RTP Media Congestion Avoidance Techniques D. Hayes, Ed.
Internet-Draft University of Oslo
Intended status: Experimental S. Ferlin
Expires: April 30, 2015 Simula Research Laboratory
M. Welzl
University of Oslo
October 27, 2014

Shared Bottleneck Detection for Coupled Congestion Control for RTP Media.


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.

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Table of Contents

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 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
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.

                               | 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).

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)
if (OWD < E_NT(OWD)) 1 else 0
if (OWD > E_NT(OWD)) 1 else 0
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: [RFC5481] to provide a summary statistic version that best aids the grouping decisions of the algorithm (see [Hayes-LCN14] section IVB).

PDV = (max_T(OWD) - E_T(OWD))
E_N(PDV) = sum_N(PDV) / N

This modifies PDV as outlined in

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)): [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.

freq_est = number_of_crossings / N
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.
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

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:

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:

Group flows whose difference in sorted freq_est is less than a threshold:
diff(freq_est) < p_f
Group flows whose difference in sorted E_N(PDV) is less than a threshold:
diff(E_N(PDV)) < (p_pdv * E_N(PDV))
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
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 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 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.
[I-D.welzl-rmcat-coupled-cc] Welzl, M., Islam, S. and S. Gjessing, "Coupled congestion control for RTP media", Internet-Draft draft-welzl-rmcat-coupled-cc-04, 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.
[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.
[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