| Network Working Group | H. Lundin |
| Internet-Draft | S. Holmer |
| Intended status: Informational | H. T. Alvestrand, Ed. |
| Expires: February 20, 2014 | |
| August 19, 2013 |
A Google Congestion Control Algorithm for Real-Time Communication
draft-alvestrand-rmcat-congestion-01
This document describes two methods of congestion control when using real-time communications on the World Wide Web (RTCWEB); one sender-based and one receiver-based.
It is published as an input document to the RMCAT working group on congestion control for media streams. The mailing list of that WG is rmcat@ietf.org.
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].
This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79.
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Congestion control is a requirement for all applications that wish to share the Internet [RFC2914].
The problem of doing congestion control for real-time media is made difficult for a number of reasons:
This memo describes two congestion control algorithms that together are seen to give reasonable performance and reasonable (not perfect) bandwidth sharing with other conferences and with TCP-using applications that share the same links.
The signaling used consists of standard RTP timestamps [RFC3550] possibly augmented with RTP transmission time offsets [RFC5450], standard RTCP feedback reports and Temporary Maximum Media Stream Bit Rate Requests (TMMBR) as defined in [RFC5104] section 3.5.4, or by using the REMB feedback report defined in [I-D.alvestrand-rmcat-remb]
The mathematics of this document have been transcribed from a more formula-friendly format.
The following notational conventions are used:
The following elements are in the system:
Together, sender side control and receiver side control implement the congestion control algorithm.
The receive-side algorithm can be further decomposed into four parts: an RTP timestamp to NTP time conversion, arrival-time filter, an over-use detector, and a remote rate-control.
It is common that multiple RTP streams are sent from the sender to the receiver. In such a situation the RTP timestamps of incoming can first be converted to a common time base using the RTP timestamp and NTP time pairs in RTCP SR reports[RFC3550]. The converted timestamps can then be used instead of RTP timestamps in the arrival-time filtering, and since all streams from the same sender have timestamps in the same time base they can all be processed by the same filter. This has the advantage of quicker reactions and reduces problems of noisy measurements due to self-inflicted cross-traffic.
In the time interval from the start of the call until a stream from the same sender has received an RTCP SR report, the receiver-side control operates in single-stream mode. In that mode only one RTP stream can be processed by the over-use detector. As soon as a stream has received one or more RTCP SR reports the receiver-side control can change to a multi-stream mode, where all RTP streams from the same sender which have received one or more RTCP SR reports can be processed by the over-use detector. When switching to the multi-stream mode the state of the over-use detector must be modified to avoid a time base mismatch. This can either be done by resetting the stored RTP timestamp values or by converting them using the newly received RTCP SR report.
This section describes an adaptive filter that continuously updates estimates of network parameters based on the timing of the received frames.
At the receiving side we are observing groups of incoming packets, where each group of packets corresponding to the same frame having timestamp T(i).
Each frame is assigned a receive time t(i), which corresponds to the time at which the whole frame has been received (ignoring any packet losses). A frame is delayed relative to its predecessor if t(i)-t(i-1)>T(i)-T(i-1), i.e., if the arrival time difference is larger than the timestamp difference.
We define the (relative) inter-arrival time, d(i) as
d(i) = t(i)-t(i-1)-(T(i)-T(i-1))
Since the time ts to send a frame of size L over a path with a capacity of C is roughly
ts = L/C
we can model the inter-arrival time as
L(i)-L(i-1)
d(i) = -------------- + w(i) = dL(i)/C+w(i)
C
Here, w(i) is a sample from a stochastic process W, which is a function of the capacity C, the current cross traffic X(i), and the current send bit rate R(i). We model W as a white Gaussian process. If we are over-using the channel we expect w(i) to increase, and if a queue on the network path is being emptied, w(i) will decrease; otherwise the mean of w(i) will be zero.
Breaking out the mean m(i) from w(i) to make the process zero mean, we get
Equation 5
d(i) = dL(i)/C + m(i) + v(i)
This is our fundamental model, where we take into account that a large frame needs more time to traverse the link than a small frame, thus arriving with higher relative delay. The noise term represents network jitter and other delay effects not captured by the model.
When graphing the values for d(i) versus dL(i) on a scatterplot, we find that most samples cluster around the center, and the outliers are clustered along a line with average slope 1/C and zero offset.
For instance, when using a regular video codec, most frames are roughly the same size after encoding (the central “cloud”); the exceptions are I-frames (or key frames) which are typically much larger than the average causing positive outliers (the I-frame itself) and negative outliers (the frame after an I-frame) on the dL axis. Audio frames on the other hand often consist of single packets of equal size, and an audio-only media stream would have its frames scattered at dL = 0.
The parameters d(i) and dL(i) are readily available for each frame i > 1, and we want to estimate C(i) and m(i) and use those estimates to detect whether or not we are over-using the bandwidth currently available. These parameters are easily estimated by any adaptive filter – we are using the Kalman filter.
Let
theta_bar(i) = [1/C(i) m(i)]^T
and call it the state of time i. We model the state evolution from time i to time i+1 as
theta_bar(i+1) = theta_bar(i) + u_bar(i)
where u_bar(i) is the zero mean white Gaussian process noise with covariance
Equation 7
Q(i) = E{u_bar(i) u_bar(i)^T}
Given equation 5 we get
Equation 8
d(i) = h_bar(i)^T theta_bar(i) + v(i) h_bar(i) = [dL(i) 1]^T
where v(i) is zero mean white Gaussian measurement noise with variance var_v = sigma(v,i)^2
The Kalman filter recursively updates our estimate
theta_hat(i) = [1/C_hat(i) m_hat(i)]^T
as
z(i) = d(i) - h_bar(i)^T * theta_hat(i-1)
theta_hat(i) = theta_hat(i-1) + z(i) * k_bar(i)
E(i-1) * h_bar(i)
k_bar(i) = --------------------------------------------
var_v_hat + h_bar(i)^T * E(i-1) * h_bar(i)
E(i) = (I - K_bar(i) * h_bar(i)^T) * E(i-1) + Q(i)
I is the 2-by-2 identity matrix.
The variance var_v = sigma(v,i)^2 is estimated using an exponential averaging filter, modified for variable sampling rate
var_v_hat = beta*sigma(v,i-1)^2 + (1-beta)*z(i)^2 beta = (1-alpha)^(30/(1000 * f_max))
where f_max = max {1/(T(j) - T(j-1))} for j in i-K+1...i is the highest rate at which frames have been captured by the camera the last K frames and alpha is a filter coefficient typically chosen as a number in the interval [0.1, 0.001]. Since our assumption that v(i) should be zero mean WGN is less accurate in some cases, we have introduced an additional outlier filter around the updates of var_v_hat. If z(i) > 3 var_v_hat the filter is updated with 3 sqrt(var_v_hat) rather than z(i). For instance v(i) will not be white in situations where packets are sent at a higher rate than the channel capacity, in which case they will be queued behind each other. In a similar way, Q(i) is chosen as a diagonal matrix with main diagonal elements given by
diag(Q(i)) = 30/(1000 * f_max)[10^-10 10^-2]^T
It is necessary to scale these filter parameters with the frame rate to make the detector respond as quickly at low frame rates as at high frame rates.
The offset estimate m(i) is compared with a threshold gamma_1. An estimate above the threshold is considered as an indication of over-use. Such an indication is not enough for the detector to signal over-use to the rate control subsystem. Not until over-use has been detected for at least gamma_2 milliseconds and at least gamma_3 frames, a definitive over-use will be signaled. However, if the offset estimate m(i) was decreased in the last update, over-use will not be signaled even if all the above conditions are met. Similarly, the opposite state, under-use, is detected when m(i) < -gamma_1. If neither over-use nor under-use is detected, the detector will be in the normal state.
The rate control at the receiving side is designed to increase the receive-side estimate of the available bandwidth A_hat as long as the detected state is normal. Doing that assures that we, sooner or later, will reach the available bandwidth of the channel and detect an over-use.
As soon as over-use has been detected the receive-side estimate of the available bandwidth is decreased. In this way we get a recursive and adaptive estimate of the available bandwidth.
In this document we make the assumption that the rate control subsystem is executed periodically and that this period is constant.
The rate control subsystem has 3 states: Increase, Decrease and Hold. "Increase" is the state when no congestion is detected; "Decrease" is the state where congestion is detected, and "Hold" is a state that waits until built-up queues have drained before going to "increase" state.
The state transitions (with blank fields meaning "remain in state") are:
State ----> | Hold |Increase |Decrease Signal----------------------------------------- v | | | Over-use | Decrease |Decrease | ----------------------------------------------- Normal | Increase | |Hold ----------------------------------------------- Under-use | |Hold |Hold -----------------------------------------------
The subsystem starts in the increase state, where it will stay until over-use or under-use has been detected by the detector subsystem. On every update the receive-side estimate of the available bandwidth is increased with a factor which is a function of the global system response time and the estimated measurement noise variance var_v_hat. The global system response time is the time from an increase that causes over-use until that over-use can be detected by the over-use detector. The variance var_v_hat affects how responsive the Kalman filter is, and is thus used as an indicator of the delay inflicted by the Kalman filter.
A_hat(i) = eta*A_hat(i-1)
1.001+B
eta(RTT, var_v_hat) = ------------------------------------------
1+e^(b(d*RTT - (c1 * var_v_hat + c2)))
Here, B, b, d, c1 and c2 are design parameters.
Since the system depends on over-using the channel to verify the current available bandwidth estimate, we must make sure that our estimate doesn't diverge from the rate at which the sender is actually sending. Thus, if the sender is unable to produce a bit stream with the bit rate the receiver is asking for, the available bandwidth estimate must stay within a given bound. Therefore we introduce a threshold
A_hat(i) < 1.5 * R_hat(i)
where R_hat(i) is the incoming bit rate measured over a T seconds window:
R_hat(i) = 1/T * sum(L(j)) for j from 1 to N(i)
N(i) is the number of frames received the past T seconds and L(j) is the payload size of frame j. Ideally T should be chosen to match the rate controller at the sender. A window between 0.5 and 1 second is recommended.
When an over-use is detected the system transitions to the decrease state, where the receive-side available bandwidth estimate is decreased to a factor times the currently incoming bit rate.
A_hat(i) = alpha*R_hat(i)
alpha is typically chosen to be in the interval [0.8, 0.95].
When the detector signals under-use to the rate control subsystem, we know that queues in the network path are being emptied, indicating that our available bandwidth estimate is lower than the actual available bandwidth. Upon that signal the rate control subsystem will enter the hold state, where the receive-side available bandwidth estimate will be held constant while waiting for the queues to stabilize at a lower level – a way of keeping the delay as low as possible. This decrease of delay is wanted, and expected, immediately after the estimate has been reduced due to over-use, but can also happen if the cross traffic over some links is reduced. In either case we want to measure the highest incoming rate during the under-use interval:
R_max = max{R_hat(i)} for i in 1..K
where K is the number of frames of under-use before returning to the normal state. R_max is a measure of the actual bandwidth available and is a good guess of what bit rate the sender should be able to transmit at. Therefore the receive-side available bandwidth estimate will be set to R_max when we transition from the hold state to the increase state.
One design decision is when to send rate control messages. The time from a change in congestion to the sending of the feedback message is a limitation on how fast the sender can react. Sending too many messages giving no new information is a waste of bandwidth - but in the case of severe congestion, feedback messages can be lost, resulting in a failure to react in a timely manner.
The conclusion is that feedback messages should be sent on a "heartbeat" schedule, allowing the sender side control to react to missing feedback messages by reducing its send rate, but they should also be sent whenever the estimated bandwidth value has changed significantly, without waiting for the heartbeat time, up to some limiting upper bound on the send rate.
The minimum interval is named t_min_fb_interval.
The maximum interval is named t_max_fb_interval.
The permissible values of these intervals will be bounded by the RTP session's RTCP bandwidth and its rtcp_frr setting.
[TODO: Get some example values for these timers]
An additional congestion controller resides at the sending side. It bases its decisions on the round-trip time, packet loss and available bandwidth estimates transmitted from the receiving side.
The available bandwidth estimates produced by the receiving side are only reliable when the size of the queues along the channel are large enough. If the queues are very short, over-use will only be visible through packet losses, which aren't used by the receiving side algorithm.
This algorithm is run every time a receive report arrives at the sender, which will happen no more often than t_min_fb_interval, and no less often than t_max_fb_interval. If no receive report is received within 2x t_max_fb_interval (indicating at least 2 lost feedback reports), the algorithm will take action as if all packets in the interval have been lost, resulting in a halving of the send rate.
The new send-side estimate is limited by the TCP Friendly Rate Control formula [RFC3448] and the receive-side estimate of the available bandwidth A(i):
8 s
As(i) >= ----------------------------------------------------------
R*sqrt(2*b*p/3) + (t_RTO*(3*sqrt(3*b*p/8) * p * (1+32*p^2)))
As(i) <= A(i)
where b is the number of packets acknowledged by a single TCP acknowledgment (set to 1 per TFRC recommendations), t_RTO is the TCP retransmission timeout value in seconds (set to 4*R) and s is the average packet size in bytes. R is the round-trip time in seconds.
(The multiplication by 8 comes because TFRC is computing bandwidth in bytes, while this document computes bandwidth in bits.)
In words: The sender-side estimate will never be larger than the receiver-side estimate, and will never be lower than the estimate from the TFRC formula.
We motivate the packet loss thresholds by noting that if the transmission channel has a small amount of packet loss due to over-use, that amount will soon increase if the sender does not adjust his bit rate. Therefore we will soon enough reach above the 10 % threshold and adjust As(i). However if the packet loss rate does not increase, the losses are probably not related to self-induced channel over-use and therefore we should not react on them.
There are three scenarios of interest, and one included for reference
In the case where both parties implement the algorithms, we expect to see most of the congestion control response to slowly varying conditions happen by TMMBR/REMB messages from recipient to sender. At most times, the sender will send less than the congestion-inducing bandwidth limit C, and when he sends more, congestion will be detected before packets are lost.
If sudden changes happen, packets will be lost, and the sender side control will trigger, limiting traffic until the congestion becomes low enough that the system switches back to the receiver-controlled state.
In the case where sender only implements, we expect to see somewhat higher loss rates and delays, but the system will still be overall TCP friendly and self-adjusting; the governing term in the calculation will be the TFRC formula.
In the case where recipient implements this algorithm and sender does not, congestion will be avoided for slow changes as long as the sender understands and obeys TMMBR/REMB; there will be no backoff for packet-loss-inducing changes in capacity. Given that some kind of congestion control is mandatory for the sender according to the TMMBR spec, this case has to be reevaluated against the specific congestion control implemented by the sender.
This algorithm has been implemented in the open-source WebRTC project.
This draft is offered as input to the congestion control discussion.
Work that can be done on this basis includes:
These are matters for further work; since some of them involve extensions that have not yet been standardized, this could take some time.
This document makes no request of IANA.
Note to RFC Editor: this section may be removed on publication as an RFC.
An attacker with the ability to insert or remove messages on the connection will, of course, have the ability to mess up rate control, causing people to send either too fast or too slow, and causing congestion.
In this case, the control information is carried inside RTP, and can be protected against modification or message insertion using SRTP, just as for the media. Given that timestamps are carried in the RTP header, which is not encrypted, this is not protected against disclosure, but it seems hard to mount an attack based on timing information only.
Thanks to Randell Jesup, Magnus Westerlund, Varun Singh, Tim Panton, Soo-Hyun Choo, Jim Gettys, Ingemar Johansson, Michael Welzl and others for providing valuable feedback on earlier versions of this draft.
| [RFC2119] | Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, March 1997. |
| [RFC3448] | Handley, M., Floyd, S., Padhye, J. and J. Widmer, "TCP Friendly Rate Control (TFRC): Protocol Specification", RFC 3448, January 2003. |
| [RFC3550] | Schulzrinne, H., Casner, S., Frederick, R. and V. Jacobson, "RTP: A Transport Protocol for Real-Time Applications", STD 64, RFC 3550, July 2003. |
| [RFC5104] | Wenger, S., Chandra, U., Westerlund, M. and B. Burman, "Codec Control Messages in the RTP Audio-Visual Profile with Feedback (AVPF)", RFC 5104, February 2008. |
| [RFC5450] | Singer, D. and H. Desineni, "Transmission Time Offsets in RTP Streams", RFC 5450, March 2009. |
| [I-D.alvestrand-rmcat-remb] | Alvestrand, H., "RTCP message for Receiver Estimated Maximum Bitrate", Internet-Draft draft-alvestrand-rmcat-remb-02, January 2013. |
| [I-D.gharai-avtcore-rtp-tfrc] | Gharai, L. and C. Perkins, "RTP with TCP Friendly Rate Control", Internet-Draft draft-gharai-avtcore-rtp-tfrc-01, September 2011. |
| [RFC2914] | Floyd, S., "Congestion Control Principles", BCP 41, RFC 2914, September 2000. |
Renamed draft to link the draft name to the RMCAT WG.
Spellcheck. Otherwise no changes, this is a "keepalive" release.