network, improper synchronisation will
be harder to conceal.
o OWD measurements are of highest precision with well synchonized
measurement systems measuring delays along a stable not congested
path. Care must be taken to avoid comparing noise and the
measurement error respectively instead of the delay.
o Packet loss, delay variation and packet reordering require a
sufficient number of these events to allow for a metric test with
the desired confidence. While one could wait for congestion or
execute the test across known bottlenecks, this may incur some
effort. A question is, whether to test these metrics under
laboratory conditions. To generalise this question: can
laboratory metric tests be tolerated for metrics whose precision
doesn't depend on synchonized clocks?
o Packet loss and delay variation probably allow for a relaxed
definition of "identical test conditions", as it may be sufficient
for test packets to share the congested interface or paths to test
for these metrics.
o In a laboratory environment, "stationary" networking conditions
can be produced without having to care about parallel resources,
applied by carriers to increase capacity. In a commercial
network, hashing functions (on addresses and ports) determine
which set of resources all the packets in a flow will traverse.
Testing in the lab may not remove the parallel resources, but it
can provide some time stability that's never assured in live
network testing.
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o Applicability of tunnels to avoid the impact of unknown parallel
resources applied by networks traversed by measuremenmts packets
during a test should be investigated.
o To determine if some aspects of the metric specifications are
clear and unambiguous, some specific conditions in the lab may be
simulated to determine if implementations measure them as
expected. This it should be tested whether all implementors read
the spec the same way. Further, reducing some sources of
variation right at the start, will make the job of statistical
comparison simpler.
o Getting access to operator information like load and packet loss
counters of a network which was used during a metric test is
improbable. But testing across a real network still is desirable
for a metric test.
4. Recommended Metric Verification Measurement Process
The proposal made by the authors of bradner-metrictest
[bradner-metrictest] is picked up and slightly enhanced:
"In order to meet their obligations under the IETF Standards Process
the IESG must be convinced that each metric specification advanced to
Draft Standard or Internet Standard status is clearly written, that
there are the required multiple verifiably equivalent
implementations, and that all options have been implemented.
"In the context of this memo, metrics are designed to measure some
characteristic of a data network. An aim of any metric definition
should be that it should be specified in a way that can reliably
measure the specific characteristic in a repeatable way."
Each metric, statistic or option of those to be validated must be
compared against a reference measurement or another implementation by
at least 5 different basic data sets, each on with sufficient size to
reach the specified level of confidence.
"In the same way, sequentially running different implementations of
software that perform the tests described in the metric document on a
stable network, or simultaneously on a network that may or may not be
stable should produce essentially the same results."
Following these assumptions any recommendation for the advancement of
a metric specification needs to be accompanied by an implementation
report, as is the case with all requests for the advancement of IETF
specifications. The implementation report needs to include a
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specific plan to test the specific metrics in the RFC in lab or real-
world networks and reports of the tests performed with two or more
implementations of the software. The test plan should cover key
parts of the specification, specify the accuracy required for each
measured metric and thus define the meaning of "statistically
equivalent" for the specific metrics being tested. Ideally, the test
plan would co-evolve with the development of the metric, since that's
when people have the most context in their thinking regarding the
different subtleties that can arise.
In particular, the implementation report MUST as a minimum document:
o The metric compared and the RFC specifying it, including the
chosen options (like e.g. the implemented selection function in
the case of IPDV).
o A complete specification of the measurement stream (mean rate,
statistical distribution of packets, packet size (or mean packet
size and their distribution), DSCP and any other measurement
stream property which could result in deviating results.
Deviations in results can be caused also if chosen IP addresses
and ports of different implementations can result in different
layer 2 or layer 3 paths due to operation of Equal Cost Multi-Path
routing in an operational network
o The duration of each measurement to be used for a metric
validation, the number of measurement points collected for each
metric during each measurement interval (i.e. the probe size) and
the level of confidence derived from this probe size for each
measurement interval
o The result of the statistical tests performed for each metric
validation.
o The measurement configuration and set up
o A parameterization of laboratory conditions and applied traffic
and network conditions allowing reproduction of these laboratory
conditions for readers of the implementation report.
"All of the tests for each set MUST be run in the same direction
between the same two points on the same network. The tests SHOULD be
run simultaneously unless the network is stable enough to ensure that
the path the data takes through the network will not change between
tests."
It is RECOMMENDED to avoid effects falsifying results of real data
networks, if validation measurements are taken over them. Obviously,
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the conditions met there can't be reproduced. As the measurement
equipment compared is designed to reliable quantify real network
performance, validating metrics under real network conditions is
desirable of course.
Data networks may forward packets differently in the case of:
o Different packet sizes chosen for different metric
implementations. A proposed countermeasure is selecting the same
packet size when validating results of two samples or a sample
against an original distribution.
o Selection of differing IP addresses and ports used by different
metric implementations during metric validation tests. If ECMP is
applied on IP or MPLS level, different paths can result (note that
it may be impossible to detect an MPLS ECMP path from an IP
endpoint). A proposed counter measure is to connect the
measurement equipment to be compared by a NAT device, or
establishing a single tunnel to transport all measurement traffic
The aim is to have the same IP addresses and port for all
measurement packets or to avoid ECMP by a layer 2 tunnel.
o Different IP options.
o Different DSCP.
The test design may have to be adapted for the purpose of the
measurement. Creation of delay and delay variation probes is simple
and straightforward, also if the measurement runs acrossa real data
network. Collecting a large number of packet loss samples on a real
data network while being sure that operational conditions are stable
may not be feasible. Further discussion on test designs to verify
specific metrics may indeed be required.
5. Acknowledgements
Gerhard Hasslinger commented a first version of this document,
suggested statistical tests and the evaluation of time series
information. Henk Uijterwaal pushed this work and Mike Hamilton
reviewed the document before publication.
6. Contributors
Scott Bradner, Vern Paxson and Allison Manking drafted bradner-
metrictest [bradner-metrictest], and major parts of it are quoted in
this document. Al Morton and Scott Bradner commented this draft
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before publication.
7. IANA Considerations
This memo includes no request to IANA.
8. Security Considerations
This draft does not raise any specific security issues.
9. References
9.1. Normative References
[RFC2026] Bradner, S., "The Internet Standards Process -- Revision
3", BCP 9, RFC 2026, October 1996.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
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.
[RFC2679] Almes, G., Kalidindi, S., and M. Zekauskas, "A One-way
Delay Metric for IPPM", RFC 2679, September 1999.
9.2. Informative References
[Autocorrelation]
N., N., "Autocorrelation", December 2008.
[Correlation]
N., N., "Correlation", June 2009.
[Precision]
N., N., "Accuracy and precision", June 2009.
[RFC5357] Hedayat, K., Krzanowski, R., Morton, A., Yum, K., and J.
Babiarz, "A Two-Way Active Measurement Protocol (TWAMP)",
RFC 5357, October 2008.
[Rule of thumb]
N., N., "Confidence interval", October 2008.
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[bradner-metrictest]
Bradner, S., Mankin, A., and V. Paxson, "Advancement of
metrics specifications on the IETF Standards Track",
draft -bradner-metricstest-03, (work in progress),
July 2007.
Authors' Addresses
Ruediger Geib (editor)
Deutsche Telekom
Heinrich Hertz Str. 3-7
Darmstadt, 64295
Germany
Phone: +49 6151 628 2747
Email: Ruediger.Geib@telekom.de
Reza Fardid
Covad Communications
2510 Zanker Road
San Jose, CA 95131
USA
Phone: +1 408 434-2042
Email: RFardid@covad.com
Geib & Fardid Expires January 7, 2010 [Page 16]