RTP Media Congestion Avoidance                             D. Hayes, Ed.
Techniques                                            University of Oslo
Internet-Draft                                                 S. Ferlin
Intended status: Experimental                 Simula Research Laboratory
Expires: January 2, 2016                                        M. Welzl
                                                      University of Oslo
                                                            July 1, 2015

   Shared Bottleneck Detection for Coupled Congestion Control for RTP


   This document describes a mechanism to detect whether end-to-end data
   flows share a common bottleneck.  It relies on summary statistics
   that are calculated by a data receiver based on continuous
   measurements and regularly fed to a grouping algorithm that runs
   wherever the knowledge is needed.  This mechanism complements the
   coupled congestion control mechanism in draft-welzl-rmcat-coupled-cc.

Status of this Memo

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   carefully, as they describe your rights and restrictions with respect
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Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  3
     1.1.  The signals  . . . . . . . . . . . . . . . . . . . . . . .  3
       1.1.1.  Packet Loss  . . . . . . . . . . . . . . . . . . . . .  3
       1.1.2.  Packet Delay . . . . . . . . . . . . . . . . . . . . .  3
       1.1.3.  Path Lag . . . . . . . . . . . . . . . . . . . . . . .  4
   2.  Definitions  . . . . . . . . . . . . . . . . . . . . . . . . .  4
     2.1.  Parameters and their Effect  . . . . . . . . . . . . . . .  5
     2.2.  Recommended Parameter Values . . . . . . . . . . . . . . .  7
   3.  Mechanism  . . . . . . . . . . . . . . . . . . . . . . . . . .  7
     3.1.  Key metrics and their calculation  . . . . . . . . . . . .  9
       3.1.1.  Mean delay . . . . . . . . . . . . . . . . . . . . . .  9
       3.1.2.  Skewness Estimate  . . . . . . . . . . . . . . . . . .  9
       3.1.3.  Variability Estimate . . . . . . . . . . . . . . . . . 10
       3.1.4.  Oscillation Estimate . . . . . . . . . . . . . . . . . 11
       3.1.5.  Packet loss  . . . . . . . . . . . . . . . . . . . . . 11
     3.2.  Flow Grouping  . . . . . . . . . . . . . . . . . . . . . . 12
       3.2.1.  Flow Grouping Algorithm  . . . . . . . . . . . . . . . 12
       3.2.2.  Using the flow group signal  . . . . . . . . . . . . . 13
     3.3.  Removing Noise from the Estimates  . . . . . . . . . . . . 13
       3.3.1.  PDV noise  . . . . . . . . . . . . . . . . . . . . . . 14
       3.3.2.  Oscillation noise  . . . . . . . . . . . . . . . . . . 14
       3.3.3.  Clock skew . . . . . . . . . . . . . . . . . . . . . . 15
     3.4.  Reducing lag and Improving Responsiveness  . . . . . . . . 15
       3.4.1.  Improving the response of the skewness estimate  . . . 16
       3.4.2.  Improving the response of the variability estimate . . 16
   4.  Measuring OWD  . . . . . . . . . . . . . . . . . . . . . . . . 17
     4.1.  Time stamp resolution  . . . . . . . . . . . . . . . . . . 17
   5.  Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 17
   6.  IANA Considerations  . . . . . . . . . . . . . . . . . . . . . 17
   7.  Security Considerations  . . . . . . . . . . . . . . . . . . . 17
   8.  Change history . . . . . . . . . . . . . . . . . . . . . . . . 18
   9.  References . . . . . . . . . . . . . . . . . . . . . . . . . . 18
     9.1.  Normative References . . . . . . . . . . . . . . . . . . . 18
     9.2.  Informative References . . . . . . . . . . . . . . . . . . 18
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 19

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1.  Introduction

   In the Internet, it is not normally known if flows (e.g., TCP
   connections or UDP data streams) traverse the same bottlenecks.  Even
   flows that have the same sender and receiver may take different paths
   and share a bottleneck or not.  Flows that share a bottleneck link
   usually compete with one another for their share of the capacity.
   This competition has the potential to increase packet loss and
   delays.  This is especially relevant for interactive applications
   that communicate simultaneously with multiple peers (such as multi-
   party video).  For RTP media applications such as RTCWEB,
   [I-D.welzl-rmcat-coupled-cc] describes a scheme that combines the
   congestion controllers of flows in order to honor their priorities
   and avoid unnecessary packet loss as well as delay.  This mechanism
   relies on some form of Shared Bottleneck Detection (SBD); here, a
   measurement-based SBD approach is described.

1.1.  The signals

   The current Internet is unable to explicitly inform endpoints as to
   which flows share bottlenecks, so endpoints need to infer this from
   whatever information is available to them.  The mechanism described
   here currently utilises packet loss and packet delay, but is not
   restricted to these.

1.1.1.  Packet Loss

   Packet loss is often a relatively rare signal.  Therefore, on its own
   it is of limited use for SBD, however, it is a valuable supplementary
   measure when it is more prevalent.

1.1.2.  Packet Delay

   End-to-end delay measurements include noise from every device along
   the path in addition to the delay perturbation at the bottleneck
   device.  The noise is often significantly increased if the round-trip
   time is used.  The cleanest signal is obtained by using One-Way-Delay

   Measuring absolute OWD is difficult since it requires both the sender
   and receiver clocks to be synchronised.  However, since the
   statistics being collected are relative to the mean OWD, a relative
   OWD measurement is sufficient.  Clock skew is not usually significant
   over the time intervals used by this SBD mechanism (see [RFC6817] A.2
   for a discussion on clock skew and OWD measurements).  However, in
   circumstances where it is significant, Section 3.3.3 outlines a way
   of adjusting the calculations to cater for it.

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   Each packet arriving at the bottleneck buffer may experience very
   different queue lengths, and therefore different waiting times.  A
   single OWD sample does not, therefore, characterize the path well.
   However, multiple OWD measurements do reflect the distribution of
   delays experienced at the bottleneck.

1.1.3.  Path Lag

   Flows that share a common bottleneck may traverse different paths,
   and these paths will often have different base delays.  This makes it
   difficult to correlate changes in delay or loss.  This technique uses
   the long term shape of the delay distribution as a base for
   comparison to counter this.

2.  Definitions

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

   Acronyms used in this document:

      OWD -- One Way Delay

      PDV -- Packet Delay Variation

      MAD -- Mean Absolute Deviation

      RTT -- Round Trip Time

      SBD -- Shared Bottleneck Detection

   Conventions used in this document:

      T       --     the base time interval over which measurements are

      N       --     the number of base time, T, intervals used in some

      sum_T(...) --  summation of all the measurements of the variable
                     in parentheses taken over the interval T

      sum(...)   --  summation of terms of the variable in parentheses

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      sum_N(...) --  summation of N terms of the variable in parentheses

      sum_NT(...) -- summation of all measurements taken over the
                     interval N*T

      E_T(...) --    the expectation or mean of the measurements of the
                     variable in parentheses over T

      E_N(...) --    the expectation or mean of the last N values of the
                     variable in parentheses

      E_M(...) --    the expectation or mean of the last M values of the
                     variable in parentheses, where M <= N.

      max_T(...) --  the maximum recorded measurement of the variable in
                     parentheses taken over the interval T

      min_T(...) --  the minimum recorded measurement of the variable in
                     parentheses taken over the interval T

      num_T(...) --  the count of measurements of the variable in
                     parentheses taken in the interval T

      num_VM(...) -- the count of valid values of the variable in
                     parentheses given M records

      PC --          a boolean variable indicating the particular flow
                     was identified as experiencing congestion in the
                     previous interval T (i.e.  Previously Congested)

      skew_est --    a measure of skewness in a OWD distribution.

      var_est --     a measure of variability in OWD measurements.

      freq_est --    a measure of low frequency oscillation in the OWD

      p_l, p_f, p_pdv, p_mad, c_s, c_h, p_s, p_d, p_v --  various
                     thresholds used in the mechanism

      M and F --     number of values related to N

2.1.  Parameters and their Effect

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   T       T should be long enough so that there are enough packets
           received during T for a useful estimate of short term mean
           OWD and variation statistics.  Making T too large can limit
           the efficacy of PDV and freq_est.  It will also increase the
           response time of the mechanism.  Making T too small will make
           the metrics noisier.

   N & M   N should be large enough provide a stable estimate of
           oscillations in OWD and average PDV.  Usually M=N, though
           having M<N may be beneficial in certain circumstances.  M*T
           needs to be long enough provide stable estimates of skewness
           and MAD (if used).

   F       F determines the number of intervals over which statistics
           are considered to be equally weighted.  When F=M recent and
           older measurements are considered equal.  Making F<M can
           increase the responsiveness of the SBD mechanism.  If F is
           too small, statistics will be too noisy.

   c_s     c_s is the threshold in skew_est used for determining whether
           a flow is experiencing congestion or not.  It should be
           slightly negative so that a very lightly loaded path does not
           give a false indication.  Setting c_s more negative makes the
           SBD mechanism less sensitive to transient and light
           congestion episodes.

   c_s     c_h adds hysteresis to the congestion determination.  It
           should be large enough to avoid constant switching in the
           determination, but low enough to ensure that grouping is not
           attempted when there is no congestion and the delay and loss
           signals cannot be relied upon.

   p_v     p_v determines the sensitivity of freq_est to noise.  Making
           it smaller will yield higher but noisier values for freq_est.
           Making it too large will render it ineffective for
           determining groups.

   p_*     Flows are separated when the skew_est|var_est|freq_est
           measure is greater than p_s|p_f|p_d|(p_pdv|p_mad).  Adjusting
           these is a compromise between false grouping of flows that do
           not share a bottleneck and false splitting of flows that do.
           Making them larger can help if the measures are very noisy,
           but reducing the noise in the statistical measures by
           adjusting T and N|M may be a better solution.

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2.2.  Recommended Parameter Values

   Reference [Hayes-LCN14] uses T=350ms, N=50, p_l = 0.1.  The other
   parameters have been tightened to reflect minor enhancements to the
   algorithm outlined in Section 3.3: c_s = -0.01, p_f = p_s = p_d =
   0.1, p_pdv = 0.2, p_v = 0.2 (or p_mad=0.1, p_v=0.7).  M=50, F=25, and
   c_h = 0.3 are additional parameters defined in the document.  These
   are values that seem to work well over a wide range of practical
   Internet conditions.

3.  Mechanism

   The mechanism described in this document is based on the observation
   that the distribution of delay measurements of packets that traverse
   a common bottleneck have similar shape characteristics.  These shape
   characteristics are described using 3 key summary statistics:

      variability (estimate var_est, see Section 3.1.3)

      skewness (estimate skew_est, see Section 3.1.2)

      oscillation (estimate freq_est, see Section 3.1.4)

   with packet loss (estimate pkt_loss, see Section 3.1.5) used as a
   supplementary statistic.

   Summary statistics help to address both the noise and the path lag
   problems by describing the general shape over a relatively long
   period of time.  This is sufficient for their application in coupled
   congestion control for RTP Media.  They can be signalled from a
   receiver, which measures the OWD and calculates the summary
   statistics, to a sender, which is the entity that is transmitting the
   media stream.  An RTP Media device may be both a sender and a
   receiver.  SBD can be performed at either a sender or a receiver or

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                                  | H2 |
                                     | L2
                         +----+  L1  |  L3  +----+
                         | H1 |------|------| H3 |
                         +----+             +----+

       A network with 3 hosts (H1, H2, H3) and 3 links (L1, L2, L3).

                                 Figure 1

   In Figure 1, there are two possible cases for shared bottleneck
   detection: a sender-based and a receiver-based case.

   1.  Sender-based: consider a situation where host H1 sends media
       streams to hosts H2 and H3, and L1 is a shared bottleneck.  H2
       and H3 measure the OWD and calculate summary statistics, which
       they send to H1 every T. H1, having this knowledge, can determine
       the shared bottleneck and accordingly control the send rates.

   2.  Receiver-based: consider that H2 is also sending media to H3, and
       L3 is a shared bottleneck.  If H3 sends summary statistics to H1
       and H2, neither H1 nor H2 alone obtain enough knowledge to detect
       this shared bottleneck; H3 can however determine it by combining
       the summary statistics related to H1 and H2, respectively.  This
       case is applicable when send rates are controlled by the
       receiver; then, the signal from H3 to the senders contains the
       sending rate.

   A discussion of the required signalling for the receiver-based case
   is beyond the scope of this document.  For the sender-based case, the
   messages and their data format will be defined here in future
   versions of this document.  We envision that an initialization
   message from the sender to the receiver could specify which key
   metrics are requested out of a possibly extensible set (pkt_loss,
   var_est, skew_est, freq_est).  The grouping algorithm described in
   this document requires all four of these metrics, and receivers MUST
   be able to provide them, but future algorithms may be able to exploit
   other metrics (e.g. metrics based on explicit network signals).
   Moreover, the initialization message could specify T, N, and the
   necessary resolution and precision (number of bits per field).

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3.1.  Key metrics and their calculation

   Measurements are calculated over a base interval, T. T should be long
   enough to provide enough samples for a good estimate of skewness, but
   short enough so that a measure of the oscillation can be made from N
   of these estimates.  Reference [Hayes-LCN14] uses T = 350ms and
   N=M=50, which are values that seem to work well over a wide range of
   practical Internet conditions.

3.1.1.  Mean delay

   The mean delay is not a useful signal for comparisons between flows
   since flows may traverse quite different paths and clocks will not
   necessarily be synchronized.  However, it is a base measure for the 3
   summary statistics.  The mean delay, E_T(OWD), is the average one way
   delay measured over T.

   To facilitate the other calculations, the last N E_T(OWD) values will
   need to be stored in a cyclic buffer along with the moving average of

      mean_delay = E_M(E_T(OWD)) = sum_M(E_T(OWD)) / M

   where M <= N. Generally M=N: setting M to be less than N allows the
   mechanism to be more responsive to changes, but potentially at the
   expense of a higher error rate (see Section 3.4 for a discussion on
   improving the responsiveness of the mechanism.)

3.1.2.  Skewness Estimate

   Skewness is difficult to calculate efficiently and accurately.
   Ideally it should be calculated over the entire period (M * T) from
   the mean OWD over that period.  However this would require storing
   every delay measurement over the period.  Instead, an estimate is
   made over M * T based on a calculation every T using the previous T's
   calculation of mean_delay.

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   The skewness is estimated using two counters, counting the number of
   one way delay samples (OWD) above and below the mean:

      skew_base_T = sum_T(OWD < mean_delay) - sum_T(OWD > mean_delay)


         if (OWD < mean_delay) 1 else 0

         if (OWD > mean_delay) 1 else 0

      and mean_delay does not include the mean of the current T

      skew_est = sum_MT(skew_base_T)/num_MT(OWD)

         where skew_est is a number between -1 and 1

   Note: Care must be taken when implementing the comparisons to ensure
   that rounding does not bias skew_est.  It is important that the mean
   is calculated with a higher precision than the samples.

3.1.3.  Variability Estimate

   Packet Delay Variation (PDV) ([RFC5481] and [ITU-Y1540]) is used as
   an estimator of the variability of the delay signal.  We define PDV
   as follows:

      PDV = PDV_max = max_T(OWD) - E_T(OWD)

      var_est = E_M(PDV) = sum_M(PDV) / M

   This modifies PDV as outlined in [RFC5481] to provide a summary
   statistic version that best aids the grouping decisions of the
   algorithm (see [Hayes-LCN14] section IVB).

   Generally the maximum is sampled well during congestion, though it is
   more sensitive to path and operating system noise.  The use of PDV =
   PDV_min = E_T(OWD) - min_T(OWD) would be less sensitive to this
   noise, but is not well sampled during congestion at the bottleneck
   and therefore not recommended.

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3.1.4.  Oscillation Estimate

   An estimate of the low frequency oscillation of the delay signal is
   calculated by counting and normalising the significant mean,
   E_T(OWD), crossings of mean_delay:

      freq_est = number_of_crossings / N

         where we define a significant mean crossing as a crossing that
         extends p_v * var_est from mean_delay.  In our experiments we
         have found that p_v = 0.2 is a good value.

   Freq_est is a number between 0 and 1.  Freq_est can be approximated
   incrementally as follows:

      With each new calculation of E_T(OWD) a decision is made as to
      whether this value of E_T(OWD) significantly crosses the current
      long term mean, mean_delay, with respect to the previous
      significant mean crossing.

      A cyclic buffer, last_N_crossings, records a 1 if there is a
      significant mean crossing, otherwise a 0.

      The counter, number_of_crossings, is incremented when there is a
      significant mean crossing and decremented when a non-zero value is
      removed from the last_N_crossings.

   This approximation of freq_est was not used in [Hayes-LCN14], which
   calculated freq_est every T using the current E_N(E_T(OWD)).  Our
   tests show that this approximation of freq_est yields results that
   are almost identical to when the full calculation is performed every

3.1.5.  Packet loss

   The proportion of packets lost is used as a supplementary measure:

      pkt_loss = sum_NT(lost packets) / sum_NT(total packets)

   Note: When pkt_loss is small it is very variable, however, when
   pkt_loss is high it becomes a stable measure for making grouping

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3.2.  Flow Grouping

3.2.1.  Flow Grouping Algorithm

   The following grouping algorithm is RECOMMENDED for SBD in the RMCAT
   context and is sufficient and efficient for small to moderate numbers
   of flows.  For very large numbers of flows (e.g. hundreds), a more
   complex clustering algorithm may be substituted.

   Since no single metric is precise enough to group flows (due to
   noise), the algorithm uses multiple metrics.  Each metric offers a
   different "view" of the bottleneck link characteristics, and used
   together they enable a more precise grouping of flows than would
   otherwise be possible.

   Flows determined to be experiencing congestion are successively
   divided into groups based on freq_est, var_est, and skew_est.

   The first step is to determine which flows are experiencing
   congestion.  This is important, since if a flow is not experiencing
   congestion its delay based metrics will not describe the bottleneck,
   but the "noise" from the rest of the path.  Skewness, with proportion
   of packets loss as a supplementary measure, is used to do this:

   1.  Grouping will be performed on flows where:

          skew_est < c_s

             || ( skew_est < c_h && PC )

             || pkt_loss > p_l

   The parameter c_s controls how sensitive the mechanism is in
   detecting congestion.  C_s = 0.0 was used in [Hayes-LCN14].  A value
   of c_s = 0.05 is a little more sensitive, and c_s = -0.05 is a little
   less sensitive.  C_h controls the hysteresis on flows that were
   grouped as experiencing congestion last time.

   These flows, flows experiencing congestion, are then progressively
   divided into groups based on the freq_est, PDV, and skew_est summary
   statistics.  The process proceeds according to the following steps:

   2.  Group flows whose difference in sorted freq_est is less than a

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          diff(freq_est) < p_f

   3.  Group flows whose difference in sorted E_N(PDV) (highest to
       lowest) is less than a threshold:

          diff(var_est) < (p_pdv * var_est)

       The threshold, (p_pdv * var_est), is with respect to the highest
       value in the difference.

   4.  Group flows whose difference in sorted skew_est or pkt_loss is
       less than a threshold:

          if pkt_loss < p_l

             diff(skew_est) < p_s


             diff(pkt_loss) < (p_d * pkt_loss)

          The threshold, (p_d * pkt_loss), is with respect to the
          highest value in the difference.

   This procedure involves sorting estimates from highest to lowest.  It
   is simple to implement, and efficient for small numbers of flows (up
   to 10-20).

3.2.2.  Using the flow group signal

   A grouping decisions is made every T from the second T, though they
   will not attain their full design accuracy until after the N'th T

   Network conditions, and even the congestion controllers, can cause
   bottlenecks to fluctuate.  A coupled congestion controller MAY decide
   only to couple groups that remain stable, say grouped together 90% of
   the time, depending on its objectives.  Recommendations concerning
   this are beyond the scope of this draft and will be specific to the
   coupled congestion controllers objectives.

3.3.  Removing Noise from the Estimates

   The following describe small changes to the calculation of the key
   metrics that help remove noise from them.  Currently these "tweaks"
   are described separately to keep the main description succinct.  In
   future revisions of the draft these enhancements may replace the
   original key metric calculations.

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3.3.1.  PDV noise

   Usually during congestion the max_T(OWD) is quite well sampled as the
   delay distribution is skewed toward the maximum.  However max_T(OWD)
   is subject to delay noise from other queues along the path as well as
   the host operating system.  Min_T(OWD) is less prone to noise along
   the path and from the host operating system, but is not well sampled
   during congestion (i.e. when there is a bottleneck).  Flows with very
   different packet send rates exacerbate the problem.

   An alternative delay variation measure that is less sensitive to
   extreme values and different send rates is Mean Absolute Deviation
   (MAD).  It can be implemented in an online manner as follows:

      var_base_T = sum_T(|OWD - E_T(OWD)|)


            |x| is the absolute value of x

            E_T(OWD) is the mean OWD calculated in the previous T

      var_est = MAD_MT = sum_MT(var_base_T)/num_MT(OWD)

   For calculation of freq_est p_v=0.7 (MAD is a smaller number than

   For the grouping threshold p_mad=0.1 instead of p_pdv (MAD is less
   noisy so the test can be tighter)

   Note that the method for improving responsiveness of MAD_MT is the
   same as that described in Section 3.4.1 for skew_est.

3.3.2.  Oscillation noise

   When a path has no congestion, var_est will be very small and the
   recorded significant mean crossings will be the result of path noise.
   Thus up to N-1 meaningless mean crossings can be a source of error at
   the point a link becomes a bottleneck and flows traversing it begin
   to be grouped.

   To remove this source of noise from freq_est:

   1.  Set the current PDV to PDV = NaN (a value representing an invalid
       record, i.e.  Not a Number) for flows that are deemed to not be
       experiencing congestion by the first skew_est based grouping test
       (see Section 3.2.1).

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   2.  Then var_est = sum_M(PDV != NaN) / num_VM(PDV)

   3.  For freq_est, only record a significant mean crossing if flow is
       experiencing congestion.

   These three changes will remove the non-congestion noise from
   freq_est.  A similar adjustment can be made for MAD based var_est.

3.3.3.  Clock skew

   Generally sender and receiver clock skew will be too small to cause
   significant errors in the estimators.  Skew_est is most sensitive to
   this type of noise.  In circumstances where clock skew is high,
   making M < N can reduce this error.

   A better method is to estimate the effect the clock skew is having on
   the summary statistics, and then adjust statistics accordingly.  A
   simple online method of doing this based on min_T(OWD) will be
   described here in a subsequent version of the draft.

3.4.  Reducing lag and Improving Responsiveness

   Measurement based shared bottleneck detection makes decisions in the
   present based on what has been measured in the past.  This means that
   there is always a lag in responding to changing conditions.  This
   mechanism is based on summary statistics taken over (N*T) seconds.
   This mechanism can be made more responsive to changing conditions by:

   1.  Reducing N and/or M -- but at the expense of having less accurate
       metrics, and/or

   2.  Exploiting the fact that more recent measurements are more
       valuable than older measurements and weighting them accordingly.

   Although more recent measurements are more valuable, older
   measurements are still needed to gain an accurate estimate of the
   distribution descriptor we are measuring.  Unfortunately, the simple
   exponentially weighted moving average weights drop off too quickly
   for our requirements and have an infinite tail.  A simple linearly
   declining weighted moving average also does not provide enough weight
   to the most recent measurements.  We propose a piecewise linear
   distribution of weights, such that the first section (samples 1:F) is
   flat as in a simple moving average, and the second section (samples
   F+1:M) is linearly declining weights to the end of the averaging
   window.  We choose integer weights, which allows incremental
   calculation without introducing rounding errors.

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3.4.1.  Improving the response of the skewness estimate

   The weighted moving average for skew_est, based on skew_est in
   Section 3.1.2, can be calculated as follows:

      skew_est = ((M-F+1)*sum(skew_base_T(1:F))

                      + sum([(M-F):1].*skew_base_T(F+1:M)))

                 / ((M-F+1)*sum(numsampT(1:F))

                      + sum([(M-F):1].*numsampT(F+1:M)))

   where numsampT is an array of the number of OWD samples in each T
   (i.e. num_T(OWD)), and numsampT(1) is the most recent; skew_base_T(1)
   is the most recent calculation of skew_base_T; 1:F refers to the
   integer values 1 through to F, and [(M-F):1] refers to an array of
   the integer values (M-F) declining through to 1; and ".*" is the
   array scalar dot product operator.

3.4.2.  Improving the response of the variability estimate

   The weighted moving average for var_est can be calculated as follows:

      var_est = ((M-F+1)*sum(PDV(1:F)) + sum([(M-F):1].*PDV(F+1:M)))

                / (F*(M-F+1) + sum([(M-F):1])

   where 1:F refers to the integer values 1 through to F, and [(M-F):1]
   refers to an array of the integer values (M-F) declining through to
   1; and ".*" is the array scalar dot product operator.  When removing
   oscillation noise (see Section 3.3.2) this calculation must be
   adjusted to allow for invalid PDV records.

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4.  Measuring OWD

   This section discusses the OWD measurements required for this
   algorithm to detect shared bottlenecks.

   The SBD mechanism described in this draft relies on differences
   between OWD measurements to avoid the practical problems with
   measuring absolute OWD (see [Hayes-LCN14] section IIIC).  Since all
   summary statistics are relative to the mean OWD and sender/receiver
   clock offsets should be approximately constant over the measurement
   periods, the offset is subtracted out in the calculation.

4.1.  Time stamp resolution

   The SBD mechanism requires timing information precise enough to be
   able to make comparisons.  As a rule of thumb, the time resolution
   should be less than one hundredth of a typical path's range of
   delays.  In general, the lower the time resolution, the more care
   that needs to be taken to ensure rounding errors do not bias the
   skewness calculation.

   Typical RTP media flows use sub-millisecond timers, which should be
   adequate in most situations.

5.  Acknowledgements

   This work was part-funded by the European Community under its Seventh
   Framework Programme through the Reducing Internet Transport Latency
   (RITE) project (ICT-317700).  The views expressed are solely those of
   the authors.

6.  IANA Considerations

   This memo includes no request to IANA.

7.  Security Considerations

   The security considerations of RFC 3550 [RFC3550], RFC 4585
   [RFC4585], and RFC 5124 [RFC5124] are expected to apply.

   Non-authenticated RTCP packets carrying shared bottleneck indications
   and summary statistics could allow attackers to alter the bottleneck
   sharing characteristics for private gain or disruption of other
   parties communication.

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8.  Change history

   Changes made to this document:

     WG-00->WG-01 :  Moved unbiased skew section to replace skew
                     estimate, more robust variability estimator, the
                     term variance replaced with variability, clock
                     drift term corrected to clock skew, revision to
                     clock skew section with a place holder, description
                     of parameters.

     02->WG-00 :     Fixed missing 0.5 in 3.3.2 and missing brace in

     01->02 :        New section describing improvements to the key
                     metric calculations that help to remove noise,
                     bias, and reduce lag.  Some revisions to the
                     notation to make it clearer.  Some tightening of
                     the thresholds.

     00->01 :        Revisions to terminology for clarity

9.  References

9.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119, March 1997.

9.2.  Informative References

              Hayes, D., Ferlin, S., and M. Welzl, "Practical Passive
              Shared Bottleneck Detection using Shape Summary
              Statistics", Proc. the IEEE Local Computer Networks
              (LCN) p150-158, September 2014, <http://heim.ifi.uio.no/

              Welzl, M., Islam, S., and S. Gjessing, "Coupled congestion
              control for RTP media", draft-welzl-rmcat-coupled-cc-04
              (work in progress), October 2014.

              ITU-T, "Internet Protocol Data Communication Service - IP
              Packet Transfer and Availability Performance Parameters",

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              Series Y: Global Information Infrastructure, Internet
              Protocol Aspects and Next-Generation Networks ,
              March 2011,

   [RFC3550]  Schulzrinne, H., Casner, S., Frederick, R., and V.
              Jacobson, "RTP: A Transport Protocol for Real-Time
              Applications", STD 64, RFC 3550, July 2003.

   [RFC4585]  Ott, J., Wenger, S., Sato, N., Burmeister, C., and J. Rey,
              "Extended RTP Profile for Real-time Transport Control
              Protocol (RTCP)-Based Feedback (RTP/AVPF)", RFC 4585,
              July 2006.

   [RFC5124]  Ott, J. and E. Carrara, "Extended Secure RTP Profile for
              Real-time Transport Control Protocol (RTCP)-Based Feedback
              (RTP/SAVPF)", RFC 5124, February 2008.

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

   [RFC6817]  Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
              "Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
              December 2012.

Authors' Addresses

   David Hayes (editor)
   University of Oslo
   PO Box 1080 Blindern
   Oslo,   N-0316

   Phone: +47 2284 5566
   Email: davihay@ifi.uio.no

   Simone Ferlin
   Simula Research Laboratory
   P.O.Box 134
   Lysaker,   1325

   Phone: +47 4072 0702
   Email: ferlin@simula.no

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   Michael Welzl
   University of Oslo
   PO Box 1080 Blindern
   Oslo,   N-0316

   Phone: +47 2285 2420
   Email: michawe@ifi.uio.no

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