1 / 9

Modeling the Effect of a Rate Smoother on TCP Congestion Control Behavior

Modeling the Effect of a Rate Smoother on TCP Congestion Control Behavior. Molly H. Shor shor@ece.orst.edu Department of Electrical and Computer Engineering Oregon State University. Kang Li, Jonathan Walpole, David C. Steere {kangli, walpole, steere}@cse.ogi.edu

hasana
Télécharger la présentation

Modeling the Effect of a Rate Smoother on TCP Congestion Control Behavior

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


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

  2. Sender Data Packets Network Receiver TCP Acknowledgment Packets Well-known Behaviors of TCP Congestion Control TCP Transmission Rate Available bandwidth 45 40 35 30 25 20 15 10 5 Time 0 10 20 30 40 0 50 • The sawtooth figure for an individual TCP • The phase plot for 2 competing TCPs

  3. 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.

  4. Modeling Temporary Rate Mismatch Rate Smoother Buffer Fill-level Rate Adjustment Pacing Control Sending Rate Calculated by TCP “Smoothed” Output +B/2 0 -B/2 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.

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

  6. Simulation of Two TCPs (one with rate smoother)

  7. 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.

  8. 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 : • 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.

  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.

More Related