1 / 29

Modeling and Taming Parallel TCP on the Wide Area Network

Modeling and Taming Parallel TCP on the Wide Area Network. Dong Lu , Yi Qiao Peter Dinda , Fabian Bustamante Department of Computer Science Northwestern University. Summary. Parallel TCP flows are frequently used

ewan
Download Presentation

Modeling and Taming Parallel TCP on the Wide Area Network

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 and Taming Parallel TCP on the Wide Area Network Dong Lu ,Yi Qiao Peter Dinda , Fabian Bustamante Department of Computer Science Northwestern University

  2. Summary • Parallel TCP flows are frequently used • What number of parallel flows will give the highest throughput with less than a p% impact on cross traffic? --- “Maximum Nondisruptive Throughput” • Our answer to this question: • Active probing at two parallelism levels • Modeling and predicting parallel TCP thropughput at other parallelism levels • Estimating impact on cross traffic, proposing a parallelism level that bounds the impact

  3. Outline • Motivation • Modeling Parallel TCP throughput • Two probes at different parallelism levels • Evaluation via wide area experiments • Taming parallel TCP • Estimating the impact on cross traffic • Evaluation via simulations

  4. Motivation • Parallel TCP flows are broadly used to achieve higher throughput on the current Internet. GridFTP is one example. However, • No practical mechanism to predict its throughput • No previous work on estimating and controlling the negative impacts on cross traffic throughput (taming parallel TCP)

  5. Motivation • Danger of using too many parallel TCP flows • Congest the end-to-end path, significantly disturb cross traffic • Diminishing Returns, or worse throughput

  6. Our solution: TameParallelTCP() struct ParallelTCPChar { int num_flows; double max_nondisruptive_thru; double cross_traffic_impact; }; ParallelTCPChar * TameParallelTCP(Address dest, double maximpact); Percentage

  7. Outline • Motivation • Modeling Parallel TCP throughput • Two probes at different parallelism levels • Evaluation via wide area experiments • Taming parallel TCP • Estimating the impact on cross traffic • Evaluation via simulations

  8. Modeling Parallel TCP throughput Single TCP throughput model [Mathis, et al, Sigcomm CCR’97] Parallel TCP throughput upper bound model [Hacker, et al, IPDPS’02] • Upper bound tight only in uncongested networks • Hard to obtain future loss rate: what is the loss rate if I add 20 parallel TCP flows?

  9. Modeling Parallel TCP throughput Single TCP throughput model [Mathis, et al, Sigcomm CCR’97] Eq(1) Eq(2) Parallel TCP throughput model (Ours) n: number of parallel flows p: loss rate RTT: round trip time MSS: max segment size b and c1: constant Eq(3)

  10. Assumptions • Parallel TCP flows share same loss rate P. Loss rate increases with parallelism level. • Supported by previous research • MSS remains stable after TCP connection setup • TCP throughput shows transient stability • Supported by previous research • Our associated work to appear in ICDCS’05 • Our model does NOT require the knowledge of RTT, MSS, p, b, and c1

  11. Modeling and predicting loss rate Eq(4) Two probes at different parallelism level: w and v Eq(5) don’t need to know know after probing then we can calculate BWn based on the two probes If we know: Empirically, we use a partial polynomial to approximate f(n): Eq(6)

  12. Eq(4) Eq(5) Eq(6) Predicting throughput at level m Eq(7)

  13. Experiments setup • Testbed: Planetlab • randomly chosen 41 pairs of hosts (41 end-to-end paths) • Throughput test tool: iperf • Methodology: A test consists of testing parallel TCP throughput with increasing parallelism levels (1~30) • Repeat each test 10 times on each path

  14. A random wide area example Measurement Prediction

  15. Low, Unbiased Relative Prediction Error

  16. Prediction Errors Unrelated To Parallelism Level 0.1 Mean relative prediction error 0 30 -0.1 Parallelism level (number of parallel TCP flows)

  17. Insensitive to parallelism levels of probes

  18. Outline • Motivation • Modeling Parallel TCP throughput • Two probes at different parallelism levels • Evaluation via wide area experiments • Taming parallel TCP • Estimating the impact on cross traffic • Evaluation via simulations

  19. Maximum Nondisruptive Throughput • The highest throughput with less than a p% impact on cross traffic (MNT)

  20. Our solution: TameParallelTCP() struct ParallelTCPChar { int num_flows; double max_nondisruptive_thru; double cross_traffic_impact; }; ParallelTCPChar * TameParallelTCP(Address dest, double maximpact); Function Return User specified

  21. Challenges • The available bandwidth on the bottleneck link is unknown • The number of cross traffic flows and their loss rates is unknown • Overhead considerations

  22. Assumptions • TCP flows share same loss rate on the bottleneck link • If the cross traffic flows have RTT similar to our parallel TCP flows • The router on the bottleneck link is using Random Early Detection (RED) like queue management policies

  23. Estimating the impact on cross traffic • Recall that after two probes, we get the value of a and b for • We set n1=1, and n2=“number of parallel TCP flows under consideration” • Then with Eq(10), we can calculate relc Eq (10)

  24. Simulation setup • Why do we need simulations? • Detailed information on cross traffic • Ns2 based simulations • TCP Reno • Each simulation is repeated 10 times

  25. Simulation topologies Cross traffic Topo 1 Parallel TCP Cross traffic Parallel TCP RED RED Topo 2 Cross traffic

  26. Low, slightly biased prediction errors 1 Probability (error<x) -0.6 0 0.6 Relative prediction error

  27. Implementing TameParallelTCP() TameParallelTCP() { Send two probes at different parallelism levels; Estimate the loss rate curve; Estimate the throughput at different parallelism levels; Estimate the impact on cross traffic at different parallelism levels; Proposed a parallelism level with estimated impact < maximpact; Return struct ParallelTCPChar; } struct ParallelTCPChar { int num_flows; double max_nondisruptive_thru; double cross_traffic_impact; };

  28. Conclusions • We have shown how to estimate parallel TCP throughput and its impact on cross traffic by sending two probes • Our evaluation using both wide area experiments and ns2 based simulations shows the effectiveness of our approach • Future work • How to relax our assumptions about the cross traffic?

  29. For more information • Tool available at: • http://plab.cs.northwestern.edu/Clairvoyance • Dong Lu, Northwestern Univ. http://www.cs.northwestern.edu/~donglu • Related work on sequential TCP characterization and prediction • Dong Lu, Yi Qiao, Peter Dinda, Fabian Bustamante, "Characterizing and Predicting TCP Throughput on the Wide Area Network", ICDCS 2005.

More Related