draft-ietf-rmcat-sbd-11.txt   rfc8382.txt 
RTP Media Congestion Avoidance Techniques D. Hayes, Ed. Internet Engineering Task Force (IETF) D. Hayes, Ed.
Internet-Draft Simula Research Laboratory Request for Comments: 8382 S. Ferlin
Intended status: Experimental S. Ferlin Category: Experimental Simula Research Laboratory
Expires: September 30, 2018 ISSN: 2070-1721 M. Welzl
M. Welzl
K. Hiorth K. Hiorth
University of Oslo University of Oslo
March 29, 2018 June 2018
Shared Bottleneck Detection for Coupled Congestion Control for RTP Shared Bottleneck Detection for Coupled Congestion Control for RTP Media
Media.
draft-ietf-rmcat-sbd-11
Abstract Abstract
This document describes a mechanism to detect whether end-to-end data This document describes a mechanism to detect whether end-to-end data
flows share a common bottleneck. It relies on summary statistics flows share a common bottleneck. This mechanism relies on summary
that are calculated based on continuous measurements and used as statistics that are calculated based on continuous measurements and
input to a grouping algorithm that runs wherever the knowledge is used as input to a grouping algorithm that runs wherever the
needed. knowledge is needed.
Status of This Memo Status of This Memo
This Internet-Draft is submitted in full conformance with the This document is not an Internet Standards Track specification; it is
provisions of BCP 78 and BCP 79. published for examination, experimental implementation, and
evaluation.
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 https://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months This document defines an Experimental Protocol for the Internet
and may be updated, replaced, or obsoleted by other documents at any community. This document is a product of the Internet Engineering
time. It is inappropriate to use Internet-Drafts as reference Task Force (IETF). It represents the consensus of the IETF
material or to cite them other than as "work in progress." community. It has received public review and has been approved for
publication by the Internet Engineering Steering Group (IESG). Not
all documents approved by the IESG are candidates for any level of
Internet Standard; see Section 2 of RFC 7841.
This Internet-Draft will expire on September 30, 2018. Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
https://www.rfc-editor.org/info/rfc8382.
Copyright Notice Copyright Notice
Copyright (c) 2018 IETF Trust and the persons identified as the Copyright (c) 2018 IETF Trust and the persons identified as the
document authors. All rights reserved. document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents Provisions Relating to IETF Documents
(https://trustee.ietf.org/license-info) in effect on the date of (https://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect carefully, as they describe your rights and restrictions with respect
to this document. Code Components extracted from this document must to this document. Code Components extracted from this document must
include Simplified BSD License text as described in Section 4.e of include Simplified BSD License text as described in Section 4.e of
the Trust Legal Provisions and are provided without warranty as the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License. described in the Simplified BSD License.
Table of Contents Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3 1. Introduction ....................................................4
1.1. The Basic Mechanism . . . . . . . . . . . . . . . . . . . 3 1.1. The Basic Mechanism ........................................4
1.2. The Signals . . . . . . . . . . . . . . . . . . . . . . . 3 1.2. The Signals ................................................4
1.2.1. Packet Loss . . . . . . . . . . . . . . . . . . . . . 3 1.2.1. Packet Loss .........................................4
1.2.2. Packet Delay . . . . . . . . . . . . . . . . . . . . 4 1.2.2. Packet Delay ........................................5
1.2.3. Path Lag . . . . . . . . . . . . . . . . . . . . . . 4 1.2.3. Path Lag ............................................5
2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4 2. Definitions .....................................................6
2.1. Parameters and Their Effect . . . . . . . . . . . . . . . 6 2.1. Parameters and Their Effects ...............................7
2.2. Recommended Parameter Values . . . . . . . . . . . . . . 7 2.2. Recommended Parameter Values ...............................8
3. Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3. Mechanism .......................................................9
3.1. SBD Feedback Requirements . . . . . . . . . . . . . . . . 8 3.1. SBD Feedback Requirements .................................10
3.1.1. Feedback When All the Logic is Placed at the Sender . 9 3.1.1. Feedback When All the Logic Is Placed at
3.1.2. Feedback When the Statistics are Calculated at the the Sender .........................................10
Receiver and SBD Performed at the Sender . . . . . . 9 3.1.2. Feedback When the Statistics Are Calculated at the
3.1.3. Feedback When Bottlenecks can be Determined at Both Receiver and SBD Is Performed at the Sender ........11
Senders and Receivers . . . . . . . . . . . . . . . . 10 3.1.3. Feedback When Bottlenecks Can Be Determined
3.2. Key Metrics and Their Calculation . . . . . . . . . . . . 10 at Both Senders and Receivers ......................11
3.2.1. Mean Delay . . . . . . . . . . . . . . . . . . . . . 10 3.2. Key Metrics and Their Calculation .........................12
3.2.2. Skewness Estimate . . . . . . . . . . . . . . . . . . 11 3.2.1. Mean Delay .........................................12
3.2.3. Variability Estimate . . . . . . . . . . . . . . . . 12 3.2.2. Skewness Estimate ..................................12
3.2.4. Oscillation Estimate . . . . . . . . . . . . . . . . 12 3.2.3. Variability Estimate ...............................13
3.2.5. Packet Loss . . . . . . . . . . . . . . . . . . . . . 13 3.2.4. Oscillation Estimate ...............................13
3.3. Flow Grouping . . . . . . . . . . . . . . . . . . . . . . 13 3.2.5. Packet Loss ........................................14
3.3.1. Flow Grouping Algorithm . . . . . . . . . . . . . . . 13 3.3. Flow Grouping .............................................14
3.3.2. Using the Flow Group Signal . . . . . . . . . . . . . 16 3.3.1. Flow-Grouping Algorithm ............................14
4. Enhancements to the Basic SBD Algorithm . . . . . . . . . . . 16 3.3.2. Using the Flow Group Signal ........................18
4.1. Reducing Lag and Improving Responsiveness . . . . . . . . 16 4. Enhancements to the Basic SBD Algorithm ........................18
4.1.1. Improving the Response of the Skewness Estimate . . . 17 4.1. Reducing Lag and Improving Responsiveness .................18
4.1.2. Improving the Response of the Variability Estimate . 19 4.1.1. Improving the Response of the Skewness Estimate ....19
4.2. Removing Oscillation Noise . . . . . . . . . . . . . . . 19 4.1.2. Improving the Response of the Variability
5. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 20 Estimate ...........................................20
5.1. Time-stamp Resolution . . . . . . . . . . . . . . . . . . 20 4.2. Removing Oscillation Noise ................................21
5.2. Clock Skew . . . . . . . . . . . . . . . . . . . . . . . 20 5. Measuring OWD ..................................................21
6. Expected Feedback from Experiments . . . . . . . . . . . . . 20 5.1. Timestamp Resolution ......................................21
7. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 21 5.2. Clock Skew ................................................22
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 21 6. Expected Feedback from Experiments .............................22
9. Security Considerations . . . . . . . . . . . . . . . . . . . 21 7. IANA Considerations ............................................22
10. Change history . . . . . . . . . . . . . . . . . . . . . . . 21 8. Security Considerations ........................................22
11. References . . . . . . . . . . . . . . . . . . . . . . . . . 23 9. References .....................................................23
11.1. Normative References . . . . . . . . . . . . . . . . . . 23 9.1. Normative References ......................................23
11.2. Informative References . . . . . . . . . . . . . . . . . 23 9.2. Informative References ....................................23
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 24 Acknowledgments ...................................................25
Authors' Addresses ................................................25
1. Introduction 1. Introduction
In the Internet, it is not normally known if flows (e.g., TCP In the Internet, it is not normally known whether flows (e.g., TCP
connections or UDP data streams) traverse the same bottlenecks. Even connections or UDP data streams) traverse the same bottlenecks. Even
flows that have the same sender and receiver may take different paths flows that have the same sender and receiver may take different paths
and may or may not share a bottleneck. Flows that share a bottleneck and may or may not share a bottleneck. Flows that share a bottleneck
link usually compete with one another for their share of the link usually compete with one another for their share of the
capacity. This competition has the potential to increase packet loss capacity. This competition has the potential to increase packet loss
and delays. This is especially relevant for interactive applications and delays. This is especially relevant for interactive applications
that communicate simultaneously with multiple peers (such as multi- that communicate simultaneously with multiple peers (such as
party video). For RTP media applications such as RTCWEB, multi-party video). For RTP media applications such as RTCWEB,
[I-D.ietf-rmcat-coupled-cc] describes a scheme that combines the [RTP-COUPLED-CC] describes a scheme that combines the congestion
congestion controllers of flows in order to honor their priorities controllers of flows in order to honor their priorities and avoid
and avoid unnecessary packet loss as well as delay. This mechanism unnecessary packet loss as well as delay. This mechanism relies on
relies on some form of Shared Bottleneck Detection (SBD); here, a some form of Shared Bottleneck Detection (SBD); here, a measurement-
measurement-based SBD approach is described. based SBD approach is described.
1.1. The Basic Mechanism 1.1. The Basic Mechanism
The mechanism groups flows that have similar statistical The mechanism groups flows that have similar statistical
characteristics together. Section 3.3.1 describes a simple method characteristics together. Section 3.3.1 describes a simple method
for achieving this, however, a major part of this draft is concerned for achieving this; however, a major part of this document is
with collecting suitable statistics for this purpose. concerned with collecting suitable statistics for this purpose.
1.2. The Signals 1.2. The Signals
The current Internet is unable to explicitly inform endpoints as to The current Internet is unable to explicitly inform endpoints as to
which flows share bottlenecks, so endpoints need to infer this from which flows share bottlenecks, so endpoints need to infer this from
whatever information is available to them. The mechanism described whatever information is available to them. The mechanism described
here currently utilizes packet loss and packet delay, but is not here currently utilizes packet loss and packet delay but is not
restricted to these. As ECN becomes more prevalent it too will restricted to these. As Explicit Congestion Notification (ECN)
become a valuable base signal. becomes more prevalent, it too will become a valuable base signal
that can be correlated to detect shared bottlenecks.
1.2.1. Packet Loss 1.2.1. Packet Loss
Packet loss is often a relatively infrequent indication that a flow Packet loss is often a relatively infrequent indication that a flow
traverses a bottleneck. Therefore, on its own it is of limited use traverses a bottleneck. Therefore, on its own it is of limited use
for SBD, however, it is a valuable supplementary measure when it is for SBD; however, it is a valuable supplementary measure when it is
more prevalent (refer to [RFC2680] section 2.5 for measuring packet more prevalent (refer to [RFC7680], Section 2.5 for measuring packet
loss). loss).
1.2.2. Packet Delay 1.2.2. Packet Delay
End-to-end delay measurements include noise from every device along End-to-end delay measurements include noise from every device along
the path in addition to the delay perturbation at the bottleneck the path, in addition to the delay perturbation at the bottleneck
device. The noise is often significantly increased if the round-trip device. The noise is often significantly increased if the round-trip
time is used. The cleanest signal is obtained by using One-Way-Delay time is used. The cleanest signal is obtained by using One-Way Delay
(OWD) (refer to [RFC7679] section 3 for a definition of OWD). (OWD) (refer to [RFC7679], Section 3 for a definition of OWD).
Measuring absolute OWD is difficult since it requires both the sender Measuring absolute OWD is difficult, since it requires both the
and receiver clocks to be synchronized. However, since the sender and receiver clocks to be synchronized. However, since the
statistics being collected are relative to the mean OWD, a relative statistics being collected are relative to the mean OWD, a relative
OWD measurement is sufficient. Clock skew is not usually significant OWD measurement is sufficient. Clock skew is not usually significant
over the time intervals used by this SBD mechanism (see [RFC6817] A.2 over the time intervals used by this SBD mechanism (see [RFC6817],
for a discussion on clock skew and OWD measurements). However, in Appendix A.2 for a discussion on clock skew and OWD measurements).
circumstances where it is significant, Section 5.2 outlines a way of However, in circumstances where it is significant, Section 5.2
adjusting the calculations to cater for it. outlines a way of adjusting the calculations to cater to it.
Each packet arriving at the bottleneck buffer may experience very Each packet arriving at the bottleneck buffer may experience very
different queue lengths, and therefore different waiting times. A different queue lengths and, therefore, different waiting times. A
single OWD sample does not, therefore, characterize the path well. single OWD sample does not, therefore, characterize the path well.
However, multiple OWD measurements do reflect the distribution of However, multiple OWD measurements do reflect the distribution of
delays experienced at the bottleneck. delays experienced at the bottleneck.
1.2.3. Path Lag 1.2.3. Path Lag
Flows that share a common bottleneck may traverse different paths, Flows that share a common bottleneck may traverse different paths,
and these paths will often have different base delays. This makes it and these paths will often have different base delays. This makes it
difficult to correlate changes in delay or loss. This technique uses difficult to correlate changes in delay or loss. This technique uses
the long term shape of the delay distribution as a base for the long-term shape of the delay distribution as a base for
comparison to counter this. comparison to counter this.
2. Definitions 2. Definitions
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in BCP "OPTIONAL" in this document are to be interpreted as described in
14 [RFC2119] RFC2119 [RFC2119] RFC8174 [RFC8174] when, and only when, BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
they appear in all capitals, as shown here. capitals, as shown here.
Acronyms used in this document: Acronyms used in this document:
OWD -- One Way Delay OWD - One-Way Delay
MAD -- Mean Absolute Deviation
RTT -- Round Trip Time MAD - Mean Absolute Deviation
SBD -- Shared Bottleneck Detection SBD - Shared Bottleneck Detection
Conventions used in this document: Conventions used in this document:
T -- the base time interval over which measurements are T the base time interval over which measurements
made are made
N -- the number of base time, T, intervals used in some N the number of base time, T, intervals used in some
calculations calculations
M -- the number of base time, T, intervals used in some M the number of base time, T, intervals used in some
calculations, where M <= N calculations, where M <= N
sum(...) -- summation of terms of the variable in parentheses sum(...) summation of terms of the variable in parentheses
sum_T(...) -- summation of all the measurements of the variable sum_T(...) summation of all the measurements of the variable in
in parentheses taken over the interval T parentheses taken over the interval T
sum_NT(...) -- summation of all measurements taken over the sum_NT(...) summation of all measurements taken over the
interval N*T interval N*T
sum_MT(...) -- summation of all measurements taken over the sum_MT(...) summation of all measurements taken over the
interval M*T interval M*T
E_T(...) -- the expectation or mean of the measurements of the E_T(...) the expectation or mean of the measurements of the
variable in parentheses over T variable in parentheses over T
E_N(...) -- the expectation or mean of the last N values of E_N(...) the expectation or mean of the last N values of the
the variable in parentheses variable in parentheses
E_M(...) -- the expectation or mean of the last M values of E_M(...) the expectation or mean of the last M values of the
the variable in parentheses variable in parentheses
num_T(...) -- the count of measurements of the variable in num_T(...) the count of measurements of the variable in
parentheses taken in the interval T parentheses taken in the interval T
num_MT(...) -- the count of measurements of the variable in num_MT(...) the count of measurements of the variable in
parentheses taken in the interval NT parentheses taken in the interval M*T
PB -- a boolean variable indicating the particular flow PB a boolean variable indicating that the particular
was identified transiting a bottleneck in the flow was identified transiting a bottleneck in the
previous interval T (i.e. Previously Bottleneck) previous interval T (i.e., "Previously Bottleneck")
skew_est -- a measure of skewness in a OWD distribution skew_est a measure of skewness in an OWD distribution
skew_base_T -- a variable used as an intermediate step in skew_base_T a variable used as an intermediate step in
calculating skew_est calculating skew_est
var_est -- a measure of variability in OWD measurements 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 var_base_T a variable used as an intermediate step in
measurements calculating var_est
pkt_loss -- a measure of the proportion of packets lost freq_est a measure of low-frequency oscillation in the OWD
measurements
p_l, p_f, p_mad, c_s, c_h, p_s, p_d, p_v -- various thresholds pkt_loss a measure of the proportion of packets lost
used in the mechanism
M and F -- number of values related to N p_l, p_f, p_mad, c_s, c_h, p_s, p_d, p_v
various thresholds used in the mechanism
2.1. Parameters and Their Effect M and F number of values related to N
T T should be long enough so that there are enough packets 2.1. Parameters and Their Effects
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 T T should be long enough so that there are enough packets
oscillations in OWD. Usually M=N, though having M<N may be received during T for a useful estimate of the short-term
beneficial in certain circumstances. M*T needs to be long mean OWD and variation statistics. Making T too large can
enough to provide stable estimates of skewness and MAD. 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.
F F determines the number of intervals over which statistics N and M N should be large enough to provide a stable estimate of
are considered to be equally weighted. When F=M recent and oscillations in OWD. Often, M=N is just fine, though
older measurements are considered equal. Making F<M can having M<N may be beneficial in certain circumstances. M*T
increase the responsiveness of the SBD mechanism. If F is needs to be long enough to provide stable estimates of
too small, statistics will be too noisy. skewness and MAD.
c_s c_s is the threshold in skew_est used for determining whether F F determines the number of intervals over which statistics
a flow is transiting a bottleneck or not. Lower values of are considered to be equally weighted. When F=M, recent
c_s require bottlenecks to be more congested to be considered and older measurements are considered equal. Making F<M
for grouping by the mechanism. c_s should be set within the can increase the responsiveness of the SBD mechanism. If F
range of +0.2 to -0.1; low enough so that lightly loaded is too small, statistics will be too noisy.
paths do not give a false indication.
p_l p_l is the threshold in pkt_loss used for determining whether c_s c_s is the threshold in skew_est used for determining
a flow is transiting a bottleneck or not. When pkt_loss is whether a flow is transiting a bottleneck or not. Lower
high it becomes a better indicator of congestion than values of c_s require bottlenecks to be more congested to
skew_est. be considered for grouping by the mechanism. c_s should be
set within the range of +0.2 to -0.1 -- low enough so that
lightly loaded paths do not give a false indication.
c_h c_h adds hysteresis to the bottleneck determination. It p_l p_l is the threshold in pkt_loss used for determining
should be large enough to avoid constant switching in the whether a flow is transiting a bottleneck or not. When
determination, but low enough to ensure that grouping is not pkt_loss is high, it becomes a better indicator of
attempted when there is no bottleneck and the delay and loss congestion than skew_est.
signals cannot be relied upon.
p_v p_v determines the sensitivity of freq_est to noise. Making c_h c_h adds hysteresis to the bottleneck determination. It
it smaller will yield higher but noisier values for freq_est. should be large enough to avoid constant switching in the
Making it too large will render it ineffective for determination but low enough to ensure that grouping is not
determining groups. attempted when there is no bottleneck and the delay and
loss signals cannot be relied upon.
p_* Flows are separated when the p_v p_v determines the sensitivity of freq_est to noise.
skew_est|var_est|freq_est|pkt_loss measure is greater than Making it smaller will yield higher but noisier values for
p_s|p_mad|p_f|p_d. Adjusting these is a compromise between freq_est. Making it too large will render it ineffective
false grouping of flows that do not share a bottleneck and for determining groups.
false splitting of flows that do. Making them larger can
help if the measures are very noisy, but reducing the noise p_* Flows are separated when the
in the statistical measures by adjusting T and N|M may be a skew_est|var_est|freq_est|pkt_loss measure is greater than
better solution. p_s|p_mad|p_f|p_d. 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.
2.2. Recommended Parameter Values 2.2. Recommended Parameter Values
Reference [Hayes-LCN14] uses T=350ms, N=50, p_l=0.1. The other [Hayes-LCN14] uses T=350ms and N=50. The other parameters have been
parameters have been tightened to reflect minor enhancements to the tightened to reflect minor enhancements to the algorithm outlined in
algorithm outlined in Section 4: c_s=0.1, p_f=p_d=0.1, p_s=0.15, Section 4: c_s=0.1, p_f=p_d=0.1, p_s=0.15, p_mad=0.1, p_v=0.7. M=30,
p_mad=0.1, p_v=0.7. M=30, F=20, and c_h = 0.3 are additional F=20, and c_h=0.3 are additional parameters defined in that document.
parameters defined in the document. These are values that seem to These are values that seem to work well over a wide range of
work well over a wide range of practical Internet conditions. practical Internet conditions.
3. Mechanism 3. Mechanism
The mechanism described in this document is based on the observation The mechanism described in this document is based on the observation
that the distribution of delay measurements of packets that traverse that when flows traverse a common bottleneck, each flow's
a common bottleneck have similar shape characteristics. These shape distribution of packet delay measurements has similar shape
characteristics are described using 3 key summary statistics: characteristics. These shape characteristics are described using
three key summary statistics --
variability (estimate var_est, see Section 3.2.3) 1. variability estimate (var_est; see Section 3.2.3)
skewness (estimate skew_est, see Section 3.2.2) 2. skewness estimate (skew_est; see Section 3.2.2)
oscillation (estimate freq_est, see Section 3.2.4) 3. oscillation estimate (freq_est; see Section 3.2.4)
with packet loss (estimate pkt_loss, see Section 3.2.5) used as a -- with packet loss (pkt_loss; see Section 3.2.5) used as a
supplementary statistic. supplementary statistic.
Summary statistics help to address both the noise and the path lag Summary statistics help to address both the noise and the path lag
problems by describing the general shape over a relatively long problems by describing the general shape over a relatively long
period of time. Each summary statistic portrays a "view" of the period of time. Each summary statistic portrays a "view" of the
bottleneck link characteristics, and when used together, they provide bottleneck link characteristics, and when used together, they provide
a robust discrimination for grouping flows. An RTP Media device may a robust discrimination for grouping flows. An RTP media device may
be both a sender and a receiver and SBD can be performed at either a be both a sender and a receiver. SBD can be performed at either a
sender or a receiver or both. sender or a receiver, or both.
In Figure 1, there are two possible locations for shared bottleneck
detection: the sender side and the receiver side.
+----+ +----+
| H2 | | H2 |
+----+ +----+
| |
| L2 | L2
| |
+----+ L1 | L3 +----+ +----+ L1 | L3 +----+
| H1 |------|------| H3 | | H1 |------|------| H3 |
+----+ +----+ +----+ +----+
A network with 3 hosts (H1, H2, H3) and 3 links (L1, L2, L3). A network with three hosts (H1, H2, H3) and three links (L1, L2, L3)
Figure 1 Figure 1
In Figure 1, there are two possible locations for shared bottleneck 1. Sender side: Consider a situation where host H1 sends media
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 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 and H3 measure the OWD and packet loss and periodically send
raw data, or the calculated summary statistics, periodically to either this raw data or the calculated summary statistics to H1
H1 every T. H1, having this knowledge, can determine the shared every T. H1, having this knowledge, can determine the shared
bottleneck and accordingly control the send rates. bottleneck and accordingly control the send rates.
2. Receiver-side: consider that H2 is also sending media to H3, and 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 L3 is a shared bottleneck. If H3 sends summary statistics to H1
and H2, neither H1 nor H2 alone obtain enough knowledge to detect and H2, neither H1 nor H2 alone obtains enough knowledge to
this shared bottleneck; H3 can however determine it by combining detect this shared bottleneck; H3 can, however, determine it by
the summary statistics related to H1 and H2, respectively. combining the summary statistics related to H1 and H2,
respectively.
3.1. SBD Feedback Requirements 3.1. SBD Feedback Requirements
There are three possible scenarios each with different feedback There are three possible scenarios, each with different feedback
requirements: requirements:
1. Both summary statistic calculations and SBD are performed at 1. Both summary statistic calculations and SBD are performed at
senders only. When sender-based congestion control is senders only. When sender-based congestion control is
implemented, this method is RECOMMENDED. implemented, this method is RECOMMENDED.
2. Summary statistics calculated on the receivers and SBD at the 2. Summary statistics are calculated on the receivers, and SBD is
senders. performed at the senders.
3. Summary statistic calculations on receivers, and SBD performed at 3. Summary statistic calculations are performed on receivers, and
both senders and receivers (beyond the current scope, but allows SBD is performed at both senders and receivers (beyond the scope
cooperative detection of bottlenecks). of this document, but allows cooperative detection of
bottlenecks).
All three possibilities are discussed for completeness in this All three possibilities are discussed for completeness in this
document, however, it is expected that feedback will take the form of document; however, it is expected that feedback will take the form of
scenario 1 and operate in conjunction with sender-based congestion scenario 1 and operate in conjunction with sender-based congestion
control mechanisms. control mechanisms.
3.1.1. Feedback When All the Logic is Placed at the Sender 3.1.1. Feedback When All the Logic Is Placed at the Sender
Having the sender calculate the summary statistics and determine the Having the sender calculate the summary statistics and determine the
shared bottlenecks based on them has the advantage of placing most of shared bottlenecks based on them has the advantage of placing most of
the functionality in one place -- the sender. the functionality in one place -- the sender.
For every packet, the sender requires accurate relative OWD For every packet, the sender requires accurate relative OWD
measurements of adequate precision, along with an indication of lost measurements of adequate precision, along with an indication of lost
packets (or the proportion of packets lost over an interval). An packets (or the proportion of packets lost over an interval). A
method to provide such measurement data with RTCP is described in method to provide such measurement data with the RTP Control Protocol
[I-D.ietf-avtcore-cc-feedback-message]. (RTCP) is described in [RTCP-CC-FEEDBACK].
Sums, var_base_T and skew_base_T are calculated incrementally as Sums, var_base_T, and skew_base_T are calculated incrementally as
relative OWD measurements are determined from the feedback messages. relative OWD measurements are determined from the feedback messages.
When the mechanism has received sufficient measurements to cover the When the mechanism has received sufficient measurements to cover the
T base time interval for all flows, the summary statistics (see base time interval T for all flows, the summary statistics (see
Section 3.2) are calculated for that T interval and flows are grouped Section 3.2) are calculated for that T interval and flows are grouped
(see Section 3.3.1). The exact timing of these calculations will (see Section 3.3.1). The exact timing of these calculations will
depend on the frequency of the feedback message. depend on the frequency of the feedback message.
3.1.2. Feedback When the Statistics are Calculated at the Receiver and 3.1.2. Feedback When the Statistics Are Calculated at the Receiver and
SBD Performed at the Sender SBD Is Performed at the Sender
This scenario minimizes feedback, but requires receivers to send This scenario minimizes feedback but requires receivers to send
selected summary statistics at an agreed regular interval. We selected summary statistics at an agreed-upon regular interval. We
envisage the following exchange of information to initialize the envisage the following exchange of information to initialize the
system: system:
o An initialization message from the sender to the receiver will o An initialization message from the sender to the receiver will
contain the following information: contain the following information:
* A list of which key metrics should be collected and relayed * A list of which key metrics should be collected and relayed
back to the sender out of a possibly extensible set (pkt_loss, back to the sender out of a possibly extensible set (pkt_loss,
var_est, skew_est, freq_est). The grouping algorithm described var_est, skew_est, and freq_est). The grouping algorithm
in this document requires all four of these metrics, and described in this document requires all four of these metrics,
receivers MUST be able to provide them, but future algorithms and receivers MUST be able to provide them, but future
may be able to exploit other metrics (e.g. metrics based on algorithms may be able to exploit other metrics (e.g., metrics
explicit network signals). based on explicit network signals).
* The values of T, N, M, and the necessary resolution and * The values of T, N, and M, and the necessary resolution and
precision of the relayed statistics. precision of the relayed statistics.
o A response message from the receiver acknowledges this message o A response message from the receiver acknowledges this message
with a list of key metrics it supports (subset of the senders with a list of key metrics it supports (subset of the sender's
list) and is able to relay back to the sender. list) and is able to relay back to the sender.
This initialization exchange may be repeated to finalize the agreed This initialization exchange may be repeated to finalize the set of
metrics should not all be supported by all receivers. It is also metrics that will be used. All agreed-upon metrics need to be
recommendable to include an identifier for the SBD algorithm version supported by all receivers. It is also recommended that an
in the initialization message from the sender, so that potential identifier for the SBD algorithm version be included in the
advances in SBD technology can be easily deployed. For reference, initialization message from the sender, so that potential advances in
the mechanism outlined in this document has the identifier SBD=01. SBD technology can be easily deployed. For reference, the mechanism
outlined in this document has the identifier "SBD=01".
After initialization the agreed summary statistics are fed back to After initialization, the agreed-upon summary statistics are fed back
the sender (nominally every T). to the sender (nominally every T).
3.1.3. Feedback When Bottlenecks can be Determined at Both Senders and 3.1.3. Feedback When Bottlenecks Can Be Determined at Both Senders and
Receivers Receivers
This type of mechanism is currently beyond the scope of the SBD This type of mechanism is currently beyond the scope of the SBD
algorithm described in this document. It is mentioned here to ensure algorithm described in this document. It is mentioned here to ensure
more advanced sender/receiver cooperative shared bottleneck that sender/receiver cooperative shared bottleneck determination
determination mechanisms remain possible in the future. mechanisms that are more advanced remain possible in the future.
It is envisaged that such a mechanism would be initialized in a It is envisaged that such a mechanism would be initialized in a
similar manner to that described in Section 3.1.2. manner similar to that described in Section 3.1.2.
After initialization both summary statistics and shared bottleneck After initialization, both summary statistics and shared bottleneck
determinations should be exchanged, nominally every T. determinations should be exchanged, nominally every T.
3.2. Key Metrics and Their Calculation 3.2. Key Metrics and Their Calculation
Measurements are calculated over a base interval, T and summarized Measurements are calculated over a base interval (T) and summarized
over N or M such intervals. All summary statistics can be calculated over N or M such intervals. All summary statistics can be calculated
incrementally. incrementally.
3.2.1. Mean Delay 3.2.1. Mean Delay
The mean delay is not a useful signal for comparisons between flows The mean delay is not a useful signal for comparisons between flows,
since flows may traverse quite different paths and clocks will not since flows may traverse quite different paths and clocks will not
necessarily be synchronized. However, it is a base measure for the 3 necessarily be synchronized. However, it is a base measure for the
summary statistics. The mean delay, E_T(OWD), is the average one way three summary statistics. The mean delay, E_T(OWD), is the average
delay measured over T. OWD measured over T.
To facilitate the other calculations, the last N E_T(OWD) values will 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 need to be stored in a cyclic buffer along with the moving average of
E_T(OWD): E_T(OWD):
mean_delay = E_M(E_T(OWD)) = sum_M(E_T(OWD)) / M 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 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 more responsive to changes, but potentially at the expense of a
higher error rate (see Section 4.1 for a discussion on improving the higher error rate (see Section 4.1 for a discussion on improving the
responsiveness of the mechanism.) responsiveness of the mechanism).
3.2.2. Skewness Estimate 3.2.2. Skewness Estimate
Skewness is difficult to calculate efficiently and accurately. Skewness is difficult to calculate efficiently and accurately.
Ideally it should be calculated over the entire period (M * T) from Ideally, it should be calculated over the entire period (M*T) from
the mean OWD over that period. However this would require storing the mean OWD over that period. However, this would require storing
every delay measurement over the period. Instead, an estimate is every delay measurement over the period. Instead, an estimate is
made over M * T based on a calculation every T using the previous T's made over M*T based on a calculation every T using the previous T's
calculation of mean_delay. calculation of mean_delay.
The base for the skewness calculation is estimated using a counter The base for the skewness calculation is estimated using a counter
initialized every T. It increments for one way delay (OWD) samples initialized every T. It increments for OWD samples below the mean
below the mean and decrements for OWD above the mean. So for each and decrements for OWD above the mean. So, for each OWD sample:
OWD sample:
if (OWD < mean_delay) skew_base_T++ if (OWD < mean_delay) skew_base_T++
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 mean_delay does not include the mean of the current T interval to
enable it to be calculated iteratively. enable it to be calculated iteratively.
skew_est = sum_MT(skew_base_T)/num_MT(OWD) skew_est = sum_MT(skew_base_T) / num_MT(OWD)
where skew_est is a number between -1 and 1 where skew_est is a number between -1 and 1.
Note: Care must be taken when implementing the comparisons to ensure Note: Care must be taken when implementing the comparisons to ensure
that rounding does not bias skew_est. It is important that the mean that rounding does not bias skew_est. It is important that the mean
is calculated with a higher precision than the samples. is calculated with a higher precision than the samples.
3.2.3. Variability Estimate 3.2.3. Variability Estimate
Mean Absolute Deviation (MAD) delay is a robust variability measure Mean Absolute Deviation (MAD) is a robust variability measure that
that copes well with different send rates. It can be implemented in copes well with different send rates. It can be implemented in an
an online manner as follows: online manner as follows:
var_base_T = sum_T(|OWD - E_T(OWD)|) var_base_T = sum_T(|OWD - E_T(OWD)|)
where where
|x| is the absolute value of x |x| is the absolute value of x
E_T(OWD) is the mean OWD calculated in the previous T E_T(OWD) is the mean OWD calculated in the previous T
var_est = MAD_MT = sum_MT(var_base_T)/num_MT(OWD) var_est = MAD_MT = sum_MT(var_base_T) / num_MT(OWD)
3.2.4. Oscillation Estimate 3.2.4. Oscillation Estimate
An estimate of the low frequency oscillation of the delay signal is An estimate of the low-frequency oscillation of the delay signal is
calculated by counting and normalizing the significant mean, calculated by counting and normalizing the significant mean,
E_T(OWD), crossings of mean_delay: E_T(OWD), crossings of mean_delay:
freq_est = number_of_crossings / N freq_est = number_of_crossings / N
where we define a significant mean crossing as a crossing that where we define a significant mean crossing as a crossing that
extends p_v * var_est from mean_delay. In our experiments we extends p_v * var_est from mean_delay. In our experiments, we
have found that p_v = 0.7 is a good value. 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 freq_est is a number between 0 and 1. freq_est can be approximated
incrementally as follows: incrementally as follows:
With each new calculation of E_T(OWD) a decision is made as to o 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 whether this value of E_T(OWD) significantly crosses the current
long term mean, mean_delay, with respect to the previous long-term mean, mean_delay, with respect to the previous
significant mean crossing. significant mean crossing.
A cyclic buffer, last_N_crossings, records a 1 if there is a o A cyclic buffer, last_N_crossings, records a 1 if there is a
significant mean crossing, otherwise a 0. significant mean crossing; otherwise, it records a 0.
The counter, number_of_crossings, is incremented when there is a o The counter, number_of_crossings, is incremented when there is a
significant mean crossing and decremented when a non-zero value is significant mean crossing and decremented when a non-zero value is
removed from the last_N_crossings. removed from the last_N_crossings.
This approximation of freq_est was not used in [Hayes-LCN14], which 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 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 tests show that this approximation of freq_est yields results that
are almost identical to when the full calculation is performed every are almost identical to when the full calculation is performed
T. every T.
3.2.5. Packet Loss 3.2.5. Packet Loss
The proportion of packets lost over the period NT is used as a The proportion of packets lost over the period NT is used as a
supplementary measure: supplementary measure:
pkt_loss = sum_NT(lost packets) / sum_NT(total packets) pkt_loss = sum_NT(lost packets) / sum_NT(total packets)
Note: When pkt_loss is small it is very variable, however, when Note: When pkt_loss is low, it is very variable; however, when
pkt_loss is high it becomes a stable measure for making grouping pkt_loss is high, it becomes a stable measure for making grouping
decisions. decisions.
3.3. Flow Grouping 3.3. Flow Grouping
3.3.1. Flow Grouping Algorithm 3.3.1. Flow-Grouping Algorithm
The following grouping algorithm is RECOMMENDED for use of SBD with The following grouping algorithm is RECOMMENDED for the use of SBD
coupled congestion control for RTP media [I-D.ietf-rmcat-coupled-cc] with coupled congestion control for RTP media [RTP-COUPLED-CC] and is
and is sufficient and efficient for small to moderate numbers of sufficient and efficient for small to moderate numbers of flows. For
flows. For very large numbers of flows (e.g. hundreds), a more very large numbers of flows (e.g., hundreds), a more complex
complex clustering algorithm may be substituted. clustering algorithm may be substituted.
Since no single metric is precise enough to group flows (due to Since no single metric is precise enough to group flows (due to
noise), the algorithm uses multiple metrics. Each metric offers a noise), the algorithm uses multiple metrics. Each metric offers a
different "view" of the bottleneck link characteristics, and used different "view" of the bottleneck link characteristics, and used
together they enable a more precise grouping of flows than would together they enable a more precise grouping of flows than would
otherwise be possible. otherwise be possible.
Flows determined to be transiting a bottleneck are successively Flows determined to be transiting a bottleneck are successively
divided into groups based on freq_est, var_est, skew_est and divided into groups based on freq_est, var_est, skew_est, and
pkt_loss. pkt_loss.
The first step is to determine which flows are transiting a The first step is to determine which flows are transiting a
bottleneck. This is important, since if a flow is not transiting a bottleneck. This is important, since if a flow is not transiting a
bottleneck its delay based metrics will not describe the bottleneck, bottleneck its delay-based metrics will not describe the bottleneck
but the "noise" from the rest of the path. Skewness, with proportion but will instead describe the "noise" from the rest of the path.
of packet loss as a supplementary measure, is used to do this: Skewness, with the 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 1. Grouping will be performed on flows that are inferred to be
traversing a bottleneck by: traversing a bottleneck by:
skew_est < c_s skew_est < c_s
|| ( skew_est < c_h & PB ) || pkt_loss > p_l || ( skew_est < c_h & PB ) || pkt_loss > p_l
The parameter c_s controls how sensitive the mechanism is in 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 detecting a bottleneck. c_s = 0.0 was used in [Hayes-LCN14]. A
of c_s = 0.1 is a little more sensitive, and c_s = -0.1 is a little value of c_s = 0.1 is a little more sensitive, and c_s = -0.1 is
less sensitive. c_h controls the hysteresis on flows that were a little less sensitive. c_h controls the hysteresis on flows
grouped as transiting a bottleneck last time. If the test result is that were grouped as transiting a bottleneck the previous time.
TRUE, PB=TRUE, otherwise PB=FALSE. If the test result is TRUE, PB=TRUE; otherwise, PB=FALSE.
These flows, flows transiting a bottleneck, are then progressively These flows (i.e., flows transiting a bottleneck) are then
divided into groups based on the freq_est, var_est, and skew_est progressively divided into groups based on the freq_est, var_est, and
summary statistics. The process proceeds according to the following skew_est summary statistics. The process proceeds according to the
steps: following steps:
2. Group flows whose difference in sorted freq_est is less than a 2. Group flows whose difference in sorted freq_est is less than a
threshold: threshold:
diff(freq_est) < p_f diff(freq_est) < p_f
3. Subdivide the groups obtained in 2. by grouping flows whose 3. Subdivide the groups obtained in step 2 by grouping flows whose
difference in sorted E_M(var_est) (highest to lowest) is less difference in sorted E_M(var_est) (highest to lowest) is less
than a threshold: than a threshold:
diff(var_est) < (p_mad * var_est) diff(var_est) < (p_mad * var_est)
The threshold, (p_mad * var_est), is with respect to the highest The threshold, (p_mad * var_est), is with respect to the highest
value in the difference. value in the difference.
4. Subdivide the groups obtained in 3. by grouping flows whose 4. Subdivide the groups obtained in step 3 by grouping flows whose
difference in sorted skew_est is less than a threshold: difference in sorted skew_est is less than a threshold:
diff(skew_est) < p_s diff(skew_est) < p_s
5. When packet loss is high enough to be reliable (pkt_loss > p_l), 5. When packet loss is high enough to be reliable (pkt_loss > p_l),
Subdivide the groups obtained in 4. by grouping flows whose subdivide the groups obtained in step 4 by grouping flows whose
difference is less than a threshold difference is less than a threshold:
diff(pkt_loss) < (p_d * pkt_loss) diff(pkt_loss) < (p_d * pkt_loss)
The threshold, (p_d * pkt_loss), is with respect to the highest The threshold, (p_d * pkt_loss), is with respect to the highest
value in the difference. value in the difference.
This procedure involves sorting estimates from highest to lowest. It This procedure involves sorting estimates from highest to lowest. It
is simple to implement, and efficient for small numbers of flows (up is simple to implement and is efficient for small numbers of flows
to 10-20). Figure 2 illustrates this algorithm. (up to 10-20). Figure 2 illustrates this algorithm.
********* *********
* Flows * * Flows *
***.**.** ***.**.**
/ ' / '
/ '--. / '--.
/ \ / \
.---v--. .----v---. .---v--. .----v---.
1. Flows traversing | Cong | | UnCong | 1. Flows traversing | Cong | | UnCong |
a bottleneck '-.--.-' '--------' a bottleneck '-.--.-' '--------'
skipping to change at page 15, line 44 skipping to change at page 17, line 44
4. Divide by | g_1ai | ... | g_1ax | ... | g_nzx | 4. Divide by | g_1ai | ... | g_1ax | ... | g_nzx |
skew_est '----.-.' '------.. '-.-.---' skew_est '----.-.' '------.. '-.-.---'
/ \ / \ / | / \ / \ / |
/ '--. v v v | / '--. v v v |
/ \ | / \ |
.-----v--. .-v------. .----v---. .-----v--. .-v------. .----v---.
5. Divide by | g_1aiA | ... | g_1aiZ | ... | g_nzxZ | 5. Divide by | g_1aiA | ... | g_1aiZ | ... | g_nzxZ |
pkt_loss '--------' '--------' '--------' pkt_loss '--------' '--------' '--------'
(when applicable) (when applicable)
Simple grouping algorithm. Simple grouping algorithm
Figure 2 Figure 2
3.3.2. Using the Flow Group Signal 3.3.2. Using the Flow Group Signal
Grouping decisions can be made every T from the second T, however Grouping decisions can be made every T from the second T; however,
they will not attain their full design accuracy until after the they will not attain their full design accuracy until after the
2*N'th T interval. We recommend that grouping decisions are not made 2*Nth T interval. We recommend that grouping decisions not be made
until 2*M T intervals. until 2*M T intervals.
Network conditions, and even the congestion controllers, can cause Network conditions, and even the congestion controllers, can cause
bottlenecks to fluctuate. A coupled congestion controller MAY decide bottlenecks to fluctuate. A coupled congestion controller MAY decide
only to couple groups that remain stable, say grouped together 90% of only to couple groups that remain stable, say grouped together 90% of
the time, depending on its objectives. Recommendations concerning the time, depending on its objectives. Recommendations concerning
this are beyond the scope of this document and will be specific to this are beyond the scope of this document and will be specific to
the coupled congestion controller's objectives. the coupled congestion controller's objectives.
4. Enhancements to the Basic SBD Algorithm 4. Enhancements to the Basic SBD Algorithm
skipping to change at page 16, line 33 skipping to change at page 18, line 33
basic mechanisms have been found to significantly improve the basic mechanisms have been found to significantly improve the
algorithm's performance under some circumstances and SHOULD be algorithm's performance under some circumstances and SHOULD be
implemented. These "tweaks" are described separately to keep the implemented. These "tweaks" are described separately to keep the
main description succinct. main description succinct.
4.1. Reducing Lag and Improving Responsiveness 4.1. Reducing Lag and Improving Responsiveness
This section describes how to improve the responsiveness of the basic This section describes how to improve the responsiveness of the basic
algorithm. algorithm.
Measurement based shared bottleneck detection makes decisions in the Measurement-based shared bottleneck detection makes decisions in the
present based on what has been measured in the past. This means that present based on what has been measured in the past. This means that
there is always a lag in responding to changing conditions. This there is always a lag in responding to changing conditions. This
mechanism is based on summary statistics taken over (N*T) seconds. mechanism is based on summary statistics taken over (N*T) seconds.
This mechanism can be made more responsive to changing conditions by: 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 1. Reducing N and/or M, but at the expense of having metrics that
metrics, and/or are less accurate, and/or
2. Exploiting the fact that more recent measurements are more 2. Exploiting the fact that measurements that are more recent are
valuable than older measurements and weighting them accordingly. more valuable than older measurements and weighting them
accordingly.
Although more recent measurements are more valuable, older Although measurements that are more recent are more valuable, older
measurements are still needed to gain an accurate estimate of the measurements are still needed to gain an accurate estimate of the
distribution descriptor we are measuring. Unfortunately, the simple distribution descriptor we are measuring. Unfortunately, the simple
exponentially weighted moving average weights drop off too quickly exponentially weighted moving average weights drop off too quickly
for our requirements and have an infinite tail. A simple linearly for our requirements and have an infinite tail. A simple linearly
declining weighted moving average also does not provide enough weight declining weighted moving average also does not provide enough weight
to the most recent measurements. We propose a piecewise linear to the measurements that are most recent. We propose a piecewise
distribution of weights, such that the first section (samples 1:F) is linear distribution of weights, such that the first section (samples
flat as in a simple moving average, and the second section (samples 1:F) is flat as in a simple moving average, and the second section
F+1:M) is linearly declining weights to the end of the averaging (samples F+1:M) is linearly declining weights to the end of the
window. We choose integer weights, which allows incremental averaging window. We choose integer weights; this allows incremental
calculation without introducing rounding errors. calculation without introducing rounding errors.
4.1.1. Improving the Response of the Skewness Estimate 4.1.1. Improving the Response of the Skewness Estimate
The weighted moving average for skew_est, based on skew_est in The weighted moving average for skew_est, based on skew_est as
Section 3.2.2, can be calculated as follows: defined in Section 3.2.2, can be calculated as follows:
skew_est = ((M-F+1)*sum(skew_base_T(1:F)) skew_est = ((M-F+1)*sum(skew_base_T(1:F))
+ sum([(M-F):1].*skew_base_T(F+1:M))) + sum([(M-F):1].*skew_base_T(F+1:M)))
/ ((M-F+1)*sum(numsampT(1:F)) / ((M-F+1)*sum(numsampT(1:F))
+ sum([(M-F):1].*numsampT(F+1:M))) + sum([(M-F):1].*numsampT(F+1:M)))
where numsampT is an array of the number of OWD samples in each T 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) (i.e., num_T(OWD)), and numsampT(1) is the most recent;
is the most recent calculation of skew_base_T; 1:F refers to the skew_base_T(1) is the most recent calculation of skew_base_T; 1:F
integer values 1 through to F, and [(M-F):1] refers to an array of refers to the integer values 1 through to F, and [(M-F):1] refers to
the integer values (M-F) declining through to 1; and ".*" is the an array of the integer values (M-F) declining through to 1; and ".*"
array scalar dot product operator. is the array scalar dot product operator.
To calculate this weighted skew_est incrementally: To calculate this weighted skew_est incrementally:
Notation: F_ - flat portion, D_ - declining portion, W_ - weighted Notation: F_ = flat portion, D_ = declining portion,
component W_ = weighted component
Initialize: sum_skewbase = 0, F_skewbase=0, W_D_skewbase=0 Initialize: sum_skewbase = 0, F_skewbase = 0, W_D_skewbase = 0
skewbase_hist = buffer length M initialize to 0 skewbase_hist = buffer of length M, initialized to 0
numsampT = buffer length M initialized to 0 numsampT = buffer of length M, initialized to 0
Steps per iteration: Steps per iteration:
1. old_skewbase = skewbase_hist(M) 1. old_skewbase = skewbase_hist(M)
2. old_numsampT = numsampT(M) 2. old_numsampT = numsampT(M)
3. cycle(skewbase_hist) 3. cycle(skewbase_hist)
4. cycle(numsampT) 4. cycle(numsampT)
skipping to change at page 18, line 47 skipping to change at page 20, line 23
10. F_numsamp = F_numsamp + numsampT(1) - numsampT(F+1) 10. F_numsamp = F_numsamp + numsampT(1) - numsampT(F+1)
11. sum_skewbase = sum_skewbase + skewbase_hist(F+1) - old_skewbase 11. sum_skewbase = sum_skewbase + skewbase_hist(F+1) - old_skewbase
12. sum_numsamp = sum_numsamp + numsampT(1) - old_numsampT 12. sum_numsamp = sum_numsamp + numsampT(1) - old_numsampT
13. skew_est = ((M-F+1)*F_skewbase + W_D_skewbase) / 13. skew_est = ((M-F+1)*F_skewbase + W_D_skewbase) /
((M-F+1)*F_numsamp+W_D_numsamp) ((M-F+1)*F_numsamp+W_D_numsamp)
Where cycle(....) refers to the operation on a cyclic buffer where where cycle(...) refers to the operation on a cyclic buffer where the
the start of the buffer is now the next element in the buffer. start of the buffer is now the next element in the buffer.
4.1.2. Improving the Response of the Variability Estimate 4.1.2. Improving the Response of the Variability Estimate
Similarly the weighted moving average for var_est can be calculated Similarly, the weighted moving average for var_est can be calculated
as follows: as follows:
var_est = ((M-F+1)*sum(var_base_T(1:F)) var_est = ((M-F+1)*sum(var_base_T(1:F))
+ sum([(M-F):1].*var_base_T(F+1:M))) + sum([(M-F):1].*var_base_T(F+1:M)))
/ ((M-F+1)*sum(numsampT(1:F)) / ((M-F+1)*sum(numsampT(1:F))
+ sum([(M-F):1].*numsampT(F+1:M))) + sum([(M-F):1].*numsampT(F+1:M)))
where numsampT is an array of the number of OWD samples in each T 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) (i.e., num_T(OWD)), and numsampT(1) is the most recent;
is the most recent calculation of skew_base_T; 1:F refers to the skew_base_T(1) is the most recent calculation of skew_base_T; 1:F
integer values 1 through to F, and [(M-F):1] refers to an array of refers to the integer values 1 through to F, and [(M-F):1] refers to
the integer values (M-F) declining through to 1; and ".*" is the an array of the integer values (M-F) declining through to 1; and ".*"
array scalar dot product operator. When removing oscillation noise is the array scalar dot product operator. When removing oscillation
(see Section 4.2) this calculation must be adjusted to allow for noise (see Section 4.2), this calculation must be adjusted to allow
invalid var_base_T records. for invalid var_base_T records.
Var_est can be calculated incrementally in the same way as skew_est var_est can be calculated incrementally in the same way as skew_est
in Section 4.1.1. However, note that the buffer numsampT is used for as shown in Section 4.1.1. However, note that the buffer numsampT is
both calculations so the operations on it should not be repeated. used for both calculations, so the operations on it should not be
repeated.
4.2. Removing Oscillation Noise 4.2. Removing Oscillation Noise
When a path has no bottleneck, var_est will be very small and the 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. 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 Thus, up to N-1 meaningless mean crossings can be a source of error
the point a link becomes a bottleneck and flows traversing it begin at the point where a link becomes a bottleneck and flows traversing
to be grouped. it begin to be grouped.
To remove this source of noise from freq_est: To remove this source of noise from freq_est:
1. Set the current var_base_T = NaN (a value representing an invalid 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 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 transiting a bottleneck by the first grouping test that is based
(see Section 3.3.1). on skew_est (see Section 3.3.1).
2. Then var_est = sum_MT(var_base_T != NaN) / num_MT(OWD) 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 3. For freq_est, only record a significant mean crossing if a given
deemed to be transiting a bottleneck. flow is deemed to be transiting a bottleneck.
These three changes can help to remove the non-bottleneck noise from These three changes can help to remove the non-bottleneck noise from
freq_est. freq_est.
5. Measuring OWD 5. Measuring OWD
This section discusses the OWD measurements required for this This section discusses the OWD measurements required for this
algorithm to detect shared bottlenecks. algorithm to detect shared bottlenecks.
The SBD mechanism described in this document relies on differences The SBD mechanism described in this document relies on differences
between OWD measurements to avoid the practical problems with between OWD measurements to avoid the practical problems with
measuring absolute OWD (see [Hayes-LCN14] section IIIC). Since all measuring absolute OWD (see [Hayes-LCN14], Section III.C). Since all
summary statistics are relative to the mean OWD and sender/receiver summary statistics are relative to the mean OWD and sender/receiver
clock offsets should be approximately constant over the measurement clock offsets should be approximately constant over the measurement
periods, the offset is subtracted out in the calculation. periods, the offset is subtracted out in the calculation.
5.1. Time-stamp Resolution 5.1. Timestamp Resolution
The SBD mechanism requires timing information precise enough to be The SBD mechanism requires timing information precise enough to be
able to make comparisons. As a rule of thumb, the time resolution 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 should be less than one hundredth of a typical path's range of
delays. In general, the coarser the time resolution, the more care delays. In general, the coarser the time resolution, the more care
that needs to be taken to ensure rounding errors do not bias the that needs to be taken to ensure that rounding errors do not bias the
skewness calculation. Frequent timing information in millisecond skewness calculation. Frequent timing information in millisecond
resolution as described by [I-D.ietf-avtcore-cc-feedback-message] resolution as described by [RTCP-CC-FEEDBACK] should be sufficient
should be sufficient for the sender to calculate relative OWD. for the sender to calculate relative OWD.
5.2. Clock Skew 5.2. Clock Skew
Generally sender and receiver clock skew will be too small to cause Generally, sender and receiver clock skew will be too small to cause
significant errors in the estimators. Skew_est and freq_est are the 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 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 calculated over a longer interval. In circumstances where clock skew
is high, basing skew_est only on the previous T's mean and ignoring is high, basing skew_est only on the previous T's mean and ignoring
freq_est provides a noisier but reliable signal. freq_est provide a noisier but reliable signal.
A more sophisticated method is to estimate the effect the clock skew A more sophisticated method is to estimate the effect the clock skew
is having on the summary statistics, and then adjust statistics is having on the summary statistics and then adjust statistics
accordingly. There are a number of techniques in the literature, accordingly. There are a number of techniques in the literature,
including [Zhang-Infocom02]. including [Zhang-Infocom02].
6. Expected Feedback from Experiments 6. Expected Feedback from Experiments
The algorithm described in this memo has so far been evaluated using The algorithm described in this memo has so far been evaluated using
simulations and small scale experiments. Real network tests using simulations and small-scale experiments. Real network tests using
RTP Media Congestion Avoidance Techniques (RMCAT) congestion control RTP Media Congestion Avoidance Techniques (RMCAT) congestion control
algorithms will help confirm the default parameter choice. For algorithms will help confirm the default parameter choice. For
example, the time interval T may need to be made longer if the packet example, the time interval T may need to be made longer if the packet
rate is very low. Implementers and testers are invited to document rate is very low. Implementers and testers are invited to document
their findings in an Internet draft. their findings in an Internet-Draft.
7. Acknowledgments
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.
8. IANA Considerations 7. IANA Considerations
This memo includes no request to IANA. This document has no IANA actions.
9. Security Considerations 8. Security Considerations
The security considerations of RFC 3550 [RFC3550], RFC 4585 The security considerations of RFC 3550 [RFC3550], RFC 4585
[RFC4585], and RFC 5124 [RFC5124] are expected to apply. [RFC4585], and RFC 5124 [RFC5124] are expected to apply.
Non-authenticated RTCP packets carrying OWD measurements, shared Non-authenticated RTCP packets carrying OWD measurements, shared
bottleneck indications, and/or summary statistics could allow bottleneck indications, and/or summary statistics could allow
attackers to alter the bottleneck sharing characteristics for private attackers to alter the bottleneck-sharing characteristics for private
gain or disruption of other parties' communication. When using SBD gain or disruption of other parties' communication. When using SBD
for coupled congestion control as described in for coupled congestion control as described in [RTP-COUPLED-CC], the
[I-D.ietf-rmcat-coupled-cc], the security considerations of security considerations of [RTP-COUPLED-CC] apply.
[I-D.ietf-rmcat-coupled-cc] apply.
10. Change history
XX RFC ED - PLEASE REMOVE THIS SECTION XXX
Changes made to this document:
WG-10->WG-11 : Genart review addressed.
WG-09->WG-10 : AD review addressed.
WG-08->WG-09 : Removed definitions that are no longer used. Added
pkt_loss definition. Refined c_s recommendation.
WG-07->WG-08 : Updates addressing https://www.ietf.org/mail-
archive/web/rmcat/current/msg01671.html Mainly
clarifications.
WG-06->WG-07 : Updates addressing
https://mailarchive.ietf.org/arch/msg/
rmcat/80B6q4nI7carGcf_ddBwx7nKvOw. Mainly
clarifications. Figure 2 to supplement grouping
algorithm description.
WG-05->WG-06 : Updates addressing WG reviews
https://mailarchive.ietf.org/arch/msg/rmcat/-
1JdrTMq1Y5T6ZNlOkrQJQ27TzE and
https://mailarchive.ietf.org/arch/msg/rmcat/
eI2Q1f8NL2SxbJgjFLR4_rEmJ_g. This has mainly
involved minor clarifications, including the moving
of 3.4.1 and 3.5 into the new Section 4, and 3.4.1
into Section 5
WG-04->WG-05 : Fix ToC formatting. Add section on expected
feedback from experiments replacing short section
on implementation status. Added comment on ECN as
a signal. Clarification of lost packet signaling.
Change term "draft" to "document" where
appropriate. American spelling. Some tightening
of the text.
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
initialization 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.
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
11. References 9. References
11.1. Normative References 9.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997, DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>. <https://www.rfc-editor.org/info/rfc2119>.
11.2. Informative References [RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in
RFC 2119 Key Words", BCP 14, RFC 8174,
DOI 10.17487/RFC8174, May 2017,
<https://www.rfc-editor.org/info/rfc8174>.
9.2. Informative References
[Hayes-LCN14] [Hayes-LCN14]
Hayes, D., Ferlin, S., and M. Welzl, "Practical Passive Hayes, D., Ferlin, S., and M. Welzl, "Practical Passive
Shared Bottleneck Detection using Shape Summary Shared Bottleneck Detection using Shape Summary
Statistics", Proc. the IEEE Local Computer Networks Statistics", Proc. IEEE Local Computer Networks (LCN),
(LCN) pp150-158, September 2014, pp. 150-158, DOI 10.1109/LCN.2014.6925767, September 2014,
<http://heim.ifi.uio.no/davihay/ <http://heim.ifi.uio.no/davihay/
hayes14__pract_passiv_shared_bottl_detec-abstract.html>. hayes14__pract_passiv_shared_bottl_detec-abstract.html>.
[I-D.ietf-avtcore-cc-feedback-message]
Sarker, Z., Perkins, C., Singh, V., and M. Ramalho, "RTP
Control Protocol (RTCP) Feedback for Congestion Control",
draft-ietf-avtcore-cc-feedback-message-01 (work in
progress), March 2018.
[I-D.ietf-rmcat-coupled-cc]
Islam, S., Welzl, M., and S. Gjessing, "Coupled congestion
control for RTP media", draft-ietf-rmcat-coupled-cc-07
(work in progress), September 2017.
[RFC2680] Almes, G., Kalidindi, S., and M. Zekauskas, "A One-way
Packet Loss Metric for IPPM", RFC 2680,
DOI 10.17487/RFC2680, September 1999,
<https://www.rfc-editor.org/info/rfc2680>.
[RFC3550] Schulzrinne, H., Casner, S., Frederick, R., and V. [RFC3550] Schulzrinne, H., Casner, S., Frederick, R., and V.
Jacobson, "RTP: A Transport Protocol for Real-Time Jacobson, "RTP: A Transport Protocol for Real-Time
Applications", STD 64, RFC 3550, DOI 10.17487/RFC3550, Applications", STD 64, RFC 3550, DOI 10.17487/RFC3550,
July 2003, <https://www.rfc-editor.org/info/rfc3550>. July 2003, <https://www.rfc-editor.org/info/rfc3550>.
[RFC4585] Ott, J., Wenger, S., Sato, N., Burmeister, C., and J. Rey, [RFC4585] Ott, J., Wenger, S., Sato, N., Burmeister, C., and J. Rey,
"Extended RTP Profile for Real-time Transport Control "Extended RTP Profile for Real-time Transport Control
Protocol (RTCP)-Based Feedback (RTP/AVPF)", RFC 4585, Protocol (RTCP)-Based Feedback (RTP/AVPF)", RFC 4585,
DOI 10.17487/RFC4585, July 2006, DOI 10.17487/RFC4585, July 2006,
<https://www.rfc-editor.org/info/rfc4585>. <https://www.rfc-editor.org/info/rfc4585>.
[RFC5124] Ott, J. and E. Carrara, "Extended Secure RTP Profile for [RFC5124] Ott, J. and E. Carrara, "Extended Secure RTP Profile for
Real-time Transport Control Protocol (RTCP)-Based Feedback Real-time Transport Control Protocol (RTCP)-Based Feedback
(RTP/SAVPF)", RFC 5124, DOI 10.17487/RFC5124, February (RTP/SAVPF)", RFC 5124, DOI 10.17487/RFC5124,
2008, <https://www.rfc-editor.org/info/rfc5124>. February 2008, <https://www.rfc-editor.org/info/rfc5124>.
[RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind, [RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
"Low Extra Delay Background Transport (LEDBAT)", RFC 6817, "Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
DOI 10.17487/RFC6817, December 2012, DOI 10.17487/RFC6817, December 2012,
<https://www.rfc-editor.org/info/rfc6817>. <https://www.rfc-editor.org/info/rfc6817>.
[RFC7679] Almes, G., Kalidindi, S., Zekauskas, M., and A. Morton, [RFC7679] Almes, G., Kalidindi, S., Zekauskas, M., and A. Morton,
Ed., "A One-Way Delay Metric for IP Performance Metrics Ed., "A One-Way Delay Metric for IP Performance Metrics
(IPPM)", STD 81, RFC 7679, DOI 10.17487/RFC7679, January (IPPM)", STD 81, RFC 7679, DOI 10.17487/RFC7679,
2016, <https://www.rfc-editor.org/info/rfc7679>. January 2016, <https://www.rfc-editor.org/info/rfc7679>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC [RFC7680] Almes, G., Kalidindi, S., Zekauskas, M., and A. Morton,
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174, Ed., "A One-Way Loss Metric for IP Performance Metrics
May 2017, <https://www.rfc-editor.org/info/rfc8174>. (IPPM)", STD 82, RFC 7680, DOI 10.17487/RFC7680,
January 2016, <https://www.rfc-editor.org/info/rfc7680>.
[RTCP-CC-FEEDBACK]
Sarker, Z., Perkins, C., Singh, V., and M. Ramalho,
"RTP Control Protocol (RTCP) Feedback for Congestion
Control", Work in Progress, draft-ietf-avtcore-cc-
feedback-message-01, March 2018.
[RTP-COUPLED-CC]
Islam, S., Welzl, M., and S. Gjessing, "Coupled congestion
control for RTP media", Work in Progress, draft-ietf-
rmcat-coupled-cc-07, September 2017.
[Zhang-Infocom02] [Zhang-Infocom02]
Zhang, L., Liu, Z., and H. Xia, "Clock synchronization Zhang, L., Liu, Z., and H. Xia, "Clock synchronization
algorithms for network measurements", Proc. the IEEE algorithms for network measurements", Proc. IEEE
International Conference on Computer Communications International Conference on Computer Communications
(INFOCOM) pp160-169, September 2002, (INFOCOM), pp. 160-169, DOI 10.1109/INFCOM.2002.1019257,
<http://dx.doi.org/10.1109/INFCOM.2002.1019257>. September 2002.
Acknowledgments
This work was partially 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.
Authors' Addresses Authors' Addresses
David Hayes (editor) David Hayes (editor)
Simula Research Laboratory Simula Research Laboratory
P.O. Box 134 P.O. Box 134
Lysaker 1325 Lysaker 1325
Norway Norway
Email: davidh@simula.no Email: davidh@simula.no
Simone Ferlin Simone Ferlin
Simula Research Laboratory
P.O. Box 134
Lysaker 1325
Norway
Email: simone@ferlin.io Email: simone@ferlin.io
Michael Welzl Michael Welzl
University of Oslo University of Oslo
PO Box 1080 Blindern P.O. Box 1080 Blindern
Oslo N-0316 Oslo N-0316
Norway Norway
Email: michawe@ifi.uio.no Email: michawe@ifi.uio.no
Kristian Hiorth Kristian Hiorth
University of Oslo University of Oslo
PO Box 1080 Blindern P.O. Box 1080 Blindern
Oslo N-0316 Oslo N-0316
Norway Norway
Email: kristahi@ifi.uio.no Email: kristahi@ifi.uio.no
 End of changes. 168 change blocks. 
487 lines changed or deleted 425 lines changed or added

This html diff was produced by rfcdiff 1.47. The latest version is available from http://tools.ietf.org/tools/rfcdiff/