IPPM Working Group                                         Rajeev Koodli
INTERNET DRAFT                                     Nokia Research Center
5 December 2001                                             R. Ravikanth

                  One-way Loss Pattern Sample Metrics

   Status of This Memo

      This document is an Internet-Draft and is in full conformance with
   all provisions of Section 10 of RFC2026.

      Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF), its areas, and its working groups.  Note that
   other groups may also distribute working documents as Internet-

      Internet-Drafts are draft documents valid for a maximum of six
   months and may be updated, replaced, or obsoleted by other documents
   at any time.  It is inappropriate to use Internet-Drafts as
   reference material or to cite them other than as "work in progress."

   The list of current Internet-Drafts can be accessed at:
   The list of Internet-Draft Shadow Directories can be accessed at:

      This memo provides information for the Internet community.  This memo
   does not specify an Internet standard of any kind.  Distribution of
   this memo is unlimited.


      The Internet exhibits certain specific types of behavior (e.g.,
   bursty packet loss) that can affect the performance seen by the users
   as well as the operators.  Previously, the focus of the IPPM had
   been on specifying base metrics such as delay, loss and connectivity
   under the framework described in [10].  However, specific Internet
   behaviors can also be captured under the umbrella of IPPM framework,
   specifying new concepts while reusing existing guidelines as much as
   possible.  This document defines metrics derived from the previously
   specified base metrics to capture loss patterns experienced by
   streams of packets on the Internet.

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Status of This Memo                                                    i

Abstract                                                               i

 1. Introduction                                                       2

 2. Terminology                                                        2

 3. The Approach                                                       2

 4. Basic Definitions                                                  3

 5.  Definitions for Samples of One-way Loss Distance, and One-way
      Loss Period                                                      4
     5.1. Metric Names  . . . . . . . . . . . . . . . . . . . . . .    4
           5.1.1. Type-P-One-Way-Loss-Distance-Stream . . . . . . .    4
           5.1.2. Type-P-One-Way-Loss-Period-Stream . . . . . . . .    4
     5.2. Metric Parameters . . . . . . . . . . . . . . . . . . . .    4
     5.3. Metric Units  . . . . . . . . . . . . . . . . . . . . . .    4
           5.3.1. Type-P-One-Way-Loss-Distance-Stream . . . . . . .    4
           5.3.2. Type-P-One-Way-Loss-Period-Stream . . . . . . . .    4
     5.4. Definitions . . . . . . . . . . . . . . . . . . . . . . .    5
           5.4.1. Type-P-One-Way-Loss-Distance-Stream . . . . . . .    5
           5.4.2. Type-P-One-Way-Loss-Period-Stream . . . . . . . .    5
           5.4.3. Examples  . . . . . . . . . . . . . . . . . . . .    5
     5.5. Methodologies . . . . . . . . . . . . . . . . . . . . . .    6
     5.6. Discussion  . . . . . . . . . . . . . . . . . . . . . . .    7
     5.7. Sampling Considerations . . . . . . . . . . . . . . . . .    7
     5.8. Errors and Uncertainties  . . . . . . . . . . . . . . . .    7

 6. Statistics                                                         8
     6.1. Type-P-One-Way-Loss-Noticeable-Rate . . . . . . . . . . .    8
     6.2. Type-P-One-Way-Loss-Period-Total  . . . . . . . . . . . .    8
     6.3. Type-P-One-Way-Loss-Period-Lengths  . . . . . . . . . . .    9
     6.4. Type-P-One-Way-Inter-Loss-Period-Lengths  . . . . . . . .    9
     6.5. Examples  . . . . . . . . . . . . . . . . . . . . . . . .    9

 7. Security Considerations:                                          10

 8. IANA Considerations                                               11

 9. Acknowledgements                                                  11

Addresses                                                             13

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   1. Introduction

      In certain real-time applications (such as packet voice and
   video), the loss pattern or loss distribution is a key parameter
   that determines the performance observed by the users.  For the
   same loss rate, two different loss distributions could potentially
   produce widely different perceptions of performance.  The impact
   of loss pattern is also extremely important for non-real-time
   applications that use an adaptive protocol such as TCP. Refer
   to  [2], [3], [5], [12] for evidence as to the importance and
   existence of loss burstiness and its effect on packet voice and video

      In this document, we propose two derived metrics, called "loss
   distance" and "loss period", with associated statistics, to capture
   packet loss patterns.  The loss period metric captures the frequency
   and length (burstiness) of loss once it starts, and the loss
   distance metric captures the spacing between the loss periods.  It is
   important to note that these metrics are derived based on the base
   metric Type-P-One-Way-packet-Loss.

   2. Terminology

      The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
   "silently ignore" in this document are to be interpreted as described
   in RFC 2119 [4].

   3. The Approach

      This document closely follows the guidelines specified in [10].
   Specifically, the concepts of singleton, sample, statistic,
   measurement principles, Type-P packets, as well as standard-formed
   packets all apply.  However, since the draft proposes to capture
   specific Internet behaviors, modifications to the sampling process
   MAY be needed.  Indeed, this is mentioned in [1], where it is
   noted that alternate sampling procedures may be useful depending
   on specific circumstances.  This draft proposes that the specific
   behaviors be captured as "derived" metrics from the base metrics the
   behaviors are related to.  The reasons for adopting this position are
   the following:

    -  it provides consistent usage of singleton metric definition for
       different behaviors (e.g., a single definition of packet loss is
       needed for capturing burst of losses, 'm out of n' losses etc.)

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    -  it allows re-use of the methodologies specified for the singleton
       metric with modifications whenever necessary

    -  it clearly separates few base metrics from many Internet

      Following the guidelines in [10], this translates to deriving
   sample metrics from the respective singletons.  The process
   of deriving sample metrics from the singletons is specified
   in [10], [1], and others.

      In the following sections, we apply this approach to a particular
   Internet behavior, namely the packet loss process.

   4. Basic Definitions

      Sequence number: Consecutive packets in a time series sample
                   are given sequence numbers that are consecutive
                   integers.  This document does not specify exactly
                   how to associate sequence numbers with packets.  The
                   sequence numbers could be contained within test
                   packets themselves, or they could be derived through
                   post-processing of the sample.

      Bursty loss: The loss involving consecutive packets of a stream.

      Loss Distance: The difference in sequence numbers of two
                   successively lost packets which may or may not be
                   separated by successfully received packets.

      Example: In a packet stream, the packet with sequence number 20
               is considered lost, followed by the packet with
               sequence number 50. The loss distance is 30.

      Loss period: Let P_i be the i'th packet.  Define f(P_i) = 1 if P_i
                   is lost, 0 otherwise.  Then, a loss period begins if
                   f(P_i) = 1 and f(P_(i-1)) = 0

      Example: Consider the following sequence of lost (denoted by x)
               and received (denoted by r) packets.

              r r r x r r x x x r x r r x x x

      Then, with `i' assigned as follows,
                                  1 1 1 1 1 1
      i:      0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5

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      f(P_i) is,

      f(P_i): 0 0 0 1 0 0 1 1 1 0 1 0 0 1 1 1

            and there are four loss periods in the above sequence
            beginning at P_3, P_6, P_10, and P_13.

   5.  Definitions for Samples of One-way Loss Distance, and One-way
      Loss Period

   5.1. Metric Names

   5.1.1. Type-P-One-Way-Loss-Distance-Stream

   5.1.2. Type-P-One-Way-Loss-Period-Stream

   5.2. Metric Parameters

      Src,         the IP address of a host

      Dst,         the IP address of a host

      T0,          a time

      Tf,          a time

      lambda,      a rate of any sampling method chosen in reciprocal of

   5.3. Metric Units

   5.3.1. Type-P-One-Way-Loss-Distance-Stream

      A sequence of pairs of the form <loss distance, loss>, where
   loss is derived from the sequence of <time, loss> in [1], and loss
   distance is either zero or a positive integer.

   5.3.2. Type-P-One-Way-Loss-Period-Stream

      A sequence of pairs of the form <loss period, loss>, where loss is
   derived from the sequence of <time, loss> in [1], and loss period is
   an integer.

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   5.4. Definitions

   5.4.1. Type-P-One-Way-Loss-Distance-Stream

      When a packet is considered lost (using the definition in [1]),
   we look at its sequence number and compare it with that of the
   previously lost packet.  The difference is the loss distance between
   the lost packet and the previously lost packet.  The sample would
   consist of <loss distance, loss> pairs.  This definition assumes that
   sequence numbers of successive test packets increase monotonically by
   one.  The loss distance associated with the very first packet loss is
   considered to be zero.

      The sequence number of a test packet can be derived from the
   timeseries sample collected by performing the loss measurement
   according to the methodology in [1].  For example, if a loss sample
   consists of <T0,0>, <T1,0>, <T2,1>, <T3,0>, <T4,0>, the sequence
   numbers of the five test packets sent at T0, T1, T2, T3, and T4 can
   be 0, 1, 2, 3 and 4 respectively, or 100, 101, 102, 103 and 104
   respectively, etc.

   5.4.2. Type-P-One-Way-Loss-Period-Stream

      We start a counter 'n' at an initial value of zero.  This
   counter is incremented by one each time a lost packet satisfies the
   definition outlined in 4.  The metric is defined as <loss period,
   loss> where "loss" is derived from the sequence of <time, loss> in
   Type-P-One-Way-Loss-Stream [1], and loss period is set to zero when
   "loss" is zero in Type-P-One-Way-Loss-Stream, and loss period is set
   to 'n' (above) when "loss" is one in Type-P-One-Way-Loss-Stream.

      Essentially, when a packet is lost, the current value of "n"
   indicates the loss period to which this packet belongs.  For a packet
   that is received successfully, the loss period is defined to be zero.

   5.4.3. Examples

      Let the following set of pairs represent a Type-P-One-Way-Loss-Stream.


      where T1, T2,..,T10 are in increasing order.

      Packets sent at T2, T5, T7, T9, T10 are lost.  The two derived
   metrics can be obtained from this sample as follows.

      (i) Type-P-One-Way-Loss-Distance-Stream:

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      Since packet 2 is the first lost packet, the associated loss
   distance is zero.  For the next lost packet (packet 5), loss distance
   is 5-2 or 3.  Similarly, for the remaining lost packets (packets
   7, 9, and 10) their loss distances are 2, 2, and 1 respectively.
   Therefore, the Type-P-One-Way-Loss-Distance-Stream is:


      (ii) The Type-P-One-Way-Loss-Period-Stream:

      The packet 2 sets the counter 'n' to 1, which is incremented
   by one for packets 5, 7 and 9 according to the definition in 4.
   However, for packet 10, the counter remains at 4, again satisfying
   the definition in 4.  Thus, the Type-P-One-Way-Loss-Period-Stream is:


   5.5. Methodologies

      The same methodology outlined in [1] can be used to conduct the
   sample experiments.  A synopsis is listed below.

      Generally, for a given Type-P, one possible methodology would
   proceed as follows:

    -  Arrange that Src and Dst have clocks that are synchronized with
       each other.  The degree of synchronization is a parameter of the
       methodology, and depends on the threshold used to determine loss
       (see below).

    -  At the Src host, select Src and Dst IP addresses, and form a test
       packet of Type-P with these addresses.

    -  At the Dst host, arrange to receive the packet.

    -  At the Src host, place a timestamp in the prepared Type-P packet,
       and send it towards Dst.

    -  If the packet arrives within a reasonable period of time, the
       one-way packet-loss is taken to be zero.

    -  If the packet fails to arrive within a reasonable period of
       time, the one-way packet-loss is taken to be one.  Note that the
       threshold of "reasonable" here is a parameter of the methodology.

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   5.6. Discussion

      The Loss-Distance-Stream metric allows one to study the separation
   between packet losses.  This could be useful in determining a "spread
   factor" associated with the packet loss rate.  In conjunction, the
   Loss-Period-Stream metric allows the study of loss burstiness for
   each occurrence of loss.  A single loss period of length 'n' can
   account for a significant portion of the overall loss rate.  Note
   that it is possible to measure distance between loss bursts separated
   by one or more successfully received packets.  (Refer to Sections 6.4
   and  6.5).

   5.7. Sampling Considerations

      The proposed metrics can be used independent of the particular
   sampling method used.  We note that Poisson sampling may not
   yield appropriate values for these metrics for certain real-time
   applications such as voice over IP, as well as to TCP-based
   applications.  For real-time applications, it may be more appropriate
   to use the ON-OFF [11] model, in which an ON period starts with
   certain probability 'p', during which certain number of packets
   are transmitted with mean 'lambda-on' according to geometric
   distribution and an OFF period starts with probability '1-p' and
   lasts for a period of time based on exponential distribution with
   rate 'lambda-off'.

      For TCP-based applications, one may use the model proposed in [7].
   See [8] for an application of the model.

   5.8. Errors and Uncertainties

      The measurement aspects, including the packet size, loss
   threshold, type of the test machine chosen etc, invariably influence
   the packet loss metric itself and hence the derived metrics described
   in this document.  Thus, when making assessment of the results
   pertaining to the metrics outlined in this document, attention must
   be paid to these matters.  See [1] for a detailed consideration of
   errors and uncertainties regarding the measurement of base packet
   loss metric.

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   6. Statistics

   6.1. Type-P-One-Way-Loss-Noticeable-Rate

      Define loss of a packet to be "noticeable" [6] if the distance
   between the lost packet and the previously lost packet is no greater
   than delta, a positive integer, where delta is the "loss constraint".

       Example:  Let delta = 99.  Let us assume that packet 50 is lost
   followed by a bursty loss of length 3 starting from packet 125.  All
   the three losses starting from packet 125 are noticeable.

      Given a Type-P-One-Way-Loss-Distance-Stream, this statistic can be
   computed simply as the number of losses that violate some constraint
   delta, divided by the number of losses.  (Alternatively, it can also
   be defined as the number of "noticeable losses" to the number of
   successfully received packets).  This statistic is useful when the
   actual distance between successive losses is important.  For example,
   many multimedia codecs can sustain losses by "concealing" the effect
   of loss by making use of past history information.  Their ability to
   do so degrades with poor history resulting from losses separated by
   close distances.  By choosing delta based on this sensitivity, one
   can measure how "noticeable" a loss might be for quality purposes.
   The noticeable loss requires a certain "spread factor" for losses
   in the timeseries.  In the above example where loss constraint is
   equal to 99, a loss rate of one percent with a spread of 100 between
   losses (e.g., 100, 200, 300, 400, 500 out of 500 packets) may be more
   desirable for some applications compared to the same loss rate with a
   spread that violates the loss constraint (e.g., 100, 175, 275, 290,
   400:  losses occurring at 175 and 290 violate delta = 99).

   6.2. Type-P-One-Way-Loss-Period-Total

      This represents the total number of loss periods, and can be
   derived from the loss period metric Type-P-One-Way-Loss-Period-Stream
   as follows:

      Type-P-One-Way-Loss-Period-Total = maximum value of the first
   entry of the set of pairs, <loss period, loss>, representing the loss
   metric Type-P-One-Way-Loss-Period-Stream.

      Note that this statistic does not describe the duration of each
   loss period itself.  If this statistic is large, it does not mean
   that the losses are more spread out than they are otherwise; one
   or more loss periods may include bursty losses.  This statistic is
   generally useful in gathering first order of approximation of loss

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   6.3. Type-P-One-Way-Loss-Period-Lengths

      This statistic is a sequence of pairs <loss period,
   length>, with the "loss period" entry ranging from 1 -
   Type-P-One-Way-Loss-Period-Total.  Thus the total number of
   pairs in this statistic equals Type-P-One-Way-Loss-Period-Total.  In
   each pair, the "length" is obtained by counting the number of pairs,
   <loss period, loss>, in the metric Type-P-One-Way-Loss-Period-Stream
   which have first entry equal to "loss period."

      Since this statistic represents the number of packets lost in each
   loss period, it is an indicator of burstiness of each loss period.
   In conjunction with loss-period-total statistic, this statistic is
   generally useful in observing which loss periods are potentially more
   influential than others from a quality perspective.

   6.4. Type-P-One-Way-Inter-Loss-Period-Lengths

      This statistic measures distance between successive loss
   periods.  It takes the form of a set of pairs <loss period,
   inter-loss-period-length>, with the "loss period" entry ranging from
   1 - Type-P-One-Way-Loss-Period-Total, and "inter-loss-period-length"
   is the loss distance between the last packet considered lost in "loss
   period" 'i-1', and the first packet considered lost in "loss period"
   'i', where 'i' ranges from 2 to Type-P-One-Way-Loss-Period-Total.
   The "inter-loss-period-length" associated with the first "loss
   period" is defined to be zero.

      This statistic allows one to consider, for example, two loss
   periods each of length greater than one (implying loss burst), but
   separated by a distance of 2 to belong to the same loss burst if such
   a consideration is deemed useful.  When the Inter-Loss-Period-Length
   between two bursty loss periods is smaller, it could affect the loss
   concealing ability of multimedia codecs since there is relatively
   smaller history.  When it is larger, an application may be able to
   rebuild its history which could dampen the effect of an impending
   loss (period).

   6.5. Examples

      We continue with the same example as in Section 5.4.3.  The three
   statistics defined above will have the following values.

    -  Let delta = 2.  In Type-P-One-Way-Loss-Distance-Stream


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       there are 3 loss distances that violate the delta of 2.  Thus,

       Type-P-One-Way-Loss-Noticeable-Rate = 3/5 ((number of noticeable
       losses)/(number of total losses))

    -  In Type-P-One-Way-Loss-Period-Stream


       the largest of the first entry in the sequence of <loss
       period,loss> pairs is 4.  Thus,

       Type-P-One-Way-Loss-Period-Total = 4

    -  In Type-P-One-Way-Loss-Period-Stream


       the lengths of individual loss periods are 1, 1, 1 and 2
       respectively.  Thus,

       Type-P-One-Way-Loss-Period-Lengths =


    -  In Type-P-One-Way-Loss-Period-Stream


       the loss periods 1 and 2 are separated by 3 (5-2), loss periods
       2 and 3 are separated by 2 (7-5), and 3 and 4 are separated by 2
       (9-7).  Thus,
       Type-P-One-Way-Inter-Loss-Period-Lengths =


   7. Security Considerations:

      Since this draft proposes sample metrics based on the base loss
   metric defined in [1], it inherits the security considerations
   mentioned in [1].

      Conducting Internet measurements raises both security and privacy
   concerns.  This document does not specify a particular implementation
   of metrics, so it does not directly affect the security of the
   Internet nor of applications which run on the Internet.  However,

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   implementations of these metrics must be mindful of security and
   privacy concerns.

      The derived sample metrics in this document are based on the loss
   metric defined in RFC-2680 [1], and thus they inherit the security
   considerations of that document.  The reader should consult [1] for a
   more detailed treatment of security considerations.

      Nevertheless, there are a few things to highlight.  First,
   the lambda specified in the Type-P-Loss-Distance-Stream and
   Type-P-Loss-Period-Stream controls the rate at which test packets
   are sent, and therefore if it is set inappropriately large could
   perturb the network under test, cause congestion, or at worst be a
   denial-of-service attack to the network under test.

      Second, privacy of user data is not a concern, since the
   underlying metric is intended to be implemented using test packets
   that contain no user information.  Even if packets contained user
   information, the derived metrics do not release data sent by the
   user.  Third, the results could be perturbed by attempting to corrupt
   or disrupt the underlying stream, for example adding extra packets
   that look just like test packets.

      In general, legitimate measurements must have their parameters
   selected carefully in order to avoid interfering with normal traffic
   in the network.  Such measurements should also be authorized and
   authenticated in some way so that attacks can be identified and

   8. IANA Considerations

      Since this document does not define a specific protocol, nor does
   it define any well-known values, there are no IANA considerations for
   this document.

   9. Acknowledgements

      Matt Zekauskas provided insightful feedback and the text for the
   Security Considerations section.  We sincerely thank him for his
   painstaking review and for supporting this work along with Merike
   Kaeo.  Thanks to Guy Almes for encouraging the work, and Vern Paxson
   for the comments during the IETF meetings.  Thanks to Steve Glass for
   making the presentation at the Oslo meeting.

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

    [2] J.-C. Bolot and A. vega Garcia, "The case for FEC-based error
        control for Packet Audio in the Internet", ACM Multimedia
        Systems, 1997.

    [3] M. S. Borella, D. Swider, S. Uludag, and G. B. Brewster,
        "Internet Packet Loss:  Measurement and Implications for
        End-to-End QoS," Proceedings, International Conference on
        Parallel Processing, August 1998.

    [4] S. Bradner, "Key words for use in RFCs to Indicate Requirement
        Levels," RFC 2119, Internet Engineering Task Force, March 1997.

    [5] M. Handley, "An examination of MBONE performance", Technical
        Report, USC/ISI, ISI/RR-97-450, July 1997

    [6] R. Koodli, "Scheduling Support for Multi-tier Quality of
        Service in Continuous Media Applications", PhD dissertation,
        Electrical and Computer Engineering Department, University of
        Massachusetts, Amherst, MA 01003, September 1997.

    [7] J. Padhye, V. Firoiu, J. Kurose and D. Towsley, "Modeling TCP
        throughput:  a simple model and its empirical validation", in
        Proceedings of SIGCOMM'98, 1998.

    [8] J. Padhye, J. Kurose, D. Towsley and R. Koodli, "A TCP-friendly
        rate adjustment protocol for continuous media flows over
        best-effort networks", short paper presentation in ACM
        SIGMETRICS'99. Available as Umass Computer Science tech report
        from ftp://gaia.cs.umass.edu/pub/Padhye98-tcp-friendly-TR.ps.gz

    [9] V. Paxson, "End-to-end Internet packet dynamics", IEEE/ACM
        Transactions on Networking, 7(3), pages 277-292, June 1999.

   [10] V. Paxson, G. Almes, J. Mahdavi, and M. Mathis, "Framework for
        IP Performance Metrics", RFC 2330, May 1998.

   [11] K. Sriram and W. Whitt, "Characterizing superposition arrival
        processes in packet multiplexers for voice and data", IEEE
        Journal on Selected Areas of Communication, pages 833-846,
        September 1986,

   [12] M. Yajnik, J. Kurose and D. Towsley, "Packet loss correlation
        in the MBONE multicast network", Proceedings of IEEE Global
        Internet, London, UK, November 1996.

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      Questions about this memo can be directed to the authors:

         Rajeev Koodli                      Rayadurgam Ravikanth
         Communications Systems Lab         Axiowave Networks Inc.
         Nokia Research Center              100 Nickerson Road
         313 Fairchild Drive                Marlborough, MA- 01752
         Mountain View, California 94043    USA
         USA                                Email:  rravikanth@axiowave.com
         Phone:  +1-650 625-2359
         EMail:  rajeev.koodli@nokia.com
         Fax:  +1 650 625-2502

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