rfc8382
Internet Engineering Task Force (IETF) D. Hayes, Ed.
Request for Comments: 8382 S. Ferlin
Category: Experimental Simula Research Laboratory
ISSN: 2070-1721 M. Welzl
K. Hiorth
University of Oslo
June 2018
Shared Bottleneck Detection for Coupled Congestion Control for RTP Media
Abstract
This document describes a mechanism to detect whether end-to-end data
flows share a common bottleneck. This mechanism relies on summary
statistics that are calculated based on continuous measurements and
used as input to a grouping algorithm that runs wherever the
knowledge is needed.
Status of This Memo
This document is not an Internet Standards Track specification; it is
published for examination, experimental implementation, and
evaluation.
This document defines an Experimental Protocol for the Internet
community. This document is a product of the Internet Engineering
Task Force (IETF). It represents the consensus of the IETF
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.
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.
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Copyright Notice
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document authors. All rights reserved.
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the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
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Table of Contents
1. Introduction ....................................................4
1.1. The Basic Mechanism ........................................4
1.2. The Signals ................................................4
1.2.1. Packet Loss .........................................4
1.2.2. Packet Delay ........................................5
1.2.3. Path Lag ............................................5
2. Definitions .....................................................6
2.1. Parameters and Their Effects ...............................7
2.2. Recommended Parameter Values ...............................8
3. Mechanism .......................................................9
3.1. SBD Feedback Requirements .................................10
3.1.1. Feedback When All the Logic Is Placed at
the Sender .........................................10
3.1.2. Feedback When the Statistics Are Calculated at the
Receiver and SBD Is Performed at the Sender ........11
3.1.3. Feedback When Bottlenecks Can Be Determined
at Both Senders and Receivers ......................11
3.2. Key Metrics and Their Calculation .........................12
3.2.1. Mean Delay .........................................12
3.2.2. Skewness Estimate ..................................12
3.2.3. Variability Estimate ...............................13
3.2.4. Oscillation Estimate ...............................13
3.2.5. Packet Loss ........................................14
3.3. Flow Grouping .............................................14
3.3.1. Flow-Grouping Algorithm ............................14
3.3.2. Using the Flow Group Signal ........................18
4. Enhancements to the Basic SBD Algorithm ........................18
4.1. Reducing Lag and Improving Responsiveness .................18
4.1.1. Improving the Response of the Skewness Estimate ....19
4.1.2. Improving the Response of the Variability
Estimate ...........................................20
4.2. Removing Oscillation Noise ................................21
5. Measuring OWD ..................................................21
5.1. Timestamp Resolution ......................................21
5.2. Clock Skew ................................................22
6. Expected Feedback from Experiments .............................22
7. IANA Considerations ............................................22
8. Security Considerations ........................................22
9. References .....................................................23
9.1. Normative References ......................................23
9.2. Informative References ....................................23
Acknowledgments ...................................................25
Authors' Addresses ................................................25
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1. Introduction
In the Internet, it is not normally known whether flows (e.g., TCP
connections or UDP data streams) traverse the same bottlenecks. Even
flows that have the same sender and receiver may take different paths
and may or may not share a bottleneck. Flows that share a bottleneck
link usually compete with one another for their share of the
capacity. This competition has the potential to increase packet loss
and delays. This is especially relevant for interactive applications
that communicate simultaneously with multiple peers (such as
multi-party video). For RTP media applications such as RTCWEB,
[RTP-COUPLED-CC] describes a scheme that combines the congestion
controllers of flows in order to honor their priorities and avoid
unnecessary packet loss as well as delay. This mechanism relies on
some form of Shared Bottleneck Detection (SBD); here, a measurement-
based SBD approach is described.
1.1. The Basic Mechanism
The mechanism groups flows that have similar statistical
characteristics together. Section 3.3.1 describes a simple method
for achieving this; however, a major part of this document is
concerned with collecting suitable statistics for this purpose.
1.2. The Signals
The current Internet is unable to explicitly inform endpoints as to
which flows share bottlenecks, so endpoints need to infer this from
whatever information is available to them. The mechanism described
here currently utilizes packet loss and packet delay but is not
restricted to these. As Explicit Congestion Notification (ECN)
becomes more prevalent, it too will become a valuable base signal
that can be correlated to detect shared bottlenecks.
1.2.1. Packet Loss
Packet loss is often a relatively infrequent indication that a flow
traverses a bottleneck. Therefore, on its own it is of limited use
for SBD; however, it is a valuable supplementary measure when it is
more prevalent (refer to [RFC7680], Section 2.5 for measuring packet
loss).
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1.2.2. Packet Delay
End-to-end delay measurements include noise from every device along
the path, in addition to the delay perturbation at the bottleneck
device. The noise is often significantly increased if the round-trip
time is used. The cleanest signal is obtained by using One-Way Delay
(OWD) (refer to [RFC7679], Section 3 for a definition of OWD).
Measuring absolute OWD is difficult, since it requires both the
sender and receiver clocks to be synchronized. However, since the
statistics being collected are relative to the mean OWD, a relative
OWD measurement is sufficient. Clock skew is not usually significant
over the time intervals used by this SBD mechanism (see [RFC6817],
Appendix A.2 for a discussion on clock skew and OWD measurements).
However, in circumstances where it is significant, Section 5.2
outlines a way of adjusting the calculations to cater to it.
Each packet arriving at the bottleneck buffer may experience very
different queue lengths and, therefore, different waiting times. A
single OWD sample does not, therefore, characterize the path well.
However, multiple OWD measurements do reflect the distribution of
delays experienced at the bottleneck.
1.2.3. Path Lag
Flows that share a common bottleneck may traverse different paths,
and these paths will often have different base delays. This makes it
difficult to correlate changes in delay or loss. This technique uses
the long-term shape of the delay distribution as a base for
comparison to counter this.
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2. Definitions
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
Acronyms used in this document:
OWD - One-Way Delay
MAD - Mean Absolute Deviation
SBD - Shared Bottleneck Detection
Conventions used in this document:
T the base time interval over which measurements
are made
N the number of base time, T, intervals used in some
calculations
M the number of base time, T, intervals used in some
calculations, where M <= N
sum(...) summation of terms of the variable in parentheses
sum_T(...) summation of all the measurements of the variable in
parentheses taken over the interval T
sum_NT(...) summation of all measurements taken over the
interval N*T
sum_MT(...) summation of all measurements taken over the
interval M*T
E_T(...) the expectation or mean of the measurements of the
variable in parentheses over T
E_N(...) the expectation or mean of the last N values of the
variable in parentheses
E_M(...) the expectation or mean of the last M values of the
variable in parentheses
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num_T(...) the count of measurements of the variable in
parentheses taken in the interval T
num_MT(...) the count of measurements of the variable in
parentheses taken in the interval M*T
PB a boolean variable indicating that the particular
flow was identified transiting a bottleneck in the
previous interval T (i.e., "Previously Bottleneck")
skew_est a measure of skewness in an OWD distribution
skew_base_T a variable used as an intermediate step in
calculating skew_est
var_est a measure of variability in OWD measurements
var_base_T a variable used as an intermediate step in
calculating var_est
freq_est a measure of low-frequency oscillation in the OWD
measurements
pkt_loss a measure of the proportion of packets lost
p_l, p_f, p_mad, c_s, c_h, p_s, p_d, p_v
various thresholds used in the mechanism
M and F number of values related to N
2.1. Parameters and Their Effects
T T should be long enough so that there are enough packets
received during T for a useful estimate of the 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 and M N should be large enough to provide a stable estimate of
oscillations in OWD. Often, M=N is just fine, though
having M<N may be beneficial in certain circumstances. M*T
needs to be long enough to provide stable estimates of
skewness and MAD.
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F F determines the number of intervals over which statistics
are considered to be equally weighted. When F=M, recent
and older measurements are considered equal. Making F<M
can increase the responsiveness of the SBD mechanism. If F
is too small, statistics will be too noisy.
c_s c_s is the threshold in skew_est used for determining
whether a flow is transiting a bottleneck or not. Lower
values of c_s require bottlenecks to be more congested to
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.
p_l p_l is the threshold in pkt_loss used for determining
whether a flow is transiting a bottleneck or not. When
pkt_loss is high, it becomes a better indicator of
congestion than skew_est.
c_h c_h adds hysteresis to the bottleneck determination. It
should be large enough to avoid constant switching in the
determination but low enough to ensure that grouping is not
attempted when there is no bottleneck and the delay and
loss signals cannot be relied upon.
p_v p_v determines the sensitivity of freq_est to noise.
Making it smaller will yield higher but noisier values for
freq_est. Making it too large will render it ineffective
for determining groups.
p_* Flows are separated when the
skew_est|var_est|freq_est|pkt_loss measure is greater than
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
[Hayes-LCN14] uses T=350ms and N=50. The other parameters have been
tightened to reflect minor enhancements to the algorithm outlined in
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,
F=20, and c_h=0.3 are additional parameters defined in that document.
These are values that seem to work well over a wide range of
practical Internet conditions.
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3. Mechanism
The mechanism described in this document is based on the observation
that when flows traverse a common bottleneck, each flow's
distribution of packet delay measurements has similar shape
characteristics. These shape characteristics are described using
three key summary statistics --
1. variability estimate (var_est; see Section 3.2.3)
2. skewness estimate (skew_est; see Section 3.2.2)
3. oscillation estimate (freq_est; see Section 3.2.4)
-- with packet loss (pkt_loss; see Section 3.2.5) used as a
supplementary statistic.
Summary statistics help to address both the noise and the path lag
problems by describing the general shape over a relatively long
period of time. Each summary statistic portrays a "view" of the
bottleneck link characteristics, and when used together, they provide
a robust discrimination for grouping flows. An RTP media device may
be both a sender and a receiver. SBD can be performed at either a
sender or a receiver, or both.
In Figure 1, there are two possible locations for shared bottleneck
detection: the sender side and the receiver side.
+----+
| H2 |
+----+
|
| L2
|
+----+ L1 | L3 +----+
| H1 |------|------| H3 |
+----+ +----+
A network with three hosts (H1, H2, H3) and three links (L1, L2, L3)
Figure 1
1. Sender side: Consider a situation where host H1 sends media
streams to hosts H2 and H3, and L1 is a shared bottleneck. H2
and H3 measure the OWD and packet loss and periodically send
either this raw data or the calculated summary statistics to H1
every T. H1, having this knowledge, can determine the shared
bottleneck and accordingly control the send rates.
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2. Receiver side: Consider that H2 is also sending media to H3, and
L3 is a shared bottleneck. If H3 sends summary statistics to H1
and H2, neither H1 nor H2 alone obtains enough knowledge to
detect this shared bottleneck; H3 can, however, determine it by
combining the summary statistics related to H1 and H2,
respectively.
3.1. SBD Feedback Requirements
There are three possible scenarios, each with different feedback
requirements:
1. Both summary statistic calculations and SBD are performed at
senders only. When sender-based congestion control is
implemented, this method is RECOMMENDED.
2. Summary statistics are calculated on the receivers, and SBD is
performed at the senders.
3. Summary statistic calculations are performed on receivers, and
SBD is performed at both senders and receivers (beyond the scope
of this document, but allows cooperative detection of
bottlenecks).
All three possibilities are discussed for completeness in this
document; however, it is expected that feedback will take the form of
scenario 1 and operate in conjunction with sender-based congestion
control mechanisms.
3.1.1. Feedback When All the Logic Is Placed at the Sender
Having the sender calculate the summary statistics and determine the
shared bottlenecks based on them has the advantage of placing most of
the functionality in one place -- the sender.
For every packet, the sender requires accurate relative OWD
measurements of adequate precision, along with an indication of lost
packets (or the proportion of packets lost over an interval). A
method to provide such measurement data with the RTP Control Protocol
(RTCP) is described in [RTCP-CC-FEEDBACK].
Sums, var_base_T, and skew_base_T are calculated incrementally as
relative OWD measurements are determined from the feedback messages.
When the mechanism has received sufficient measurements to cover the
base time interval T for all flows, the summary statistics (see
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
depend on the frequency of the feedback message.
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3.1.2. Feedback When the Statistics Are Calculated at the Receiver and
SBD Is Performed at the Sender
This scenario minimizes feedback but requires receivers to send
selected summary statistics at an agreed-upon regular interval. We
envisage the following exchange of information to initialize the
system:
o An initialization message from the sender to the receiver will
contain the following information:
* A list of which key metrics should be collected and relayed
back to the sender out of a possibly extensible set (pkt_loss,
var_est, skew_est, and freq_est). The grouping algorithm
described in this document requires all four of these metrics,
and receivers MUST be able to provide them, but future
algorithms may be able to exploit other metrics (e.g., metrics
based on explicit network signals).
* The values of T, N, and M, and the necessary resolution and
precision of the relayed statistics.
o A response message from the receiver acknowledges this message
with a list of key metrics it supports (subset of the sender's
list) and is able to relay back to the sender.
This initialization exchange may be repeated to finalize the set of
metrics that will be used. All agreed-upon metrics need to be
supported by all receivers. It is also recommended that an
identifier for the SBD algorithm version be included in the
initialization message from the sender, so that potential advances in
SBD technology can be easily deployed. For reference, the mechanism
outlined in this document has the identifier "SBD=01".
After initialization, the agreed-upon summary statistics are fed back
to the sender (nominally every T).
3.1.3. Feedback When Bottlenecks Can Be Determined at Both Senders and
Receivers
This type of mechanism is currently beyond the scope of the SBD
algorithm described in this document. It is mentioned here to ensure
that sender/receiver cooperative shared bottleneck determination
mechanisms that are more advanced remain possible in the future.
It is envisaged that such a mechanism would be initialized in a
manner similar to that described in Section 3.1.2.
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After initialization, both summary statistics and shared bottleneck
determinations should be exchanged, nominally every T.
3.2. Key Metrics and Their Calculation
Measurements are calculated over a base interval (T) and summarized
over N or M such intervals. All summary statistics can be calculated
incrementally.
3.2.1. Mean Delay
The mean delay is not a useful signal for comparisons between flows,
since flows may traverse quite different paths and clocks will not
necessarily be synchronized. However, it is a base measure for the
three summary statistics. The mean delay, E_T(OWD), is the average
OWD measured over T.
To facilitate the other calculations, the last N E_T(OWD) values will
need to be stored in a cyclic buffer along with the moving average of
E_T(OWD):
mean_delay = E_M(E_T(OWD)) = sum_M(E_T(OWD)) / M
where M <= N. Setting M to be less than N allows the mechanism to be
more responsive to changes, but potentially at the expense of a
higher error rate (see Section 4.1 for a discussion on improving the
responsiveness of the mechanism).
3.2.2. Skewness Estimate
Skewness is difficult to calculate efficiently and accurately.
Ideally, it should be calculated over the entire period (M*T) from
the mean OWD over that period. However, this would require storing
every delay measurement over the period. Instead, an estimate is
made over M*T based on a calculation every T using the previous T's
calculation of mean_delay.
The base for the skewness calculation is estimated using a counter
initialized every T. It increments for OWD samples below the mean
and decrements for OWD above the mean. So, for each OWD sample:
if (OWD < mean_delay) skew_base_T++
if (OWD > mean_delay) skew_base_T--
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mean_delay does not include the mean of the current T interval to
enable it to be calculated iteratively.
skew_est = sum_MT(skew_base_T) / num_MT(OWD)
where skew_est is a number between -1 and 1.
Note: Care must be taken when implementing the comparisons to ensure
that rounding does not bias skew_est. It is important that the mean
is calculated with a higher precision than the samples.
3.2.3. Variability Estimate
Mean Absolute Deviation (MAD) is a robust variability measure that
copes well with different send rates. It can be implemented in an
online manner as follows:
var_base_T = sum_T(|OWD - E_T(OWD)|)
where
|x| is the absolute value of x
E_T(OWD) is the mean OWD calculated in the previous T
var_est = MAD_MT = sum_MT(var_base_T) / num_MT(OWD)
3.2.4. Oscillation Estimate
An estimate of the low-frequency oscillation of the delay signal is
calculated by counting and normalizing the significant mean,
E_T(OWD), crossings of mean_delay:
freq_est = number_of_crossings / N
where we define a significant mean crossing as a crossing that
extends p_v * var_est from mean_delay. In our experiments, we
have found that p_v = 0.7 is a good value.
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freq_est is a number between 0 and 1. freq_est can be approximated
incrementally as follows:
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
long-term mean, mean_delay, with respect to the previous
significant mean crossing.
o A cyclic buffer, last_N_crossings, records a 1 if there is a
significant mean crossing; otherwise, it records a 0.
o The counter, number_of_crossings, is incremented when there is a
significant mean crossing and decremented when a non-zero value is
removed from the last_N_crossings.
This approximation of freq_est was not used in [Hayes-LCN14], which
calculated freq_est every T using the current E_N(E_T(OWD)). Our
tests show that this approximation of freq_est yields results that
are almost identical to when the full calculation is performed
every T.
3.2.5. Packet Loss
The proportion of packets lost over the period NT is used as a
supplementary measure:
pkt_loss = sum_NT(lost packets) / sum_NT(total packets)
Note: When pkt_loss is low, it is very variable; however, when
pkt_loss is high, it becomes a stable measure for making grouping
decisions.
3.3. Flow Grouping
3.3.1. Flow-Grouping Algorithm
The following grouping algorithm is RECOMMENDED for the use of SBD
with coupled congestion control for RTP media [RTP-COUPLED-CC] and is
sufficient and efficient for small to moderate numbers of flows. For
very large numbers of flows (e.g., hundreds), a more complex
clustering algorithm may be substituted.
Since no single metric is precise enough to group flows (due to
noise), the algorithm uses multiple metrics. Each metric offers a
different "view" of the bottleneck link characteristics, and used
together they enable a more precise grouping of flows than would
otherwise be possible.
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Flows determined to be transiting a bottleneck are successively
divided into groups based on freq_est, var_est, skew_est, and
pkt_loss.
The first step is to determine which flows are transiting a
bottleneck. This is important, since if a flow is not transiting a
bottleneck its delay-based metrics will not describe the bottleneck
but will instead describe the "noise" from the rest of the path.
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
traversing a bottleneck by:
skew_est < c_s
|| ( skew_est < c_h & PB ) || pkt_loss > p_l
The parameter c_s controls how sensitive the mechanism is in
detecting a bottleneck. c_s = 0.0 was used in [Hayes-LCN14]. A
value of c_s = 0.1 is a little more sensitive, and c_s = -0.1 is
a little less sensitive. c_h controls the hysteresis on flows
that were grouped as transiting a bottleneck the previous time.
If the test result is TRUE, PB=TRUE; otherwise, PB=FALSE.
These flows (i.e., flows transiting a bottleneck) are then
progressively divided into groups based on the freq_est, var_est, and
skew_est summary statistics. The process proceeds according to the
following steps:
2. Group flows whose difference in sorted freq_est is less than a
threshold:
diff(freq_est) < p_f
3. Subdivide the groups obtained in step 2 by grouping flows whose
difference in sorted E_M(var_est) (highest to lowest) is less
than a threshold:
diff(var_est) < (p_mad * var_est)
The threshold, (p_mad * var_est), is with respect to the highest
value in the difference.
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4. Subdivide the groups obtained in step 3 by grouping flows whose
difference in sorted skew_est is less than a threshold:
diff(skew_est) < p_s
5. When packet loss is high enough to be reliable (pkt_loss > p_l),
subdivide the groups obtained in step 4 by grouping flows whose
difference is less than a threshold:
diff(pkt_loss) < (p_d * pkt_loss)
The threshold, (p_d * pkt_loss), is with respect to the highest
value in the difference.
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This procedure involves sorting estimates from highest to lowest. It
is simple to implement and is efficient for small numbers of flows
(up to 10-20). Figure 2 illustrates this algorithm.
*********
* Flows *
***.**.**
/ '
/ '--.
/ \
.---v--. .----v---.
1. Flows traversing | Cong | | UnCong |
a bottleneck '-.--.-' '--------'
/ \
/ \
/ \
.--v--. v-----.
2. Divide by | g_1 | ... | g_n |
freq_est '---.-. '----..
/ \ / \
/ '--. v '------.
/ \ \
.----v-. .-v----. .---v--.
3. Divide by | g_1a | ... | g_1z | ... | g_nz |
var_est '---.-.' '-----.. '-.-.--'
/ \ / \ / |
/ '-----. v v v |
/ \ |
.-v-----. .-v-----. .---v---.
4. Divide by | g_1ai | ... | g_1ax | ... | g_nzx |
skew_est '----.-.' '------.. '-.-.---'
/ \ / \ / |
/ '--. v v v |
/ \ |
.-----v--. .-v------. .----v---.
5. Divide by | g_1aiA | ... | g_1aiZ | ... | g_nzxZ |
pkt_loss '--------' '--------' '--------'
(when applicable)
Simple grouping algorithm
Figure 2
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3.3.2. Using the Flow Group Signal
Grouping decisions can be made every T from the second T; however,
they will not attain their full design accuracy until after the
2*Nth T interval. We recommend that grouping decisions not be made
until 2*M T intervals.
Network conditions, and even the congestion controllers, can cause
bottlenecks to fluctuate. A coupled congestion controller MAY decide
only to couple groups that remain stable, say grouped together 90% of
the time, depending on its objectives. Recommendations concerning
this are beyond the scope of this document and will be specific to
the coupled congestion controller's objectives.
4. Enhancements to the Basic SBD Algorithm
The SBD algorithm as specified in Section 3 was found to work well
for a broad variety of conditions. The following enhancements to the
basic mechanisms have been found to significantly improve the
algorithm's performance under some circumstances and SHOULD be
implemented. These "tweaks" are described separately to keep the
main description succinct.
4.1. Reducing Lag and Improving Responsiveness
This section describes how to improve the responsiveness of the basic
algorithm.
Measurement-based shared bottleneck detection makes decisions in the
present based on what has been measured in the past. This means that
there is always a lag in responding to changing conditions. This
mechanism is based on summary statistics taken over (N*T) seconds.
This mechanism can be made more responsive to changing conditions by:
1. Reducing N and/or M, but at the expense of having metrics that
are less accurate, and/or
2. Exploiting the fact that measurements that are more recent are
more valuable than older measurements and weighting them
accordingly.
Although measurements that are more recent are more valuable, older
measurements are still needed to gain an accurate estimate of the
distribution descriptor we are measuring. Unfortunately, the simple
exponentially weighted moving average weights drop off too quickly
for our requirements and have an infinite tail. A simple linearly
declining weighted moving average also does not provide enough weight
to the measurements that are most recent. We propose a piecewise
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linear distribution of weights, such that the first section (samples
1:F) is flat as in a simple moving average, and the second section
(samples F+1:M) is linearly declining weights to the end of the
averaging window. We choose integer weights; this allows incremental
calculation without introducing rounding errors.
4.1.1. Improving the Response of the Skewness Estimate
The weighted moving average for skew_est, based on skew_est as
defined in Section 3.2.2, can be calculated as follows:
skew_est = ((M-F+1)*sum(skew_base_T(1:F))
+ sum([(M-F):1].*skew_base_T(F+1:M)))
/ ((M-F+1)*sum(numsampT(1:F))
+ sum([(M-F):1].*numsampT(F+1:M)))
where numsampT is an array of the number of OWD samples in each T
(i.e., num_T(OWD)), and numsampT(1) is the most recent;
skew_base_T(1) is the most recent calculation of skew_base_T; 1:F
refers to the integer values 1 through to F, and [(M-F):1] refers to
an array of the integer values (M-F) declining through to 1; and ".*"
is the array scalar dot product operator.
To calculate this weighted skew_est incrementally:
Notation: F_ = flat portion, D_ = declining portion,
W_ = weighted component
Initialize: sum_skewbase = 0, F_skewbase = 0, W_D_skewbase = 0
skewbase_hist = buffer of length M, initialized to 0
numsampT = buffer of length M, initialized to 0
Steps per iteration:
1. old_skewbase = skewbase_hist(M)
2. old_numsampT = numsampT(M)
3. cycle(skewbase_hist)
4. cycle(numsampT)
5. numsampT(1) = num_T(OWD)
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6. skewbase_hist(1) = skew_base_T
7. F_skewbase = F_skewbase + skew_base_T - skewbase_hist(F+1)
8. W_D_skewbase = W_D_skewbase + (M-F)*skewbase_hist(F+1)
- sum_skewbase
9. W_D_numsamp = W_D_numsamp + (M-F)*numsampT(F+1) - sum_numsamp
+ F_numsamp
10. F_numsamp = F_numsamp + numsampT(1) - numsampT(F+1)
11. sum_skewbase = sum_skewbase + skewbase_hist(F+1) - old_skewbase
12. sum_numsamp = sum_numsamp + numsampT(1) - old_numsampT
13. skew_est = ((M-F+1)*F_skewbase + W_D_skewbase) /
((M-F+1)*F_numsamp+W_D_numsamp)
where cycle(...) refers to the operation on a cyclic buffer where the
start of the buffer is now the next element in the buffer.
4.1.2. Improving the Response of the Variability Estimate
Similarly, the weighted moving average for var_est can be calculated
as follows:
var_est = ((M-F+1)*sum(var_base_T(1:F))
+ sum([(M-F):1].*var_base_T(F+1:M)))
/ ((M-F+1)*sum(numsampT(1:F))
+ sum([(M-F):1].*numsampT(F+1:M)))
where numsampT is an array of the number of OWD samples in each T
(i.e., num_T(OWD)), and numsampT(1) is the most recent;
skew_base_T(1) is the most recent calculation of skew_base_T; 1:F
refers to the integer values 1 through to F, and [(M-F):1] refers to
an array of the integer values (M-F) declining through to 1; and ".*"
is the array scalar dot product operator. When removing oscillation
noise (see Section 4.2), this calculation must be adjusted to allow
for invalid var_base_T records.
var_est can be calculated incrementally in the same way as skew_est
as shown in Section 4.1.1. However, note that the buffer numsampT is
used for both calculations, so the operations on it should not be
repeated.
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4.2. Removing Oscillation Noise
When a path has no bottleneck, var_est will be very small and the
recorded significant mean crossings will be the result of path noise.
Thus, up to N-1 meaningless mean crossings can be a source of error
at the point where a link becomes a bottleneck and flows traversing
it begin to be grouped.
To remove this source of noise from freq_est:
1. Set the current var_base_T = NaN (a value representing an invalid
record, i.e., Not a Number) for flows that are deemed to not be
transiting a bottleneck by the first grouping test that is based
on skew_est (see Section 3.3.1).
2. Then, var_est = sum_MT(var_base_T != NaN) / num_MT(OWD).
3. For freq_est, only record a significant mean crossing if a given
flow is deemed to be transiting a bottleneck.
These three changes can help to remove the non-bottleneck noise from
freq_est.
5. Measuring OWD
This section discusses the OWD measurements required for this
algorithm to detect shared bottlenecks.
The SBD mechanism described in this document relies on differences
between OWD measurements to avoid the practical problems with
measuring absolute OWD (see [Hayes-LCN14], Section III.C). Since all
summary statistics are relative to the mean OWD and sender/receiver
clock offsets should be approximately constant over the measurement
periods, the offset is subtracted out in the calculation.
5.1. Timestamp Resolution
The SBD mechanism requires timing information precise enough to be
able to make comparisons. As a rule of thumb, the time resolution
should be less than one hundredth of a typical path's range of
delays. In general, the coarser the time resolution, the more care
that needs to be taken to ensure that rounding errors do not bias the
skewness calculation. Frequent timing information in millisecond
resolution as described by [RTCP-CC-FEEDBACK] should be sufficient
for the sender to calculate relative OWD.
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5.2. Clock Skew
Generally, sender and receiver clock skew will be too small to cause
significant errors in the estimators. skew_est and freq_est are the
most sensitive to this type of noise due to their use of a mean OWD
calculated over a longer interval. In circumstances where clock skew
is high, basing skew_est only on the previous T's mean and ignoring
freq_est provide a noisier but reliable signal.
A more sophisticated method is to estimate the effect the clock skew
is having on the summary statistics and then adjust statistics
accordingly. There are a number of techniques in the literature,
including [Zhang-Infocom02].
6. Expected Feedback from Experiments
The algorithm described in this memo has so far been evaluated using
simulations and small-scale experiments. Real network tests using
RTP Media Congestion Avoidance Techniques (RMCAT) congestion control
algorithms will help confirm the default parameter choice. For
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
their findings in an Internet-Draft.
7. IANA Considerations
This document has no IANA actions.
8. Security Considerations
The security considerations of RFC 3550 [RFC3550], RFC 4585
[RFC4585], and RFC 5124 [RFC5124] are expected to apply.
Non-authenticated RTCP packets carrying OWD measurements, shared
bottleneck indications, and/or summary statistics could allow
attackers to alter the bottleneck-sharing characteristics for private
gain or disruption of other parties' communication. When using SBD
for coupled congestion control as described in [RTP-COUPLED-CC], the
security considerations of [RTP-COUPLED-CC] apply.
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9. References
9.1. 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,
<https://www.rfc-editor.org/info/rfc2119>.
[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, D., Ferlin, S., and M. Welzl, "Practical Passive
Shared Bottleneck Detection using Shape Summary
Statistics", Proc. IEEE Local Computer Networks (LCN),
pp. 150-158, DOI 10.1109/LCN.2014.6925767, September 2014,
<http://heim.ifi.uio.no/davihay/
hayes14__pract_passiv_shared_bottl_detec-abstract.html>.
[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, <https://www.rfc-editor.org/info/rfc3550>.
[RFC4585] Ott, J., Wenger, S., Sato, N., Burmeister, C., and J. Rey,
"Extended RTP Profile for Real-time Transport Control
Protocol (RTCP)-Based Feedback (RTP/AVPF)", RFC 4585,
DOI 10.17487/RFC4585, July 2006,
<https://www.rfc-editor.org/info/rfc4585>.
[RFC5124] Ott, J. and E. Carrara, "Extended Secure RTP Profile for
Real-time Transport Control Protocol (RTCP)-Based Feedback
(RTP/SAVPF)", RFC 5124, DOI 10.17487/RFC5124,
February 2008, <https://www.rfc-editor.org/info/rfc5124>.
[RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
"Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
DOI 10.17487/RFC6817, December 2012,
<https://www.rfc-editor.org/info/rfc6817>.
Hayes, et al. Experimental [Page 23]
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[RFC7679] Almes, G., Kalidindi, S., Zekauskas, M., and A. Morton,
Ed., "A One-Way Delay Metric for IP Performance Metrics
(IPPM)", STD 81, RFC 7679, DOI 10.17487/RFC7679,
January 2016, <https://www.rfc-editor.org/info/rfc7679>.
[RFC7680] Almes, G., Kalidindi, S., Zekauskas, M., and A. Morton,
Ed., "A One-Way Loss Metric for IP Performance Metrics
(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, L., Liu, Z., and H. Xia, "Clock synchronization
algorithms for network measurements", Proc. IEEE
International Conference on Computer Communications
(INFOCOM), pp. 160-169, DOI 10.1109/INFCOM.2002.1019257,
September 2002.
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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
David Hayes (editor)
Simula Research Laboratory
P.O. Box 134
Lysaker 1325
Norway
Email: davidh@simula.no
Simone Ferlin
Simula Research Laboratory
P.O. Box 134
Lysaker 1325
Norway
Email: simone@ferlin.io
Michael Welzl
University of Oslo
P.O. Box 1080 Blindern
Oslo N-0316
Norway
Email: michawe@ifi.uio.no
Kristian Hiorth
University of Oslo
P.O. Box 1080 Blindern
Oslo N-0316
Norway
Email: kristahi@ifi.uio.no
Hayes, et al. Experimental [Page 25]
ERRATA
