Network Working Group V. Raisanen
Request for Comments: 3432 Nokia
Category: Standards Track G. Grotefeld
Motorola
A. Morton
AT&T Labs
November 2002

Network performance measurement with periodic streams

Status of this Memo

This document specifies an Internet standards track protocol for the
Internet community, and requests discussion and suggestions for
improvements. Please refer to the current edition of the "Internet
Official Protocol Standards" (STD 1) for the standardization state
and status of this protocol. Distribution of this memo is unlimited.

Copyright Notice

Copyright (C) The Internet Society (2002). All Rights Reserved.

Abstract

This memo describes a periodic sampling method and relevant metrics
for assessing the performance of IP networks. First, the memo
motivates periodic sampling and addresses the question of its value
as an alternative to the Poisson sampling described in RFC2330. The
benefits include applicability to active and passive measurements,
simulation of constant bit rate (CBR) traffic (typical of multimedia
communication, or nearly CBR, as found with voice activity
detection), and several instances in which analysis can be
simplified. The sampling method avoids predictability by mandating
random start times and finite length tests. Following descriptions
of the sampling method and sample metric parameters, measurement
methods and errors are discussed. Finally, we give additional
information on periodic measurements, including security
considerations.

Table of Contents

1. Conventions used in this document........................... 2
2. IntrodUCtion................................................ 3
2.1 Motivation.............................................. 3
3. Periodic Sampling Methodology............................... 4
4. Sample metrics for periodic streams......................... 5
4.1 Metric name............................................. 5
4.2 Metric parameters....................................... 5
4.3 High level description of the procedure to collect a
sample.................................................. 7
4.4 Discussion.............................................. 8
4.5 Additional Methodology ASPects.......................... 9
4.6 Errors and uncertainties................................ 9
4.7 Reporting............................................... 13
5. Additional discussion on periodic sampling.................. 14
5.1 Measurement applications................................ 15
5.2 Statistics calculable from one sample................... 18
5.3 Statistics calculable from multiple samples............. 18
5.4 Background conditions................................... 19
5.5 Considerations related to delay......................... 19
6. Security Considerations..................................... 19
6.1 Denial of Service Attacks............................... 19
6.2 User data confidentiality............................... 20
6.3 Interference with the metric............................ 20
7. IANA Considerations......................................... 20
8. Normative References........................................ 20
9. Informative References...................................... 21
10. Acknowledgments............................................. 21
11. Author's Addresses.......................................... 22
12. Full Copyright Statement.................................... 23

1. Conventions used in this document

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 BCP 14, RFC2119 [2].
Although RFC2119 was written with protocols in mind, the key words
are used in this document for similar reasons. They are used to
ensure that the results of measurements from two different
implementations are comparable, and to note instances in which an
implementation could perturb the network.

2. Introduction

This memo describes a sampling method and performance metrics
relevant to certain applications of IP networks. The original driver
for this work was Quality of Service of interactive periodic streams,
such as multimedia conferencing over IP, but the idea of periodic
sampling and measurement has wider applicability. Interactive
multimedia traffic is used as an example below to illustrate the
concept.

Transmitting equally sized packets (or mostly same-size packets)
through a network at regular intervals simulates a constant bit-rate
(CBR), or a nearly CBR multimedia bit stream. Hereafter, these
packets are called periodic streams. Cases of "mostly same-size
packets" may be found in applications that have multiple coding
methods (e.g. digitally coded comfort noise during silence gaps in
speech).

In the following sections, a sampling methodology and metrics are
presented for periodic streams. The measurement results may be used
in derivative metrics such as average and maximum delays. The memo
seeks to formalize periodic stream measurements to achieve comparable
results between independent implementations.

2.1 Motivation

As noted in the IPPM framework RFC2330 [3], a sample metric using
regularly spaced singleton tests has some limitations when considered
from a general measurement point of view: only part of the network
performance spectrum is sampled. However, some applications also
sample this limited performance spectrum and their performance may be
of critical interest.

Periodic sampling is useful for the following reasons:

* It is applicable to passive measurement, as well as active
measurement.

* An active measurement can be configured to match the
characteristics of media flows, and simplifies the estimation of
application performance.

* Measurements of many network impairments (e.g., delay variation,
consecutive loss, reordering) are sensitive to the sampling
frequency. When the impairments themselves are time-varying (and
the variations are somewhat rare, yet important), a constant
sampling frequency simplifies analysis.

* Frequency Domain analysis is simplified when the samples are
equally spaced.

Simulation of CBR flows with periodic streams encourages dense
sampling of network performance, since typical multimedia flows have
10 to 100 packets in each second. Dense sampling permits the
characterization of network phenomena with short duration.

3. Periodic Sampling Methodology

The Framework RFC[3] points out the following potential problems
with Periodic Sampling:

1. The performance sampled may be synchronized with some other
periodic behavior, or the samples may be anticipated and the
results manipulated. Unpredictable sampling is preferred.

2. Active measurements can cause congestion, and periodic sampling
might drive congestion-aware senders into a synchronized state,
producing atypical results.

Poisson sampling produces an unbiased sample for the various IP
performance metrics, yet there are situations where alternative
sampling methods are advantageous (as discussed under Motivation).

We can prescribe periodic sampling methods that address the problems
listed above. Predictability and some forms of synchronization can
be mitigated through the use of random start times and limited stream
duration over a test interval. The periodic sampling parameters
produce bias, and judicious selection can produce a known bias of
interest. The total traffic generated by this or any sampling method
should be limited to avoid adverse affects on non-test traffic
(packet size, packet rate, and sample duration and frequency should
all be considered).

The configuration parameters of periodic sampling are:
+ T, the beginning of a time interval where a periodic sample is
desired.
+ dT, the duration of the interval for allowed sample start times.
+ T0, a time that MUST be selected at random from the interval
[T, T+dT] to start generating packets and taking measurements.
+ Tf, a time, greater than T0, for stopping generation of packets
for a sample (Tf may be relative to T0 if desired).
+ incT, the nominal duration of inter-packet interval, first bit to
first bit.

T0 may be drawn from a uniform distribution, or T0 = T + Unif(0,dT).
Other distributions may also be appropriate. Start times in
successive time intervals MUST use an independent value drawn from
the distribution. In passive measurement, the arrival of user media
flows may have sufficient randomness, or a randomized start time of
the measurement during a flow may be needed to meet this requirement.

When a mix of packet sizes is desired, passive measurements usually
possess the sequence and statistics of sizes in actual use, while
active measurements would need to reproduce the intended distribution
of sizes.

4. Sample metrics for periodic streams

The sample metric presented here is similar to the sample metric
Type-P-One-way-Delay-Poisson-Stream presented in RFC2679[4].
Singletons defined in [3] and [4] are applicable here.

4.1 Metric name

Type-P-One-way-Delay-Periodic-Stream

4.2 Metric parameters

4.2.1 Global metric parameters

These parameters apply in the following sub-sections (4.2.2, 4.2.3,
and 4.2.4).

Parameters that each Singleton usually includes:
+ Src, the IP address of a host
+ Dst, the IP address of a host
+ IPV, the IP version (IPv4/IPv6) used in the measurement
+ dTloss, a time interval, the maximum waiting time for a packet
before declaring it lost.
+ packet size p(j), the desired number of bytes in the Type-P
packet, where j is the size index.

Optional parameters:
+ PktType, any additional qualifiers (transport address)
+ Tcons, a time interval for consolidating parameters collected at
the measurement points.

While a number of applications will use one packet size (j = 1),
other applications may use packets of different sizes (j > 1).
Especially in cases of congestion, it may be useful to use packets
smaller than the maximum or predominant size of packets in the
periodic stream.

A topology where Src and Dst are separate from the measurement points
is assumed.

4.2.2 Parameters collected at the measurement point MP(Src)

Parameters that each Singleton usually includes:
+ Tstamp(Src)[i], for each packet [i], the time of the packet as
measured at MP(Src)

Additional parameters:
+ PktID(Src) [i], for each packet [i], a unique identification or
sequence number.
+ PktSi(Src) [i], for each packet [i], the actual packet size.

Some applications may use packets of different sizes, either because
of application requirements or in response to IP performance
eXPerienced.

4.2.3 Parameters collected at the measurement point MP(Dst)

+ Tstamp(Dst)[i], for each packet [i], the time of the packet as
measured at MP(Dst)
+ PktID(Dst) [i], for each packet [i], a unique identification or
sequence number.
+ PktSi(Dst) [i], for each packet [i], the actual packet size.

Optional parameters:
+ dTstop, a time interval, used to add to time Tf to determine when
to stop collecting metrics for a sample
+ PktStatus [i], for each packet [i], the status of the packet
received. Possible status includes OK, packet header corrupt,
packet payload corrupt, duplicate, fragment. The criteria to
determine the status MUST be specified, if used.

4.2.4 Sample Metrics resulting from combining parameters at MP(Src)
and MP(Dst)

Using the parameters above, a delay singleton would be calculated as
follows:

+ Delay [i], for each packet [i], the time interval
Delay[i] = Tstamp(Dst)[i] - Tstamp(Src)[i]

For the following conditions, it will not be possible to compute
delay singletons:

Spurious: There will be no Tstamp(Src)[i] time
Not received: There will be no Tstamp (Dst) [i]
Corrupt packet header: There will be no Tstamp (Dst) [i]
Duplicate: Only the first non-corrupt copy of the packet
received at Dst should have Delay [i] computed.

A sample metric for average delay is as follows

AveDelay = (1/N)Sum(from i=1 to N, Delay[i])

assuming all packets i= 1 through N have valid singletons.

A delay variation [5] singleton can also be computed:

+ IPDV[i], for each packet [i] except the first one, delay variation
between successive packets would be calculated as

IPDV[i] = Delay[i] - Delay [i-1]

IPDV[i] may be negative, zero, or positive. Delay singletons for
packets i and i-1 must be calculable or IPDV[i] is undefined.

An example metric for the IPDV sample is the range:

RangeIPDV = max(IPDV[]) - min(IPDV[])

4.3 High level description of the procedure to collect a sample

Beginning on or after time T0, Type-P packets are generated by Src
and sent to Dst until time Tf is reached with a nominal interval
between the first bit of successive packets of incT, as measured at
MP(Src). incT may be nominal due to a number of reasons: variation
in packet generation at Src, clock issues (see section 4.6), etc.
MP(Src) records the parameters above only for packets with timestamps
between and including T0 and Tf having the required Src, Dst, and any
other qualifiers. MP (Dst) also records for packets with time stamps
between T0 and (Tf + dTstop).

Optionally at a time Tf + Tcons (but eventually in all cases), the
data from MP(Src) and MP(Dst) are consolidated to derive the sample
metric results. To prevent stopping data collection too soon, dTcons
should be greater than or equal to dTstop. Conversely, to keep data
collection reasonably efficient, dTstop should be some reasonable
time interval (seconds/minutes/hours), even if dTloss is infinite or
extremely long.

4.4 Discussion

This sampling methodology is intended to quantify the delays and the
delay variation as experienced by multimedia streams of an
application. Due to the definitions of these metrics, packet loss
status is also recorded. The nominal interval between packets
assesses network performance variations on a specific time scale.

There are a number of factors that should be taken into account when
collecting a sample metric of Type-P-One-way-Delay-Periodic-Stream.

+ The interval T0 to Tf should be specified to cover a long enough
time interval to represent a reasonable use of the application
under test, yet not excessively long in the same context (e.g.
phone calls last longer than 100ms, but less than one week).

+ The nominal interval between packets (incT) and the packet size(s)
(p(j)) should not define an equivalent bit rate that exceeds the
capacity of the egress port of Src, the ingress port of Dst, or
the capacity of the intervening network(s), if known. There may
be exceptional cases to test the response of the application to
overload conditions in the transport networks, but these cases
should be strictly controlled.

+ Real delay values will be positive. Therefore, it does not make
sense to report a negative value as a real delay. However, an
individual zero or negative delay value might be useful as part of
a stream when trying to discover a distribution of the delay
errors.

+ Depending on measurement topology, delay values may be as low as
100 usec to 10 msec, whereby it may be important for Src and Dst
to synchronize very closely. GPS systems afford one way to
achieve synchronization to within several 10s of usec. Ordinary
application of NTP may allow synchronization to within several
msec, but this depends on the stability and symmetry of delay
properties among the NTP agents used, and this delay is what we
are trying to measure.

+ A given methodology will have to include a way to determine
whether a packet was lost or whether delay is merely very large
(and the packet is yet to arrive at Dst). The global metric
parameter dTloss defines a time interval such that delays larger
than dTloss are interpreted as losses. {Comment: For many
applications, the treatment of a large delay as infinite/loss will
be inconsequential. A TCP data packet, for example, that arrives
only after several multiples of the usual RTT may as well have
been lost.}

4.5 Additional Methodology Aspects

As with other Type-P-* metrics, the detailed methodology will depend
on the Type-P (e.g., protocol number, UDP/TCP port number, size,
precedence).

4.6 Errors and uncertainties

The description of any specific measurement method should include an
accounting and analysis of various sources of error or uncertainty.
The Framework RFC[3] provides general guidance on this point, but we
note here the following specifics related to periodic streams and
delay metrics:

+ Error due to variation of incT. The reasons for this can be
uneven process scheduling, possibly due to CPU load.

+ Errors or uncertainties due to uncertainties in the clocks of the
MP(Src) and MP(Dst) measurement points.

+ Errors or uncertainties due to the difference between 'wire time'
and 'host time'.

4.6.1. Errors or uncertainties related to Clocks

The uncertainty in a measurement of one-way delay is related, in
part, to uncertainties in the clocks of MP(Src) and MP(Dst). In the
following, we refer to the clock used to measure when the packet was
measured at MP(Src) as the MP(Src) clock and we refer to the clock
used to measure when the packet was received at MP(Dst) as the
MP(Dst) clock. Alluding to the notions of synchronization, accuracy,
resolution, and skew, we note the following:

+ Any error in the synchronization between the MP(Src) clock and the
MP(Dst) clock will contribute to error in the delay measurement.
We say that the MP(Src) clock and the MP(Dst) clock have a
synchronization error of Tsynch if the MP(Src) clock is Tsynch
ahead of the MP(Dst) clock. Thus, if we know the value of Tsynch
exactly, we could correct for clock synchronization by adding
Tsynch to the uncorrected value of Tstamp(Dst)[i] - Tstamp(Src)
[i].

+ The resolution of a clock adds to uncertainty about any time
measured with it. Thus, if the MP(Src) clock has a resolution of
10 msec, then this adds 10 msec of uncertainty to any time value
measured with it. We will denote the resolution of the source
clock and the MP(Dst) clock as ResMP(Src) and ResMP(Dst),
respectively.

+ The skew of a clock is not so much an additional issue as it is a
realization of the fact that Tsynch is itself a function of time.
Thus, if we attempt to measure or to bound Tsynch, this
measurement or calculation must be repeated periodically. Over
some periods of time, this function can be approximated as a
linear function plus some higher order terms; in these cases, one
option is to use knowledge of the linear component to correct the
clock. Using this correction, the residual Tsynch is made
smaller, but remains a source of uncertainty that must be
accounted for. We use the function Esynch(t) to denote an upper
bound on the uncertainty in synchronization. Thus, Tsynch(t) <=
Esynch(t).

Taking these items together, we note that naive computation
Tstamp(Dst)[i] - Tstamp(Src) [i] will be off by Tsynch(t) +/-
(ResMP(SRc) + ResMP(Dst)). Using the notion of Esynch(t), we note
that these clock-related problems introduce a total uncertainty of
Esynch(t)+ Rsource + Rdest. This estimate of total clock-related
uncertainty should be included in the error/uncertainty analysis of
any measurement implementation.

4.6.2. Errors or uncertainties related to wire time vs host time

We would like to measure the time between when a packet is measured
and time-stamped at MP(Src) and when it arrives and is time-stamped
at MP(Dst); we refer to these as "wire times." However, if
timestamps are applied by software on Src and Dst, then this software
can only directly measure the time between when Src generates the
packet just prior to sending the test packet and when Dst has started
to process the packet after having received the test packet; we refer
to these two points as "host times".

To the extent that the difference between wire time and host time is
accurately known, this knowledge can be used to correct for wire time
measurements. The corrected value more accurately estimates the
desired (host time) metric, and visa-versa.

To the extent, however, that the difference between wire time and
host time is uncertain, this uncertainty must be accounted for in an
analysis of a given measurement method. We denote by Hsource an
upper bound on the uncertainty in the difference between wire time of
MP(Src) and host time on the Src host, and similarly define Hdest for
the difference between the host time on the Dst host and the wire
time of MP(Dst). We then note that these problems introduce a total
uncertainty of Hsource+Hdest. This estimate of total wire-vs-host
uncertainty should be included in the error/uncertainty analysis of
any measurement implementation.

4.6.3. Calibration

Generally, the measured values can be decomposed as follows:

measured value = true value + systematic error + random error

If the systematic error (the constant bias in measured values) can be
determined, it can be compensated for in the reported results.

reported value = measured value - systematic error

therefore

reported value = true value + random error

The goal of calibration is to determine the systematic and random
error generated by the instruments themselves in as much detail as
possible. At a minimum, a bound ("e") should be found such that the
reported value is in the range (true value - e) to (true value + e)
at least 95 percent of the time. We call "e" the calibration error
for the measurements. It represents the degree to which the values
produced by the measurement instrument are repeatable; that is, how
closely an actual delay of 30 ms is reported as 30 ms. {Comment: 95
percent was chosen due to reasons discussed in [4], briefly
summarized as (1) some confidence level is desirable to be able to
remove outliers, which will be found in measuring any physical
property; (2) a particular confidence level should be specified so
that the results of independent implementations can be compared.}

From the discussion in the previous two sections, the error in
measurements could be bounded by determining all the individual
uncertainties, and adding them together to form:

Esynch(t) + ResMP(Src) + ResMP(Dst) + Hsource + Hdest

However, reasonable bounds on both the clock-related uncertainty
captured by the first three terms and the host-related uncertainty
captured by the last two terms should be possible by careful design
techniques and calibrating the instruments using a known, isolated,
network in a lab.

For example, the clock-related uncertainties are greatly reduced
through the use of a GPS time source. The sum of Esynch(t) +
ResMP(Src) + ResMP(Dst) is small, and is also bounded for the
duration of the measurement because of the global time source. The
host-related uncertainties, Hsource + Hdest, could be bounded by

connecting two instruments back-to-back with a high-speed serial link
or isolated LAN segment. In this case, repeated measurements are
measuring the same one-way delay.

If the test packets are small, such a network connection has a
minimal delay that may be approximated by zero. The measured delay
therefore contains only systematic and random error in the
instrumentation. The "average value" of repeated measurements is the
systematic error, and the variation is the random error. One way to
compute the systematic error, and the random error, to a 95%
confidence, is to repeat the experiment many times - at least
hundreds of tests. The systematic error would then be the median.
The random error could then be found by removing the systematic error
from the measured values. The 95% confidence interval would be the
range from the 2.5th percentile to the 97.5th percentile of these
deviations from the true value. The calibration error "e" could then
be taken to be the largest absolute value of these two numbers, plus
the clock-related uncertainty. {Comment: as described, this bound is
relatively loose since the uncertainties are added, and the absolute
value of the largest deviation is used. As long as the resulting
value is not a significant fraction of the measured values, it is a
reasonable bound. If the resulting value is a significant fraction
of the measured values, then more exact methods will be needed to
compute the calibration error.}

Note that random error is a function of measurement load. For
example, if many paths will be measured by one instrument, this might
increase interrupts, process scheduling, and disk I/O (for example,
recording the measurements), all of which may increase the random
error in measured singletons. Therefore, in addition to minimal load
measurements to find the systematic error, calibration measurements
should be performed with the same measurement load that the
instruments will see in the field.

We wish to reiterate that this statistical treatment refers to the
calibration of the instrument; it is used to "calibrate the meter
stick" and say how well the meter stick reflects reality.

4.6.4 Errors in incT

The nominal interval between packets, incT, can vary during either
active or passive measurements. In passive measurement, packet
headers may include a timestamp applied prior to most of the protocol
stack, and the actual sending time may vary due to processor
scheduling. For example, H.323 systems are required to have packets
ready for the network stack within 5 ms of their ideal time. There
may be additional variation from the network between the Src and the

MP(Src). Active measurement systems may encounter similar errors,
but to a lesser extent. These errors must be accounted for in some
types of analysis.

4.7 Reporting

The calibration and context in which the method is used MUST be
carefully considered, and SHOULD always be reported along with metric
results. We next present five items to consider: the Type-P of test
packets, the threshold of delay equivalent to loss, error
calibration, the path traversed by the test packets, and background
conditions at Src, Dst, and the intervening networks during a sample.
This list is not exhaustive; any additional information that could be
useful in interpreting applications of the metrics should also be
reported.

4.7.1. Type-P

As noted in the Framework document [3], the value of a metric may
depend on the type of IP packets used to make the measurement, or
"type-P". The value of Type-P-One-way-Periodic-Delay could change if
the protocol (UDP or TCP), port number, size, or arrangement for
special treatment (e.g., IP precedence or RSVP) changes. The exact
Type-P used to make the measurements MUST be reported.

4.7.2. Threshold for delay equivalent to loss

In addition, the threshold for delay equivalent to loss (or
methodology to determine this threshold) MUST be reported.

4.7.3. Calibration results

+ If the systematic error can be determined, it SHOULD be removed
from the measured values.
+ You SHOULD also report the calibration error, e, such that the
true value is the reported value plus or minus e, with 95%
confidence (see the last section.)
+ If possible, the conditions under which a test packet with finite
delay is reported as lost due to resource exhaustion on the
measurement instrument SHOULD be reported.

4.7.4. Path

The path traversed by the packets SHOULD be reported, if possible.
In general, it is impractical to know the precise path a given packet
takes through the network. The precise path may be known for certain
Type-P packets on short or stable paths. If Type-P includes the
record route (or loose-source route) option in the IP header, and the

path is short enough, and all routers on the path support record (or
loose-source) route, then the path will be precisely recorded.

This may be impractical because the route must be short enough. Many
routers do not support (or are not configured for) record route, and
use of this feature would often artificially worsen the performance
observed by removing the packet from common-case processing.

However, partial information is still valuable context. For example,
if a host can choose between two links (and hence two separate routes
from Src to Dst), then the initial link used is valuable context.
{Comment: For example, with one commercial setup, a Src on one NAP
can reach a Dst on another NAP by either of several different
backbone networks.}

5. Additional discussion on periodic sampling

Fig.1 illustrates measurements on multiple protocol levels that are
relevant to this memo. The user's focus is on transport quality
evaluation from the application point of view. However, to properly
separate the quality contribution of the operating system and codec
on packet voice, for example, it is beneficial to be able to measure
quality at the IP level [6]. Link layer monitoring provides a way of
accounting for link layer characteristics such as bit error rates.

---------------
application
---------------
transport <--
---------------
network <--
---------------
link <--
---------------
physical
---------------

Fig. 1: Different possibilities for performing measurements: a
protocol view. Above, "application" refers to all layers above L4
and is not used in the OSI sense.

In general, the results of measurements may be influenced by
individual application requirements/responses related to the
following issues:

+ Lost packets: Applications may have varying tolerance to lost
packets. Another consideration is the distribution of lost
packets (i.e. random or bursty).

+ Long delays: Many applications will consider packets delayed
longer than a certain value to be equivalent to lost packets (i.e.
real time applications).
+ Duplicate packets: Some applications may be perturbed if duplicate
packets are received.
+ Reordering: Some applications may be perturbed if packets arrive
out of sequence. This may be in addition to the possibility of
exceeding the "long" delay threshold as a result of being out of
sequence.
+ Corrupt packet header: Most applications will probably treat a
packet with a corrupt header as equivalent to a lost packet.
+ Corrupt packet payload: Some applications (e.g. digital voice
codecs) may accept corrupt packet payload. In some cases, the
packet payload may contain application specific forward error
correction (FEC) that can compensate for some level of corruption.
+ Spurious packet: Dst may receive spurious packets (i.e. packets
that are not sent by the Src as part of the metric). Many
applications may be perturbed by spurious packets.

Depending, e.g., on the observed protocol level, some issues listed
above may be indistinguishable from others by the application, it may
be important to preserve the distinction for the operators of Src,
Dst, and/or the intermediate network(s).

5.1 Measurement applications

This sampling method provides a way to perform measurements
irrespective of the possible QoS mechanisms utilized in the IP
network. As an example, for a QoS mechanism without hard guarantees,
measurements may be used to ascertain that the "best" class gets the
service that has been promised for the traffic class in question.
Moreover, an operator could study the quality of a cheap, low-
guarantee service implemented using possible slack bandwidth in other
classes. Such measurements could be made either in studying the
feasibility of a new service, or on a regular basis.

IP delivery service measurements have been discussed within the
International Telecommunications Union (ITU). A framework for IP
service level measurements (with references to the framework for IP
performance [3]) that is intended to be suitable for service planning
has been approved as I.380 [7]. ITU-T Recommendation I.380 covers
abstract definitions of performance metrics. This memo describes a
method that is useful, both for service planning and end-user testing
purposes, in both active and passive measurements.

Delay measurements can be one-way [3,4], paired one-way, or round-
trip [8]. Accordingly, the measurements may be performed either with
synchronized or unsynchronized Src/Dst host clocks. Different
possibilities are listed below.

The reference measurement setup for all measurement types is shown in
Fig. 2.

----------------< IP >--------------------

------- ------- -------- --------
Src MP MP Dst
------- (Src) (Dst) --------
------- --------

Fig. 2: Example measurement setup.

An example of the use of the method is a setup with a source host
(Src), a destination host (Dst), and corresponding measurement points
(MP(Src) and MP(Dst)) as shown in Figure 2. Separate equipment for
measurement points may be used if having Src and/or Dst conduct the
measurement may significantly affect the delay performance to be
measured. MP(Src) should be placed/measured close to the egress
point of packets from Src. MP(Dst) should be placed/measure close
to the ingress point of packets for Dst. "Close" is defined as a
distance sufficiently small so that application-level performance
characteristics measured (such as delay) can be expected to follow
the corresponding performance characteristic between Src and Dst to
an adequate accuracy. The basic principle here is that measurement
results between MP(Src) and MP(Dst) should be the same as for a
measurement between Src and Dst, within the general error margin
target of the measurement (e.g., < 1 ms; number of lost packets is
the same). If this is not possible, the difference between MP-MP
measurement and Src-Dst measurement should preferably be systematic.

The test setup just described fulfills two important criteria:

1) The test is made with realistic stream metrics, emulating - for
example - a full-duplex Voice over IP (VoIP) call.

2) Either one-way or round-trip characteristics may be oBTained.

It is also possible to have intermediate measurement points between
MP(Src) and MP(Dst), but that is beyond the scope of this document.

5.1.1 One way measurement

In the interests of specifying metrics that are as generally
applicable as possible, application-level measurements based on one-
way delays are used in the example metrics. The implication of
application-level measurement for bi-directional applications, such
as interactive multimedia conferencing, is discussed below.

Performing a single one-way measurement only yields information on
network behavior in one direction. Moreover, the stream at the
network transport level does not emulate accurately a full-duplex
multimedia connection.

5.1.2 Paired one way measurement

Paired one way delay refers to two multimedia streams: Src to Dst and
Dst to Src for the same Src and Dst. By way of example, for some
applications, the delay performance of each one way path is more
important than the round trip delay. This is the case for delay-
limited signals such as VoIP. Possible reasons for the difference
between one-way delays is different routing of streams from Src to
Dst vs. Dst to Src.

For example, a paired one way measurement may show that Src to Dst
has an average delay of 30ms, while Dst to Src has an average delay
of 120ms. To a round trip delay measurement, this example would look
like an average of 150ms delay. Without the knowledge of the
asymmetry, we might miss a problem that the application at either end
may have with delays averaging more than 100ms.

Moreover, paired one way delay measurement emulates a full-duplex
VoIP call more accurately than a single one-way measurement only.

5.1.3 Round trip measurement

From the point of view of periodic multimedia streams, round-trip
measurements have two advantages: they avoid the need of host clock
synchronization and they allow for a simulation of full-duplex
communication. The former aspect means that a measurement is easily
performed, since no special equipment or NTP setup is needed. The
latter property means that measurement streams are transmitted in
both directions. Thus, the measurement provides information on
quality of service as experienced by two-way applications.

The downsides of round-trip measurement are the need for more
bandwidth than a one-way test and more complex accounting of packet
loss. Moreover, the stream that is returning towards the original
sender may be more bursty than the one on the first "leg" of the

round-trip journey. The last issue, however, means in practice that
the returning stream may experience worse QoS than the out-going one,
and the performance estimates thus obtained are pessimistic ones.
The possibility of asymmetric routing and queuing must be taken into
account during an analysis of the results.

Note that with suitable arrangements, round-trip measurements may be
performed using paired one way measurements.

5.2 Statistics calculable from one sample

Some statistics may be particularly relevant to applications
simulated by periodic streams, such as the range of delay values
recorded during the sample.

For example, a sample metric generates 100 packets at MP(Src) with
the following measurements at MP(Dst):

+ 80 packets received with delay [i] <= 20 ms
+ 8 packets received with delay [i] > 20 ms
+ 5 packets received with corrupt packet headers
+ 4 packets from MP(Src) with no matching packet recorded at
MP(Dst) (effectively lost)
+ 3 packets received with corrupt packet payload and delay
[i] <= 20 ms
+ 2 packets that duplicate one of the 80 packets received correctly
as indicated in the first item

For this example, packets are considered acceptable if they are
received with less than or equal to 20ms delays and without corrupt
packet headers or packet payload. In this case, the percentage of
acceptable packets is 80/100 = 80%.

For a different application that will accept packets with corrupt
packet payload and no delay bounds (so long as the packet is
received), the percentage of acceptable packets is (80+8+3)/100 =
91%.

5.3 Statistics calculable from multiple samples

There may be value in running multiple tests using this method to
collect a "sample of samples". For example, it may be more
appropriate to simulate 1,000 two-minute VoIP calls rather than a
single 2,000 minute call. When considering a collection of multiple
samples, issues like the interval between samples (e.g. minutes,
hours), composition of samples (e.g. equal Tf-T0 duration, different

packet sizes), and network considerations (e.g. run different samples
over different intervening link-host combinations) should be taken
into account. For items like the interval between samples, the usage
pattern for the application of interest should be considered.

When computing statistics for multiple samples, more general
statistics (e.g. median, percentile, etc.) may have relevance with a
larger number of packets.

5.4 Background conditions

In many cases, the results may be influenced by conditions at Src,
Dst, and/or any intervening networks. Factors that may affect the
results include: traffic levels and/or bursts during the sample, link
and/or host failures, etc. Information about the background
conditions may only be available by external means (e.g. phone calls,
television) and may only become available days after samples are
taken.

5.5 Considerations related to delay

For interactive multimedia sessions, end-to-end delay is an important
factor. Too large a delay reduces the quality of the multimedia
session as perceived by the participants. One approach for managing
end-to-end delays on an Internet path involving heterogeneous link
layer technologies is to use per-domain delay quotas (e.g. 50 ms for
a particular IP domain). However, this scheme has clear
inefficiencies, and can over-constrain the problem of achieving some
end-to-end delay objective. A more flexible implementation ought to
address issues like the possibility of asymmetric delays on paths,
and sensitivity of an application to delay variations in a given
domain. There are several alternatives as to the delay statistic one
ought to use in managing end-to-end QoS. This question, although
very interesting, is not within the scope of this memo and is not
discussed further here.

6. Security Considerations

6.1 Denial of Service Attacks

This method generates a periodic stream of packets from one host
(Src) to another host (Dst) through intervening networks. This
method could be abused for denial of service attacks directed at Dst
and/or the intervening network(s).

Administrators of Src, Dst, and the intervening network(s) should
establish bilateral or multi-lateral agreements regarding the timing,
size, and frequency of collection of sample metrics. Use of this

method in excess of the terms agreed between the participants may be
cause for immediate rejection, discard of packets, or other
escalation procedures defined between the affected parties.

6.2 User data confidentiality

Active use of this method generates packets for a sample, rather than
taking samples based on user data, and does not threaten user data
confidentiality. Passive measurement must restrict attention to the
headers of interest. Since user payloads may be temporarily stored
for length analysis, suitable precautions MUST be taken to keep this
information safe and confidential.

6.3 Interference with the metric

It may be possible to identify that a certain packet or stream of
packets is part of a sample. With that knowledge at Dst and/or the
intervening networks, it is possible to change the processing of the
packets (e.g. increasing or decreasing delay) that may distort the
measured performance. It may also be possible to generate additional
packets that appear to be part of the sample metric. These
additional packets are likely to perturb the results of the sample
measurement.

To discourage the kind of interference mentioned above, packet
interference checks, such as cryptographic hash, MAY be used.

7. IANA Considerations

Since this method and metric do not define a protocol or well-known
values, there are no IANA considerations in this memo.

8. Normative References

[1] Bradner, S., "The Internet Standards Process -- Revision 3", BCP
9, RFC2026, October 1996.

[2] Bradner, S., "Key words for use in RFCs to Indicate Requirement
Levels", BCP 14, RFC2119, March 1997.

[3] Paxson, V., Almes, G., Mahdavi, J. and M. Mathis, "Framework for
IP Performance Metrics", RFC2330, May 1998.

[4] Almes, G., Kalidindi, S. and M. Zekauskas, "A one-way delay
metric for IPPM", RFC2679, September 1999.

[5] Demichelis, C. and P. Chimento, "IP Packet Delay Variation
Metric for IP Performance Metrics (IPPM)", RFC3393, November
2002.

9. Informative References

[6] "End-to-end Quality of Service in TIPHON systems; Part 5: Quality
of Service (QoS) measurement methodologies", ETSI TS 101 329-5
V1.1.2, January 2002.

[7] International Telecommunications Union, "Internet protocol data
communication service _ IP packet transfer and availability
performance parameters", Telecommunications Sector
Recommendation I.380 (re-numbered Y.1540), February 1999.

[8] Almes, G., Kalidindi, S. and M. Zekauskas, "A round-trip delay
metric for IPPM", RFC2681, September 1999.

10. Acknowledgments

The authors wish to thank the chairs of the IPPM WG (Matt Zekauskas
and Merike Kaeo) for comments that have made the present document
more clear and focused. Howard Stanislevic and Will Leland have also
presented useful comments and questions. We also gratefully
acknowledge Henk Uijterwaal's continued challenge to develop the
motivation for this method. The authors have built on the
substantial foundation laid by the authors of the framework for IP
performance [3].

11. Author's Addresses

Vilho Raisanen
Nokia Networks
P.O. Box 300
FIN-00045 Nokia Group
Finland

Phone: +358 7180 8000
Fax: +358 9 4376 6852
EMail: Vilho.Raisanen@nokia.com

Glenn Grotefeld
Motorola, Inc.
1501 W. Shure Drive, MS 2F1
Arlington Heights, IL 60004 USA

Phone: +1 847 435-0730
Fax: +1 847 632-6800
EMail: g.grotefeld@motorola.com

Al Morton
AT&T Labs
Room D3 - 3C06
200 Laurel Ave. South
Middletown, NJ 07748 USA

Phone: +1 732 420 1571
Fax: +1 732 368 1192
EMail: acmorton@att.com

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