One-way loss patterns: One-way Loss Pattern Sample Metrics

Network Working Group                                          R. Koodli
Request for Comments: 3357                         Nokia Research Center
Category: Informational                                     R. Ravikanth
                                                                Axiowave
                                                             August 2002


                  One-way Loss Pattern Sample Metrics

Status of this Memo

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

Copyright Notice

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

Abstract

   Using the base loss metric defined in RFC 2680, this document defines
   two derived metrics "loss distance" and "loss period", and the
   associated statistics that together capture loss patterns experienced
   by packet streams on the Internet.  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.  The loss
   pattern or loss distribution is a key parameter that determines the
   performance observed by the users for certain real-time applications
   such as packet voice and video.  For the same loss rate, two
   different loss distributions could potentially produce widely
   different perceptions of performance.

Table of Contents

   1. Introduction                                                     3
   2. Terminology                                                      3
   3. The Approach                                                     3
   4. Basic Definitions                                                4
   5.  Definitions for Samples of One-way Loss Distance, and One-way
        Loss Period                                                    5
       5.1. Metric Names  . . . . . . . . . . . . . . . . . . . . . .  5
             5.1.1. Type-P-One-Way-Loss-Distance-Stream . . . . . . .  5
             5.1.2. Type-P-One-Way-Loss-Period-Stream . . . . . . . .  5
       5.2. Metric Parameters . . . . . . . . . . . . . . . . . . . .  5
       5.3. Metric Units  . . . . . . . . . . . . . . . . . . . . . .  5
             5.3.1. Type-P-One-Way-Loss-Distance-Stream . . . . . . .  5
             5.3.2. Type-P-One-Way-Loss-Period-Stream . . . . . . . .  5
       5.4. Definitions . . . . . . . . . . . . . . . . . . . . . . .  6
             5.4.1. Type-P-One-Way-Loss-Distance-Stream . . . . . . .  6
             5.4.2. Type-P-One-Way-Loss-Period-Stream . . . . . . . .  6
             5.4.3. Examples  . . . . . . . . . . . . . . . . . . . .  6
       5.5. Methodologies . . . . . . . . . . . . . . . . . . . . . .  7
       5.6. Discussion  . . . . . . . . . . . . . . . . . . . . . . .  8
       5.7. Sampling Considerations . . . . . . . . . . . . . . . . .  8
       5.8. Errors and Uncertainties  . . . . . . . . . . . . . . . .  8
   6. Statistics                                                       9
       6.1. Type-P-One-Way-Loss-Noticeable-Rate . . . . . . . . . . .  9
       6.2. Type-P-One-Way-Loss-Period-Total  . . . . . . . . . . . .  9
       6.3. Type-P-One-Way-Loss-Period-Lengths  . . . . . . . . . . . 10
       6.4. Type-P-One-Way-Inter-Loss-Period-Lengths  . . . . . . . . 10
       6.5. Examples  . . . . . . . . . . . . . . . . . . . . . . . . 10
   7. Security Considerations                                         11
       7.1. Denial of Service Attacks . . . . . . . . . . . . . . . . 12
       7.2. Privacy / Confidentiality . . . . . . . . . . . . . . . . 12
       7.3. Integrity . . . . . . . . . . . . . . . . . . . . . . . . 12
   8. IANA Considerations                                             12
   9. Acknowledgements                                                12
   10. Normative References                                           12
   11. Informative References                                         13
   Authors' Addresses                                                 14
   Full Copyright Statement                                           15

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 [4], [5], [6],
   [11] for evidence as to the importance and existence of loss
   burstiness and its effect on packet voice and video applications.

   Previously, the focus of the IPPM had been on specifying base metrics
   such as delay, loss and connectivity under the framework described in
   RFC 2330.  However, specific Internet behaviors can also be captured
   under the umbrella of the IPPM framework, specifying new concepts
   while reusing existing guidelines as much as possible.  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 NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", "OPTIONAL", and
   "silently ignore" in this document are to be interpreted as described
   in BCP 14, RFC 2119 [2].

3. The Approach

   This document closely follows the guidelines specified in [3].
   Specifically, the concepts of singleton, sample, statistic,
   measurement principles, Type-P packets, as well as standard-formed
   packets all apply.  However, since the document 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 document 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.)

   -  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 behaviors

   Following the guidelines in [3], this translates to deriving sample
   metrics from the respective singletons.  The process of deriving
   sample metrics from the singletons is specified in [3], [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

   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
                seconds

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.

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.

   {<T1,0>,<T2,1>,<T3,0>,<T4,0>,<T5,1>,<T6,0>,<T7,1>,<T8,0>,
    <T9,1>,<T10,1>}

   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:

   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:

   {<0,0>,<0,1>,<0,0>,<0,0>,<3,1>,<0,0>,<2,1>,<0,0>,<2,1>,<1,1>}

   (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:

   {<0,0>,<1,1>,<0,0>,<0,0>,<2,1>,<0,0>,<3,1>,<0,0>,<4,1>,<4,1>}

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:

   -  Assume 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.

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 [10] model, in which an ON period starts with a
   certain probability 'p', during which a 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 [8].
   See [9] 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 an 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.

6. Statistics

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

   Define loss of a packet to be "noticeable" [7] 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 approximation of loss spread.

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 their 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

         {<0,0>,<0,1>,<0,0>,<0,0>,<3,1>,<0,0>,<2,1>,<0,0>,<2,1>,<1,1>},
      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

         {<0,0>,<1,1>,<0,0>,<0,0>,<2,1>,<0,0>,<3,1>,<0,0>,<4,1>,<4,1>},

      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

         {<0,0>,<1,1>,<0,0>,<0,0>,<2,1>,<0,0>,<3,1>,<0,0>,<4,1>,<4,1>},

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

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

         {<1,1>,<2,1>,<3,1>,<4,2>}

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

         {<0,0>,<1,1>,<0,0>,<0,0>,<2,1>,<0,0>,<3,1>,<0,0>,<4,1>,<4,1>},

      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 =

         {<1,0>,<2,3>,<3,2>,<4,2>}

7. Security Considerations

   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,
   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.

7.1. Denial of Service Attacks

   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, it could perturb
   the network under test, cause congestion, or at worst be a denial-
   of-service attack to the network under test.  Legitimate measurements
   must have their parameters selected carefully in order to avoid
   interfering with normal traffic in the network.

7.2. Privacy / Confidentiality

   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.

7.3. Integrity

   Results could be perturbed by attempting to corrupt or disrupt the
   underlying stream, for example adding extra packets that look just
   like test packets.  To ensure that test packets are valid and have
   not been altered during transit, packet authentication and integrity
   checks, such as a signed cryptographic hash, MAY be used.

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.  Merike Kao helped revising the
   Security Considerations and the Abstract to conform with RFC
   guidelines.  We thank both of them.  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.

10. Normative References

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

   [2]  Bradner, S., "Key words for use in RFCs to Indicate Requirement
        Levels", BCP 14, RFC 2119, March 1997.

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

11. Informative References

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

   [5]  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.

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

   [7]  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.

   [8]  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.

   [9]  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

   [10] 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,

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

Authors' Addresses

   Rajeev Koodli
   Communications Systems Lab
   Nokia Research Center
   313 Fairchild Drive
   Mountain View, CA 94043
   USA

   Phone: +1-650 625-2359
   Fax: +1 650 625-2502
   EMail: rajeev.koodli@nokia.com


   Rayadurgam Ravikanth
   Axiowave Networks Inc.
   200 Nickerson Road
   Marlborough, MA 01752
   USA

   EMail: rravikanth@axiowave.com

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