1. Fast Pattern-Based Throughput Prediction for TCP Bulk Transfers
Pattern-Based Throughput Prediction for Bulk TCP Transfers
05/10/2005
Fast Pattern-Based Throughput Prediction for TCP Bulk Transfers
Tsung-i (Mark) Huang
Jaspal Subhlok
University of Houston
GAN’05 / May 10, 2005
GAN'05

2. Pattern-Based Throughput Prediction for Bulk TCP Transfers
05/10/2005
Outline
Background
Problem Description
Methodology
Experiments and Results
Conclusion and Future Works
TMH - GAN'05, 05/10/2005
GAN'05

3. Pattern-Based Throughput Prediction for Bulk TCP Transfers
05/10/2005
“Are we there yet?”
When you need Throughput Prediction?
File download: xx minutes left: MS IE vs. Mozilla
Mirror site selection: Knoppix: Florida State Univ. (fsu.edu) or TU Ilmenau, Germany (tu-ilmenau.de)
Resource selection in a grid environment
Cache selection for web content
delivery services
Test.
TMH - GAN'05, 05/10/2005
GAN'05

4. Which site will give the best throughput?
Pattern-Based Throughput Prediction for Bulk TCP Transfers
05/10/2005
Which site will give the best throughput?
Current approaches and tools:
Geographical distance
Ping (ICMP)
Download 512 KBytes (fixed size) – NWS / iperf
Download 10 seconds (fixed duration) - iperf
Last two approaches are most accurate:
How much data to download / How long?
Is “Bandwidth * Delay” the answer? One size fits all?
“All or nothing” – no result is available until the
end of transmission
TMH - GAN'05, 05/10/2005
GAN'05

5. Pattern-Based Throughput Prediction for Bulk TCP Transfers
05/10/2005
Problem Description
Predicted future throughput can be used in mirror/replica site selection
Predict throughput of a TCP bulk transfer
Single TCP stream
Input: Time Series of (Arrival time, Bytes received)
Output: Predicted future throughput
Make a prediction of future throughput after 10 ~ 100 RTTs
Utilize knowledge of TCP flow patterns
Assume TCP flow patterns will repeat later in the same TCP stream
TMH - GAN'05, 05/10/2005
GAN'05

6. Pattern-Based Throughput Prediction for Bulk TCP Transfers
05/10/2005
TCP Flow Patterns
Textbook Examples:
(a) Rate Control
(b) Congestion Control
In Reality:
(c) Rate Control with delay
(d) Mixed Congestion Control
TMH - GAN'05, 05/10/2005
GAN'05

7. Approach to Throughput Prediction
Pattern-Based Throughput Prediction for Bulk TCP Transfers
05/10/2005
Approach to Throughput Prediction
Analyze Time-Series (TS1) of (Arrival Time, Bytes received) to get a meaningful throughput Time-Series
Possible solutions:
Instant throughput: throughput since previous TCP segment
Fixed Interval throughput: avg throughput over a fixed time period
Per RTT throughput: partition using fixed SYN-ACK RTT
Idea: TCP sends a window full of data segments every RTT
Partition Time-Series (TS1 ) with fixed SYN-ACK RTT, and get per RTT Throughput (TS2 )
Analyze per RTT Throughput Time-Series (TS2 ) to predict future throughput
Compare different prediction methods across all traces
TMH - GAN'05, 05/10/2005
GAN'05

8. TCP Segment Partitioning (1)
Pattern-Based Throughput Prediction for Bulk TCP Transfers
05/10/2005
TCP Segment Partitioning (1)
SYN-ACK RTT
= 176 ms
Over 1 GBytes/sec
About 220 Bytes/sec
per RTT Throughput
Fixed Interval
of 100 ms
Log Scaled
121 KB/sec
40 KB/sec
Instant throughput shows wide-range of fluctuation.
Fixed Interval throughput shows less fluctuation.
TMH - GAN'05, 05/10/2005
GAN'05

9. TCP Segment Partitioning (2)
Pattern-Based Throughput Prediction for Bulk TCP Transfers
05/10/2005
TCP Segment Partitioning (2)
RTT estimation
Use fixed SYN-ACK RTT
Simple and effective
Partition TCP segments into per RTT throughput time series
SYN
ACK
RTT
TMH - GAN'05, 05/10/2005
GAN'05

10. Throughput Prediction (1)
Flat
TCP Patterns
Rate Control limited (RC)
Congestion Control limited (CC)
Identify basic elements
Flat regions
Exponential Climb regions
Linear Climb regions
Drop points
Exponential Climb
Linear Climb
Drop points
TMH - GAN'05, 05/10/2005

11. Throughput Prediction (2)
Peak of slow start
Data points up to end of 1st slow
start are ignored for prediction
initial slow start does not repeat
RC-based prediction
Use flat regions
CC-based prediction
Use complete CC cycles
Window-based prediction
If no clear pattern observed
Peak of slow start
TMH - GAN'05, 05/10/2005

12. Experiments (1) - Setup
Download data files from 290 web sites (Debian/Gentoo mirrors)
Use TCPDUMP to capture receiver’s traffic
Record SYN-ACK RTTs
Include Retransmitted packets (0.09%)
Average file size is 30 MBytes
461 traces collected at Univ. of Houston
Traces are analyzed using perl scripts
TMH - GAN'05, 05/10/2005

13. Experiments (2) – Prediction Methods
Prediction methods compared
Moving Average (MA) – avg throughput of previous 10 RTTs
Exponential Weighted Moving Average (EWMA)
Aggregate throughput – average past throughput (same as cumulative average); use this as predicted throughput
TCP Pattern prediction
Average error in predicted future throughput
Cut off at 100% if over, in case measured future throughput is very small
predicted throughput – measured throughput
measured throughput
x 100%
TMH - GAN'05, 05/10/2005

14. Illustration of Prediction (1)
Make a prediction for next 200 RTTs:
Peak of
slow start
per RTT throughput
Aggregate
TCP Pattern
Drop at 27th RTT
25th RTT
40th RTT
Window size (in RTTs)
Prediction at 25th RTT
Aggregate Throughput Prediction: average throughput
of 0~25 RTTs
TCP Throughput Prediction: average throughput of
9~25 RTTs (RC-based prediction)
Prediction at 40th RTT
TCP Throughput Prediction: using Window-based
prediction after 27th RTTs (a significant drop)
TMH - GAN'05, 05/10/2005

15. Illustration of Prediction (2)
Make a prediction for next 200 RTTs:
per RTT throughput
Aggregate
Moving Average
EWMA
TCP Pattern
per RTT throughput
Aggregate
TCP Pattern
Window size (in RTTs)
Closer to 0,
better the
prediction.
Avg error against measured future throughput of next 200 RTTs
(for example, at 20th RTT, avg throughput of 21~220 RTTs is used)
TMH - GAN'05, 05/10/2005

16. Illustration of Prediction (3)
Make a prediction for next 200 RTTs:
One complete CC cycle
per RTT throughput
Aggregate
Moving Average
EWMA
TCP Pattern
per RTT throughput
Aggregate
TCP Pattern
Prediction made at 65th RTT using 3 CC complete cycles
Closer to 0,
better the
prediction.
Throughput prediction using Congestion-Control based patterns.
TMH - GAN'05, 05/10/2005

17. Results (1) – predict next 200 RTTs at different time
per RTT throughput
Aggregate
TCP Pattern
per RTT throughput
Aggregate
Moving Average
EWMA
TCP Pattern
per RTT throughput
Moving Average
EWMA
TCP Pattern
30th RTT
Aggregate is not accurate for small window size (< 30 RTTs)
MA / EWMA generally not as accurate
TMH - GAN'05, 05/10/2005

18. Results (2) – predict at 15th RTT for different time in the future
per RTT throughput
Aggregate
TCP Pattern
per RTT throughput
Aggregate
Moving Average
EWMA
TCP Pattern
per RTT throughput
Moving Average
EWMA
TCP Pattern
When only limited data is available,
Aggregate is not accurate
MA performs best; TCP Pattern is close
TMH - GAN'05, 05/10/2005

19. Results (3) – predict at 25th RTT for different time in the future
per RTT throughput
Aggregate
TCP Pattern
per RTT throughput
Aggregate
Moving Average
EWMA
TCP Pattern
per RTT throughput
Moving Average
EWMA
TCP Pattern
More data is available,
Aggregate performs better
TCP Pattern performs best; MA is close
TMH - GAN'05, 05/10/2005

20. Results (4) – predict at 50th RTT for different time in the future
per RTT throughput
Aggregate
TCP Pattern
per RTT throughput
Aggregate
Moving Average
EWMA
TCP Pattern
per RTT throughput
Moving Average
EWMA
TCP Pattern
Even more data is available,
TCP Pattern best and Aggregate is close
MA now performs worse, due to dynamic of TCP flows
TMH - GAN'05, 05/10/2005

21. Summary of Results
Aggregate is accurate with sufficient data, not with a few RTTs of data
MA performs very well for a few RTTs of data
EWMA is not a good predictor
TCP Pattern generally performs better or as well as other methods
TMH - GAN'05, 05/10/2005

22. Summary of Results (table view)
Methods
Small # of RTTs of data
Large # of RTTs of data
Aggregate
Worse (3)
Better (2)
Moving Average
Best (1)
EWMA
Worst (4)
TCP Pattern
TMH - GAN'05, 05/10/2005

23. Conclusion and Future Works
TCP-pattern based throughput prediction is as good or better than other methods.
Good predictions within 25 RTTs (or ~ 5 sec).
Patterns observed: 65% Rate Control, few Congestion Control
Methods using Aggregate (e.g. NWS) can not be expected to work well for small test files
What’s next?
Identify more patterns
Add a degree of confidence for each prediction
Multiple TCP streams
TMH - GAN'05, 05/10/2005

24. That’s all, folks!
Thank You!
TMH - GAN'05, 05/10/2005

25. Supplement Slides
TMH - GAN'05, 05/10/2005

26. Characteristics of collected traces (1)
Terms
Values
Comments
Number of traces
461
Downloaded file size
26-34 MB
Avg: 30 MB
Unique web sites
290
Debian/Gentoo
Avg # segment per trace
24,062
(min/max/median) = (17,025/69,866/24,412)
Retransmitted segments
0.09%
97 out of 461 traces
Avg # retransmitted segments per trace
103.6
(min/max/median) = (0/2,672/4)
Avg SYN-ACT RTT
sec
(min/max/median) = (0.02/2.91/0.155)
Avg # RTTs per trace
2,589
(min/max/median) = (143/110,673/662)
TMH - GAN'05, 05/10/2005

27. Characteristics of collected traces (2)
Classification: one trace presents over 50% “some type” of patterns.
Type
#traces %
Comments
Rate Control
301
65.29%
35 traces (7.59%) have big gaps (> 10 RTTs)
Congestion Avoidance 30
6.51%
Mixed or Congestion Control
130
28.20%
51 traces (11.06%) are very low in volume (up to 8~12 pkts/RTT (vs ~44 pkts/RTT))
Total
461
100.00%
TMH - GAN'05, 05/10/2005

28. Some Trace Patterns (300 RTTs)
TMH - GAN'05, 05/10/2005
Under-estimated RTT; 100 RTTs

29. Results (0.5) – predict next 100 RTTs at different time
per RTT throughput
Aggregate
Moving Average
EWMA
TCP Pattern
TMH - GAN'05, 05/10/2005

30. Results (1.5) – predict next 400 RTTs at different time
per RTT throughput
Aggregate
Moving Average
EWMA
TCP Pattern
TMH - GAN'05, 05/10/2005

31. Bandwidth Bandwidth: Bottleneck Bandwidth (BB)
The amount of data that can be pushed through a link in unit time. Usually measured in bits or bytes per second.
Bottleneck Bandwidth (BB)
Available Bandwidth (AB)
Throughput (T)
T ≤ AB ≤ BB
TMH - GAN'05, 05/10/2005