Network Working Group                                          A. Morton
Internet-Draft                                           G. Ramachandran
Intended status: Informational                               G. Maguluri
Expires: September 12, November 11, 2012                                     AT&T Labs
                                                          March 11,
                                                            May 10, 2012

   Reporting IP Network Performance Metrics: Different Points of View


   Consumers of IP network performance metrics have many different uses
   in mind.  The memo provides "long-term" reporting considerations
   (e.g., hours, days, weeks or months, as opposed to 10 seconds), based
   on analysis of the two key audience points-of-view.  It describes how
   the audience categories affect the selection of metric parameters and
   options when seeking info that serves their needs.

Requirements Language

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

Status of this Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

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   This Internet-Draft will expire on September 12, November 11, 2012.

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Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  4
   2.  Purpose and Scope  . . . . . . . . . . . . . . . . . . . . . .  4
   3.  Reporting Results  . . . . . . . . . . . . . . . . . . . . . .  5
     3.1.  Overview of Metric Statistics  . . . . . . . . . . . . . .  5
     3.2.  Long-Term Reporting Considerations . . . . . . . . . . . .  6
   4.  Effect of POV on the Loss Metric . . . . . . . . . . . . . . .  8
     4.1.  Loss Threshold . . . . . . . . . . . . . . . . . . . . . .  8
       4.1.1.  Network Characterization . . . . . . . . . . . . . . .  8
       4.1.2.  Application Performance  . . . . . . . . . . . . . . . 10 11
     4.2.  Errored Packet Designation . . . . . . . . . . . . . . . . 10 11
     4.3.  Causes of Lost Packets . . . . . . . . . . . . . . . . . . 10 11
     4.4.  Summary for Loss . . . . . . . . . . . . . . . . . . . . . 11 12
   5.  Effect of POV on the Delay Metric  . . . . . . . . . . . . . . 11 12
     5.1.  Treatment of Lost Packets  . . . . . . . . . . . . . . . . 11 12
       5.1.1.  Application Performance  . . . . . . . . . . . . . . . 12 13
       5.1.2.  Network Characterization . . . . . . . . . . . . . . . 12 13
       5.1.3.  Delay Variation  . . . . . . . . . . . . . . . . . . . 13 14
       5.1.4.  Reordering . . . . . . . . . . . . . . . . . . . . . . 14 15
     5.2.  Preferred Statistics . . . . . . . . . . . . . . . . . . . 14 15
     5.3.  Summary for Delay  . . . . . . . . . . . . . . . . . . . . 15 16
   6.  Reporting Raw Capacity Metrics . . . . . . . . . . . . . . . . 15 16
     6.1.  Type-P Parameter . . . . . . . . . . . . . . . . . . . . . 16 17
     6.2.  A priori Factors . . . . . . . . . . . . . . . . . . . . . 16 17
     6.3.  IP-layer Capacity  . . . . . . . . . . . . . . . . . . . . 16 17
     6.4.  IP-layer Utilization . . . . . . . . . . . . . . . . . . . 17 18
     6.5.  IP-layer Available Capacity  . . . . . . . . . . . . . . . 17 18
     6.6.  Variability in Utilization and Avail. Available Capacity  . . . . . . 18 19
       6.6.1.  General Summary of Variability . . . . . . . . . . . . 18 19
   7.  Reporting Restricted Capacity Metrics  . . . . . . . . . . . . 19 20
     7.1.  Type-P Parameter and Type-C Parameter  . . . . . . . . . . 20 21
     7.2.  A priori Factors . . . . . . . . . . . . . . . . . . . . . 20 21
     7.3.  Measurement Interval . . . . . . . . . . . . . . . . . . . 20 21
     7.4.  Bulk Transfer Capacity Reporting . . . . . . . . . . . . . 21 22
     7.5.  Variability in Bulk Transfer Capacity  . . . . . . . . . . 22 23
   8.  Reporting on Test Streams and Sample Size  . . . . . . . . . . 22 23
     8.1.  Test Stream Characteristics  . . . . . . . . . . . . . . . 22 23
     8.2.  Sample Size  . . . . . . . . . . . . . . . . . . . . . . . 23 24
   9.  IANA Considerations  . . . . . . . . . . . . . . . . . . . . . 23 24
   10. Security Considerations  . . . . . . . . . . . . . . . . . . . 23 24
   11. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 23 25
   12. References . . . . . . . . . . . . . . . . . . . . . . . . . . 24 25
     12.1. Normative References . . . . . . . . . . . . . . . . . . . 24 25
     12.2. Informative References . . . . . . . . . . . . . . . . . . 25 26
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 25 26

1.  Introduction

   When designing measurements of IP networks and presenting the
   results, a result,
   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: categories for the report of results:

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

   2.  Application Performance Estimation - describes the network
       conditions in a way that facilitates determining affects effects on user
       applications, and ultimately the users themselves.  This point-
       of-view looks outward, toward the user(s), accepting the network
       as-is.  This report consumer intends to estimate a network-dependent 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 scope of this memo primarily covers the design and reporting of
   the loss and delay metrics [RFC2680] [RFC2679].  It will also discuss
   the delay variation [RFC3393] and reordering metrics [RFC4737] where

   With capacity metrics growing in relevance to the industry, the memo
   also covers POV and reporting considerations for metrics resulting
   from the Bulk Transfer Capacity Framework [RFC3148] and Network
   Capacity Definitions [RFC5136].  These memos effectively describe two
   different categories of metrics,

   o  Restricted: [RFC3148] includes restrictions of congestion control
      and the notion of unique data bits delivered, and

   o  Raw: [RFC5136] using a definition of raw capacity without the
      restrictions of data uniqueness or congestion-awareness. congestion awareness.

   It might seem at first glance that each of these metrics has an
   obvious audience (Raw = Network Characterization, Restricted =
   Application Performance), but reality is more complex and consistent
   with the overall topic of capacity measurement and reporting.  For
   example, TCP is usually used in Restricted capacity measurement
   methods, while UDP appears in Raw capacity measurement.  The Raw and
   Restricted capacity metrics will be treated in separate sections,
   although they share one common reporting issue: representing
   variability in capacity metric results as part of a long-term report.

   Sampling, or the design of the active packet stream that is the basis
   for the measurements, is also discussed.

3.  Reporting Results

   This section gives an overview of recommendations, followed by
   additional considerations for reporting results in the "long-term", "long term",
   based on the discussion and conclusions of the major sections that

3.1.  Overview of Metric Statistics

   This section gives an overview of reporting recommendations for the
   loss, delay, and delay variation metrics.

   The minimal report on measurements MUST must include both Loss and Delay

   For Packet Loss, the loss ratio defined in [RFC2680] is a sufficient
   starting point, especially the existing guidance for setting the loss
   threshold waiting time.  We have calculated a waiting time above in section
   4.1.1 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 and a waiting time of approximately 50
   seconds is considered to be manageable in practice.

   We note that a loss ratio calculated according to [Y.1540] would
   exclude errored packets from the numerator.  In practice, the
   difference between these two loss metrics is small if any, depending
   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 for loss.  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 [RFC5481] and [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 99.9 percentile minus the minimum
   will give a value that can be compared with objectives in [Y.1541].

   For Capacity, both Raw and Restricted, reporting the variability in a
   useful way is identified as the main challenge.  The Min, Max, and
   Range statistics are suggested along with a ratio of Max to Min and
   moving averages.  In the end, a simple plot of the singleton results
   over time may succeed where summary metrics fail, or serve to confirm
   that the summaries are valid.

3.2.  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, (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, or

   o  the ability to inspect results of a single measurement interval
      for deeper analysis,

   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 [RFC5835] 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 had been conducted.  A memo to address addressing 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. time".

   NOTE that numerical thresholds of acceptability are not set in IETF
   performance work and are explicitly therefore excluded from the IPPM charter. scope of this

   In all measurement, it is important to avoid unintended
   synchronization with network events.  This topic is treated in
   [RFC2330] for Poisson-distributed inter-packet time streams, and
   [RFC3432] for Periodic streams.  Both avoid synchronization through
   use of random start times.

   There are network conditions where it is simply more useful to report
   the connectivity status of the Source-Destination path, and to
   distinguish time intervals where connectivity can be demonstrated
   from other time intervals (where connectivity does not appear to
   exist).  [RFC2678] specifies a number of one-way and two two-way
   connectivity metrics of increasing complexity.  In this memo, we
   RECOMMEND that long term reporting of loss, delay, and other metrics
   be limited to time intervals where connectivity can be demonstrated,
   and other intervals be summarized as percent of time where
   connectivity does not appear to exist.  We note that this same
   approach has been adopted in ITU-T Recommendation [Y.1540] where
   performance parameters are only valid during periods of service
   "availability" (evaluated according to a function based on packet
   loss, and sustained periods of loss ratio greater than a threshold
   are declared "unavailable").

4.  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.

4.1.  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.  We estimate a practical bound on
   waiting time below.

4.1.1.  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. delay (in active measurement,
   the Source and Destination hosts contribute minimal delay).  The
   normal path
   delay delay, D, across n hops queues (where TTL is decremented at a
   node with a queue) and n+1 links without encountering a loop, D, is

        Path model with n=5
          Source --- q1 --- q2 --- q3 --- q4 --- q5 --- Destination
                 t0     t1     t2     t3     t4     t5

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

                        Figure 1: Normal Path Delay

   and the time spent in the loop with L hops, queues, is
            Path model with n=5 and L=3
            Time in one Loop = (qx+tx + qy+ty + qz+tz)

                                   qy -- qz
                                    |  ?/exit?
              Src --- q1 --- q2 ---/    q3 --- q4 --- q5 --- Dst
                  t0     t1     t2         t3     t4     t5

                       j + L-1
                        \                          (TTL - n)
                 R = C   >  (t  + q ) where C    = ---------
                        /     i    i         max       L
                        i=j           where j is the hop number where the loop begins

                Figure 2: Delay due to Rotations in a Loop

   where n is the total number of queues in the non-loop path (with n+1
   links), j is the queue number where the loop begins, C is the number
   of times a packet circles the loop, and where TTL is the packet's initial
   Time-to-Live value at the source (or Hop Count in IPv6).

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

   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 real-
   world circumstance.

   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

4.1.2.  Application Performance

   Fortunately, application performance estimation activities are not
   adversely affected by the long estimated worst-case transfer time. limit on waiting time,
   because most applications will use shorter time thresholds.  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 re-designating such
   packets as lost.  Thus, the measurement system can use a single loss
   waiting time and support both application and network performance
   POVs simultaneously.

4.2.  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

4.3.  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
   5.  Layers below the source-to-destination IP layer can discard
       packets that fail error checking checking, and link-layer checksums often
       cover the entire packet

   After waiting sufficient time,

   It is reasonable to consider a packet loss can probably that has not arrived after a
   large amount of time to be attributed lost (due to one of these causes. the causes above)
   because packets do not "live forever" in the network, or have
   infinite delay.

4.4.  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.

5.  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.

5.1.  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.

5.1.1.  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' receiver's packet processing takes only one of two
   alternative directions (or "forks" (a "fork" in the road):

   o  Packets that arrive within expected tolerance are handled by
      processes that remove
      removing headers, restore restoring smooth delivery timing (as in a de-jitter de-
      jitter buffer), restore restoring sending order, check checking for errors in
      payloads, and many other operations.

   o  Packets that do not arrive when expected spawn other processes
      that attempt lead to attempted
      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

5.1.2.  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 the network's inability to deliver them (in a
   timely way) is relegated 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, orthogonal, in that they convey
   non-overlapping information about the network under test.  This is a
   valuable property, whose absence is discussed below.

   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 "positive infinity" is
   assigned to all lost packets.  Figure 3 shows a CDF where a small
   fraction of packets are lost.

                 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. infinity.
   Questions are bound to arise, and tend to detract from the goal of
   informing the consumer with a performance report.

5.1.3.  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 of [RFC3393] 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."

   We note that the argument above applies to all forms of packet delay
   variation that can be constructed using the "selection function"
   concept of [RFC3393].  In recent work the two main forms of delay
   variation metrics have been compared and the results are summarized
   in [RFC5481].

5.1.4.  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.

5.2.  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 on 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 95
   percentile 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
   under unusual circumstances, such as 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

   Plots of the delay distribution may also be useful when single-value
   statistics indicate that new conditions are present.  An empirically-
   derived probability distribution function will usually describe
   multiple modes more efficiently than any other form of result.

5.3.  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 packets that
   are truly lost and those that are delayed packets with a sufficiently
   long waiting time, and to designate the delay of non-arriving packets
   as undefined.

6.  Reporting Raw Capacity Metrics

   Raw capacity refers to the metrics defined in [RFC5136] which do not
   include restrictions such as data uniqueness or flow-control response
   to congestion.

   The metrics considered are IP-layer Capacity, Utilization (or used
   capacity), and Available Capacity, for individual links and complete
   paths.  These three metrics form a triad: knowing one metric
   constrains the other two (within their allowed range), and knowing
   two determines the third.  The link metrics have another key aspect
   in common: they are single-measurement-point metrics at the egress of
   a link.  The path Capacity and Available Capacity are derived by
   examining the set of single-point link measurements and taking the
   minimum value.

6.1.  Type-P Parameter

   The concept of "packets of type-P" Type-P" is defined in [RFC2330].  The
   Type-P categorization has critical relevance in all forms of capacity
   measurement and reporting.  The ability to categorize packets based
   on header fields for assignment to different queues and scheduling
   mechanisms is now common place.  When un-used resources are shared
   across queues, the conditions in all packet categories will affect
   capacity and related measurements.  This is one source of variability
   in the results that all audiences would prefer to see reported in a
   useful and easily understood way.

   Communication of Type-P in OWAMP within the One-way Active Measurement
   Protocol (OWAMP) and TWAMP the Two-way Active Measurement Protocol (TWAMP)
   is essentially confined to the Diffserv Codepoint [RFC4656].  DSCP is
   the most common qualifier for type-P. Type-P.

   Each audience will have a set of type-P Type-P qualifications and value
   combinations that are of interest.  Measurements and reports SHOULD should
   have the flexibility to report per-type and aggregate performance.

6.2.  A priori Factors

   The audience for Network Characterization may have detailed
   information about each link that comprises a complete path (due to
   ownership, for example), or some of the links in the path but not
   others, or none of the links.

   There are cases where the measurement audience only has information
   on one of the links (the local access link), and wishes to measure
   one or more of the raw capacity metrics.  This scenario is quite
   common, and has spawned a substantial number of experimental
   measurement methods (e.g., ).
   Many of these methods respect that their users want a result fairly
   quickly and in a one-trial.  Thus, the measurement interval is kept
   short (a few seconds to a minute).  For long-term reporting, a sample
   of short term results need to be summarized.

6.3.  IP-layer Capacity

   For links, this metric's theoretical maximum value can be determined
   from the physical layer bit rate and the bit rate reduction due to
   the layers between the physical layer and IP.  When measured, this
   metric takes additional factors into account, such as the ability of
   the sending device to process and forward traffic under various
   conditions.  For example, the arrival of routing updates may spawn
   high priority processes that reduce the sending rate temporarily.
   Thus, the measured capacity of a link will be variable, and the
   maximum capacity observed applies to a specific time, time interval,
   and other relevant circumstances.

   For paths composed of a series of links, it is easy to see how the
   sources of variability for the results grow with each link in the
   path.  Results variability will be discussed in more detail below.

6.4.  IP-layer Utilization

   The ideal metric definition of Link Utilization [RFC5136] is based on
   the actual usage (bits successfully received during a time interval)
   and the Maximum Capacity for the same interval.

   In practice, Link Utilization can be calculated by counting the IP-
   layer (or other layer) octets received over a time interval and
   dividing by the theoretical maximum of octets that could have been
   delivered in the same interval.  A commonly used time interval is 5
   minutes, and this interval has been sufficient to support network
   operations and design for some time. 5 minutes is somewhat long
   compared with the expected download time for web pages, but short
   with respect to large file transfers and TV program viewing.  It is
   fair to say that considerable variability is concealed by reporting a
   single (average) Utilization value for each 5 minute interval.  Some
   performance management systems have begun to make 1 minute averages

   There is also a limit on the smallest useful measurement interval.
   Intervals on the order of the serialization time for a single Maximum
   Transmission Unit (MTU) packet will observe on/off behavior and
   report 100% or 0%.  The smallest interval needs to be some multiple
   of MTU serialization time for averaging to be effective.

6.5.  IP-layer Available Capacity

   The Available Capacity of a link can be calculated using the Capacity
   and Utilization metrics.

   When Available capacity of a link or path is estimated through some
   measurement technique, the following parameters SHOULD should be reported:

   o  Name and reference to the exact method of measurement

   o  IP packet length, octets (including IP header)

   o  Maximum Capacity that can be assessed in the measurement

   o  The time duration of the measurement

   o  All other parameters specific to the measurement method

   Many methods of Available capacity measurement have a maximum
   capacity that they can measure, and this maximum may be less than the
   actual Available capacity of the link or path.  Therefore, it is
   important to know the capacity value beyond which there will be no
   measured improvement.

   The Application Design audience may have a desired target capacity
   value and simply wish to assess whether there is sufficient Available
   Capacity.  This case simplifies measurement of link and path capacity
   to some degree, as long as the measurable maximum exceeds the target

6.6.  Variability in Utilization and Avail. Available Capacity

   As with most metrics and measurements, assessing the consistency or
   variability in the results gives the user an intuitive feel for the
   degree (or confidence) that any one value is representative of other
   results, or the spread of the underlying distribution of the
   singleton measurements.

   How can Utilization be measured and summarized to describe the
   potential variability in a useful way?

   How can the variability in Available Capacity estimates be reported,
   so that the confidence in the results is also conveyed?

   We suggest some methods below:

6.6.1.  General Summary of Variability

   With a set of singleton Utilization or Available Capacity estimates,
   each representing a time interval needed to ascertain the estimate,
   we seek to describe the variation over the set of singletons as
   though reporting summary statistics of a distribution.  Three useful
   summary statistics are:

   o  Minimum,

   o  Maximum,

   o  Range

   An alternate way to represent the Range is as ratio of Maximum to
   Minimum value.  This enables an easily understandable statistic to
   describe the range observed.  For example, when Maximum = 3*Minimum,
   then the Max/Min Ratio is 3 and users may see variability of this
   order.  On the other hand, Capacity estimates with a Max/Min Ratio
   near 1 are quite consistent and near the central measure or statistic

   For an on-going series of singleton estimates, a moving average of n
   estimates may provide a single value estimate to more easily
   distinguish substantial changes in performance over time.  For
   example, in a window of n singletons observed in time interval, t, a
   percentage change of x% is declared to be a substantial change and
   reported as an exception.

   Often, the most informative summary of the results is a two-axis plot
   rather than a table of statistics, where time is plotted on the
   x-axis and the singleton value on the y-axis.  The time-series plot
   can illustrate sudden changes in an otherwise stable range, identify
   bi-modality easily, and help quickly assess correlation with other
   time-series.  Plots of frequency of the singleton values are likewise
   useful tools to visualize the variation.

7.  Reporting Restricted Capacity Metrics

   Restricted capacity refers to the metrics defined in [RFC3148] which
   include criteria of data uniqueness or flow-control response to

   In primary metric considered is Bulk Transfer Capacity (BTC) for
   complete paths.  [RFC3148] defines

   BTC = data_sent / elapsed_time

   for a connection with congestion-aware congestion aware flow control, where data_sent
   is the total of unique payload bits (no headers).

   We note that this definition *differs* from the raw capacity
   definition in Section 2.3.1 of [RFC5136], where IP-layer Capacity
   *includes* all bits in the IP header and payload.  This means that
   Restricted Capacity BTC is already operating at a disadvantage when
   compared to the raw capacity at layers below TCP.  Further, there are
   cases where one IP-layer is encapsulated in another IP-layer or other
   form of tunneling protocol, designating more and more of the
   fundamental transport capacity as header bits that are pure overhead
   to the BTC measurement.

   We also note that Raw and Restricted Capacity metrics are not
   orthogonal in the sense defined in Section 5.1.2 above.  The
   information they covey about the network under test is certainly
   overlapping, but they reveal two different and important aspects of

   When thinking about the triad of raw capacity metrics, BTC is most
   akin to the "IP-Type-P Available Path Capacity", at least in the eyes
   of a network user who seeks to know what transmission performance a
   path might support.

7.1.  Type-P Parameter and Type-C Parameter

   The concept of "packets of type-P" Type-P" is defined in [RFC2330].  The
   considerations for Restricted Capacity are identical to the raw
   capacity section on this topic, with the addition that the various
   fields and options in the TCP header MUST must be included in the

   The vast array of TCP flow control options are not well-captured by
   Type-P, because they do not exist in the TCP header bits.  Therefore,
   we introduce a new notion here: TCP Configuration of "Type-C".  The
   elements of Type-C describe all of the settings for TCP options and
   congestion control algorithm variables, including the main form of
   congestion control in use.  Readers should consider the parameters
   and variables of [RFC3148] and [RFC6349] when constructing Type-C.

7.2.  A priori Factors

   The audience for Network Characterization may have detailed
   information about each link that comprises a complete path (due to
   ownership, for example), or some of the links in the path but not
   others, or none of the links.

   There are cases where the measurement audience only has information
   on one of the links (the local access link), and wishes to measure
   one or more BTC metrics.  The discussion of Section 6.2 applies here
   as well.

7.3.  Measurement Interval

   There are limits on a useful measurement interval for BTC.  Three
   factors that influence the interval duration are listed below:

   1.  Measurements may choose to include or exclude the 3-way handshake
       of TCP connection establishment, which requires at least 1.5 *
       RTT and contains both the delay of the path and the host
       processing time for responses.  However, user experience includes
       the 3-way handshake for all new TCP connections.

   2.  Measurements may choose to include or exclude Slow-Start,
       preferring instead to focus on a portion of the transfer that
       represents "equilibrium" (which needs to be defined for
       particular circumstances if used).  However, user experience
       includes the Slow-Start for all new TCP connections.

   3.  Measurements may choose to use a fixed block of data to transfer,
       where the size of the block has a relationship to the file size
       of the application of interest.  This approach yields variable
       size measurement intervals, where a path with faster BTC is
       measured for less time than a path with slower BTC, and this has
       implications when path impairments are time-varying, or
       transient.  Users are likely to turn their immediate attention
       elsewhere when a very large file must be transferred, thus they
       do not directly experience such a long transfer -- they see the
       result (success or fail) and possibly an objective measurement of
       the transfer time (which will likely include the 3-way handshake,
       Slow-start, and application file management processing time as
       well as the BTC).

   Individual measurement intervals may be short or long, but there is a
   need to report the results on a long-term basis that captures the BTC
   variability experienced between each interval.  Consistent BTC is a
   valuable commodity along with the value attained.

7.4.  Bulk Transfer Capacity Reporting

   When BTC of a link or path is estimated through some measurement
   technique, the following parameters SHOULD should be reported:

   o  Name and reference to the exact method of measurement

   o  Maximum Transmission Unit (MTU)

   o  Maximum BTC that can be assessed in the measurement configuration

   o  The time and duration of the measurement

   o  The number of BTC connections used simultaneously

   o  *All* other parameters specific to the measurement method,
      especially the Congestion Control algorithm in use

   See also [RFC6349].

   Many methods of Bulk Transfer Capacity measurement have a maximum
   capacity that they can measure, and this maximum may be less than the
   available capacity of the link or path.  Therefore, it is important
   to specify the measured BTC value beyond which there will be no
   measured improvement.

   The Application Design audience may have a desired target capacity
   value and simply wish to assess whether there is sufficient BTC.
   This case simplifies measurement of link and path capacity to some
   degree, as long as the measurable maximum exceeds the target

7.5.  Variability in Bulk Transfer Capacity

   As with most metrics and measurements, assessing the consistency or
   variability in the results gives the user an intuitive feel for the
   degree (or confidence) that any one value is representative of other
   results, or the underlying distribution from which these singleton
   measurements have come.

   With two questions looming:

   1.  What ways can BTC be measured and summarized to describe the
       potential variability in a useful way?

   2.  How can the variability in BTC estimates be reported, so that the
       confidence in the results is also conveyed?


   We suggest the methods of Section 6.6.1 above, and the additional
   results presentations given in [RFC6349].

8.  Reporting on 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.

8.1.  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.

8.2.  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.

   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.

   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.

   The Appendix of [RFC2330] indicates that sample size of 128
   singletons worked well for goodness-of-fit testing, while a much
   larger size almost always failed (8192 singletons).

9.  IANA Considerations

   This document makes no request of IANA.

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

10.  Security Considerations

   The security considerations that apply to any active measurement of
   live networks are relevant here as well.  See [RFC4656]. [RFC4656] for mandatory
   to implement security features that intend to mitigate attacks
   described in the corresponding security considerations section.

   Measurement systems conducting long-term measurements are more
   exposed to threats as a by-product of ports open longer to perform
   their task, and more easily detected measurement activity on those
   ports.  Further, use of long packet waiting times affords an attacker
   a better opportunity to prepare and launch a replay attack.

11.  Acknowledgements

   The authors thank: Phil Chimento for his suggestion to employ
   conditional distributions for Delay, Steve Konish Jr. for his careful
   review and suggestions, Dave McDysan and Don McLachlan for useful
   comments based on their long experience with measurement and
   reporting, Daniel Genin for his observation of non-orthogonality
   between Raw and Restricted Capacity metrics (and our omission of this
   fact), and Matt Zekauskas for suggestions on organizing the memo for
   easier consumption.

12.  References

12.1.  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.

   [RFC2678]  Mahdavi, J. and V. Paxson, "IPPM Metrics for Measuring
              Connectivity", RFC 2678, September 1999.

   [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.

   [RFC3148]  Mathis, M. and M. Allman, "A Framework for Defining
              Empirical Bulk Transfer Capacity Metrics", RFC 3148,
              July 2001.

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

   [RFC3432]  Raisanen, V., Grotefeld, G., and A. Morton, "Network
              performance measurement with periodic streams", RFC 3432,
              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.

   [RFC5136]  Chimento, P. and J. Ishac, "Defining Network Capacity",
              RFC 5136, February 2008.

12.2.  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.

              Shalunov, S. and M. Swany, "Reporting IP Performance
              Metrics to Users", draft-ietf-ippm-reporting-06 (work in
              progress), March 2011.

   [RFC5481]  Morton, A. and B. Claise, "Packet Delay Variation
              Applicability Statement", RFC 5481, March 2009.

   [RFC5835]  Morton, A. and S. Van den Berghe, "Framework for Metric
              Composition", RFC 5835, April 2010.

   [RFC6349]  Constantine, B., Forget, G., Geib, R., and R. Schrage,
              "Framework for TCP Throughput Testing", RFC 6349,
              August 2011.

   [Y.1540]   ITU-T Recommendation Y.1540, "Internet protocol data
              communication service - IP packet transfer and
              availability performance parameters", December  2011.

   [Y.1541]   ITU-T Recommendation Y.1540, "Network Performance
              Objectives for IP-Based Services", February  2011.

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