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RTP Media Congestion Avoidance Techniques                  D. Hayes, Ed.
Internet-Draft                                        University of Oslo
Intended status: Experimental                                  S. Ferlin
Expires: September 22, 2016                   Simula Research Laboratory
                                                                M. Welzl
                                                               K. Hiorth
                                                      University of Oslo
                                                          March 21, 2016


   Shared Bottleneck Detection for Coupled Congestion Control for RTP
                                 Media.
                        draft-ietf-rmcat-sbd-04

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-ietf-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
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   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 22, 2016.

Copyright Notice

   Copyright (c) 2016 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



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   publication of this document.  Please review these documents
   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.  Parameters and their Effect . . . . . . . . . . . . . . .   7
     2.2.  Recommended Parameter Values  . . . . . . . . . . . . . .   8
   3.  Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . .   8
     3.1.  SBD feedback requirements . . . . . . . . . . . . . . . .   9
       3.1.1.  Feedback when all the        logic is placed at
               the sender  . . . . . . . . . . . . . . . . . . . . .  10
       3.1.2.  Feedback when the         statistics are
               calculated at the         receiver and SBD at
               the sender  . . . . . . . . . . . . . . . . . . . . .  10
       3.1.3.  Feedback when         bottlenecks         can be
               determined at         both senders         and
               receivers . . . . . . . . . . . . . . . . . . . . . .  11
     3.2.  Key metrics and their calculation . . . . . . . . . . . .  11
       3.2.1.  Mean delay  . . . . . . . . . . . . . . . . . . . . .  11
       3.2.2.  Skewness Estimate . . . . . . . . . . . . . . . . . .  11
       3.2.3.  Variability Estimate  . . . . . . . . . . . . . . . .  12
       3.2.4.  Oscillation Estimate  . . . . . . . . . . . . . . . .  12
       3.2.5.  Packet loss . . . . . . . . . . . . . . . . . . . . .  13
     3.3.  Flow Grouping . . . . . . . . . . . . . . . . . . . . . .  13
       3.3.1.  Flow Grouping Algorithm . . . . . . . . . . . . . . .  13
       3.3.2.  Using the flow group signal . . . . . . . . . . . . .  15
     3.4.  Removing Noise from the Estimates . . . . . . . . . . . .  15
       3.4.1.  Oscillation noise . . . . . . . . . . . . . . . . . .  15
       3.4.2.  Clock skew  . . . . . . . . . . . . . . . . . . . . .  16
     3.5.  Reducing lag and Improving        Responsiveness  . . . .  16
       3.5.1.  Improving the response of    the skewness estimate  .  17
       3.5.2.  Improving the response of    the variability estimate  19
   4.  Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . .  19
     4.1.  Time stamp resolution . . . . . . . . . . . . . . . . . .  19
   5.  Implementation status . . . . . . . . . . . . . . . . . . . .  20
   6.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  20
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  20
   8.  Security Considerations . . . . . . . . . . . . . . . . . . .  20



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   9.  Change history  . . . . . . . . . . . . . . . . . . . . . . .  20
   10. References  . . . . . . . . . . . . . . . . . . . . . . . . .  21
     10.1.  Normative References . . . . . . . . . . . . . . . . . .  21
     10.2.  Informative References . . . . . . . . . . . . . . . . .  21
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  22

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.ietf-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



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   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 3.4.2 outlines a way
   of adjusting the calculations to cater for it.

   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

      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
                     made.

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

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





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      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

      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

      PB --          a boolean variable indicating the particular flow
                     was identified transiting a bottleneck in the
                     previous interval T (i.e.  Previously Bottleneck)

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

     skew_base_T --  a variable used as an intermediate step in
                     calculating skew_est.

      var_est --     a measure of variability in OWD measurements.

     var_base_T --   a variable used as an intermediate step in
                     calculating var_est.

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




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      p_l, p_f, 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

   .













































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2.1.  Parameters and their Effect

   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 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 to provide a stable estimate of
           oscillations in OWD.  Usually M=N, though having M<N may be
           beneficial in certain circumstances.  M*T needs to be long
           enough to provide stable estimates of skewness and MAD.

   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 transiting a bottleneck 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 slight
           bottlenecks.

   c_h     c_h adds hysteresis to the botteneck 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 bottleneck 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_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 3.4: c_s=-0.01, p_f=p_d=0.1, p_s=0.15,
   p_mad=0.1, p_v=0.7.  M=30, F=20, 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 3.2.3)

      skewness (estimate skew_est, see Section 3.2.2)

      oscillation (estimate freq_est, see Section 3.2.4)

   with packet loss (estimate pkt_loss, see Section 3.2.5) 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.  Each summary statistic portrays a "view" of the
   bottleneck link characteristics, and when used together, they provide
   a robust discrimination for grouping flows.  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
   both.
















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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 locations for shared bottleneck
   detection: sender-side and receiver-side.

   1.  Sender-side: 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 packet loss and either send back this
       raw data, or the calculated summary statistics, periodically to
       H1 every T.  H1, having this knowledge, can determine the shared
       bottleneck and accordingly control the send rates.

   2.  Receiver-side: 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.

3.1.  SBD feedback requirements

   There are three possible scenarios each with different feedback
   requirements:

   1.  Both summary statistic calculations and SBD are performed at
       senders only.

   2.  Summary statistics calculated on the receivers and SBD at the
       senders.

   3.  Summary statistic calculations on receivers, and SBD performed at
       both senders and receivers (beyond the current scope, but allows
       cooperative detection of bottlenecks).







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3.1.1.  Feedback when all the logic is placed at the sender

   Having the sender calculate the summary statistics and determine the
   shared bottlenecks based on them has the advantage of placing most of
   the functionality in one place -- the sender.

   The sender requires precise accurate OWD measurements for every
   packet, along with the proportion of packets lost over the interval
   T, to be sent from the receivers to the senders every T.

   An initialisation message may be required to agree on the feedback
   interval.

3.1.2.  Feedback when the statistics are calculated at the receiver and
        SBD at the sender

   This scenario minimises feedback, but requires receivers to send
   selected summary statistics at an agreed regular interval.  We
   envisage the following exchange of information to initialise the
   system:

   o  An initialization message from the sender to the receiver will
      contain the following information:

      *  A protocol identifier (SBD=01).  This is to future proof the
         message exchange so that potential advances in SBD technology
         can be easily deployed.  All following initialisation elements
         relate to the mechanism outlined in this document which will
         have the identifier SBD=01.

      *  A list of which key metrics should be collected and relayed
         back to the sender 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).

      *  The values of T, N, M, and the necessary resolution and
         precision of the relayed statistics.

   o  A response message from the receiver acknowledges this message
      with a list of key metrics it supports (subset of the senders
      list) and is able to relay back to the sender.

   This initialisation exchange may be repeated to finalize the agreed
   metrics should not all be supported by all receivers.




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   After initialisation the agreed summary statistics will be fed back
   to the sender every T.

3.1.3.  Feedback when bottlenecks can be determined at both senders and
        receivers

   This type of mechanism is currently beyond the scope of SBD in RMCAT.
   It is mentioned here to ensure more advanced sender/receiver
   cooperative shared bottleneck determination mechanisms remain
   possible in the future.

   It is envisaged that such a mechanism would be initialised in a
   similar manner to that described in Section 3.1.2.

   After initialisation both summary statistics and shared bottleneck
   determinations will need to be exchanged every T.

3.2.  Key metrics and their calculation

   Measurements are calculated over a base interval, T and summarized
   over N or M such intervals.  All summary statistics can be calculated
   incrementally.

3.2.1.  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.  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 3.5 for a discussion on improving the
   responsiveness of the mechanism.)

3.2.2.  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



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   made over M * T based on a calculation every T using the previous T's
   calculation of mean_delay.

   The base for the skewness calculation is estimated using a counter
   initialised every T.  It increments for one way delay samples (OWD)
   below the mean and decrements for OWD above the mean.  So for each
   OWD sample:

      if (OWD < mean_delay) skew_base_T++

      if (OWD > mean_delay) skew_base_T--

   The mean_delay does not include the mean of the current T interval to
   enable it to be calculated iteratively.

   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.

3.2.3.  Variability Estimate

   Mean Absolute Deviation (MAD) delay is a robust variability measure
   that copes well with different send rates.  It can be implemented in
   an online manner as follows:

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

         where

            |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

   For the grouping threshold p_mad=0.1

3.2.4.  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:



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      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 = 0.7 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
   T.

3.2.5.  Packet loss

   The proportion of packets lost over the period NT 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.

3.3.  Flow Grouping

3.3.1.  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.





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   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 transiting a bottleneck are successively
   divided into groups based on freq_est, var_est, skew_est and
   pkt_loss.

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

   1.  Grouping will be performed on flows that are inferred to be
       traversing a bottleneck by:

          skew_est < c_s

             || ( skew_est < c_h & PB ) || pkt_loss > p_l

   The parameter c_s controls how sensitive the mechanism is in
   detecting a bottleneck.  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 transiting a bottleneck last time.  If the test
   result is TRUE, PB=TRUE, otherwise PB=FALSE.

   These flows, flows transiting a bottleneck, are then progressively
   divided into groups based on the freq_est, var_est, 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_M(var_est) (highest to
       lowest) is less than a threshold:

          diff(var_est) < (p_mad * var_est)

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




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

          diff(skew_est) < p_s

   5.  When packet loss is high enough to be reliable (pkt_loss > p_l),
       group flows whose difference is less than a threshold

          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).

3.3.2.  Using the flow group signal

   Grouping decisions can be made every T from the second T, however
   they will not attain their full design accuracy until after the
   2*N'th T interval.  We recommend that grouping decisions are not made
   until 2*M T intervals.

   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.

3.4.  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.

3.4.1.  Oscillation noise

   When a path has no bottleneck, 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:



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   1.  Set the current var_base_T = NaN (a value representing an invalid
       record, i.e. Not a Number) for flows that are deemed to not be
       transiting a bottleneck by the first skew_est based grouping test
       (see Section 3.3.1).

   2.  Then var_est = sum_MT(var_base_T != NaN) / num_MT(OWD)

   3.  For freq_est, only record a significant mean crossing if flow
       deemed to be transiting a bottleneck.

   These three changes can help to remove the non-bottleneck noise from
   freq_est.

3.4.2.  Clock skew

   Generally sender and receiver clock skew will be too small to cause
   significant errors in the estimators.  Skew_est and freq_est are the
   most sensitive to this type of noise due to their use of a mean OWD
   calculated over a longer interval.  In circumstances where clock skew
   is high, basing skew_est only on the previous T's mean and ignoring
   freq_est provides a noisier but reliable signal.

   A more sophisticated method is to estimate the effect the clock skew
   is having on the summary statistics, and then adjust statistics
   accordingly.  There are a number of techniques in the literature,
   including [Zhang-Infocom02].

3.5.  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



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   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.

3.5.1.  Improving the response of the skewness estimate

   The weighted moving average for skew_est, based on skew_est in
   Section 3.2.2, 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.
























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   To calculate this weighted skew_est incrementally:

   Notation:    F_ - flat portion, D_ - declining portion, W_ - weighted
                component

   Initialise:  sum_skewbase = 0, F_skewbase=0, W_D_skewbase=0

                skewbase_hist = buffer length M initialize to 0

                numsampT = buffer length M initialzed to 0

   Steps per iteration:

   1.   old_skewbase = skewbase_hist(M)

   2.   old_numsampT = numsampT(M)

   3.   cycle(skewbase_hist)

   4.   cycle(numsampT)

   5.   numsampT(1) = num_T(OWD)

   6.   skewbase_hist(1) = skew_base_T

   7.   F_skewbase = F_skewbase + skew_base_T - skewbase_hist(F+1)

   8.   W_D_skewbase = W_D_skewbase + (M-F)*skewbase_hist(F+1)
          - sum_skewbase

   9.   W_D_numsamp = W_D_numsamp + (M-F)*numsampT(F+1) - sum_numsamp
          + F_numsamp

   10.  F_numsamp = F_numsamp + numsampT(1) - numsampT(F+1)

   11.  sum_skewbase = sum_skewbase + skewbase_hist(F+1) - old_skewbase

   12.  sum_numsamp = sum_numsamp + numsampT(1) - old_numsampT

   13.  skew_est = ((M-F+1)*F_skewbase + W_D_skewbase) /
          ((M-F+1)*F_numsamp+W_D_numsamp)

   Where cycle(....) refers to the operation on a cyclic buffer where
   the start of the buffer is now the next element in the buffer.







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3.5.2.  Improving the response of the variability estimate

   Similarly the weighted moving average for var_est can be calculated
   as follows:



      var_est =  ((M-F+1)*sum(var_base_T(1:F))

                      + sum([(M-F):1].*var_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.  When removing oscillation noise
   (see Section 3.4.1) this calculation must be adjusted to allow for
   invalid var_base_T records.

   Var_est can be calculated incrementally in the same way as skew_est
   in Section 3.5.1.  However, note that the buffer numsampT is used for
   both calculations so the operations on it should not be repeated.

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.



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

5.  Implementation status

   The University of Oslo is currently working on an implementation of
   this in the Chromium browser.

6.  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.

7.  IANA Considerations

   This memo includes no request to IANA.

8.  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.

9.  Change history

   Changes made to this document:

    WG-03->WG-04 :   Add M to terminology table, suggest skew_est based
                     on previous T and no freq_est in clock skew
                     section, feedback requirements as a separate sub
                     section.

    WG-02->WG-03 :   Correct misspelled author

    WG-01->WG-02 :   Removed ambiguity associated with the term
                     "congestion".  Expanded the description of
                     initialisation messages.  Removed PDV metric.
                     Added description of incremental weighted metric
                     calculations for skew_est.  Various clarifications
                     based on implementation work.  Fixed typos and
                     tuned parameters.




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     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
                     3.3.3

     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

10.  References

10.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <http://www.rfc-editor.org/info/rfc2119>.

10.2.  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) pp150-158, September 2014,
              <http://heim.ifi.uio.no/davihay/
              hayes14__pract_passiv_shared_bottl_detec-abstract.html>.

   [I-D.ietf-rmcat-coupled-cc]
              Islam, S., Welzl, M., and S. Gjessing, "Coupled congestion
              control for RTP media", draft-ietf-rmcat-coupled-cc-00
              (work in progress), September 2015.

   [RFC3550]  Schulzrinne, H., Casner, S., Frederick, R., and V.
              Jacobson, "RTP: A Transport Protocol for Real-Time
              Applications", STD 64, RFC 3550, DOI 10.17487/RFC3550,
              July 2003, <http://www.rfc-editor.org/info/rfc3550>.





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   [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,
              DOI 10.17487/RFC4585, July 2006,
              <http://www.rfc-editor.org/info/rfc4585>.

   [RFC5124]  Ott, J. and E. Carrara, "Extended Secure RTP Profile for
              Real-time Transport Control Protocol (RTCP)-Based Feedback
              (RTP/SAVPF)", RFC 5124, DOI 10.17487/RFC5124, February
              2008, <http://www.rfc-editor.org/info/rfc5124>.

   [RFC6817]  Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
              "Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
              DOI 10.17487/RFC6817, December 2012,
              <http://www.rfc-editor.org/info/rfc6817>.

   [Zhang-Infocom02]
              Zhang, L., Liu, Z., and H. Xia, "Clock synchronization
              algorithms for network measurements", Proc. the IEEE
              International Conference on Computer Communications
              (INFOCOM) pp160-169, September 2002,
              <http://dx.doi.org/10.1109/INFCOM.2002.1019257>.

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








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

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


   Kristian Hiorth
   University of Oslo
   PO Box 1080 Blindern
   Oslo  N-0316
   Norway

   Email: kristahi@ifi.uio.no


































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