Characterizing and Predicting TCP Throughput on the Wide Area Network

1
Characterizing and Predicting TCP Throughput on
the Wide Area Network

• Dong Lu, Yi Qiao,
• Peter Dinda, Fabian Bustamante
• Department of Computer Science
• Northwestern University
• http//plab.cs.northwestern.edu

2
Overview

• Algorithm for predicting the TCP throughput as
  function of flow size
• Minimal active probing
• Dynamic probe rate adjustment
• Explaining flow size / throughput correlation
• Explaining why simple active probing fails
• Large scale empirical study

3
Outline

• Why TCP throughput prediction?
• Particulars of study
• Flow size / TCP throughput correlation
• Issues with simple benchmarking
• DualPats algorithm
• Stability and dynamic rate adjustment

4
Goal

• A library call
• BW PredictTransfer(src,dst,numbytes)
• Expected Time numbytes/BW
• Ideally, we want a confidence interval
• (BWLow,BWHigh) PredictTransfer(src,dst,numbytes,
  p)

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Available Bandwidth

• Maximum rate a path can offer a flow without
  slowing other flows
• pathchar, cprobe, nettimer, delphi, IGI,
  pathchirp, pathload
• Available bandwidth can differ significantly from
  TCP throughput
• Not real time, takes at least tens of seconds to
  run

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Simple TCP Benchmarking

• Benchmark paths with a single small probe
• BW ProbeSize/Time
• Widely used Network Weather Service (NWS) and
  others (Remos benchmarking collector)
• Not accurate for large transfers on the current
  high speed Internet
• Numerous papers show this and attempt to fix it

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Fixing Simple TCP Benchmarking

• Logs Sundharshan correlate real transfer
  measurements with benchmarking measurements
• Recent transfers needed
• Similar size transfers needed
• Measurements at application chosen times
• CDF-matching Swany correlate CDF of real
  transfer measurements with CDF of benchmarking
  measurements
• Recent transfers still needed
• Measurements at application chosen times

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Analysis of TCP

• Extensive research on TCP throughput modeling in
  networking community
• Really intended to build better TCPs
• Difficult to use models online because of hard to
  measure parameters
• Future loss rate and RTT
• Note we measure goodput

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Our Measurement Study

• PlanetLab and additional machines
• Located all over the world
• Measurements of throughput
• Wide open socket buffers (1-3 MB)
• Simple ttcp-like client/server
• scp
• GridFTP
• Four separate sets of measurements

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Distribution Set

• For analysis of TCP throughput stability and
  distributions
• 60 randomly chosen paths among PlanetLab machines
• 1.6 million transfers (client/server)
• 100 KB, 200 KB, 400 KB, 10 MB flows
• 3000 consecutive transfers per pathflow size

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Correlation Set

• For studying correlation between throughput and
  flow size, initial testing of algorithm
• 60 randomly chosen paths among PlanetLab machines
• 2.4 million transfers, 270 thousand runs,
  client/server
• 100 KB, 200 KB, 400 KB, 10 MB flows
• Run sweep flow size for path

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Verification Set

• Test algorithm
• 30 randomly chosen paths among PlanetLab machines
  and others
• 4800 transfers, 300 runs, scp and GridFTP
• 5 KB to 1 GB flows
• Run sweep flow size for path

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Online Evaluation Set

• Test online algorithm
• 50 randomly chosen paths among PlanetLab machines
  and others
• 14000 transfers, scp and GridFTP
• 40 MB or 160 MB file, randomly chosen size
• 10 days

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Strong Correlation Between TCP Throughput and
Flow Size
Correlation and Verification Sets
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Why Does The Correlation Exist?

• Slow start and user effects Zhang
• Extant flows
• Non-negligible startup overheads
• Control messages in scp and GridFTP
• Residual slow start effect
• SACK results in slow convergence to equilibrium

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Why Simple Benchmarking Fails
Need more than one probe to capture correlation
Probes are too small
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Our Approach
Two consecutive probes, both larger than the
noise region
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Our Approach

• Two consecutive probes are integrated into a
  single probe
• 400KB, 800 KB in single 800 KB probe

Probe two
Probe one
T2
0
T1
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Our Approach
Flow size
Transfer Time
Solve For A and B
Predict Throughput For Some Other Transfer
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Model Fit is Excellent
Low and Normally Distributed Relative Errors At
All Flow Sizes
Correlation Set
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Stability

• How long does the TCP throughput function remain
  stable?
• How frequently should we probe the path?
• Whats the distribution of throughput around the
  function (i.e., the error)?

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Throughput is Stable For Long Periods
Increasing Max/Min Throughput in Interval
Correlation Set
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Throughput Is Normally Distributed In An Interval
Distribution Set
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Online DualPats Algorithm

• Fetch probe sequence for destination
• Start probing process if no data exists
• Project probe sequence ahead
• 20 point moving average over values with current
  sampling interval
• Apply model using projected data
• Return result
• confidence interval computed using normality
  assumptions

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Dynamic Sampling Rate

• Adjust sampling interval to correspond to the
  paths stable intervals
• Limit rate (20 to 1200 seconds)
• Additive increase / additive decrease of based on
  difference between last two probes
• lt 5 gt increase interval
• gt 15 gt decrease interval

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Finding Sufficiently Large Probe Size

• Default values 400 KB / 800 KB
• Upper bound
• Additive increase until prediction error are less
  than threshold, all with same sign.

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Evaluation

• Slight conservative bias
• gt90 of predictions have lt 35 error

1
Pmean error lt X
Mean relative error
Mean abs(relative error)
0.4
-0.4
0
Relative error
Online Evaluation Set
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Conclusions

• Algorithm for predicting the TCP throughput as
  function of flow size
• Minimal active probing
• Dynamic probe rate adjustment
• Explaining flow size / throughput correlation
• Explaining why simple active probing fails
• Large scale empirical study

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For MoreInfo

• Prescience Lab
• http//plab.cs.northwestern.edu
• Aqua Lab
• http//aqualab.cs.northwestern.edu
• D. Lu, Y. Qiao, P. Dinda, and F. Bustamante,
  Modeling and Taming Parallel TCP on the Wide Area
  Network, IPDPS 2005 .
• Y. Qiao, J. Skicewicz, P. Dinda, An Empirical
  Study of the Multiscale Predictability of Network
  Traffic, HPDC 2004.