1 Modeling the Effect of a Rate Smoother on TCP Congestion Control Behavior Kang Li, Jonathan Walpole, David C. Steere {kangli, walpole, Department of Computer Science and Engineering Oregon Graduate Institute Molly H. Shor Department of Electrical and Computer Engineering Oregon State University

2 Sender Receiver TCP Data Packets Network Acknowledgment Packets Well-known Behaviors of TCP Congestion Control Time TCP Transmission Rate Available bandwidth The phase plot for 2 competing TCPs The sawtooth figure for an individual TCP

3 Trajectories of Various TCP-Friendly Congestion Controls Competing with a TCP 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. 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

4 Modeling Temporary Rate Mismatch Forward and Wait Sending Rate Calculated by TCP “Smoothed” Output Pacing Control Rate Smoother Mismatch window (a virtual Buffer) Buffer Fill-levelRate Adjustment 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. TCP with a Rate Smoother Component 0+B/2-B/2

5 Smoothing is simulated based on the following equations: TCP congestion avoidance is simulated by: –When no congestion signal –When congestion signal arrives Simulation in Matlab Rate Smoother TCP AIMD Pacing Control

6 Simulation of Two TCPs (one with rate smoother)

7 Simulation Results (1) System Plot under Uniform Packet Losses AB 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.

8 Simulation Results (2) The Impact of Non-Uniform Packet Losses 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 : –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. A B

9 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.