Network Working Group                                          A. Morton
Internet-Draft                                           G. Ramachandran
Intended status: Informational                               G. Maguluri
Expires: July 12, 2009                                         AT&T Labs
                                                         January 8, 2009

              Reporting Metrics: Different Points of View

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   Consumers of IP network performance metrics have many different uses

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   in mind.  This memo categorizes the different audience points of
   view.  It describes how the categories affect the selection of metric
   parameters and options when seeking info that serves their needs.
   The memo then proceeds to discuss "long-term" reporting
   considerations (e.g, days, weeks or months, as opposed to 10

Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   document are to be interpreted as described in RFC 2119 [RFC2119].

Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  3
   2.  Purpose and Scope  . . . . . . . . . . . . . . . . . . . . . .  3
   3.  Effect of POV on the Loss Metric . . . . . . . . . . . . . . .  4
     3.1.  Loss Threshold . . . . . . . . . . . . . . . . . . . . . .  4
       3.1.1.  Network Characterization . . . . . . . . . . . . . . .  4
       3.1.2.  Application Performance  . . . . . . . . . . . . . . .  6
     3.2.  Errored Packet Designation . . . . . . . . . . . . . . . .  6
     3.3.  Causes of Lost Packets . . . . . . . . . . . . . . . . . .  6
     3.4.  Summary for Loss . . . . . . . . . . . . . . . . . . . . .  7
   4.  Effect of POV on the Delay Metric  . . . . . . . . . . . . . .  7
     4.1.  Treatment of Lost Packets  . . . . . . . . . . . . . . . .  7
       4.1.1.  Application Performance  . . . . . . . . . . . . . . .  7
       4.1.2.  Network Characterization . . . . . . . . . . . . . . .  8
       4.1.3.  Delay Variation  . . . . . . . . . . . . . . . . . . .  9
       4.1.4.  Reordering . . . . . . . . . . . . . . . . . . . . . . 10
     4.2.  Preferred Statistics . . . . . . . . . . . . . . . . . . . 10
     4.3.  Summary for Delay  . . . . . . . . . . . . . . . . . . . . 11
   5.  Test Streams and Sample Size . . . . . . . . . . . . . . . . . 11
     5.1.  Test Stream Characteristics  . . . . . . . . . . . . . . . 11
     5.2.  Sample Size  . . . . . . . . . . . . . . . . . . . . . . . 11
   6.  Reporting Results  . . . . . . . . . . . . . . . . . . . . . . 12
     6.1.  Overview of Metric Statistics  . . . . . . . . . . . . . . 12
     6.2.  Long-Term Reporting Considerations . . . . . . . . . . . . 13
   7.  IANA Considerations  . . . . . . . . . . . . . . . . . . . . . 14
   8.  Security Considerations  . . . . . . . . . . . . . . . . . . . 14
   9.  Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 14
   10. References . . . . . . . . . . . . . . . . . . . . . . . . . . 15
     10.1. Normative References . . . . . . . . . . . . . . . . . . . 15
     10.2. Informative References . . . . . . . . . . . . . . . . . . 15
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 16

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1.  Introduction

   When designing measurements of IP networks and presenting the
   results, knowledge of the audience is a key consideration.  To
   present a useful and relevant portrait of network conditions, one
   must answer the following question:

   "How will the results be used?"

   There are two main audience categories:

   1.  Network Characterization - describes conditions in an IP network
       for quality assurance, troubleshooting, modeling, etc.  The
       point-of-view looks inward, toward the network, and the consumer
       intends their actions there.

   2.  Application Performance Estimation - describes the network
       conditions in a way that facilitates determining affects on user
       applications, and ultimately the users themselves.  This point-
       of-view looks outward, toward the user(s), accepting the network
       as-is.  This consumer intends to estimate a network-dependent
       aspect of performance, or design some aspect of an application's
       accommodation of the network.  (These are *not* application
       metrics, they are defined at the IP layer.)

   This memo considers how these different points-of-view affect both
   the measurement design (parameters and options of the metrics) and
   statistics reported when serving their needs.

   The IPPM framework [RFC2330] and other RFCs describing IPPM metrics
   provide a background for this memo.

2.  Purpose and Scope

   The purpose of this memo is to clearly delineate two points-of-view
   (POV) for using measurements, and describe their effects on the test
   design, including the selection of metric parameters and reporting
   the results.

   The current scope of this memo is primarily limited to design and
   reporting of the loss and delay metrics [RFC2680] [RFC2679], but will
   also discuss the delay variation and reordering metrics where
   applicable.  Sampling, or the design of the active packet stream that
   is the basis for the measurements, is also discussed.

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3.  Effect of POV on the Loss Metric

   This section describes the ways in which the Loss metric can be tuned
   to reflect the preferences of the two audience categories, or
   different POV.  The waiting time to declare a packet lost, or loss
   threshold is one area where there would appear to be a difference,
   but the ability to post-process the results may resolve it.

3.1.  Loss Threshold

   RFC 2680 [RFC2680] defines the concept of a waiting time for packets
   to arrive, beyond which they are declared lost.  The text of the RFC
   declines to recommend a value, instead saying that "good engineering,
   including an understanding of packet lifetimes, will be needed in
   practice."  Later, in the methodology, they give reasons for waiting
   "a reasonable period of time", and leaving the definition of
   "reasonable" intentionally vague.

3.1.1.  Network Characterization

   Practical measurement experience has shown that unusual network
   circumstances can cause long delays.  One such circumstance is when
   routing loops form during IGP re-convergence following a failure or
   drastic link cost change.  Packets will loop between two routers
   until new routes are installed, or until the IPv4 Time-to-Live (TTL)
   field (or the IPv6 Hop Limit) decrements to zero.  Very long delays
   on the order of several seconds have been measured [Casner] [Cia03].

   Therefore, network characterization activities prefer a long waiting
   time in order to distinguish these events from other causes of loss
   (such as packet discard at a full queue, or tail drop).  This way,
   the metric design helps to distinguish more reliably between packets
   that might yet arrive, and those that are no longer traversing the

   It is possible to calculate a worst-case waiting time, assuming that
   a routing loop is the cause.  We model the path between Source and
   Destination as a series of delays in links (t) and queues (q), as
   these two are the dominant contributors to delay.  The normal path
   delay across n hops without encountering a loop, D, is

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                           D = t  +   >   t  + q
                                0    /     i    i
                                    i = 1

                        Figure 1: Normal Path Delay

   and the time spent in the loop with L hops, is

                       i + L-1
                        \                         (TTL - n)
                 R = C   >   t  + q  where C    = ---------
                        /     i    i        max       L

                Figure 2: Delay due to Rotations in a Loop

   and where C is the number of times a packet circles the loop.

   If we take the delays of all links and queues as 100ms each, the
   TTL=255, the number of hops n=5 and the hops in the loop L=4, then

   D = 1.1 sec and R ~= 50 sec, and D + R ~= 51.1 seconds

   We note that the link delays of 100ms would span most continents, and
   a constant queue length of 100ms is also very generous.  When a loop
   occurs, it is almost certain to be resolved in 10 seconds or less.
   The value calculated above is an upper limit for almost any realistic

   A waiting time threshold parameter, dT, set consistent with this
   calculation would not truncate the delay distribution (possibly
   causing a change in its mathematical properties), because the packets
   that might arrive have been given sufficient time to traverse the

   It is worth noting that packets that are stored and deliberately
   forwarded at a much later time constitute a replay attack on the
   measurement system, and are beyond the scope of normal performance

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3.1.2.  Application Performance

   Fortunately, application performance estimation activities are not
   adversely affected by the estimated worst-case transfer time.
   Although the designer's tendency might be to set the Loss Threshold
   at a value equivalent to a particular application's threshold, this
   specific threshold can be applied when post-processing the
   measurements.  A shorter waiting time can be enforced by locating
   packets with delays longer than the application's threshold, and re-
   designating such packets as lost.  Thus, the measurement system can
   use a single loss threshold and support both application and network
   performance POVs simultaneously.

3.2.  Errored Packet Designation

   RFC 2680 designates packets that arrive containing errors as lost
   packets.  Many packets that are corrupted by bit errors are discarded
   within the network and do not reach their intended destination.

   This is consistent with applications that would check the payload
   integrity at higher layers, and discard the packet.  However, some
   applications prefer to deal with errored payloads on their own, and
   even a corrupted payload is better than no packet at all.

   To address this possibility, and to make network characterization
   more complete, it is recommended to distinguish between packets that
   do not arrive (lost) and errored packets that arrive (conditionally

3.3.  Causes of Lost Packets

   Although many measurement systems use a waiting time to determine if
   a packet is lost or not, most of the waiting is in vain.  The packets
   are no-longer traversing the network, and have not reached their

   There are many causes of packet loss, including:

   1.  Queue drop, or discard

   2.  Corruption of the IP header, or other essential header info

   3.  TTL expiration (or use of a TTL value that is too small)

   4.  Link or router failure

   After waiting sufficient time, packet loss can probably be attributed
   to one of these causes.

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3.4.  Summary for Loss

   Given that measurement post-processing is possible (even encouraged
   in the definitions of IPPM metrics), measurements of loss can easily
   serve both points of view:

   o  Use a long waiting time to serve network characterization and
      revise results for specific application delay thresholds as

   o  Distinguish between errored packets and lost packets when possible
      to aid network characterization, and combine the results for
      application performance if appropriate.

4.  Effect of POV on the Delay Metric

   This section describes the ways in which the Delay metric can be
   tuned to reflect the preferences of the two consumer categories, or
   different POV.

4.1.  Treatment of Lost Packets

   The Delay Metric [RFC2679] specifies the treatment of packets that do
   not successfully traverse the network: their delay is undefined.

   " >>The *Type-P-One-way-Delay* from Src to Dst at T is undefined
   (informally, infinite)<< means that Src sent the first bit of a
   Type-P packet to Dst at wire-time T and that Dst did not receive that

   It is an accepted, but informal practice to assign infinite delay to
   lost packets.  We next look at how these two different treatments
   align with the needs of measurement consumers who wish to
   characterize networks or estimate application performance.  Also, we
   look at the way that lost packets have been treated in other metrics:
   delay variation and reordering.

4.1.1.  Application Performance

   Applications need to perform different functions, dependent on
   whether or not each packet arrives within some finite tolerance.  In
   other words, a receivers' packet processing takes one of two
   directions (or "forks" in the road):

   o  Packets that arrive within expected tolerance are handled by
      processes that remove headers, restore smooth delivery timing (as
      in a de-jitter buffer), restore sending order, check for errors in

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      payloads, and many other operations.

   o  Packets that do not arrive when expected spawn other processes
      that attempt recovery from the apparent loss, such as
      retransmission requests, loss concealment, or forward error
      correction to replace the missing packet.

   So, it is important to maintain a distinction between packets that
   actually arrive, and those that do not.  Therefore, it is preferable
   to leave the delay of lost packets undefined, and to characterize the
   delay distribution as a conditional distribution (conditioned on

4.1.2.  Network Characterization

   In this discussion, we assume that both loss and delay metrics will
   be reported for network characterization (at least).

   Assume packets that do not arrive are reported as Lost, usually as a
   fraction of all sent packets.  If these lost packets are assigned
   undefined delay, then network's inability to deliver them (in a
   timely way) is captured only in the loss metric when we report
   statistics on the Delay distribution conditioned on the event of
   packet arrival (within the Loss waiting time threshold).  We can say
   that the Delay and Loss metrics are Orthogonal, in that they convey
   non-overlapping information about the network under test.

   However, if we assign infinite delay to all lost packets, then:

   o  The delay metric results are influenced both by packets that
      arrive and those that do not.

   o  The delay singleton and the loss singleton do not appear to be
      orthogonal (Delay is finite when Loss=0, Delay is infinite when

   o  The network is penalized in both the loss and delay metrics,
      effectively double-counting the lost packets.

   As further evidence of overlap, consider the Cumulative Distribution
   Function (CDF) of Delay when the value positive infinity is assigned
   to all lost packets.  Figure 3 shows a CDF where a small fraction of
   packets are lost.

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                 1 | - - - - - - - - - - - - - - - - - -+
                   |                                    |
                   |          _..----''''''''''''''''''''
                   |      ,-''
                   |    ,'
                   |   /                         Mass at
                   |  /                          +infinity
                   | /                           = fraction
                   ||                            lost
                 0 |_____________________________________

                   0               Delay               +o0

     Figure 3: Cumulative Distribution Function for Delay when Loss =

   We note that a Delay CDF that is conditioned on packet arrival would
   not exhibit this apparent overlap with loss.

   Although infinity is a familiar mathematical concept, it is somewhat
   disconcerting to see any time-related metric reported as infinity, in
   the opinion of the authors.  Questions are bound to arise, and tend
   to detract from the goal of informing the consumer with a performance

4.1.3.  Delay Variation

   [RFC3393] excludes lost packets from samples, effectively assigning
   an undefined delay to packets that do not arrive in a reasonable
   time.  Section 4.1 describes this specification and its rationale
   (ipdv = inter-packet delay variation in the quote below).

   "The treatment of lost packets as having "infinite" or "undefined"
   delay complicates the derivation of statistics for ipdv.
   Specifically, when packets in the measurement sequence are lost,
   simple statistics such as sample mean cannot be computed.  One
   possible approach to handling this problem is to reduce the event
   space by conditioning.  That is, we consider conditional statistics;
   namely we estimate the mean ipdv (or other derivative statistic)
   conditioned on the event that selected packet pairs arrive at the
   destination (within the given timeout).  While this itself is not
   without problems (what happens, for example, when every other packet
   is lost), it offers a way to make some (valid) statements about ipdv,
   at the same time avoiding events with undefined outcomes."

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4.1.4.  Reordering

   [RFC4737]defines metrics that are based on evaluation of packet
   arrival order, and include a waiting time to declare a packet lost
   (to exclude them from further processing).

   If packets are assigned a delay value, then the reordering metric
   would declare any packets with infinite delay to be reordered,
   because their sequence numbers will surely be less than the "Next
   Expected" threshold when (or if) they arrive.  But this practice
   would fail to maintain orthogonality between the reordering metric
   and the loss metric.  Confusion can be avoided by designating the
   delay of non-arriving packets as undefined, and reserving delay
   values only for packets that arrive within a sufficiently long
   waiting time.

4.2.  Preferred Statistics

   Today in network characterization, the sample mean is one statistic
   that is almost ubiquitously reported.  It is easily computed and
   understood by virtually everyone in this audience category.  Also,
   the sample is usually filtered on packet arrival, so that the mean is
   based a conditional distribution.

   The median is another statistic that summarizes a distribution,
   having somewhat different properties from the sample mean.  The
   median is stable in distributions with a few outliers or without
   them.  However, the median's stability prevents it from indicating
   when a large fraction of the distribution changes value. 50% or more
   values would need to change for the median to capture the change.

   Both the median and sample mean have difficulty with bimodal
   distributions.  The median will reside in only one of the modes, and
   the mean may not lie in either mode range.  For this and other
   reasons, additional statistics such as the minimum, maximum, and 95%-
   ile have value when summarizing a distribution.

   When both the sample mean and median are available, a comparison will
   sometimes be informative, because these two statistics are equal only
   when the delay distribution is perfectly symmetrical.

   Also, these statistics are generally useful from the Application
   Performance POV, so there is a common set that should satisfy

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4.3.  Summary for Delay

   From the perspectives of:

   1.  application/receiver analysis, where subsequent processing
       depends on whether the packet arrives or times-out,

   2.  straightforward network characterization without double-counting
       defects, and

   3.  consistency with Delay variation and Reordering metric

   the most efficient practice is to distinguish between truly lost and
   delayed packets with a sufficiently long waiting time, and to
   designate the delay of non-arriving packets as undefined.

5.  Test Streams and Sample Size

   This section discusses two key aspects of measurement that are
   sometimes omitted from the report: the description of the test stream
   on which the measurements are based, and the sample size.

5.1.  Test Stream Characteristics

   Network Characterization has traditionally used Poisson-distributed
   inter-packet spacing, as this provides an unbiased sample.  The
   average inter-packet spacing may be selected to allow observation of
   specific network phenomena.  Other test streams are designed to
   sample some property of the network, such as the presence of
   congestion, link bandwidth, or packet reordering.

   If measuring a network in order to make inferences about applications
   or receiver performance, then there are usually efficiencies derived
   from a test stream that has similar characteristics to the sender.
   In some cases, it is essential to synthesize the sender stream, as
   with Bulk Transfer Capacity estimates.  In other cases, it may be
   sufficient to sample with a "known bias", e.g., a Periodic stream to
   estimate real-time application performance.

5.2.  Sample Size

   Sample size is directly related to the accuracy of the results, and
   plays a critical role in the report.  Even if only the sample size
   (in terms of number of packets) is given for each value or summary
   statistic, it imparts a notion of the confidence in the result.

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   In practice, the sample size will be selected taking both statistical
   and practical factors into account.  Among these factors are:

   1.  The estimated variability of the quantity being measured

   2.  The desired confidence in the result (although this may be
       dependent on assumption of the underlying distribution of the
       measured quantity).

   3.  The effects of active measurement traffic on user traffic

   4.  etc.

   A sample size may sometimes be referred to as "large".  This is a
   relative, and qualitative term.  It is preferable to describe what
   one is attempting to achieve with their sample.  For example, stating
   an implication may be helpful: this sample is large enough such that
   a single outlying value at ten times the "typical" sample mean (the
   mean without the outlying value) would influence the mean by no more
   than X.

6.  Reporting Results

   This section gives an overview of recommendations, followed by
   additional considerations for reporting results in the "long-term".

6.1.  Overview of Metric Statistics

   This section gives an overview of reporting recommendations for the
   loss, delay, and delay variation metrics based on the discussion and
   conclusions of the preceding sections.

   The minimal report on measurements MUST include both Loss and Delay

   For Packet Loss, the loss ratio defined in [RFC2680] is a sufficient
   starting point, especially the guidance for setting the loss
   threshold waiting time.  We have calculated a waiting time above that
   should be sufficient to differentiate between packets that are truly
   lost or have long finite delays under general measurement
   circumstances, 51 seconds.  Knowledge of specific conditions can help
   to reduce this threshold, but 51 seconds is considered to be
   manageable in practice.

   We note that a loss ratio calculated according to [Y.1540] would
   exclude errored packets form the numerator.  In practice, the
   difference between these two loss metrics is small if any, depending

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   on whether the last link prior to the destination contributes errored

   For Packet Delay, we recommend providing both the mean delay and the
   median delay with lost packets designated undefined (as permitted by
   [RFC2679]).  Both statistics are based on a conditional distribution,
   and the condition is packet arrival prior to a waiting time dT, where
   dT has been set to take maximum packet lifetimes into account, as
   discussed above.  Using a long dT helps to ensure that delay
   distributions are not truncated.

   For Packet Delay Variation (PDV), the minimum delay of the
   conditional distribution should be used as the reference delay for
   computing PDV according to [Y.1540] or [RFC3393].  A useful value to
   report is a pseudo range of delay variation based on calculating the
   difference between a high percentile of delay and the minimum delay.
   For example, the 99.9%-ile minus the minimum will give a value that
   can be compared with objectives in [Y.1541].

6.2.  Long-Term Reporting Considerations

   [I-D.ietf-ippm-reporting] describes methods to conduct measurements
   and report the results on a near-immediate time scale (10 seconds,
   which we consider to be "short-term").

   Measurement intervals and reporting intervals need not be the same
   length.  Sometimes, the user is only concerned with the performance
   levels achieved over a relatively long interval of time (e.g, days,
   weeks, or months, as opposed to 10 seconds).  However, there can be
   risks involved with running a measurement continuously over a long
   period without recording intermediate results:

   o  Temporary power failure may cause loss of all the results to date.

   o  Measurement system timing synchronization signals may experience a
      temporary outage, causing sub-sets of measurements to be in error
      or invalid.

   o  Maintenance may be necessary on the measurement system, or its
      connectivity to the network under test.

   For these and other reasons, such as

   o  the constraint to collect measurements on intervals similar to
      user session length, or

   o  the dual-use of measurements in monitoring activities where
      results are needed on a period of a few minutes,

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   there is value in conducting measurements on intervals that are much
   shorter than the reporting interval.

   There are several approaches for aggregating a series of measurement
   results over time in order to make a statement about the longer
   reporting interval.  One approach requires the storage of all metric
   singletons collected throughout the reporting interval, even though
   the measurement interval stops and starts many times.

   Another approach is described in [I-D.ietf-ippm-framework-compagg] as
   "temporal aggregation".  This approach would estimate the results for
   the reporting interval based on many individual measurement interval
   statistics (results) alone.  The result would ideally appear in the
   same form as though a continuous measurement was conducted.  A memo
   to address the details of temporal aggregation is yet to be prepared.

   Yet another approach requires a numerical objective for the metric,
   and the results of each measurement interval are compared with the
   objective.  Every measurement interval where the results meet the
   objective contribute to the fraction of time with performance as
   specified.  When the reporting interval contains many measurement
   intervals it is possible to present the results as "metric A was less
   than or equal to objective X during Y% of time.

   NOTE that numerical thresholds are not set in IETF performance work
   and are explicitly excluded from the IPPM charter.

7.  IANA Considerations

   This document makes no request of IANA.

   Note to RFC Editor: this section may be removed on publication as an

8.  Security Considerations

   The security considerations that apply to any active measurement of
   live networks are relevant here as well.  See [RFC4656].

9.  Acknowledgements

   The authors would like to thank Phil Chimento for his suggestion to
   employ conditional distributions for Delay, and Steve Konish Jr. for
   his careful review and suggestions.

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10.  References

10.1.  Normative References

   [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.

   [RFC2680]  Almes, G., Kalidindi, S., and M. Zekauskas, "A One-way
              Packet Loss Metric for IPPM", RFC 2680, September 1999.

   [RFC3393]  Demichelis, C. and P. Chimento, "IP Packet Delay Variation
              Metric for IP Performance Metrics (IPPM)", RFC 3393,
              November 2002.

   [RFC4656]  Shalunov, S., Teitelbaum, B., Karp, A., Boote, J., and M.
              Zekauskas, "A One-way Active Measurement Protocol
              (OWAMP)", RFC 4656, September 2006.

   [RFC4737]  Morton, A., Ciavattone, L., Ramachandran, G., Shalunov,
              S., and J. Perser, "Packet Reordering Metrics", RFC 4737,
              November 2006.

10.2.  Informative References

   [Casner]   "A Fine-Grained View of High Performance Networking, NANOG
              22 Conf.;", May
              20-22 2001.

   [Cia03]    "Standardized Active Measurements on a Tier 1 IP Backbone,
              IEEE Communications Mag., pp 90-97.", June 2003.

              Morton, A., "Framework for Metric Composition",
              draft-ietf-ippm-framework-compagg-07 (work in progress),
              October 2008.

              Shalunov, S. and M. Swany, "Reporting IP Performance
              Metrics to Users", draft-ietf-ippm-reporting-02 (work in
              progress), July 2008.

Morton, et al.            Expires July 12, 2009                [Page 15]

Internet-Draft              Reporting Metrics               January 2009

   [Y.1540]   ITU-T Recommendation Y.1540, "Internet protocol data
              communication service - IP packet transfer and
              availability performance parameters", December  2002.

   [Y.1541]   ITU-T Recommendation Y.1540, "Network Performance
              Objectives for IP-Based Services", February  2006.

Authors' Addresses

   Al Morton
   AT&T Labs
   200 Laurel Avenue South
   Middletown, NJ  07748

   Phone: +1 732 420 1571
   Fax:   +1 732 368 1192

   Gomathi Ramachandran
   AT&T Labs
   200 Laurel Avenue South
   Middletown, New Jersey  07748

   Phone: +1 732 420 2353

   Ganga Maguluri
   AT&T Labs
   200 Laurel Avenue
   Middletown, New Jersey  07748

   Phone: 732-420-2486

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