Fast PatternBased Throughput Prediction for TCP Bulk Transfers

1
Fast Pattern-Based Throughput Prediction for
TCP Bulk Transfers

• Tsung-i (Mark) Huang
• Jaspal Subhlok
• University of Houston
• GAN05 / May 10, 2005

2
Outline

• Background
• Problem Description
• Methodology
• Experiments and Results
• Conclusion and Future Works

3
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

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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

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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

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TCP Flow Patterns

• Textbook Examples

(a) Rate Control
(b) Congestion Control

• In Reality

(c) Rate Control with delay
(d) Mixed Congestion Control
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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

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TCP Segment Partitioning (1)
SYN-ACK RTT 176 ms
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.
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TCP Segment Partitioning (2)

• RTT estimation
• Use fixed SYN-ACK RTT
• Simple and effective
• Partition TCP segments into per RTT throughput
  time series

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Throughput Prediction (1)

• TCP Patterns
• Rate Control limited (RC)
• Congestion Control limited (CC)
• Identify basic elements
• Flat regions
• Exponential Climb regions
• Linear Climb regions
• Drop points

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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

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Experiments (1) - Setup

• Download data files from 290 web sites
  (Debian/Gentoo mirrors)
• Use TCPDUMP to capture receivers 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

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

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Illustration of Prediction (1)
Make a prediction for next 200 RTTs
Drop at 27th RTT
Window size (in RTTs)
25th RTT
40th RTT
Prediction at 25th RTT

• Aggregate Throughput Prediction average
  throughput
• of 025 RTTs

• TCP Throughput Prediction average throughput of
  
• 925 RTTs (RC-based prediction)

Prediction at 40th RTT

• TCP Throughput Prediction using Window-based
• prediction after 27th RTTs (a significant drop)

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Illustration of Prediction (2)
Make a prediction for next 200 RTTs
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
  21220 RTTs is used)

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Illustration of Prediction (3)
Make a prediction for next 200 RTTs
One complete CC cycle
Prediction made at 65th RTT using 3 CC complete
cycles
Closer to 0, better the prediction.
Throughput prediction using Congestion-Control
based patterns.
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Results (1) predict next 200 RTTs at different
time
30th RTT

• Aggregate is not accurate for small window size
  (lt 30 RTTs)

• MA / EWMA generally not as accurate

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Results (2) predict at 15th RTT for different
time in the future

• When only limited data is available,

• Aggregate is not accurate

• MA performs best TCP Pattern is close

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Results (3) predict at 25th RTT for different
time in the future

• More data is available,

• Aggregate performs better

• TCP Pattern performs best MA is close

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Results (4) predict at 50th RTT for different
time in the future

• Even more data is available,

• TCP Pattern best and Aggregate is close

• MA now performs worse, due to dynamic of TCP
  flows

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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

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Summary of Results (table view)
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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
• Whats next?
• Identify more patterns
• Add a degree of confidence for each prediction
• Multiple TCP streams

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Thats all, folks!

• Thank You!

25
Supplement Slides
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Characteristics of collected traces (1)
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Characteristics of collected traces (2)

• Classification one trace presents over 50
  some type of patterns.

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Some Trace Patterns (300 RTTs)
Under-estimated RTT 100 RTTs
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Results (0.5) predict next 100 RTTs at
different time
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Results (1.5) predict next 400 RTTs at
different time
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Bandwidth

• Bandwidth
• 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