EquationBased Congestion Control for Unicast Applications Sally Floyd, Mark Handley, Jitendra Padhye

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Equation-Based Congestion Control for Unicast
ApplicationsSally Floyd, Mark Handley, Jitendra
Padhye and Jörg Widmer

• Presented by Ankur Upadhyaya for CPSC 538A

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Presentation Outline

• PART 1 TCP Congestion Control
• PART 2 Equation-Based Congestion Control with
  TFRC
• PART 3 Evaluating TFRC
• PART 4 Related and Future Work
• PART 5 Discussion

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PART 1 TCP Congestion Control
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What is congestion?

• If several packets arriving at a router contend
  for an outgoing link, some must be queued in a
  buffer.

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What is congestion?

• When the router buffer fills, new packets must be
  dropped. When such losses become common,
  congestion is said to occur.

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How is congestion handled on the Internet?

• The predominant approach is the congestion
  control mechanism employed by TCP.
• Initially, however, TCP implementations did not
  address the problem of congestion.
• The result was the frequent congestion collapse
  of the Internet.

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How is congestion handled on the Internet?

• The current stability of the Internet depends on
  TCPs end-to-end congestion control.
• Note that TCP has many different implementations
  (e.g. TCP Vegas, TCP Reno, TCP Tahoe, Sack TCP,
  etc.). While implementing the same protocol,
  these can still have substantially different
  behaviors.

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How does TCP congestion control work?

• TCP congestion control consists of three
  mechanisms
• (1) Additive Increase/Multiplicative Decrease
  (AIMD)
• (2) Slowstart
• (3) Fast Retransmit and Fast Recovery

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What is AIMD?

• Number of packets a sender can put on the network
  is limited not only by the advertised send window
  but also by a congestion window.
• Slowly (additively) increase the congestion
  window size as long as there is no congestion.
  Typically, the congestion window is increased by
  one packet for each window sent without a packet
  drop.
• Quickly (multiplicatively) decrease the
  congestion window size once congestion is
  detected. Typically, the congestion window is
  halved for each window containing a packet drop.

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What is AIMD?

• AIMD results in the saw-tooth pattern shown
  below in the plot of sending rate versus time.

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What is slowstart?

• The slow linear growth provided by additive
  increase is inappropriate when we are ramping up
  a TCP connection from a standing start.
• Slowstart increases the sending rate
  exponentially by putting two packets on the
  network for each acknowledgement received.

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Fast Retransmit and Fast Recovery

• Fast retransmit and fast recovery are performance
  optimizations not directly relevant to our
  discussion of TFRC.

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Fast Retransmit and Fast Recovery

• Fast recovery work by always sending the ACK for
  the last packet received in a contiguous
  sequence. This way the source can use repeated
  ACKs to infer a probable loss and immediately
  retransmit, rather than waiting for an explicit
  timeout.

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Fast Retransmit and Fast Recovery

• When AIMD and slow start were originally
  combined, the congestion window size was always
  dropped to its minimum value upon detecting
  congestion.
• Slow start was then used to raise the sending
  rate to one-half the window size at which
  congestion was detected. Additive increase was
  used from there.
• Fast recovery uses ACKs already in the pipe to
  allow us to drop directly to one-half the window
  size at which congestion was detected.
  Essentially, we skip the slow start in the step
  above.

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What is wrong with TCP?

• TCPs AIMD results in an unstable sending rate.
  This is unsuitable for some real-time
  applications.
• TCP congestion control is coupled with a service
  model that is too rich for many real-time
  applications. It can be argued, for example,
  that retransmission-based reliability is not
  desirable for streaming video.

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PART 2 Equation-Based Congestion Control
with TFRC
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Are we limited to TCP congestion control?

• While the multiplicative decrease of ½ used by
  TCP is sufficient to control congestion it is not
  necessarily a requirement. In fact, a less
  aggressive and more stable factor of 7/8 is
  suitable.
• This opens the door to consideration of
  mechanisms not based on AIMD and so, possibly
  more stable.
• One such mechanism is equation-based congestion
  control.

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What is equation-based congestion control?

• In equation-based congestion control, the maximum
  acceptable sending rate is given by a control
  equation.
• The control equation expresses the maximum
  acceptable sending rate as a function of
  parameters that (1) are derived from receiver
  feedback and (2) characterize the level of
  network congestion.
• The sender adapts to ensure that it is working
  within the maximum acceptable rate.

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What is TFRC?

• TFRC stands for TCP Friendly Rate Control.
• TFRC is a best-effort transport protocol for
  unicast traffic that employs equation-based
  congestion control.
• The point of TFRC is to provide a more smoothly
  changing sending rate while still remaining
  responsive to network congestion. It achieves
  this albeit at the cost of a more moderate
  response to transient changes in the level of
  network congestion.

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Does TFRC play nicely with TCP?

• If TFRC is to be deployed in the general
  Internet, it will have to compete with TCP
  traffic.
• Thus, TFRC must use a control equation that
  ensures TCP-compatible flows. A flow is
  TCP-compatible if in its steady state it uses no
  more bandwidth than a conformant TCP operating
  under comparable conditions.
• If not TCP-compatible, TFRC could starve TCP
  traffic in a FIFO queue.

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Does TFRC play nicely with TCP?

• At the same time TFRC must also be aggressive
  enough to ensure that it is not starved by TCP in
  a FIFO queue.
• In general, acceptable performance can only be
  achieved for multiple flows competing in a FIFO
  queue if they have comparable sending rates.

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What is the TFRC control equation?

• Given the requirement of TCP-compatibility, TFRC
  uses the TCP response function as its control
  equation. This function characterizes the
  steady-state sending rate of TCP as a function of
  the round-trip time and steady-state loss event
  rate.
• Note that a loss event is defined as the dropping
  of one or more packets within a single round-trip
  time.

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What is the TFRC control equation?

• One formulation of the TCP response function is
• This gives an upper bound T on the sending rate
  in bytes/second as a function of the packet size
  s, round-trip time R, steady state loss event
  rate p and the TCP timeout value tRTO.

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How do we compute the control equation parameters?

• Recall that the control equation parameters are
  derived from receiver feedback.
• The receiver sends this feedback at least once
  per RTT, if it received packets in that RTT.
• Note that if the sender fails to receive feedback
  after several RTTs, it begins to slow the sending
  rate and ultimately stops altogether.

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Computing R

• To compute R, the sender labels each packet with
  a sequence number.
• The feedback returned by the receiver contains
  the most recent sequence number received along
  with the time since it was received. From this,
  the sender derives a measurement of R of R.

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Computing R

• The sender updates an exponentially weighted
  moving average (EWMA), SRTT, with R using a
  weight w
• SRTTnew w R ( 1 w ) SRTTold
• SRTT is taken as R in the control equation.

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Computing tRTO

• tRTO is computed as follows, where SRTT is the
  current EWMA of the RTT and RTTvar is the
  variance of the RTT measurements taken thus far
• tRTO 4RTT
• Note that this is exactly how TCP initially seeds
  tRTO.

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Computing tRTO

• It is important to remember that tRTO is only
  relevant as a control equation parameter.
  Because TFRC is unreliable, there are no
  retransmissions taking place!

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Computing p

• p is computed at the receiver and forwarded to
  the sender in periodic feedback.
• The Average Loss Interval method is used to
  compute p.
• A loss interval is defined as the number of
  packets between loss events.

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Computing p

• Average Loss Interval computes a weighted average
  of loss rate over the last n loss intervals, with
  equal weights on the n/2 most recent intervals.
• The loss intervals are numbered i 0, 1, n,
  where s0 is the most recent loss interval and sn
  is the oldest. Note that s0 is not yet
  terminated by a loss.

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Computing p

• Let wi be the weight for si. Note that w0 1.
  The other wi can be calculated as
• Thus, for n 8, w1 through w8 are 1, 1, 1, 1,
  0.8, 0.6, 0.4, 0.2, respectively.

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Computing p

• Now, we compute the average loss interval, s, as
  follows
• s max(s(1,n), s(0,n-1))
• Note that we compute both s(1,n) and s(0,n-1) and
  take a maximum only because s0 is not yet
  terminated by a loss. We only want to count it
  if it is large enough to increase the average.

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Computing p

• A technique called history discounting is applied
  to the Average Loss Interval to ensure a more
  timely response to a sustained decrease in
  congestion.
• In history discounting, the weights given to
  older loss intervals are smoothly discounted once
  s0 has grown to twice the size of the average
  interval.
• Finally, we compute p as 1/s.

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How exactly does TFRC use the control equation?

• Each time the receiver sends feedback, the sender
  will update the parameters R, tRTO and p and
  compute a new value of T.
• If the current sending rate, Tactual is less than
  T, then sender will raise Tactual as close to T
  as possible, thereby exploiting the available
  network capacity.
• If Tactual is more than T, then the sender will
  reduce Tactual until it is less than or equal to
  T.

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How does this achieve smoother congestion control?

• Use of the Average Loss Interval method to
  compute p ensures that this parameter is
  insulated from any abrupt changes.
• Given the control equation, it is clear that T,
  as a function of p, is similarly smooth.
• As T governs the sending rate, the desired
  stability is achieved.

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How does this achieve smoother congestion control?

• In fact, it has been shown that with a fixed RTT
  and no history discounting, TFRCs sending rate
  increases by at most 0.14 packets/RTT. Once
  history discounting is applied, this goes up to
  0.22 packets/RTT.
• It has also been shown that TFRC requires from 4
  to 8 RTTs to halve its sending rate in response
  to persistent congestion.

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How does this achieve smoother congestion control?

• This smoothness is illustrated in the figure
  below. Note that here, the loss rate is 1
  before time 6, 10 until time 9 and 0.5
  thereafter.

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Some key refinements to TFRC

• (1) Controlling inter-packet spacing to improve
  stability.
• (2) Implementing slowstart for TFRC.

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Controlling Inter-Packet Spacing to Improve
Stability

• If the EWMA weight for R is too high, TFRC reacts
  strongly to changes in RTT. This can lead to
  oscillations if we have a small number of TFRC
  flows sharing a high-bandwidth link. Why?
• If the EMWA weight for R is too low, short term
  oscillations in sending rate can arise. Why?
• It turns out that these problems can be solved by
  appropriately setting the inter-packet spacing
  (i.e. the time in which a single packet is sent).

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Controlling Inter-Packet Spacing to Improve
Stability

• The inter-packet spacing is set as in the
  equation below.
• Here R0 is the most recent RTT sample and M is
  the average of the square-roots of the RTTs,
  calculated using the EWMA using the same constant
  used to calculate the mean RTT.

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Controlling Inter-Packet Spacing to Improve
Stability

• The smoothing effect of this optimization can be
  seen in the graphs below.

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Implementing Slowstart for TFRC

• In TCPs slowstart, the send rates overshoot of
  the network capacity is limited to twice the
  network capacity by only putting out two packets
  onto the network for each ACK received.
• Because TFRC does not use explicit ACKs, however,
  it does not have this natural self-limiting
  property.

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Implementing Slowstart for TFRC

• TFRC solves this problem by having the receiver
  feed back the rate at which packets arrived
  during the last measured RTT. If a loss
  occurred, slowstart is stopped. Otherwise, the
  send rate is set to

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PART 3 Evaluating TFRC
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Does TFRC work?

• To demonstrate the feasibility of large-scale
  deployment of TFRC you would need to
• (1) Show that it performs well in isolation.
• (2) Show that it performs well over a wide range
  of network conditions.
• (3) Show that it coexists well with many kinds
  of TCP traffic of different flavors.

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Does TFRC work?

• Based on extensive tests on the Internet, the
  Dummynet network emulator and the ns network
  simulator, TFRC meets these three conditions
  remarkably well.

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Does TFRC work?

• The graphs below illustrate this coexistence and
  smoothness.

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Some Important Metrics

• Two important metrics used in the performance
  evaluation of TFRC are
• (1) Coefficient of Variance (CoV)
• (2) Equivalence (ed,a,b(t))

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Coefficient of Variance (CoV)

• The Coefficient of Variance is simply the
  standard deviation divided by the mean for a
  particular set of measurements.
• This serves as a normalized measure of
  variability.

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Equivalence (ed,a,b(t))

• Define the send rate Rd,F(t) of a data flow F
  using s-byte packets at time t at a timescale d
  as

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Equivalence (ed,a,b(t))

• Then, the equivalence ed,a,b(t) can be used to
  compare the send rates of two flows a and b at
  time t with timescale d. Note that by using a
  minimum we get a value normalized between 0 and
  1.

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PART 4 Related and Future Work
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Related Work

• Active Queue Management is an area in which
  congestion is dealt with by first anticipating it
  at the bottleneck routers. Random Early
  Detection (RED) is a predominant example of this
  approach.
• Explicit Congestion Notification is an approach
  to congestion control in which flows are directly
  informed of congestion by bottleneck routers.

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Related Work

• Congestion control for multicast is an active
  area of research. Bullet is an example of a
  multicast system that attempts to address
  congestion.
• There are numerous equation-based and
  non-equation based protocols that address
  congestion (e.g. RAP, TEAR, SCTP, DCCP, binomial
  congestion control, TFRCP, etc.).

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Future Work

• Researching how the choice of control equation
  and parameters can be placed on a more rigorous
  foundation. Some contend that control theory may
  be relevant in this regard (e.g. XCP).
• Application of equation-based technique to
  multicast congestion control.
• Explore performance of equation-based techniques
  in an environment with Explicit Congestion
  Notification (ECN).

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Future Work

• Study of duplex traffic under TFRC.

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PART 5 Discussion
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Discussion

• What were the main contributions of the work?
• What were the advantages and disadvantages of the
  approach?
• How does it compare to the other papers and
  related work?
• What are potential avenues for improvement?