1. 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. 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. 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. 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. 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. 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. 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. 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] 1.Upper bound tight only in uncongested networks 2.Hard to obtain future loss rate: what is the loss rate if I add 20 parallel TCP flows?

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

10. 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 c 1

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

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

13. 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. 14 A random wide area example Measurement Prediction

15. 15 Low, Unbiased Relative Prediction Error

16. 16 Prediction Errors Unrelated To Parallelism Level 030 -0.1 0.1 Parallelism level (number of parallel TCP flows) Mean relative prediction error

17. 17 Insensitive to parallelism levels of probes

18. 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. 19 Maximum Nondisruptive Throughput The highest throughput with less than a p% impact on cross traffic (MNT)

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

21. 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. 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. 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. 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. 25 Simulation topologies RED Cross traffic Parallel TCP Cross traffic RED Parallel TCP Topo 1 Topo 2

26. 26 Low, slightly biased prediction errors 00.6-0.6 1 Relative prediction error Probability (error<x)

27. 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. 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. 29 For more information Tool available at: –http://plab.cs.northwestern.edu/Clairvoyancehttp://plab.cs.northwestern.edu/Clairvoyance Dong Lu, Northwestern Univ. http://www.cs.northwestern.edu/~donglu 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.