Network Working Group                                             X. Zhu
Internet-Draft                                                    R. Pan
Intended status: Experimental                                 M. Ramalho
Expires: April 11, 2016                                          S. Mena
                                                                P. Jones
                                                                   J. Fu
                                                           Cisco Systems
                                                             S. D'Aronco
                                                             C. Ganzhorn
                                                         October 9, 2015

     NADA: A Unified Congestion Control Scheme for Real-Time Media


   This document describes NADA (network-assisted dynamic adaptation), a
   novel congestion control scheme for interactive real-time media
   applications, such as video conferencing.  In the proposed scheme,
   the sender regulates its sending rate based on either implicit or
   explicit congestion signaling, in a unified approach.  The scheme can
   benefit from explicit congestion notification (ECN) markings from
   network nodes.  It also maintains consistent sender behavior in the
   absence of such markings, by reacting to queuing delays and packet
   losses instead.

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
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   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 April 11, 2016.

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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
   ( in effect on the date of
   publication of this document.  Please review these documents
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   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
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  System Overview . . . . . . . . . . . . . . . . . . . . . . .   3
   4.  Core Congestion Control Algorithm . . . . . . . . . . . . . .   4
     4.1.  Mathematical Notations  . . . . . . . . . . . . . . . . .   5
     4.2.  Receiver-Side Algorithm . . . . . . . . . . . . . . . . .   7
     4.3.  Sender-Side Algorithm . . . . . . . . . . . . . . . . . .   9
   5.  Practical Implementation of NADA  . . . . . . . . . . . . . .  10
     5.1.  Receiver-Side Operation . . . . . . . . . . . . . . . . .  10
       5.1.1.  Estimation of one-way delay and queuing delay . . . .  11
       5.1.2.  Estimation of packet loss/marking ratio . . . . . . .  11
       5.1.3.  Estimation of receiving rate  . . . . . . . . . . . .  11
     5.2.  Sender-Side Operation . . . . . . . . . . . . . . . . . .  12
       5.2.1.  Rate shaping buffer . . . . . . . . . . . . . . . . .  12
       5.2.2.  Adjusting video target rate and sending rate  . . . .  13
   6.  Discussions and Further Investigations  . . . . . . . . . . .  13
     6.1.  Choice of delay metrics . . . . . . . . . . . . . . . . .  13
     6.2.  Method for delay, loss, and marking ratio estimation  . .  14
     6.3.  Impact of parameter values  . . . . . . . . . . . . . . .  14
     6.4.  Sender-based vs. receiver-based calculation . . . . . . .  15
     6.5.  Incremental deployment  . . . . . . . . . . . . . . . . .  16
   7.  Implementation Status . . . . . . . . . . . . . . . . . . . .  16
   8.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  16
   9.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  17
   10. References  . . . . . . . . . . . . . . . . . . . . . . . . .  17
     10.1.  Normative References . . . . . . . . . . . . . . . . . .  17
     10.2.  Informative References . . . . . . . . . . . . . . . . .  17
   Appendix A.  Network Node Operations  . . . . . . . . . . . . . .  19
     A.1.  Default behavior of drop tail queues  . . . . . . . . . .  19
     A.2.  RED-based ECN marking . . . . . . . . . . . . . . . . . .  19
     A.3.  Random Early Marking with Virtual Queues  . . . . . . . .  20

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   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  21

1.  Introduction

   Interactive real-time media applications introduce a unique set of
   challenges for congestion control.  Unlike TCP, the mechanism used
   for real-time media needs to adapt quickly to instantaneous bandwidth
   changes, accommodate fluctuations in the output of video encoder rate
   control, and cause low queuing delay over the network.  An ideal
   scheme should also make effective use of all types of congestion
   signals, including packet loss, queuing delay, and explicit
   congestion notification (ECN) [RFC3168] markings.  The requirements
   for the congestion control algorithm are outlined in

   This document describes an experimental congestion control scheme
   called network-assisted dynamic adaptation (NADA).  The NADA design
   benefits from explicit congestion control signals (e.g., ECN
   markings) from the network, yet also operates when only implicit
   congestion indicators (delay and/or loss) are available.  In
   addition, it supports weighted bandwidth sharing among competing
   video flows.  The signaling mechanism consists of standard RTP
   timestamp [RFC3550] and standard RTCP feedback reports.

2.  Terminology

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

3.  System Overview

   Figure 1 shows the end-to-end system for real-time media transport
   that NADA operates in.

     +---------+  r_vin  +--------+        +--------+     +----------+
     |  Media  |<--------|  RTP   |        |Network |     |   RTP    |
     | Encoder |========>| Sender |=======>|  Node  |====>| Receiver |
     +---------+  r_vout +--------+ r_send +--------+     +----------+
                             /|\                                |
                              |                                 |
                                    RTCP Feedback Report

                         Figure 1: System Overview

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   o  Media encoder with rate control capabilities.  It encodes the
      source media stream into an RTP stream with target bit rate r_vin.
      The actual output rate from the encoder r_vout may fluctuate
      around the target r_vin.  In addition, the encoder can only change
      its bit rate at rather coarse time intervals, e.g., once every 0.5

   o  RTP sender: responsible for calculating the NADA reference rate
      based on network congestion indicators (delay, loss, or ECN
      marking reports from the receiver), for updating the video encoder
      with a new target rate r_vin, and for regulating the actual
      sending rate r_send accordingly.  The RTP sender also provides an
      RTP timestamp for each outgoing packet.

   o  RTP receiver: responsible for measuring and estimating end-to-end
      delay based on sender RTP timestamp, packet loss and ECN marking
      ratios, as well as receiving rate (r_recv) of the flow.  It
      calculates the aggregated congestion signal (x_n) that accounts
      for queuing delay, ECN marking, and packet losses, and determines
      the mode for sender rate adaptation (rmode) based on whether the
      flow has encountered any standing non-zero congestion.  The
      receiver sends periodic RTCP reports back to the sender,
      containing values of x_n, rmode, and r_recv.

   o  Network node with several modes of operation.  The system can work
      with the default behavior of a simple drop tail queue.  It can
      also benefit from advanced AQM features such as PIE, FQ-CoDel,
      RED-based ECN marking, and PCN marking using a token bucket
      algorithm.  Note that network node operation is out of scope for
      the design of NADA.

4.  Core Congestion Control Algorithm

   Like TCP-Friendly Rate Control (TFRC) [Floyd-CCR00] [RFC5348], NADA
   is a rate-based congestion control algorithm.  In its simplest form,
   the sender reacts to the collection of network congestion indicators
   in the form of an aggregated congestion signal, and operates in one
   of two modes:

   o  Accelerated ramp-up: when the bottleneck is deemed to be
      underutilized, the rate increases multiplicatively with respect to
      the rate of previously successful transmissions.  The rate
      increase mutliplier (gamma) is calculated based on observed round-
      trip-time and target feedback interval, so as to limit self-
      inflicted queuing delay.

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   o  Gradual rate update: in the presence of non-zero aggregate
      congestion signal, the sending rate is adjusted in reaction to
      both its value (x_n) and its change in value (x_diff).

   This section introduces the list of mathematical notations and
   describes the core congestion control algorithm at the sender and
   receiver, respectively.  Additional details on recommended practical
   implementations are described in Section 5.1 and Section 5.2.

4.1.  Mathematical Notations

   This section summarizes the list of variables and parameters used in
   the NADA algorithm.

     | Notation     | Variable Name                                   |
     | t_curr       | Current timestamp                               |
     | t_last       | Last time sending/receiving a feedback          |
     | delta        | Observed interval between current and previous  |
     |              | feedback reports: delta = t_curr-t_last         |
     | r_n          | Reference rate based on network congestion      |
     | r_send       | Sending rate                                    |
     | r_recv       | Receiving rate                                  |
     | r_vin        | Target rate for video encoder                   |
     | r_vout       | Output rate from video encoder                  |
     | d_base       | Estimated baseline delay                        |
     | d_fwd        | Measured and filtered one-way delay             |
     | d_n          | Estimated queueing delay                        |
     | d_tilde      | Equivalent delay after non-linear warping       |
     | p_mark       | Estimated packet ECN marking ratio              |
     | p_loss       | Estimated packet loss ratio                     |
     | x_n          | Aggregate congestion signal                     |
     | x_prev       | Previous value of aggregate congestion signal   |
     | x_diff       | Change in aggregate congestion signal w.r.t.    |
     |              | its previous value: x_diff = x_n - x_prev       |
     | rmode        | Rate update mode: (0 = accelerated ramp-up;     |
     |              | 1 = gradual update)                             |
     | gamma        | Rate increase multiplier in accelerated ramp-up |
     |              | mode                                            |
     | rtt          | Estimated round-trip-time at sender             |
     | buffer_len   | Rate shaping buffer occupancy measured in bytes |

                       Figure 2: List of variables.

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    | Notation     | Parameter Name                   | Default Value  |
    | PRIO         | Weight of priority of the flow   |    1.0
    | RMIN         | Minimum rate of application      |    150 Kbps    |
    |              | supported by media encoder       |                |
    | RMAX         | Maximum rate of application      |    1.5 Mbps    |
    |              | supported by media encoder       |                |
    | X_REF        | Reference congestion level       |    20ms        |
    | KAPPA        | Scaling parameter for gradual    |    0.5         |
    |              | rate update calculation          |                |
    | ETA          | Scaling parameter for gradual    |    2.0         |
    |              | rate update calculation          |                |
    | TAU          | Upper bound of RTT in gradual    |    500ms       |
    |              | rate update calculation          |                |
    | DELTA        | Target feedback interval         |    100ms       |
    | LOGWIN       | Observation window in time for   |    500ms       |
    |              | calculating packet summary       |                |
    |              | statistics at receiver           |                |
    | QEPS         | Threshold for determining queuing|     10ms       |
    |              | delay build up at receiver       |                |
    | QTH          | Delay threshold for non-linear   |    100ms       |
    |              | warping                          |                |
    | QMAX         | Delay upper bound for non-linear |    400ms       |
    |              | warping                          |                |
    | DLOSS        | Delay penalty for loss           |    1.0s        |
    | DMARK        | Delay penalty for ECN marking    |    200ms       |
    | GAMMA_MAX    | Upper bound on rate increase     |     20%        |
    |              | ratio for accelerated ramp-up    |                |
    | QBOUND       | Upper bound on self-inflicted    |    50ms        |
    |              | queuing delay during ramp up     |                |
    | FPS          | Frame rate of incoming video     |     30         |
    | BETA_S       | Scaling parameter for modulating |    0.1         |
    |              | outgoing sending rate            |                |
    | BETA_V       | Scaling parameter for modulating |    0.1         |
    |              | video encoder target rate        |                |
    | ALPHA        | Smoothing factor in exponential  |    0.1         |
    |              | smoothing of packet loss and     |                |
    |              | marking ratios                   |

                  Figure 3: List of algorithm parameters.

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4.2.  Receiver-Side Algorithm

   The receiver-side algorithm can be outlined as below:

  On initialization:
    set d_base = +INFINITY
    set p_loss = 0
    set p_mark = 0
    set r_recv = 0
    set both t_last and t_curr as current time

  On receiving a media packet:
    obtain current timestamp t_curr
    obtain from packet header sending time stamp t_sent
    obtain one-way delay measurement: d_fwd = t_curr - t_sent
    update baseline delay: d_base = min(d_base, d_fwd)
    update queuing delay:  d_n = d_fwd - d_base
    update packet loss ratio estimate p_loss
    update packet marking ratio estimate p_mark
    update measurement of receiving rate r_recv

  On time to send a new feedback report (t_curr - t_last > DELTA):
    calculate non-linear warping of delay d_tilde if packet loss exists
    calculate aggregate congestion signal x_n
    determine mode of rate adaptation for sender: rmode
    send RTCP feedback report containing values of: rmode, x_n, and r_recv
    update t_last = t_curr

   In order for a delay-based flow to hold its ground when competing
   against loss-based flows (e.g., loss-based TCP), it is important to
   distinguish between different levels of observed queuing delay.  For
   instance, a moderate queuing delay value below 100ms is likely self-
   inflicted or induced by other delay-based flows, whereas a high
   queuing delay value of several hundreds of milliseconds may indicate
   the presence of a loss-based flow that does not refrain from
   increased delay.

   When packet losses are observed, the estimated queuing delay follows
   a non-linear warping inspired by the delay-adaptive congestion window
   backoff policy in [Budzisz-TON11]:

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                / d_n,                  if     d_n<QTH;
                |     (QMAX - d_n)^4
     d_tilde = <  QTH ----------------, if QTH<d_n<QMAX  (1)
                |     (QMAX - QTH)^4
                \  0,                   otherwise.

   Here, the queuing delay value is unchanged when it is below the first
   threshold QTH; it is scaled down following a non-linear curve when
   its value falls between QTH and QMAX; above QMAX, the high queuing
   delay value no longer counts toward congestion control.

   The aggregate congestion signal is:

       x_n = d_tilde + p_mark*DMARK + p_loss*DLOSS.      (2)

   Here, DMARK is prescribed delay penalty associated with ECN markings
   and DLOSS is prescribed delay penalty associated with packet losses.
   The value of DLOSS and DMARK does not depend on configurations at the
   network node, but does assume that ECN markings, when available,
   occur before losses.  Furthermore, the values of DLOSS and DMARK need
   to be set consistently across all NADA flows for them to compete

   In the absence of packet marking and losses, the value of x_n reduces
   to the observed queuing delay d_n.  In that case the NADA algorithm
   operates in the regime of delay-based adaptation.

   Given observed per-packet delay and loss information, the receiver is
   also in a good position to determine whether the network is
   underutilized and recommend the corresponding rate adaptation mode
   for the sender.  The criteria for operating in accelerated ramp-up
   mode are:

   o  No recent packet losses within the observation window LOGWIN; and

   o  No build-up of queuing delay: d_fwd-d_base < QEPS for all previous
      delay samples within the observation window LOGWIN.

   Otherwise the algorithm operates in graduate update mode.

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4.3.  Sender-Side Algorithm

   The sender-side algorithm is outlined as follows:

     on initialization:
       set r_n = RMIN
       set rtt = 0
       set x_prev = 0
       set t_last and t_curr as current time

     on receiving feedback report:
       obtain current timestamp: t_curr
       obtain values of rmode, x_n, and r_recv from feedback report
       update estimation of rtt
       measure feedback interval: delta = t_curr - t_last
       if rmode == 0:
         update r_n following accelerated ramp-up rules
         update r_n following gradual update rules
       clip rate r_n within the range of [RMIN, RMAX]
       x_prev = x_n
       t_last = t_curr

   In accelerated ramp-up mode, the rate r_n is updated as follows:

       gamma = min(GAMMA_MAX, -----------)     (3)

       r_n  =  (1+gamma) r_recv             (4)

   The rate increase multiplier gamma is calculated as a function of
   upper bound of self-inflicted queuing delay (QBOUND), round-trip-time
   (rtt), and target feedback interval DELTA.  It has a maximum value of
   GAMMA_MAX.  The rationale behind (3)-(4) is that the longer it takes
   for the sender to observe self-inflicted queuing delay build-up, the
   more conservative the sender should be in increasing its rate, hence
   the smaller the rate increase multiplier.

   In gradual update mode, the rate r_n is updated as:

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       x_offset = x_n - PRIO*X_REF*RMAX/r_n           (5)

       x_diff   = x_n - x_prev                        (6)

                          delta    x_offset
       r_n = r_n - KAPPA*-------*------------*r_n
                           TAU       TAU

                 - KAPPA*ETA*---------*r_n            (7)

   The rate changes in proportion to the previous rate decision.  It is
   affected by two terms: offset of the aggregate congestion signal from
   its value at equilibrium (x_offset) and its change (x_diff).
   Calculation of x_offset depends on maximum rate of the flow (RMAX),
   its weight of priority (PRIO), as well as a reference congestion
   signal (X_REF).  The value of X_REF is chosen that the maximum rate
   of RMAX can be achieved when the observed congestion signal level is
   below PRIO*X_REF.

   At equilibrium, the aggregated congestion signal stablizes at x_n =
   PRIO*X_REF*RMAX/r_n.  This ensures that when multiple flows share the
   same bottleneck and observe a common value of x_n, their rates at
   equilibrium will be proportional to their respective priority levels
   (PRIO) and maximum rate (RMAX).

   As mentioned in the sender-side algorithm, the final rate is clipped
   within the dynamic range specified by the application:

           r_n = min(r_n, RMAX)          (8)

           r_n = max(r_n, RMIN)          (9)

   The above operations ignore many practical issues such as clock
   synchronization between sender and receiver, filtering of noise in
   delay measurements, and base delay expiration.  These will be
   addressed in later sections describing practical implementation of
   the NADA algorithm.

5.  Practical Implementation of NADA

5.1.  Receiver-Side Operation

   The receiver continuously monitors end-to-end per-packet statistics
   in terms of delay, loss, and/or ECN marking ratios.  It then
   aggregates all forms of congestion indicators into the form of an
   equivalent delay and periodically reports this back to the sender.

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   In addition, the receiver tracks the receiving rate of the flow and
   includes that in the feedback message.

5.1.1.  Estimation of one-way delay and queuing delay

   The delay estimation process in NADA follows a similar approach as in
   earlier delay-based congestion control schemes, such as LEDBAT
   [RFC6817].  NADA estimates the forward delay as having a constant
   base delay component plus a time varying queuing delay component.
   The base delay is estimated as the minimum value of one-way delay
   observed over a relatively long period (e.g., tens of minutes),
   whereas the individual queuing delay value is taken to be the
   difference between one-way delay and base delay.

   The individual sample values of queuing delay should be further
   filtered against various non-congestion-induced noise, such as spikes
   due to processing "hiccup" at the network nodes.  Current
   implementation employs a 15-tab minimum filter over per-packet
   queuing delay estimates.

5.1.2.  Estimation of packet loss/marking ratio

   The receiver detects packet losses via gaps in the RTP sequence
   numbers of received packets.  Packets arriving out-of-order are
   discarded, and count towards losses.  The instantaneous packet loss
   ratio p_inst is estimated as the ratio between the number of missing
   packets over the number of total transmitted packets within the
   recent observation window LOGWIN.  The packet loss ratio p_loss is
   obtained after exponential smoothing:

       p_loss = ALPHA*p_inst + (1-ALPHA)*p_loss.   (10)

   The filtered result is reported back to the sender as the observed
   packet loss ratio p_loss.

   Estimation of packet marking ratio p_mark follows the same procedure
   as above.  It is assumed that ECN marking information at the IP
   header can be passed to the transport layer by the receiving

5.1.3.  Estimation of receiving rate

   It is fairly straighforward to estimate the receiving rate r_recv.
   NADA maintains a recent observation window with time span of LOGWIN,
   and simply divides the total size of packets arriving during that
   window over the time span.  The receiving rate (r_recv) is included
   as part of the feedback report.

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5.2.  Sender-Side Operation

   Figure 4 provides a detailed view of the NADA sender.  Upon receipt
   of an RTCP feedback report from the receiver, the NADA sender
   calculates the reference rate r_n as specified in Section 4.3.  It
   further adjusts both the target rate for the live video encoder r_vin
   and the sending rate r_send over the network based on the updated
   value of r_n and rate shaping buffer occupancy buffer_len.

   The NADA sender behavior stays the same in the presence of all types
   of congestion indicators: delay, loss, and ECN marking.  This unified
   approach allows a graceful transition of the scheme as the network
   shifts dynamically between light and heavy congestion levels.

                      |  Calculate     | <---- RTCP report
                      | Reference Rate |
                              | r_n
                 |                          |
                \|/                        \|/
         +-----------------+           +---------------+
         | Calculate Video |           |   Calculate   |
         |  Target Rate    |           | Sending Rate  |
         +-----------------+           +---------------+
             |        /|\                 /|\      |
       r_vin |         |                   |       |
            \|/        +-------------------+       |
         +----------+          | buffer_len        |  r_send
         |  Video   | r_vout  -----------+        \|/
         |  Encoder |-------->   |||||||||=================>
         +----------+         -----------+    RTP packets
                             Rate Shaping Buffer

                      Figure 4: NADA Sender Structure

5.2.1.  Rate shaping buffer

   The operation of the live video encoder is out of the scope of the
   design for the congestion control scheme in NADA.  Instead, its
   behavior is treated as a black box.

   A rate shaping buffer is employed to absorb any instantaneous
   mismatch between encoder rate output r_vout and regulated sending
   rate r_send.  Its current level of occupancy is measured in bytes and
   is denoted as buffer_len.

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   A large rate shaping buffer contributes to higher end-to-end delay,
   which may harm the performance of real-time media communications.
   Therefore, the sender has a strong incentive to prevent the rate
   shaping buffer from building up.  The mechanisms adopted are:

   o  To deplete the rate shaping buffer faster by increasing the
      sending rate r_send; and

   o  To limit incoming packets of the rate shaping buffer by reducing
      the video encoder target rate r_vin.

5.2.2.  Adjusting video target rate and sending rate

   The target rate for the live video encoder deviates from the network
   congestion control rate r_n based on the level of occupancy in the
   rate shaping buffer:

       r_vin = r_n - BETA_V*8*buffer_len*FPS.     (11)

   The actual sending rate r_send is regulated in a similar fashion:

       r_send = r_n + BETA_S*8*buffer_len*FPS.    (12)

   In (11) and (12), the first term indicates the rate calculated from
   network congestion feedback alone.  The second term indicates the
   influence of the rate shaping buffer.  A large rate shaping buffer
   nudges the encoder target rate slightly below -- and the sending rate
   slightly above -- the reference rate r_n.

   Intuitively, the amount of extra rate offset needed to completely
   drain the rate shaping buffer within the duration of a single video
   frame is given by 8*buffer_len*FPS, where FPS stands for the frame
   rate of the video.  The scaling parameters BETA_V and BETA_S can be
   tuned to balance between the competing goals of maintaining a small
   rate shaping buffer and deviating the system from the reference rate

6.  Discussions and Further Investigations

6.1.  Choice of delay metrics

   The current design works with relative one-way-delay (OWD) as the
   main indication of congestion.  The value of the relative OWD is
   obtained by maintaining the minimum value of observed OWD over a
   relatively long time horizon and subtract that out from the observed
   absolute OWD value.  Such an approach cancels out the fixed
   difference between the sender and receiver clocks.  It has been
   widely adopted by other delay-based congestion control approaches

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   such as [RFC6817].  As discussed in [RFC6817], the time horizon for
   tracking the minimum OWD needs to be chosen with care: it must be
   long enough for an opportunity to observe the minimum OWD with zero
   queuing delay along the path, and sufficiently short so as to timely
   reflect "true" changes in minimum OWD introduced by route changes and
   other rare events.

   The potential drawback in relying on relative OWD as the congestion
   signal is that when multiple flows share the same bottleneck, the
   flow arriving late at the network experiencing a non-empty queue may
   mistakenly consider the standing queuing delay as part of the fixed
   path propagation delay.  This will lead to slightly unfair bandwidth
   sharing among the flows.

   Alternatively, one could move the per-packet statistical handling to
   the sender instead and use relative round-trip-time (RTT) in lieu of
   relative OWD, assuming that per-packet acknowledgements are
   available.  The main drawback of RTT-based approach is the noise in
   the measured delay in the reverse direction.

   Note that the choice of either delay metric (relative OWD vs. RTT)
   involves no change in the proposed rate adaptation algorithm.
   Therefore, comparing the pros and cons regarding which delay metric
   to adopt can be kept as an orthogonal direction of investigation.

6.2.  Method for delay, loss, and marking ratio estimation

   Like other delay-based congestion control schemes, performance of
   NADA depends on the accuracy of its delay measurement and estimation
   module.  Appendix A in [RFC6817] provides an extensive discussion on
   this aspect.

   The current recommended practice of simply applying a 15-tab minimum
   filter suffices in guarding against processing delay outliers
   observed in wired connections.  For wireless connections with a
   higher packet delay variation (PDV), more sophisticated techniques on
   de-noising, outlier rejection, and trend analysis may be needed.

   More sophisticated methods in packet loss ratio calculation, such as
   that adopted by [Floyd-CCR00], will likely be beneficial.  These
   alternatives are currently under investigation.

6.3.  Impact of parameter values

   In the gradual rate update mode, the parameter TAU indicates the
   upper bound of round-trip-time (RTT) in feedback control loop.
   Typically, the observed feedback interval delta is close to the
   target feedback interval DELTA, and the relative ratio of delta/TAU

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   versus ETA dictates the relative strength of influence from the
   aggregate congestion signal offset term (x_offset) versus its recent
   change (x_diff), respectively.  These two terms are analogous to the
   integral and proportional terms in a proportional-integral (PI)
   controller.  The recommended choice of TAU=500ms, DELTA=100ms and ETA
   = 2.0 corresponds to a relative ratio of 1:10 between the gains of
   the integral and proportional terms.  Consequently, the rate
   adaptation is mostly driven by the change in the congestion signal
   with a long-term shift towards its equilibrium value driven by the
   offset term.  Finally, the scaling parameter KAPPA determines the
   overall speed of the adaptation and needs to strike a balance between
   responsiveness and stability.

   The choice of the target feedback interval DELTA needs to strike the
   right balance between timely feedback and low RTCP feedback message
   counts.  A target feedback interval of DELTA=100ms is recommended,
   corresponding to a feedback bandwidth of 16Kbps with 200 bytes per
   feedback message --- less than 0.1% overhead for a 1 Mbps flow.
   Furthermore, both simulation studies and frequency-domain analysis
   have established that a feedback interval below 250ms will not break
   up the feedback control loop of NADA congestion control.

   In calculating the non-linear warping of delay in (1), the current
   design uses fixed values of QTH and QMAX.  It is possible to adapt
   the value of both based on past observations of queuing delay in the
   presence of packet losses.

   In calculating the aggregate congestion signal x_n, the choice of
   DMARK and DLOSS influence the steady-state packet loss/marking ratio
   experienced by the flow at a given available bandwidth.  Higher
   values of DMARK and DLOSS result in lower steady-state loss/marking
   ratios, but are more susceptible to the impact of individual packet
   loss/marking events.  While the value of DMARK and DLOSS are fixed
   and predetermined in the current design, a scheme for automatically
   tuning these values based on desired bandwidth sharing behavior in
   the presence of other competing loss-based flows (e.g., loss-based
   TCP) is under investigation.

   [Editor's note: Choice of start value: is this in scope of congestion
   control, or should this be decided by the application?]

6.4.  Sender-based vs. receiver-based calculation

   In the current design, the aggregated congestion signal x_n is
   calculated at the receiver, keeping the sender operation completely
   independent of the form of actual network congestion indications
   (delay, loss, or marking).  Alternatively, one can move the logics of
   (1) and (2) to the sender.  Such an approach requires slightly higher

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   overhead in the feedback messages, which should contain individual
   fields on queuing delay (d_n), packet loss ratio (p_loss), packet
   marking ratio (p_mark), receiving rate (r_recv), and recommended rate
   adaptation mode (rmode).

6.5.  Incremental deployment

   One nice property of NADA is the consistent video endpoint behavior
   irrespective of network node variations.  This facilitates gradual,
   incremental adoption of the scheme.

   To start off with, the proposed congestion control mechanism can be
   implemented without any explicit support from the network, and relies
   solely on observed one-way delay measurements and packet loss ratios
   as implicit congestion signals.

   When ECN is enabled at the network nodes with RED-based marking, the
   receiver can fold its observations of ECN markings into the
   calculation of the equivalent delay.  The sender can react to these
   explicit congestion signals without any modification.

   Ultimately, networks equipped with proactive marking based on token
   bucket level metering can reap the additional benefits of zero
   standing queues and lower end-to-end delay and work seamlessly with
   existing senders and receivers.

7.  Implementation Status

   The NADA scheme has been implemented in [ns-2] and [ns-3] simulation
   platforms.  Extensive ns-2 simulation evaluations of an earlier
   version of the draft are documented in [Zhu-PV13].  Evaluation
   results of the current draft over several test cases in
   [I-D.ietf-rmcat-eval-test] have been presented at recent IETF
   meetings [IETF-90][IETF-91].

   The scheme has also been implemented and evaluated in a lab setting
   as described in [IETF-90].  Preliminary evaluation results of NADA in
   single-flow and multi-flow scenarios have been presented in

8.  IANA Considerations

   This document makes no request of IANA.

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9.  Acknowledgements

   The authors would like to thank Randell Jesup, Luca De Cicco, Piers
   O'Hanlon, Ingemar Johansson, Stefan Holmer, Cesar Ilharco Magalhaes,
   Safiqul Islam, Mirja Kuhlewind, and Karen Elisabeth Egede Nielsen for
   their valuable questions and comments on earlier versions of this

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,

   [RFC3168]  Ramakrishnan, K., Floyd, S., and D. Black, "The Addition
              of Explicit Congestion Notification (ECN) to IP", RFC
              3168, DOI 10.17487/RFC3168, September 2001,

   [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, <>.

              Singh, V. and J. Ott, "Evaluating Congestion Control for
              Interactive Real-time Media", draft-ietf-rmcat-eval-
              criteria-03 (work in progress), March 2015.

              Sarker, Z., Singh, V., Zhu, X., and M. Ramalho, "Test
              Cases for Evaluating RMCAT Proposals", draft-ietf-rmcat-
              eval-test-02 (work in progress), September 2015.

              Jesup, R. and Z. Sarker, "Congestion Control Requirements
              for Interactive Real-Time Media", draft-ietf-rmcat-cc-
              requirements-09 (work in progress), December 2014.

10.2.  Informative References

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   [RFC2309]  Braden, B., Clark, D., Crowcroft, J., Davie, B., Deering,
              S., Estrin, D., Floyd, S., Jacobson, V., Minshall, G.,
              Partridge, C., Peterson, L., Ramakrishnan, K., Shenker,
              S., Wroclawski, J., and L. Zhang, "Recommendations on
              Queue Management and Congestion Avoidance in the
              Internet", RFC 2309, DOI 10.17487/RFC2309, April 1998,

   [RFC5348]  Floyd, S., Handley, M., Padhye, J., and J. Widmer, "TCP
              Friendly Rate Control (TFRC): Protocol Specification", RFC
              5348, DOI 10.17487/RFC5348, September 2008,

   [RFC6660]  Briscoe, B., Moncaster, T., and M. Menth, "Encoding Three
              Pre-Congestion Notification (PCN) States in the IP Header
              Using a Single Diffserv Codepoint (DSCP)", RFC 6660, DOI
              10.17487/RFC6660, July 2012,

   [RFC6817]  Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
              "Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
              DOI 10.17487/RFC6817, December 2012,

              Floyd, S., Handley, M., Padhye, J., and J. Widmer,
              "Equation-based Congestion Control for Unicast
              Applications", ACM SIGCOMM Computer Communications Review
              vol. 30, no. 4, pp. 43-56, October 2000.

              Budzisz, L., Stanojevic, R., Schlote, A., Baker, F., and
              R. Shorten, "On the Fair Coexistence of Loss- and Delay-
              Based TCP", IEEE/ACM Transactions on Networking vol. 19,
              no. 6, pp. 1811-1824, December 2011.

              Zhu, X. and R. Pan, "NADA: A Unified Congestion Control
              Scheme for Low-Latency Interactive Video", in Proc. IEEE
              International Packet Video Workshop (PV'13) San Jose, CA,
              USA, December 2013.

   [ns-2]     "The Network Simulator - ns-2",

   [ns-3]     "The Network Simulator - ns-3", <>.

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   [IETF-90]  Zhu, X., Ramalho, M., Ganzhorn, C., Jones, P., and R. Pan,
              "NADA Update: Algorithm, Implementation, and Test Case
              Evalua6on Results", July 2014,

   [IETF-91]  Zhu, X., Pan, R., Ramalho, M., Mena, S., Ganzhorn, C.,
              Jones, P., and S. D'Aronco, "NADA Algorithm Update and
              Test Case Evaluations", November 2014,

Appendix A.  Network Node Operations

   NADA can work with different network queue management schemes and
   does not assume any specific network node operation.  As an example,
   this appendix describes three variants of queue management behavior
   at the network node, leading to either implicit or explicit
   congestion signals.

   In all three flavors described below, the network queue operates with
   the simple first-in-first-out (FIFO) principle.  There is no need to
   maintain per-flow state.  The system can scale easily with a large
   number of video flows and at high link capacity.

A.1.  Default behavior of drop tail queues

   In a conventional network with drop tail or RED queues, congestion is
   inferred from the estimation of end-to-end delay and/or packet loss.
   Packet drops at the queue are detected at the receiver, and
   contributes to the calculation of the aggregated congestion signal
   x_n.  No special action is required at network node.

A.2.  RED-based ECN marking

   In this mode, the network node randomly marks the ECN field in the IP
   packet header following the Random Early Detection (RED) algorithm
   [RFC2309].  Calculation of the marking probability involves the
   following steps:

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       on packet arrival:
           update smoothed queue size q_avg as:
               q_avg = w*q + (1-w)*q_avg.

           calculate marking probability p as:

              / 0,                    if q < q_lo;
              |        q_avg - q_lo
          p= <  p_max*--------------, if q_lo <= q < q_hi;
              |         q_hi - q_lo
              \ p = 1,                if q >= q_hi.

   Here, q_lo and q_hi corresponds to the low and high thresholds of
   queue occupancy.  The maximum marking probability is p_max.

   The ECN markings events will contribute to the calculation of an
   equivalent delay x_n at the receiver.  No changes are required at the

A.3.  Random Early Marking with Virtual Queues

   Advanced network nodes may support random early marking based on a
   token bucket algorithm originally designed for Pre-Congestion
   Notification (PCN) [RFC6660].  The early congestion notification
   (ECN) bit in the IP header of packets are marked randomly.  The
   marking probability is calculated based on a token-bucket algorithm
   originally designed for the Pre-Congestion Notification (PCN)
   [RFC6660].  The target link utilization is set as 90%; the marking
   probability is designed to grow linearly with the token bucket size
   when it varies between 1/3 and 2/3 of the full token bucket limit.

   * upon packet arrival, meter packet against token bucket (r,b);

   * update token level b_tk;

   * calculate the marking probability as:

            / 0,                     if b-b_tk < b_lo;
            |          b-b_tk-b_lo
       p = <  p_max* --------------, if b_lo<= b-b_tk <b_hi;
            |           b_hi-b_lo
            \ 1,                     if b-b_tk>=b_hi.

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   Here, the token bucket lower and upper limits are denoted by b_lo and
   b_hi, respectively.  The parameter b indicates the size of the token
   bucket.  The parameter r is chosen to be below capacity, resulting in
   slight under-utilization of the link.  The maximum marking
   probability is p_max.

   The ECN markings events will contribute to the calculation of an
   equivalent delay x_n at the receiver.  No changes are required at the
   sender.  The virtual queuing mechanism from the PCN-based marking
   algorithm will lead to additional benefits such as zero standing

Authors' Addresses

   Xiaoqing Zhu
   Cisco Systems
   12515 Research Blvd., Building 4
   Austin, TX  78759


   Rong Pan
   Cisco Systems
   3625 Cisco Way
   San Jose, CA  95134


   Michael A. Ramalho
   Cisco Systems, Inc.
   8000 Hawkins Road
   Sarasota, FL  34241

   Phone: +1 919 476 2038

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   Sergio Mena de la Cruz
   Cisco Systems
   EPFL, Quartier de l'Innovation, Batiment E
   Ecublens, Vaud  1015


   Paul E. Jones
   Cisco Systems
   7025 Kit Creek Rd.
   Research Triangle Park, NC  27709


   Jiantao Fu
   Cisco Systems
   707 Tasman Drive
   Milpitas, CA  95035


   Stefano D'Aronco
   Ecole Polytechnique Federale de Lausanne
   EPFL STI IEL LTS4, ELD 220 (Batiment ELD), Station 11
   Lausanne  CH-1015


   Charles Ganzhorn
   7900 International Drive, International Plaza, Suite 400
   Bloomington, MN  55425


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