Transcript Modeling the Effect of a Rate Smoother on TCP Congestion Control Behavior Kang Li, Jonathan Walpole, David C.

Modeling the Effect of a Rate Smoother on TCP
Congestion Control Behavior
Kang Li, Jonathan Walpole, David C. Steere
{kangli, walpole, steere}@cse.ogi.edu
Department of Computer Science and
Engineering
Oregon Graduate Institute
Molly H. Shor
[email protected]
Department of Electrical and Computer
Engineering
Oregon State University
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Well-known Behaviors of TCP Congestion Control
Sender
Data
Packets
TCP
Network
Receiver
Acknowledgment
Packets
TCP
Transmission
Rate
45
40
35
30
25
20
15
10
5
0
Available bandwidth
20
50
• The sawtooth figure for an
individual TCP
Time
• The phase plot for 2
competing TCPs
2
Trajectories of Various TCP-Friendly Congestion
Controls Competing with a TCP
A: TCP-friendliness by Varying
TCP AIMD Parameters
B:TCP-friendliness by Damping
TCP’s Rate Variations
C: An Arbitrary Trajectory that Tracks
Around the Fair Share Point
• There exists many limit cycles that oscillate around the equal fair sharing point
• However, we have assumed all the competing flows back off together.
– If the assumption is false, they may experience different congestion signals.
– Temporary rate mismatches may lead to non-uniform losses across flows;
– Different network buffering states may affect the timing of packet losses.
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Modeling Temporary Rate Mismatch
Rate Smoother
Buffer Fill-level
Rate Adjustment
Pacing Control
Sending Rate
Calculated by TCP
+B/2
0
-B/2
“Smoothed” Output
Forward and Wait
Mismatch window
(a virtual Buffer)
TCP with a Rate Smoother Component
• We add a rate smoother to TCP to control the rate mismatch:
– The pacing period and other control parameters can be tuned.
– Many existed and new pacing and smoothing algorithms can be simulated.
– By tracking a TCP’s throughput, the rate smoother provides an
implementation of an Equation-Based TCP-friendly Congestion Control.
• To study the effect of smoothing on TCP, we built a Matlab simulation
and a Linux-based implementation.
4
Rate Smoother
Simulation in Matlab
• Smoothing is simulated based on
the following equations:
r (t )  rout (t )  rin (t )
rout (t )  P * r (t )  I *  r (t )  D *
Pacing
Control
dr (t )
dt
• TCP congestion avoidance is
simulated by:
– When no congestion signal
dr(t )  * MSS

dt
RTT 2 (t )
TCP AIMD
dRTT (t ) r (t )  R

dt
R
– When congestion signal arrives
r (t )   * r (t )
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Simulation of Two TCPs (one with rate smoother)
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Simulation Results
(1) System Plot under Uniform Packet Losses
A
•
•
B
Uniform Losses – The same congestion signal for all TCP flows.
The system trajectory converges to a limit cycle that oscillates around the equal
bandwidth sharing point. (Figure A)
– Same phase plot as Figure 3-B with an additional dimension for buffer fill-level.
•
The rate produced by AIMD algorithm is used as the input to the rate smoother.
(Figure B)
– An alternative would be to use the TCP throughput equation as a function of congestion
signals as the input to the rate smoother.
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Simulation Results
(2) The Impact of Non-Uniform Packet Losses
A
•
•
B
Non-Uniform Losses – Rate-dependent congestion signal for each TCP flow.
Bandwidth Sharing Ratios depend on loss distributions.
– Figures A and B show the backing-off probability and average throughput ratio for a
set of loss distribution models in which a TCP’s backing-off probability P is a
function of its current transmission rate r : P(r )  1  a * eb*r
– The ratio is close to 1 when the distribution is proportional to the rate (b=1/100) or
when it is close to a uniform distribution (b=10).
•
Next step: simulate feedback between loss distributions and rate mismatches.
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Conclusion & Future Work
• Conclusion
– No big conclusion yet,
– Feedback control based conceptual model and simulation tools lead
to clear understanding of TCP congestion control behavior.
– Developed a generic model and implementation of Rate Smoothing
based on feedback control.
• Future Work
–
–
–
–
Simulate feedback between loss distributions and rate mismatches.
Combine the model with some realistic loss event distributions.
Extend model from a continuous to a hybrid event-driven system.
Build a tunable paced TCP implementation that exposes smoothing
control parameters to applications.
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