1. Methodologies for
Networking Research
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2. Measurementquot;
V. Paxson. quot;End-to-end Internet packet dynamics,”quot;
J. Padhye, V. Firoiu, D. Towesley, and J. Kurose quot;Modeling
TCP Throughput: A Simple Model and its Empirical
Validation,”
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3. “Reality Check”
Are our assumptions reasonable? Is our
mathematical model a good estimation of the real
world?
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4. e.g., from Paxson’s studyquot;
1. packet losses are busrtyquot;
2. OTT != RTT/2
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7. Experimentationquot;
e.g., V. Jacobson. “Congestion Control and Avoidancequot;
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8. Deal with implementation
issues
Sometimes unforeseen complexities (e.g. own research
experience in Unreliable TCP)
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9. Understand the Behavior
of Systems
Some systems are too complex to understand with
“thought experiments” alone.
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12. Analysisquot;
D. Chiu and R. Jain, quot;Analysis of the increase and decrease
algorithms for congestion avoidance in computer
networks,”quot;
J. Padhye, V. Firoiu, D. Towesley, and J. Kurose quot;Modeling
TCP Throughput: A Simple Model and its Empirical
Validation,”
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13. Explore with Complete
Control
We can understand the basic forces that affect the
system. e.g. TCP throughput is inversely propotional
to √p
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14. Simplify complex systemsquot;
If too simplified, important behavior could be missed
(TCP throughput without timeout)
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15. Simulationquot;
K. Fall and S. Floyd, quot;Simulation-based comparison of
Tahoe, Reno, and SACK TCP,quot;quot;
S. Floyd, K. Fall, quot;Promoting the Use of End-to-End
Congestion Control in the Internet,”quot;
S. Floyd, V. Jacobson, quot;Random Early Detection
Gateways for Congestion Avoidance,quot;
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16. Check Correctness of
Analysisquot;
If simulation uses the same assumptions/model as the
analysis, this simply verifies the correctness of the
mathematical derivations.
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17. Check Correctness of
Analysisquot;
Simulation can relax some assumptions, use more
complex models, etc. to test the limits of analysis.
(Real measurement/experiments still needed to check
the usefulness of analysis results)
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18. Explore Complex Systemsquot;
Some systems are too difficult/impossible to analyzed
e.g. Internet
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28. 2.!
Internet is huge
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29. 570,937,778
Number of Hosts as of July 2008
http://www.isc.org/index.pl?/ops/ds/host-count-history.php
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30. 3.!
Internet is changing
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32. 17 October 2008
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http://www.dtc.umn.edu/mints/
33. Median File Transfer
Time
Size
March 1998
10.9 kB
December 1998
5.6 kB
December 1999
10.9 kB
June 2000
62 kB
November 2000
10 kB
Measurement at LBNL: Statistical property
of Internet changes as well.
34. Why is Internet hard to
simulate?quot;
1. Heterogeneous quot;
2. Huge quot;
3. Changing
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35. Suppose you come up
with the greatest
BitTorrent
improvement ever..
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36. You want to simulate it
to make sure it works
before you release it
(and call the press)
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37. What Internet topology
should you use in your
simulation?
How end hosts are connected? What are the
properties of the links?
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38. Topology changes constantlyquot;
Companies keep info secretsquot;
Routes may changequot;
Routes may be asymmetric
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39. You will need to simulate over
a wide range of connectivity
and link properties
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40. Suppose you come up
with the greatest TCP
optimization ever..
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41. You want to know if it
is fair to existing TCP
versions before you
write your SIGCOMM
paper..
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42. Which TCP version to
use?
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43. Using “fingerprinting”,
831 different TCP
implementations and
versions are identified.
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44. Which to use? Which
to ignore?
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45. What applications to run?
What type of traffic to
generate?quot;
Telnet? FTP? Web? BitTorrent?
Skype?
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46. How congested should
the network be?
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47. How congested should
the network be?
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48. Example from Sally Floyd:
RED vs DropTail
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72. 5. Heavy Tail
Distributions
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73. Self-Similarity in World Wide Web Traffic: Evidence and
Possible Causes, by Mark E. Crovella and Azer Bestavros
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74. 1. Looking for
Invariants
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75. 2. Explore
Parameter Space
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