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
In other words, how to use parallel TCP with controlled impact on the cross traffic?
Also, it is not helpful to use arbitrarily large flow numbers, because of the “diminishing returns”.

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)
parallelism level : number of parallel TCP flows
Cut words

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
User specify the maximum percentage impact she is willing to have on cross traffic

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]
More recent TCP model for the single TCP and why we didn’t use it?
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
Our associated work to appear in ICDCS’05
Our model does NOT require the knowledge of RTT, MSS, p, b, and c1
MSS, b, and c1 will be canceled during the derivation

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
a and b are constants for the given path
Add legend for the colors
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. Predicting throughput at level m
Eq(6)
Eq(4)
Eq(5)
a and b are constants for the given path
colorissue
Eq(7)

13. Experiments setup Testbed: Planetlab Throughput test tool: iperf
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
Note on graph that parallelism levels 1 and 10 were measured to derive the rest: highlight on the graph
Only show the measurement and the partial order two results
Title tells the con

15. Low, Unbiased Relative Prediction Error
Paths ID are ordered by the std.
Test bed: planetlab, 42 paths. We present mean and std of relative errors on each path.
We used iperf for the experiments.

16. Prediction Errors Unrelated To Parallelism Level
0.1
Mean relative prediction error 30
There is no clear trend as parallelism level increases. This implies that our prediction error is independent of parallelism level
-0.1
Parallelism level (number of parallel TCP flows)

17. Insensitive to parallelism levels of probes
What is probe 1-8 …
No need to read the individual points. Supposed to be like noise … no connection between pairs of choice and the errors.

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 specify the maximum percentage impact she is willing to have on cross traffic
Q and A into the slide
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
Ignore the math, with the two probes we can get this relc
Eq (10)

24. Simulation setup Why do we need simulations? Ns2 based 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
Label the topo
Parallel TCP
RED
RED
Topo 2
Cross traffic

26. Low, slightly biased prediction errors1
Probability (error<x)
CDF of relative prediction errors
-0.6
0.6
Relative prediction error

27. Implementing 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; };
Pseudo code

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?
Rewrite the sentence -> con
More on future work

29. For more information Tool available at:
Dong Lu, Northwestern Univ.
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.