RTP Media Congestion Avoidance D. Hayes, Ed.
Techniques University of Oslo
Internet-Draft S. Ferlin
Intended status: Experimental Simula Research Laboratory
Expires: April 30, 2015 M. Welzl
University of Oslo
October 27, 2014
Shared Bottleneck Detection for Coupled Congestion Control for RTP
Media.
draft-hayes-rmcat-sbd-01
Abstract
This document describes a mechanism to detect whether end-to-end data
flows share a common bottleneck. It relies on summary statistics
that are calculated by a data receiver based on continuous
measurements and regularly fed to a grouping algorithm that runs
wherever the knowledge is needed. This mechanism complements the
coupled congestion control mechanism in draft-welzl-rmcat-coupled-cc.
Status of this Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. The signals . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.1. Packet Loss . . . . . . . . . . . . . . . . . . . . . 3
1.1.2. Packet Delay . . . . . . . . . . . . . . . . . . . . . 3
1.1.3. Path Lag . . . . . . . . . . . . . . . . . . . . . . . 4
2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1. Parameter Values . . . . . . . . . . . . . . . . . . . . . 5
3. Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3.1. Key metrics and their calculation . . . . . . . . . . . . 7
3.1.1. Mean delay . . . . . . . . . . . . . . . . . . . . . . 7
3.1.2. Skewness Estimate . . . . . . . . . . . . . . . . . . 7
3.1.3. Variance Estimate . . . . . . . . . . . . . . . . . . 8
3.1.4. Oscillation Estimate . . . . . . . . . . . . . . . . . 8
3.1.5. Packet loss . . . . . . . . . . . . . . . . . . . . . 9
3.2. Flow Grouping . . . . . . . . . . . . . . . . . . . . . . 9
3.2.1. Flow Grouping Algorithm . . . . . . . . . . . . . . . 9
3.2.2. Using the flow group signal . . . . . . . . . . . . . 10
4. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 11
4.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 11
5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 11
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
7. Security Considerations . . . . . . . . . . . . . . . . . . . 11
8. References . . . . . . . . . . . . . . . . . . . . . . . . . . 12
8.1. Normative References . . . . . . . . . . . . . . . . . . . 12
8.2. Informative References . . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 13
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1. Introduction
In the Internet, it is not normally known if flows (e.g., TCP
connections or UDP data streams) traverse the same bottlenecks. Even
flows that have the same sender and receiver may take different paths
and share a bottleneck or not. Flows that share a bottleneck link
usually compete with one another for their share of the capacity.
This competition has the potential to increase packet loss and
delays. This is especially relevant for interactive applications
that communicate simultaneously with multiple peers (such as multi-
party video). For RTP media applications such as RTCWEB,
[I-D.welzl-rmcat-coupled-cc] describes a scheme that combines the
congestion controllers of flows in order to honor their priorities
and avoid unnecessary packet loss as well as delay. This mechanism
relies on some form of Shared Bottleneck Detection (SBD); here, a
measurement-based SBD approach is described.
1.1. The signals
The current Internet is unable to explicitly inform endpoints as to
which flows share bottlenecks, so endpoints need to infer this from
packet loss and packet delay.
1.1.1. Packet Loss
Packet loss is often a relatively rare signal. Therefore, on its own
it is of limited use for SBD, however, it is a valuable supplementary
measure when it is more prevalent.
1.1.2. Packet Delay
End-to-end delay measurements include noise from every device along
the path in addition to the delay perturbation at the bottleneck
device. The noise is often significantly increased if the round-trip
time is used. The cleanest signal is obtained by using One-Way-Delay
(OWD).
Measuring absolute OWD is difficult since it requires both the sender
and receiver clocks to be synchronised. However, since the
statistics being collected are relative to the mean OWD, a relative
OWD measurement is sufficient. Clock drift is not usually
significant over the time intervals used by this SBD mechanism (see
[RFC6817] A.2 for a discussion on clock drift and OWD measurements).
Each packet arriving at the bottleneck buffer may experience very
different queue lengths, and therefore different waiting times. A
single OWD sample does therefore not characterize the actual OWD of a
path well. However, multiple OWD measurements do reflect the
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distribution of delays experienced at the bottleneck.
1.1.3. Path Lag
Flows that share a common bottleneck may traverse different paths,
and these paths will often have different base delays. This makes it
difficult to correlate changes in delay or loss. This technique uses
the long term shape of the delay distribution as a base for
comparison to counter this.
2. Definitions
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
Acronyms used in this document:
OWD -- One Way Delay
RTT -- Round Trip Time
SBD -- Shared Bottleneck Detection
Conventions used in this document:
T -- the base time interval over which measurements are
made.
N -- the number of base time, T, intervals used in some
calculations.
sum_T(...) -- summation of all the measurements of the variable
in parentheses taken over the interval T
sum_N(...) -- summation of N terms of the variable in parentheses
sum_NT(...) -- summation of all measurements taken over the
interval N*T
E_T(...) -- the expectation or mean of the measurements of the
variable in parentheses over T
E_N(...) -- The expectation or mean of the last N values of the
variable in parentheses
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max_T(...) -- the maximum recorded measurement of the variable in
parentheses taken over the interval T
min_T(...) -- the minimum recorded measurement of the variable in
parentheses taken over the interval T
num_T(...) -- the count of measurements of the variable in
parenthesis taken in the interval T
p_l, p_f, p_pdf, c_s, p_s, p_d, p_v -- various thresholds used in
the mechanism.
2.1. Parameter Values
Reference [Hayes-LCN14] uses T=350ms, N=50, p_l = 0.1, p_f = 0.2,
p_pdf = 0.3, c_s = 0.0, p_s = p_d = p_v = 0.2. These are values that
seem to work well over a wide range of practical Internet conditions.
3. Mechanism
The mechanism described in this document is based on the observation
that the distribution of delay measurements of packets from flows
that share a common bottleneck have similar shape characteristics.
These shape characteristics are described using 3 key summary
statistics:
variance (estimate PDV, see Section 3.1.3)
skewness (estimate skew_est, see Section 3.1.2)
oscillation (estimate freq_est, see Section 3.1.4)
Summary statistics help to address both the noise and the path lag
problems by describing the general shape over a relatively long
period of time. This is sufficient for their application in coupled
congestion control for RTP Media. They can be signalled from a
receiver, which measures the OWD and calculates the summary
statistics, to a sender, which is the entity that is transmitting the
media stream. An RTP Media device may be both a sender and a
receiver. SBD can be performed at either Sender or receiver or both.
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+----+
| H2 |
+----+
|
| L2
|
+----+ L1 | L3 +----+
| H1 |------|------| H3 |
+----+ +----+
A network with 3 hosts (H1, H2, H3) and 3 links (L1, L2, L3).
Figure 1
In Figure 1, there are two possible cases for shared bottleneck
detection: a sender-based and a receiver-based case.
1. Sender-based: consider a situation where host H1 sends media
streams to hosts H2 and H3, and L1 is a shared bottleneck. H2
and H3 measure the OWD and calculate summary statistics, which
they send to H1 every T. H1, having this knowledge, can determine
the shared bottleneck and accordingly control the send rates.
2. Receiver-based: consider that H2 is also sending media to H3, and
L3 is a shared bottleneck. If H3 sends summary statistics to H1
and H2, neither H1 nor H2 alone obtain enough knowledge to detect
this shared bottleneck; H3 can however determine it by combining
the summary statistics related to H1 and H2, respectively. This
case is applicable when send rates are controlled by the
receiver; then, the signal from H3 to the senders contains the
sending rate.
A discussion of the required signaling for the receiver-based case is
beyond the scope of this document. For the sender-based case, the
messages and their data format will be defined here in future
versions of this document. We envision that an initialization
message from the sender to the receiver could specify which key
metrics are requested out of a possibly extensible set (PL_NT, PDV,
skew_est, freq_est). The grouping algorithm described in this
document requires all four of these metrics, and receivers MUST be
able to provide them, but future algorithms may be able to exploit
other metrics (e.g. metrics based on explicit network signals).
Moreover, the initialization message could specify T, N, and the
necessary resolution and precision (number of bits per field).
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3.1. Key metrics and their calculation
Measurements are calculated over a base interval, T. T should be long
enough to provide enough samples for a good estimate of skewness, but
short enough so that a measure of the oscillation can be made from N
of these estimates. Reference [Hayes-LCN14] uses T = 350ms and N =
50, which are values that seem to work well over a wide range of
practical Internet conditions.
3.1.1. Mean delay
The mean delay is not a useful signal for comparisons between flows
since flows may traverse quite different paths and clocks will not
necessarily be synchronized. However, it is a base measure for the 3
summary statistics. The mean delay, E_T(OWD), is the average one way
delay measured over T.
To facilitate the other calculations, the last N E_T(OWD) values will
need to be stored in a cyclic buffer along with the moving average of
E_T(OWD):
E_N(E_T(OWD)) = sum_N(E_T(OWD)) / N
3.1.2. Skewness Estimate
Skewness is difficult to calculate efficiently and accurately.
Ideally it should be calculated over the entire period (N * T) from
the mean OWD over that period. However this would require storing
every delay measurement over the period. Instead, an estimate is
made over T using the previous calculation of E_T(OWD). Comparisons
are made using the mean of N skew estimates.
The skewness is estimated using two counters, counting the number of
one way delay samples (OWD) above and below the mean:
skew_est = (sum_T(OWD < E_NT(OWD)) - sum_T(OWD > E_NT(OWD))) /
num_T(OWD)
where
if (OWD < E_NT(OWD)) 1 else 0
if (OWD > E_NT(OWD)) 1 else 0
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skew_est is a number between -1 and 1
E_N(skew_est) = sum_N(skew_est) / N
For implementation ease, E_NT(OWD) does not include the mean of the
current T interval. Care must be taken when implementing the
comparisons to ensure that rounding does not bias skew_est.
3.1.3. Variance Estimate
Packet Delay Variation (PDV) ([RFC5481] and [ITU-Y1540]) is used as
an estimator of the variance of the delay signal. We define PDV as
follows:
PDV = (max_T(OWD) - E_T(OWD))
E_N(PDV) = sum_N(PDV) / N
This modifies PDV as outlined in [RFC5481] to provide a summary
statistic version that best aids the grouping decisions of the
algorithm (see [Hayes-LCN14] section IVB).
The use of PDV = (min_T(OWD) - E_T(OWD)) is currently being
investigated as an alternative that is less sensitive to noise.
3.1.4. Oscillation Estimate
An estimate of the low frequency oscillation of the delay signal is
calculated by counting and normalising the significant mean,
E_T(OWD), crossings of E_N(E_T(OWD)):
freq_est = number_of_crossings / N
Where
we define a significant mean crossing as a crossing that
extends p_v * E_N(PDV) from E_N(E_T(OWD)). In our experiments
we have found that p_v = 0.2 is a good value.
Freq_est is a number between 0 and 1. Freq_est can be approximated
incrementally as follows:
With each new calculation of E_T(OWD) a decision is made as to
whether this value of E_T(OWD) significantly crosses the current
long term mean, E_N(E_T(OWD), with respect to the previous
significant mean crossing.
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A cyclic buffer, last_N_crossings, records a 1 if there is a
significant mean crossing, otherwise a 0.
The counter, number_of_crossings, is incremented when there is a
significant mean crossing and subtracted from when a non zero
value is removed from the last_N_crossings.
This approximation of freq_est was not used in [Hayes-LCN14], which
calculated freq_est every T using the current E_N(E_T(OWD)). Our
tests show that this approximation of freq_est yields results that
are almost identical to when the full calculation is performed every
T.
3.1.5. Packet loss
The proportion of packets lost is used as a supplementary measure:
PL_NT = sum_NT(lost packets) / sum_NT(total packets)
3.2. Flow Grouping
3.2.1. Flow Grouping Algorithm
The following grouping algorithm is RECOMMENDED for SBD in this
context and is sufficient and efficient for small to moderate numbers
of flows. For very large numbers of flows (e.g. hundreds), a more
complex clustering algorithm may be substituted.
Since no single metric is precise enough to group flows (due to
noise), the algorithm uses multiple metrics. Each metric offers a
different "view" of the bottleneck link characteristics, and used
together enable a more precise grouping of flows than would otherwise
be possible.
Flows determined to be experiencing congestion are successively
divided into groups based on freq_est, PDV, and skew_est.
The first step is to determine which flows are experiencing
congestion. This is important, since if a flow is not experiencing
congestion its delay based metrics will not describe the bottleneck,
but the "noise" from the rest of the path. Skewness, with proportion
of packets loss as a supplementary measure, is used to do this:
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1. Grouping will be performed on flows where:
E_N(skew_est) < c_s || PL_NT > p_l.
The parameter c_s controls how sensitive the mechanism is in
detecting congestion. C_s = 0.0 was used in [Hayes-LCN14]. A value
of c_s = 0.05 is a little more sensitive, and c_s = -0.05 is a little
less sensitive.
These flows, flows experiencing congestion, are then progressively
divided into groups based on the freq_est, PDV, and skew_est summary
statistics. The process proceeds according to the following steps:
2. Group flows whose difference in sorted freq_est is less than a
threshold:
diff(freq_est) < p_f
3. Group flows whose difference in sorted E_N(PDV) is less than a
threshold:
diff(E_N(PDV)) < (p_pdv * E_N(PDV))
4. Group flows whose difference in sorted E_N(skew_est) or PL_NT is
less than a threshold:
if PL_NT < p_l
diff(E_N(skewness)) < p_s
otherwise
diff(PL_NT) < p_d
This procedure involves sorting the groups, according to the measure
being used to divide them. It is simple to implement, and efficient
for small numbers of flows, such as are expected in RTCWEB.
3.2.2. Using the flow group signal
A grouping decisions is made every T from the second T, though they
will not attain their full design accuracy until after the N'th T
interval.
Network conditions 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
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this draft and will be specific to the coupled congestion controllers
objectives.
4. Measuring OWD
This section discusses the OWD measurements required for this
algorithm to detect shared bottlenecks.
The SBD mechanism described in this draft relies on differences
between OWD measurements to avoid the practical problems with
measuring absolute OWD (see [Hayes-LCN14] section IIIC). Since all
summary statistics are relative to the mean OWD and sender/receiver
clock offsets are approximately constant over the measurement
periods, the offset is subtracted out in the calculation.
4.1. Time stamp resolution
The SBD mechanism requires timing information precise enough to be
able to make comparisons. As a rule of thumb, the time resolution
should be less than one hundredth of a typical path's range of
delays. In general, the lower the time resolution, the more care
that needs to be taken to ensure rounding errors don't bias the
skewness calculation.
Typical RTP media flows use sub-millisecond timers, which should be
adequate in most situations.
5. Acknowledgements
This work was part-funded by the European Community under its Seventh
Framework Programme through the Reducing Internet Transport Latency
(RITE) project (ICT-317700). The views expressed are solely those of
the authors.
6. IANA Considerations
This memo includes no request to IANA.
7. Security Considerations
The security considerations of RFC 3550 [RFC3550], RFC 4585
[RFC4585], and RFC 5124 [RFC5124] are expected to apply.
Non-authenticated RTCP packets carrying shared bottleneck indications
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and summary statistics could allow attackers to alter the bottleneck
sharing characteristics for private gain or disruption of other
parties communication.
8. References
8.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
8.2. Informative References
[Hayes-LCN14]
Hayes, D., Ferlin, S., and M. Welzl, "Practical Passive
Shared Bottleneck Detection using Shape Summary
Statistics", Proc. the IEEE Local Computer Networks
(LCN) p150-158, September 2014, <http://heim.ifi.uio.no/
davihay/
hayes14__pract_passiv_shared_bottl_detec-abstract.html>.
[I-D.welzl-rmcat-coupled-cc]
Welzl, M., Islam, S., and S. Gjessing, "Coupled congestion
control for RTP media", draft-welzl-rmcat-coupled-cc-04
(work in progress), October 2014.
[ITU-Y1540]
ITU-T, "Internet protocol data communication service - IP
packet transfer and availability performance parameters",
Series Y: Global Information Infrastructure, Internet
Protocol Aspects and Next-Generation Networks ,
March 2011,
<http://www.itu.int/rec/T-REC-Y.1540-201103-I/en>.
[RFC3550] Schulzrinne, H., Casner, S., Frederick, R., and V.
Jacobson, "RTP: A Transport Protocol for Real-Time
Applications", STD 64, RFC 3550, July 2003.
[RFC4585] Ott, J., Wenger, S., Sato, N., Burmeister, C., and J. Rey,
"Extended RTP Profile for Real-time Transport Control
Protocol (RTCP)-Based Feedback (RTP/AVPF)", RFC 4585,
July 2006.
[RFC5124] Ott, J. and E. Carrara, "Extended Secure RTP Profile for
Real-time Transport Control Protocol (RTCP)-Based Feedback
(RTP/SAVPF)", RFC 5124, February 2008.
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[RFC5481] Morton, A. and B. Claise, "Packet Delay Variation
Applicability Statement", RFC 5481, March 2009.
[RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
"Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
December 2012.
Authors' Addresses
David Hayes (editor)
University of Oslo
PO Box 1080 Blindern
Oslo, N-0316
Norway
Phone: +47 2284 5566
Email: davihay@ifi.uio.no
Simone Ferlin
Simula Research Laboratory
P.O.Box 134
Lysaker, 1325
Norway
Phone: +47 4072 0702
Email: ferlin@simula.no
Michael Welzl
University of Oslo
PO Box 1080 Blindern
Oslo, N-0316
Norway
Phone: +47 2285 2420
Email: michawe@ifi.uio.no
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