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Versions: 00 01 02 draft-ietf-rmcat-sbd

RTP Media Congestion Avoidance                             D. Hayes, Ed.
Techniques                                            University of Oslo
Internet-Draft                                                 S. Ferlin
Intended status: Experimental                 Simula Research Laboratory
Expires: September 4, 2015                                      M. Welzl
                                                      University of Oslo
                                                           March 3, 2015


   Shared Bottleneck Detection for Coupled Congestion Control for RTP
                                 Media.
                        draft-hayes-rmcat-sbd-02

Abstract

   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

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

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at http://datatracker.ietf.org/drafts/current/.

   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."

   This Internet-Draft will expire on September 4, 2015.

Copyright Notice

   Copyright (c) 2015 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (http://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents



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   carefully, as they describe your rights and restrictions with respect
   to this document.  Code Components extracted from this document must
   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.


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.  Parameter Values . . . . . . . . . . . . . . . . . . . . .  5
   3.  Mechanism  . . . . . . . . . . . . . . . . . . . . . . . . . .  6
     3.1.  Key metrics and their calculation  . . . . . . . . . . . .  7
       3.1.1.  Mean delay . . . . . . . . . . . . . . . . . . . . . .  7
       3.1.2.  Skewness Estimate  . . . . . . . . . . . . . . . . . .  8
       3.1.3.  Variance Estimate  . . . . . . . . . . . . . . . . . .  9
       3.1.4.  Oscillation Estimate . . . . . . . . . . . . . . . . .  9
       3.1.5.  Packet loss  . . . . . . . . . . . . . . . . . . . . . 10
     3.2.  Flow Grouping  . . . . . . . . . . . . . . . . . . . . . . 10
       3.2.1.  Flow Grouping Algorithm  . . . . . . . . . . . . . . . 10
       3.2.2.  Using the flow group signal  . . . . . . . . . . . . . 12
     3.3.  Removing Noise from the Estimates  . . . . . . . . . . . . 12
       3.3.1.  Oscillation noise  . . . . . . . . . . . . . . . . . . 12
       3.3.2.  Clock drift  . . . . . . . . . . . . . . . . . . . . . 13
       3.3.3.  Bias in the skewness measure . . . . . . . . . . . . . 14
     3.4.  Reducing lag and Improving Responsiveness  . . . . . . . . 14
       3.4.1.  Improving the response of the skewness estimate  . . . 15
       3.4.2.  Improving the response of the variance estimate  . . . 15
   4.  Measuring OWD  . . . . . . . . . . . . . . . . . . . . . . . . 16
     4.1.  Time stamp resolution  . . . . . . . . . . . . . . . . . . 16
   5.  Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 16
   6.  IANA Considerations  . . . . . . . . . . . . . . . . . . . . . 16
   7.  Security Considerations  . . . . . . . . . . . . . . . . . . . 16
   8.  Change history . . . . . . . . . . . . . . . . . . . . . . . . 17
   9.  References . . . . . . . . . . . . . . . . . . . . . . . . . . 17
     9.1.  Normative References . . . . . . . . . . . . . . . . . . . 17
     9.2.  Informative References . . . . . . . . . . . . . . . . . . 17
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 18








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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
   (OWD).

   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 drift is not usually
   significant over the time intervals used by this SBD mechanism (see
   [RFC6817] A.2 for a discussion on clock drift and OWD measurements).
   However, in circumstances where it is significant, Section 3.3.2
   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",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119 [RFC2119].

   Acronyms used in this document:

      OWD -- One Way Delay

      PDV -- Packet Delay Variation

      RTT -- Round Trip Time

      SBD -- Shared Bottleneck Detection

   Conventions used in this document:

      T     --     the base time interval over which measurements are
                   made.

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

      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

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






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      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)

      CD_T --      an estimate of the effect of Clock Drift on the mean
                   OWD per T

      CD_Adj(...) --  Mean OWD adjusted for clock drift

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

      N, M, and F --  number of values (calculated over T).

2.1.  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.  M=50, F=10, 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,
   but are the subject of ongoing tests.




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3.  Mechanism

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

      variance (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 Sender or receiver or both.

                                  +----+
                                  | 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.



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   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).

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
   E_T(OWD):

      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



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   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 T using the previous calculation of mean_delay.
   Comparisons are made using the mean of M skew estimates (an
   alternative that removes bias in the mean is given in Section 3.3.3).

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



      skew_est_T =  (sum_T(OWD < mean_delay)

                    - sum_T(OWD > mean_delay)) / num_T(OWD)

         where

            if (OWD < mean_delay) 1 else 0

            if (OWD > mean_delay) 1 else 0

         skew_est_T is a number between -1 and 1

      skew_est = E_M(skew_est_T) = sum_M(skew_est_T) / M

   For implementation ease, mean_delay does not include the mean of the
   current T interval.

   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.














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3.1.3.  Variance Estimate

   Packet Delay Variation (PDV) ([RFC5481] and [ITU-Y1540]) is used as
   an estimator of the variance 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).

   The use of PDV = PDV_min = E_T(OWD) - min_T(OWD) is currently being
   investigated as an alternative that is less sensitive to noise.  The
   drawback of using PDV_min is that it does not distinguish between
   groups of flows with similar values of skew_est as well as PDV_max
   (see [Hayes-LCN14] section IVB).

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.







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      The counter, number_of_crossings, is incremented when there is a
      significant mean crossing and subtracted from 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
   T.

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
   decisions.

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:








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   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
       threshold:

          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

          otherwise

             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,
   such as are expected in RTCWEB.



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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
   interval.

   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.

3.3.1.  Oscillation noise

   When a path has no congestion, the PDV 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, ie 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).

   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.








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3.3.2.  Clock drift

   Generally sender and receiver clock drift 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 drift is high,
   making M < N can reduce this error.

   A better method is to estimate the effect the clock drift is having
   on the E_N(E_T(OWD)), and then adjust mean_delay accordingly.  A
   simple method of doing this follows:

      First divide the N E_T(OWD) values into two halves (N/2 in each)
      -- old and new.

      Calculate a mean of the old half:

         Older_mean = E_old(E_T(OWD)) / N/2

      Calculate a mean of the new (most recent) half:

         Newer_mean = E_new(E_T(OWD)) / N/2

      A linear estimate of the Clock Drift per T estimates is:

         CD_T = (Newer_mean - Older_mean)/N/2

      An adjusted mean estimate then is:

         mean_delay = CD_Adj(E_M(E_T(OWD))) = E_M(E_T(OWD)) + CD_T * M/2

   CD_Adj can be thought of as a prediction of what the long term mean
   will be in the current measurement period T. It is used as the basis
   for skew_est and freq_est.


















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3.3.3.  Bias in the skewness measure

   If successive calculations of skew_est are made with very different
   numbers of samples (num_T(OWD)), the simple calculation of
   E_M(skew_est) used for grouping decisions will be biased by the
   intervals that have few samples samples.  This bias can be corrected
   if necessary as follows.



      skew_base_T = (sum_T(OWD < mean_delay

                    - sum_T(OWD > mean_delay)

         skew_est = sum_MT(skew_base_T)/num_MT(OWD)

   This calculation requires slightly more state, since an
   implementation will need to maintain two cyclic buffers storing
   skew_base_T and num_T(OWD) respectively to manage the rolling
   summations (note only one cyclic buffer is needed for the calculation
   of skew_est outlined previously).

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 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 3.3.3, 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 (ie
   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 variance 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 3.3.1) 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:

     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-LCN14]
              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/
              davihay/
              hayes14__pract_passiv_shared_bottl_detec-abstract.html>.

   [I-D.welzl-rmcat-coupled-cc]
              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-Y1540]
              ITU-T, "Internet Protocol Data Communication Service - IP
              Packet Transfer and Availability Performance Parameters",
              Series Y: Global Information Infrastructure, Internet
              Protocol Aspects and Next-Generation Networks ,
              March 2011,
              <http://www.itu.int/rec/T-REC-Y.1540-201103-I/en>.

   [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



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              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
   Norway

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


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

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


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

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






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