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Internet Engineering Task Force (IETF) S. Islam
Request for Comments: 8699 M. Welzl
Category: Experimental S. Gjessing
ISSN: 2070-1721 University of Oslo
January 2020
Coupled Congestion Control for RTP Media
Abstract
When multiple congestion-controlled Real-time Transport Protocol
(RTP) sessions traverse the same network bottleneck, combining their
controls can improve the total on-the-wire behavior in terms of
delay, loss, and fairness. This document describes such a method for
flows that have the same sender, in a way that is as flexible and
simple as possible while minimizing the number of changes needed to
existing RTP applications. This document also specifies how to apply
the method for the Network-Assisted Dynamic Adaptation (NADA)
congestion control algorithm and provides suggestions on how to apply
it to other congestion control algorithms.
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/rfc8699.
Copyright Notice
Copyright (c) 2020 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(https://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
to this document. Code Components extracted from this document must
include Simplified BSD License text as described in Section 4.e of
the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
Table of Contents
1. Introduction
2. Definitions
3. Limitations
4. Architectural Overview
5. Roles
5.1. SBD
5.2. FSE
5.3. Flows
5.3.1. Example Algorithm 1 - Active FSE
5.3.2. Example Algorithm 2 - Conservative Active FSE
6. Application
6.1. NADA
6.2. General Recommendations
7. Expected Feedback from Experiments
8. IANA Considerations
9. Security Considerations
10. References
10.1. Normative References
10.2. Informative References
Appendix A. Application to GCC
Appendix B. Scheduling
Appendix C. Example Algorithm - Passive FSE
C.1. Example Operation (Passive)
Acknowledgements
Authors' Addresses
1. Introduction
When there is enough data to send, a congestion controller attempts
to increase its sending rate until the path's capacity has been
reached. Some controllers detect path capacity by increasing the
sending rate further, until packets are ECN-marked [RFC8087] or
dropped, and then decreasing the sending rate until that stops
happening. This process inevitably creates undesirable queuing delay
when multiple congestion-controlled connections traverse the same
network bottleneck, and each connection overshoots the path capacity
as it determines its sending rate.
The Congestion Manager (CM) [RFC3124] couples flows by providing a
single congestion controller. It is hard to implement because it
requires an additional congestion controller and removes all per-
connection congestion control functionality, which is quite a
significant change to existing RTP-based applications. This document
presents a method to combine the behavior of congestion control
mechanisms that is easier to implement than the Congestion Manager
[RFC3124] and also requires fewer significant changes to existing
RTP-based applications. It attempts to roughly approximate the CM
behavior by sharing information between existing congestion
controllers. It is able to honor user-specified priorities, which is
required by WebRTC [RTCWEB-OVERVIEW] [RFC7478].
The described mechanisms are believed safe to use, but they are
experimental and are presented for wider review and operational
evaluation.
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.
Available Bandwidth:
The available bandwidth is the nominal link capacity minus the
amount of traffic that traversed the link during a certain time
interval, divided by that time interval.
Bottleneck:
The first link with the smallest available bandwidth along the
path between a sender and receiver.
Flow:
A flow is the entity that congestion control is operating on.
It could, for example, be a transport-layer connection or an
RTP stream [RFC7656], regardless of whether or not this RTP
stream is multiplexed onto an RTP session with other RTP
streams.
Flow Group Identifier (FGI):
A unique identifier for each subset of flows that is limited by
a common bottleneck.
Flow State Exchange (FSE):
The entity that maintains information that is exchanged between
flows.
Flow Group (FG):
A group of flows having the same FGI.
Shared Bottleneck Detection (SBD):
The entity that determines which flows traverse the same
bottleneck in the network or the process of doing so.
3. Limitations
Sender-side only:
Shared bottlenecks can exist when multiple flows originate from
the same sender or when flows from different senders reach the
same receiver (see Section 3 of [RFC8382]). Coupled congestion
control, as described here, only supports the former case, not
the latter, as it operates inside a single host on the sender
side.
Shared bottlenecks do not change quickly:
As per the definition above, a bottleneck depends on cross
traffic, and since such traffic can heavily fluctuate,
bottlenecks can change at a high frequency (e.g., there can be
oscillation between two or more links). This means that, when
flows are partially routed along different paths, they may
quickly change between sharing and not sharing a bottleneck.
For simplicity, here it is assumed that a shared bottleneck is
valid for a time interval that is significantly longer than the
interval at which congestion controllers operate. Note that,
for the only SBD mechanism defined in this document
(multiplexing on the same five-tuple), the notion of a shared
bottleneck stays correct even in the presence of fast traffic
fluctuations; since all flows that are assumed to share a
bottleneck are routed in the same way, if the bottleneck
changes, it will still be shared.
4. Architectural Overview
Figure 1 shows the elements of the architecture for coupled
congestion control: the Flow State Exchange (FSE), Shared Bottleneck
Detection (SBD), and Flows. The FSE is a storage element that can be
implemented in two ways: active and passive. In the active version,
it initiates communication with flows and SBD. However, in the
passive version, it does not actively initiate communication with
flows and SBD; its only active role is internal state maintenance
(e.g., an implementation could use soft state to remove a flow's data
after long periods of inactivity). Every time a flow's congestion
control mechanism would normally update its sending rate, the flow
instead updates information in the FSE and performs a query on the
FSE, leading to a sending rate that can be different from what the
congestion controller originally determined. Using information
about/from the currently active flows, SBD updates the FSE with the
correct Flow Group Identifiers (FGIs).
This document describes both active and passive versions. While the
passive algorithm works better for congestion controls with RTT-
independent convergence, it can still produce oscillations on short
time scales. The passive algorithm, described in Appendix C, is
therefore considered highly experimental and not safe to deploy
outside of testbed environments. Figure 2 shows the interaction
between flows and the FSE using the variable names defined in
Section 5.2.
------- <--- Flow 1
| FSE | <--- Flow 2 ..
------- <--- .. Flow N
^
| |
------- |
| SBD | <-------|
-------
Figure 1: Coupled congestion control architecture
Flow#1(cc) FSE Flow#2(cc)
---------- --- ----------
#1 JOIN ----register--> REGISTER
REGISTER <--register-- JOIN #1
#2 CC_R(1) ----UPDATE----> UPDATE (in)
#3 NEW RATE <---FSE_R(1)-- UPDATE (out) --FSE_R(2)-> #3 NEW RATE
Figure 2: Flow-FSE interactions
Since everything shown in Figure 1 is assumed to operate on a single
host (the sender) only, this document only describes aspects that
have an influence on the resulting on-the-wire behavior. It does
not, for instance, define how many bits must be used to represent
FGIs or in which way the entities communicate.
Implementations can take various forms; for instance, all the
elements in the figure could be implemented within a single
application, thereby operating on flows generated by that application
only. Another alternative could be to implement both the FSE and SBD
together in a separate process that different applications
communicate with via some form of Inter-Process Communication (IPC).
Such an implementation would extend the scope to flows generated by
multiple applications. The FSE and SBD could also be included in the
Operating System kernel. However, only one type of coupling
algorithm should be used for all flows. Combinations of multiple
algorithms at different aggregation levels (e.g., the Operating
System coupling application aggregates with one algorithm, and
applications coupling their flows with another) have not been tested
and are therefore not recommended.
5. Roles
This section gives an overview of the roles of the elements of
coupled congestion control and provides an example of how coupled
congestion control can operate.
5.1. SBD
SBD uses knowledge about the flows to determine which flows belong in
the same Flow Group (FG) and assigns FGIs accordingly. This
knowledge can be derived in three basic ways:
1. From multiplexing: It can be based on the simple assumption that
packets sharing the same five-tuple (IP source and destination
address, protocol, and transport-layer port number pair) and
having the same values for the Differentiated Services Code Point
(DSCP) and the ECN field in the IP header are typically treated
in the same way along the path. This method is the only one
specified in this document; SBD MAY consider all flows that use
the same five-tuple, DSCP, and ECN field value to belong to the
same FG. This classification applies to certain tunnels or RTP
flows that are multiplexed over one transport (cf.
[TRANSPORT-MULTIPLEX]). Such multiplexing is also a recommended
usage of RTP in WebRTC [RTCWEB-RTP-USAGE].
2. Via configuration: e.g., by assuming that a common wireless
uplink is also a shared bottleneck.
3. From measurements: e.g., by considering correlations among
measured delay and loss as an indication of a shared bottleneck.
The methods above have some essential trade-offs. For example,
multiplexing is a completely reliable measure, but it is limited in
scope to two endpoints (i.e., it cannot be applied to couple
congestion controllers of one sender talking to multiple receivers).
A measurement-based SBD mechanism is described in [RFC8382].
Measurements can never be 100% reliable, in particular because they
are based on the past, but applying coupled congestion control
involves making an assumption about the future; it is therefore
recommended to implement cautionary measures, e.g., by disabling
coupled congestion control if enabling it causes a significant
increase in delay and/or packet loss. Measurements also take time,
which entails a certain delay for turning on coupling (refer to
[RFC8382] for details). When this is possible, it can be more
efficient to statically configure shared bottlenecks (e.g., via a
system configuration or user input) based on assumptions about the
network environment.
5.2. FSE
The FSE contains a list of all flows that have registered with it.
For each flow, the FSE stores the following:
* a unique flow number f to identify the flow.
* the FGI of the FG that it belongs to (based on the definitions in
this document, a flow has only one bottleneck and can therefore be
in only one FG).
* a priority P(f), which is a number greater than zero.
* The rate used by the flow in bits per second, FSE_R(f).
* The desired rate DR(f) of flow f. This can be smaller than
FSE_R(f) if the application feeding into the flow has less data to
send than FSE_R(f) would allow or if a maximum value is imposed on
the rate. In the absence of such limits, DR(f) must be set to the
sending rate provided by the congestion control module of flow f.
Note that the absolute range of priorities does not matter; the
algorithm works with a flow's priority portion of the sum of all
priority values. For example, if there are two flows, flow 1 with
priority 1 and flow 2 with priority 2, the sum of the priorities is
3. Then, flow 1 will be assigned 1/3 of the aggregate sending rate,
and flow 2 will be assigned 2/3 of the aggregate sending rate.
Priorities can be mapped to the "very-low", "low", "medium", or
"high" priority levels described in [WEBRTC-TRANS] by simply using
the values 1, 2, 4, and 8, respectively.
In the FSE, each FG contains one static variable, S_CR, which is the
sum of the calculated rates of all flows in the same FG. This value
is used to calculate the sending rate.
The information listed here is enough to implement the sample flow
algorithm given below. FSE implementations could easily be extended
to store, e.g., a flow's current sending rate for statistics
gathering or future potential optimizations.
5.3. Flows
Flows register themselves with SBD and FSE when they start,
deregister from the FSE when they stop, and carry out an UPDATE
function call every time their congestion controller calculates a new
sending rate. Via UPDATE, they provide the newly calculated rate
and, optionally (if the algorithm supports it), the desired rate.
The desired rate is less than the calculated rate in case of
application-limited flows; otherwise, it is the same as the
calculated rate.
Below, two example algorithms are described. While other algorithms
could be used instead, the same algorithm must be applied to all
flows. Names of variables used in the algorithms are explained
below.
CC_R(f) The rate received from the congestion controller of flow f
when it calls UPDATE.
FSE_R(f) The rate calculated by the FSE for flow f.
DR(f) The desired rate of flow f.
S_CR The sum of the calculated rates of all flows in the same
FG; this value is used to calculate the sending rate.
FG A group of flows having the same FGI and hence, sharing the
same bottleneck.
P(f) The priority of flow f, which is received from the flow's
congestion controller; the FSE uses this variable for
calculating FSE_R(f).
S_P The sum of all the priorities.
TLO The total leftover rate; the sum of rates that could not be
assigned to flows that were limited by their desired rate.
AR The aggregate rate that is assigned to flows that are not
limited by their desired rate.
5.3.1. Example Algorithm 1 - Active FSE
This algorithm was designed to be the simplest possible method to
assign rates according to the priorities of flows. Simulation
results in [FSE] indicate that it does not, however, significantly
reduce queuing delay and packet loss.
(1) When a flow f starts, it registers itself with SBD and the FSE.
FSE_R(f) is initialized with the congestion controller's initial
rate. SBD will assign the correct FGI. When a flow is assigned
an FGI, it adds its FSE_R(f) to S_CR.
(2) When a flow f stops or pauses, its entry is removed from the
list.
(3) Every time the congestion controller of the flow f determines a
new sending rate CC_R(f), the flow calls UPDATE, which carries
out the tasks listed below to derive the new sending rates for
all the flows in the FG. A flow's UPDATE function uses three
local (i.e., per-flow) temporary variables: S_P, TLO, and AR.
(a) It updates S_CR.
S_CR = S_CR + CC_R(f) - FSE_R(f)
(b) It calculates the sum of all the priorities, S_P, and
initializes FSE_R.
S_P = 0
for all flows i in FG do
S_P = S_P + P(i)
FSE_R(i) = 0
end for
(c) It distributes S_CR among all flows, ensuring that each
flow's desired rate is not exceeded.
TLO = S_CR
while(TLO-AR>0 and S_P>0)
AR = 0
for all flows i in FG do
if FSE_R[i] < DR[i] then
if TLO * P[i] / S_P >= DR[i] then
TLO = TLO - DR[i]
FSE_R[i] = DR[i]
S_P = S_P - P[i]
else
FSE_R[i] = TLO * P[i] / S_P
AR = AR + TLO * P[i] / S_P
end if
end if
end for
end while
(d) It distributes FSE_R to all the flows.
for all flows i in FG do
send FSE_R(i) to the flow i
end for
5.3.2. Example Algorithm 2 - Conservative Active FSE
This algorithm changes algorithm 1 to conservatively emulate the
behavior of a single flow by proportionally reducing the aggregate
rate on congestion. Simulation results in [FSE] indicate that it can
significantly reduce queuing delay and packet loss.
Step (a) of the UPDATE function is changed as described below. This
also introduces a local variable DELTA, which is used to calculate
the difference between CC_R(f) and the previously stored FSE_R(f).
To prevent flows from either ignoring congestion or overreacting, a
timer keeps them from changing their rates immediately after the
common rate reduction that follows a congestion event. This timer is
set to two RTTs of the flow that experienced congestion because it is
assumed that a congestion event can persist for up to one RTT of that
flow, with another RTT added to compensate for fluctuations in the
measured RTT value.
(a) It updates S_CR based on DELTA.
if Timer has expired or was not set then
DELTA = CC_R(f) - FSE_R(f)
if DELTA < 0 then // Reduce S_CR proportionally
S_CR = S_CR * CC_R(f) / FSE_R(f)
Set Timer for 2 RTTs
else
S_CR = S_CR + DELTA
end if
end if
6. Application
This section specifies how the FSE can be applied to specific
congestion control mechanisms and makes general recommendations that
facilitate applying the FSE to future congestion controls.
6.1. NADA
Network-Assisted Dynamic Adaptation (NADA) [RFC8698] is a congestion
control scheme for WebRTC. It calculates a reference rate r_ref upon
receiving an acknowledgment and then, based on the reference rate,
calculates a video target rate r_vin and a sending rate for the
flows, r_send.
When applying the FSE to NADA, the UPDATE function call described in
Section 5.3 gives the FSE NADA's reference rate r_ref. The
recommended algorithm for NADA is the Active FSE in Section 5.3.1.
In step 3 (d), when the FSE_R(i) is "sent" to the flow i, r_ref
(r_vin and r_send) of flow i is updated with the value of FSE_R(i).
6.2. General Recommendations
This section provides general advice for applying the FSE to
congestion control mechanisms.
Receiver-side calculations:
When receiver-side calculations make assumptions about the rate
of the sender, the calculations need to be synchronized, or the
receiver needs to be updated accordingly. This applies to TCP
Friendly Rate Control (TFRC) [RFC5348], for example, where
simulations showed somewhat less favorable results when using
the FSE without a receiver-side change [FSE].
Stateful algorithms:
When a congestion control algorithm is stateful (e.g., during
the TCP slow start, congestion avoidance, or fast recovery
phase), these states should be carefully considered such that
the overall state of the aggregate flow is correct. This may
require sharing more information in the UPDATE call.
Rate jumps:
The FSE-based coupling algorithms can let a flow quickly
increase its rate to its fair share, e.g., when a new flow
joins or after a quiescent period. In case of window-based
congestion controls, this may produce a burst that should be
mitigated in some way. An example of how this could be done
without using a timer is presented in [ANRW2016], using TCP as
an example.
7. Expected Feedback from Experiments
The algorithm described in this memo has so far been evaluated using
simulations covering all the tests for more than one flow from
[RMCAT-PROPOSALS] (see [IETF-93] and [IETF-94]). Experiments should
confirm these results using at least the NADA congestion control
algorithm with real-life code (e.g., browsers communicating over an
emulated network covering the conditions in [RMCAT-PROPOSALS]). The
tests with real-life code should be repeated afterwards in real
network environments and monitored. Experiments should investigate
cases where the media coder's output rate is below the rate that is
calculated by the coupling algorithm (FSE_R(i) in algorithms 1
(Section 5.3.1) and 2 (Section 5.3.2)). Implementers and testers are
invited to document their findings in an Internet-Draft.
8. IANA Considerations
This document has no IANA actions.
9. Security Considerations
In scenarios where the architecture described in this document is
applied across applications, various cheating possibilities arise,
e.g., supporting wrong values for the calculated rate, desired rate,
or priority of a flow. In the worst case, such cheating could either
prevent other flows from sending or make them send at a rate that is
unreasonably large. The end result would be unfair behavior at the
network bottleneck, akin to what could be achieved with any UDP-based
application. Hence, since this is no worse than UDP in general,
there seems to be no significant harm in using this in the absence of
UDP rate limiters.
In the case of a single-user system, it should also be in the
interest of any application programmer to give the user the best
possible experience by using reasonable flow priorities or even
letting the user choose them. In a multi-user system, this interest
may not be given, and one could imagine the worst case of an "arms
race" situation where applications end up setting their priorities to
the maximum value. If all applications do this, the end result is a
fair allocation in which the priority mechanism is implicitly
eliminated and no major harm is done.
Implementers should also be aware of the Security Considerations
sections of [RFC3124], [RFC5348], and [RFC7478].
10. References
10.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC3124] Balakrishnan, H. and S. Seshan, "The Congestion Manager",
RFC 3124, DOI 10.17487/RFC3124, June 2001,
<https://www.rfc-editor.org/info/rfc3124>.
[RFC5348] Floyd, S., Handley, M., Padhye, J., and J. Widmer, "TCP
Friendly Rate Control (TFRC): Protocol Specification",
RFC 5348, DOI 10.17487/RFC5348, September 2008,
<https://www.rfc-editor.org/info/rfc5348>.
[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>.
[RFC8698] Zhu, X., Pan, R., Ramalho, M., and S. Mena, "Network-
Assisted Dynamic Adaptation (NADA): A Unified Congestion
Control Scheme for Real-Time Media", RFC 8698,
DOI 10.17487/RFC8698, January 2020,
<https://www.rfc-editor.org/info/rfc8698>.
10.2. Informative References
[ANRW2016] Islam, S. and M. Welzl, "Start Me Up: Determining and
Sharing TCP's Initial Congestion Window", ACM, IRTF, ISOC
Applied Networking Research Workshop 2016 (ANRW 2016),
DOI 10.1145/2959424.2959440, Proceedings of the 2016
Applied Networking Research Workshop Pages 52-54, July
2016, <https://doi.org/10.1145/2959424.2959440>.
[FSE] Islam, S., Welzl, M., Gjessing, S., and N. Khademi,
"Coupled Congestion Control for RTP Media", ACM SIGCOMM
Capacity Sharing Workshop (CSWS 2014) and ACM SIGCOMM CCR
44(4) 2014, March 2014,
<http://safiquli.at.ifi.uio.no/paper/fse-tech-report.pdf>.
[FSE-NOMS] Islam, S., Welzl, M., Hayes, D., and S. Gjessing,
"Managing real-time media flows through a flow state
exchange", IEEE NOMS 2016, DOI 10.1109/NOMS.2016.7502803,
<https://doi.org/10.1109/NOMS.2016.7502803>.
[GCC-RTCWEB]
Holmer, S., Lundin, H., Carlucci, G., Cicco, L., and S.
Mascolo, "A Google Congestion Control Algorithm for Real-
Time Communication", Work in Progress, Internet-Draft,
draft-ietf-rmcat-gcc-02, 8 July 2016,
<https://tools.ietf.org/html/draft-ietf-rmcat-gcc-02>.
[IETF-93] Islam, S., Welzl, M., and S. Gjessing, "Updates on
'Coupled Congestion Control for RTP Media'", RTP Media
Congestion Avoidance Techniques (rmcat) Working Group,
IETF 93, July 2015,
<https://www.ietf.org/proceedings/93/rmcat.html>.
[IETF-94] Islam, S., Welzl, M., and S. Gjessing, "Updates on
'Coupled Congestion Control for RTP Media'", RTP Media
Congestion Avoidance Techniques (rmcat) Working Group,
IETF 94, November 2015,
<https://www.ietf.org/proceedings/94/rmcat.html>.
[RFC7478] Holmberg, C., Hakansson, S., and G. Eriksson, "Web Real-
Time Communication Use Cases and Requirements", RFC 7478,
DOI 10.17487/RFC7478, March 2015,
<https://www.rfc-editor.org/info/rfc7478>.
[RFC7656] Lennox, J., Gross, K., Nandakumar, S., Salgueiro, G., and
B. Burman, Ed., "A Taxonomy of Semantics and Mechanisms
for Real-Time Transport Protocol (RTP) Sources", RFC 7656,
DOI 10.17487/RFC7656, November 2015,
<https://www.rfc-editor.org/info/rfc7656>.
[RFC8087] Fairhurst, G. and M. Welzl, "The Benefits of Using
Explicit Congestion Notification (ECN)", RFC 8087,
DOI 10.17487/RFC8087, March 2017,
<https://www.rfc-editor.org/info/rfc8087>.
[RFC8382] Hayes, D., Ed., Ferlin, S., Welzl, M., and K. Hiorth,
"Shared Bottleneck Detection for Coupled Congestion
Control for RTP Media", RFC 8382, DOI 10.17487/RFC8382,
June 2018, <https://www.rfc-editor.org/info/rfc8382>.
[RMCAT-PROPOSALS]
Sarker, Z., Singh, V., Zhu, X., and M. Ramalho, "Test
Cases for Evaluating RMCAT Proposals", Work in Progress,
Internet-Draft, draft-ietf-rmcat-eval-test-10, 23 May
2019, <https://tools.ietf.org/html/draft-ietf-rmcat-eval-
test-10>.
[RTCWEB-OVERVIEW]
Alvestrand, H., "Overview: Real Time Protocols for
Browser-based Applications", Work in Progress, Internet-
Draft, draft-ietf-rtcweb-overview-19, 11 November 2017,
<https://tools.ietf.org/html/draft-ietf-rtcweb-overview-
19>.
[RTCWEB-RTP-USAGE]
Perkins, C., Westerlund, M., and J. Ott, "Web Real-Time
Communication (WebRTC): Media Transport and Use of RTP",
Work in Progress, Internet-Draft, draft-ietf-rtcweb-rtp-
usage-26, 17 March 2016, <https://tools.ietf.org/html/
draft-ietf-rtcweb-rtp-usage-26>.
[TRANSPORT-MULTIPLEX]
Westerlund, M. and C. Perkins, "Multiple RTP Sessions on a
Single Lower-Layer Transport", Work in Progress, Internet-
Draft, draft-westerlund-avtcore-transport-multiplexing-07,
October 2013, <https://tools.ietf.org/html/draft-
westerlund-avtcore-transport-multiplexing-07>.
[WEBRTC-TRANS]
Alvestrand, H., "Transports for WebRTC", Work in Progress,
Internet-Draft, draft-ietf-rtcweb-transports-17, 26
October 2016, <https://tools.ietf.org/html/draft-ietf-
rtcweb-transports-17>.
Appendix A. Application to GCC
Google Congestion Control (GCC) [GCC-RTCWEB] is another congestion
control scheme for RTP flows that is under development. GCC is not
yet finalized, but at the time of this writing, the rate control of
GCC employs two parts: controlling the bandwidth estimate based on
delay and controlling the bandwidth estimate based on loss. Both are
designed to estimate the available bandwidth, A_hat.
When applying the FSE to GCC, the UPDATE function call described in
Section 5.3 gives the FSE GCC's estimate of available bandwidth
A_hat. The recommended algorithm for GCC is the Active FSE in
Section 5.3.1. In step 3 (d) of this algorithm, when the FSE_R(i) is
"sent" to the flow i, A_hat of flow i is updated with the value of
FSE_R(i).
Appendix B. Scheduling
When flows originate from the same host, it would be possible to use
only one sender-side congestion controller that determines the
overall allowed sending rate and then use a local scheduler to assign
a proportion of this rate to each RTP session. This way, priorities
could also be implemented as a function of the scheduler. The
Congestion Manager (CM) [RFC3124] also uses such a scheduling
function.
Appendix C. Example Algorithm - Passive FSE
Active algorithms calculate the rates for all the flows in the FG and
actively distribute them. In a passive algorithm, UPDATE returns a
rate that should be used instead of the rate that the congestion
controller has determined. This can make a passive algorithm easier
to implement; however, when round-trip times of flows are unequal,
flows with shorter RTTs may (depending on the congestion control
algorithm) update and react to the overall FSE state more often than
flows with longer RTTs, which can produce unwanted side effects.
This problem is more significant when the congestion control
convergence depends on the RTT. While the passive algorithm works
better for congestion controls with RTT-independent convergence, it
can still produce oscillations on short time scales. The algorithm
described below is therefore considered highly experimental and not
safe to deploy outside of testbed environments. Results of a
simplified passive FSE algorithm with both NADA and GCC can be found
in [FSE-NOMS].
In the passive version of the FSE, TLO (Total Leftover Rate) is a
static variable per FG that is initialized to 0. Additionally, S_CR
is limited to increase or decrease as conservatively as a flow's
congestion controller decides in order to prohibit sudden rate jumps.
(1) When a flow f starts, it registers itself with SBD and the FSE.
FSE_R(f) and DR(f) are initialized with the congestion
controller's initial rate. SBD will assign the correct FGI.
When a flow is assigned an FGI, it adds its FSE_R(f) to S_CR.
(2) When a flow f stops or pauses, it sets its DR(f) to 0 and sets
P(f) to -1.
(3) Every time the congestion controller of the flow f determines a
new sending rate CC_R(f), assuming the flow's new desired rate
new_DR(f) to be "infinity" in case of a bulk data transfer with
an unknown maximum rate, the flow calls UPDATE, which carries
out the tasks listed below to derive the flow's new sending
rate, Rate(f). A flow's UPDATE function uses a few local (i.e.,
per-flow) temporary variables, which are all initialized to 0:
DELTA, new_S_CR, and S_P.
(a) For all the flows in its FG (including itself), it
calculates the sum of all the calculated rates, new_S_CR.
Then, it calculates DELTA: the difference between FSE_R(f)
and CC_R(f).
for all flows i in FG do
new_S_CR = new_S_CR + FSE_R(i)
end for
DELTA = CC_R(f) - FSE_R(f)
(b) It updates S_CR, FSE_R(f), and DR(f).
FSE_R(f) = CC_R(f)
if DELTA > 0 then // the flow's rate has increased
S_CR = S_CR + DELTA
else if DELTA < 0 then
S_CR = new_S_CR + DELTA
end if
DR(f) = min(new_DR(f),FSE_R(f))
(c) It calculates the leftover rate TLO, removes the terminated
flows from the FSE, and calculates the sum of all the
priorities, S_P.
for all flows i in FG do
if P(i)<0 then
delete flow
else
S_P = S_P + P(i)
end if
end for
if DR(f) < FSE_R(f) then
TLO = TLO + (P(f)/S_P) * S_CR - DR(f))
end if
(d) It calculates the sending rate, Rate(f).
Rate(f) = min(new_DR(f), (P(f)*S_CR)/S_P + TLO)
if Rate(f) != new_DR(f) and TLO > 0 then
TLO = 0 // f has 'taken' TLO
end if
(e) It updates DR(f) and FSE_R(f) with Rate(f).
if Rate(f) > DR(f) then
DR(f) = Rate(f)
end if
FSE_R(f) = Rate(f)
The goals of the flow algorithm are to achieve prioritization,
improve network utilization in the face of application-limited flows,
and impose limits on the increase behavior such that the negative
impact of multiple flows trying to increase their rate together is
minimized. It does that by assigning a flow a sending rate that may
not be what the flow's congestion controller expected. It therefore
builds on the assumption that no significant inefficiencies arise
from temporary application-limited behavior or from quickly jumping
to a rate that is higher than the congestion controller intended.
How problematic these issues really are depends on the controllers in
use and requires careful per-controller experimentation. The coupled
congestion control mechanism described here also does not require all
controllers to be equal; effects of heterogeneous controllers, or
homogeneous controllers being in different states, are also subject
to experimentation.
This algorithm gives the leftover rate of application-limited flows
to the first flow that updates its sending rate, provided that this
flow needs it all (otherwise, its own leftover rate can be taken by
the next flow that updates its rate). Other policies could be
applied, e.g., to divide the leftover rate of a flow equally among
all other flows in the FGI.
C.1. Example Operation (Passive)
In order to illustrate the operation of the passive coupled
congestion control algorithm, this section presents a toy example of
two flows that use it. Let us assume that both flows traverse a
common 10 Mbit/s bottleneck and use a simplistic congestion
controller that starts out with 1 Mbit/s, increases its rate by 1
Mbit/s in the absence of congestion, and decreases it by 2 Mbit/s in
the presence of congestion. For simplicity, flows are assumed to
always operate in a round-robin fashion. Rate numbers below without
units are assumed to be in Mbit/s. For illustration purposes, the
actual sending rate is also shown for every flow in FSE diagrams even
though it is not really stored in the FSE.
Flow #1 begins. It is a bulk data transfer and considers itself to
have top priority. This is the FSE after the flow algorithm's step
1:
----------------------------------------
| # | FGI | P | FSE_R | DR | Rate |
| | | | | | |
| 1 | 1 | 1 | 1 | 1 | 1 |
----------------------------------------
S_CR = 1, TLO = 0
Its congestion controller gradually increases its rate. Eventually,
at some point, the FSE should look like this:
-----------------------------------------
| # | FGI | P | FSE_R | DR | Rate |
| | | | | | |
| 1 | 1 | 1 | 10 | 10 | 10 |
-----------------------------------------
S_CR = 10, TLO = 0
Now, another flow joins. It is also a bulk data transfer and has a
lower priority (0.5):
------------------------------------------
| # | FGI | P | FSE_R | DR | Rate |
| | | | | | |
| 1 | 1 | 1 | 10 | 10 | 10 |
| 2 | 1 | 0.5 | 1 | 1 | 1 |
------------------------------------------
S_CR = 11, TLO = 0
Now, assume that the first flow updates its rate to 8, because the
total sending rate of 11 exceeds the total capacity. Let us take a
closer look at what happens in step 3 of the flow algorithm.
CC_R(1) = 8. new_DR(1) = infinity.
(3a) new_S_CR = 11; DELTA = 8 - 10 = -2.
(3b) FSE_R(1) = 8. DELTA is negative, hence S_CR = 9; DR(1) = 8
(3c) S_P = 1.5.
(3d) new sending rate Rate(1) = min(infinity, 1/1.5 * 9 + 0) = 6.
(3e) FSE_R(1) = 6.
The resulting FSE looks as follows:
-------------------------------------------
| # | FGI | P | FSE_R | DR | Rate |
| | | | | | |
| 1 | 1 | 1 | 6 | 8 | 6 |
| 2 | 1 | 0.5 | 1 | 1 | 1 |
-------------------------------------------
S_CR = 9, TLO = 0
The effect is that flow #1 is sending with 6 Mbit/s instead of the 8
Mbit/s that the congestion controller derived. Let us now assume
that flow #2 updates its rate. Its congestion controller detects
that the network is not fully saturated (the actual total sending
rate is 6+1=7) and increases its rate.
CC_R(2) = 2. new_DR(2) = infinity.
(3a) new_S_CR = 7; DELTA = 2 - 1 = 1.
(3b) FSE_R(2) = 2. DELTA is positive, hence S_CR = 9 + 1 = 10;
DR(2) = 2.
(3c) S_P = 1.5.
(3d) Rate(2) = min(infinity, 0.5/1.5 * 10 + 0) = 3.33.
(3e) DR(2) = FSE_R(2) = 3.33.
The resulting FSE looks as follows:
-------------------------------------------
| # | FGI | P | FSE_R | DR | Rate |
| | | | | | |
| 1 | 1 | 1 | 6 | 8 | 6 |
| 2 | 1 | 0.5 | 3.33 | 3.33 | 3.33 |
-------------------------------------------
S_CR = 10, TLO = 0
The effect is that flow #2 is now sending with 3.33 Mbit/s, which is
close to half of the rate of flow #1 and leads to a total utilization
of 6(#1) + 3.33(#2) = 9.33 Mbit/s. Flow #2's congestion controller
has increased its rate faster than the controller actually expected.
Now, flow #1 updates its rate. Its congestion controller detects
that the network is not fully saturated and increases its rate.
Additionally, the application feeding into flow #1 limits the flow's
sending rate to at most 2 Mbit/s.
CC_R(1) = 7. new_DR(1) = 2.
(3a) new_S_CR = 9.33; DELTA = 1.
(3b) FSE_R(1) = 7, DELTA is positive, hence S_CR = 10 + 1 = 11;
DR(1) = min(2, 7) = 2.
(3c) S_P = 1.5; DR(1) < FSE_R(1), hence TLO = 1/1.5 * 11 - 2 = 5.33.
(3d) Rate(1) = min(2, 1/1.5 * 11 + 5.33) = 2.
(3e) FSE_R(1) = 2.
The resulting FSE looks as follows:
-------------------------------------------
| # | FGI | P | FSE_R | DR | Rate |
| | | | | | |
| 1 | 1 | 1 | 2 | 2 | 2 |
| 2 | 1 | 0.5 | 3.33 | 3.33 | 3.33 |
-------------------------------------------
S_CR = 11, TLO = 5.33
Now, the total rate of the two flows is 2 + 3.33 = 5.33 Mbit/s, i.e.,
the network is significantly underutilized due to the limitation of
flow #1. Flow #2 updates its rate. Its congestion controller
detects that the network is not fully saturated and increases its
rate.
CC_R(2) = 4.33. new_DR(2) = infinity.
(3a) new_S_CR = 5.33; DELTA = 1.
(3b) FSE_R(2) = 4.33. DELTA is positive, hence S_CR = 12; DR(2) =
4.33.
(3c) S_P = 1.5.
(3d) Rate(2) = min(infinity, 0.5/1.5 * 12 + 5.33 ) = 9.33.
(3e) FSE_R(2) = 9.33, DR(2) = 9.33.
The resulting FSE looks as follows:
-------------------------------------------
| # | FGI | P | FSE_R | DR | Rate |
| | | | | | |
| 1 | 1 | 1 | 2 | 2 | 2 |
| 2 | 1 | 0.5 | 9.33 | 9.33 | 9.33 |
-------------------------------------------
S_CR = 12, TLO = 0
Now, the total rate of the two flows is 2 + 9.33 = 11.33 Mbit/s.
Finally, flow #1 terminates. It sets P(1) to -1 and DR(1) to 0. Let
us assume that it terminated late enough for flow #2 to still
experience the network in a congested state, i.e., flow #2 decreases
its rate in the next iteration.
CC_R(2) = 7.33. new_DR(2) = infinity.
(3a) new_S_CR = 11.33; DELTA = -2.
(3b) FSE_R(2) = 7.33. DELTA is negative, hence S_CR = 9.33; DR(2) =
7.33.
(3c) Flow 1 has P(1) = -1, hence it is deleted from the FSE. S_P =
0.5.
(3d) Rate(2) = min(infinity, 0.5/0.5*9.33 + 0) = 9.33.
(3e) FSE_R(2) = DR(2) = 9.33.
The resulting FSE looks as follows:
-------------------------------------------
| # | FGI | P | FSE_R | DR | Rate |
| | | | | | |
| 2 | 1 | 0.5 | 9.33 | 9.33 | 9.33 |
-------------------------------------------
S_CR = 9.33, TLO = 0
Acknowledgements
This document benefited from discussions with and feedback from
Andreas Petlund, Anna Brunstrom, Colin Perkins, David Hayes, David
Ros (who also gave the FSE its name), Ingemar Johansson, Karen
Nielsen, Kristian Hiorth, Martin Stiemerling, Mirja Kühlewind,
Spencer Dawkins, Varun Singh, Xiaoqing Zhu, and Zaheduzzaman Sarker.
The authors would like to especially thank Xiaoqing Zhu and Stefan
Holmer for helping with NADA and GCC, and Anna Brunstrom as well as
Julius Flohr for helping us correct the active algorithm for the case
of application-limited flows.
This work was partially funded by the European Community under its
Seventh Framework Program through the Reducing Internet Transport
Latency (RITE) project (ICT-317700).
Authors' Addresses
Safiqul Islam
University of Oslo
PO Box 1080 Blindern
N-0316 Oslo
Norway
Phone: +47 22 84 08 37
Email: safiquli@ifi.uio.no
Michael Welzl
University of Oslo
PO Box 1080 Blindern
N-0316 Oslo
Norway
Phone: +47 22 85 24 20
Email: michawe@ifi.uio.no
Stein Gjessing
University of Oslo
PO Box 1080 Blindern
N-0316 Oslo
Norway
Phone: +47 22 85 24 44
Email: steing@ifi.uio.no