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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5. 17 October 2008
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6. 17 October 2008
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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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10. 17 October 2008
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11. 17 October 2008
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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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19. Helps Develop Intuitionquot;
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20. Measurementquot;
Experimentationquot; } Real World
Analysisquot;
Simulationquot; } Abstract Model
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21. “Difficulties in Simulating
the Internet” quot;
Sally Floyd, Van Paxsonquot;
ACM/IEEE TON, 9(4) August 2001

22. Why is Internet hard to
simulate?
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23. 1.!
Internet is diverse
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24. End-hosts: phones,
desktops, servers, iPod, Wii
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25. Links: Ethernet, WiFi,
Satellite, Dial-up, 3G
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26. Transport: TCP variants,
UDP, DCCP
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27. Applications: games,
videos, web, ftp, bittorrent
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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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31. http://www.isc.org/ds/
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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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49. 17 October 2008
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50. 17 October 2008
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51. Example from Sally Floyd:
Using TFRC for VoIP
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52. 17 October 2008
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53. 17 October 2008
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54. We can focus our
simulation on dominant
technology/application
today..
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55. TCP: NewReno SACKSquot;
OS: Windows Linuxquot;
Applications: Web, FTP
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56. What about tomorrow?
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57. WiMax? quot;
Sensors? quot;
Virtual World?quot;
DCCP?
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58. 10 years ago, you came
up with a router
mechanism to improve
TCP Reno.. quot;
No one cares today.
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59. How to verify the
simulator itself?
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60. So, how?
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61. Looking for
Invariants
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62. 1. Diurnal Patterns
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63. 17 October 2008
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64. hour #constrained
---- ------------
00 139 2.5% -----------------------------------------------------X
01 144 2.6% ------------------------------------------------------X
02 146 2.6% -------------------------------------------------------X
03 140 2.5% -----------------------------------------------------X
04 119 2.1% ---------------------------------------------X
05 89 1.6% ----------------------------------X
06 69 1.2% --------------------------X
07 55 1.0% ---------------------X
08 45 0.8% -----------------X
09 40 0.7% ---------------X
10 40 0.7% ---------------X
11 42 0.8% ----------------X
12 51 0.9% -------------------X
13 57 1.0% ---------------------X
14 68 1.2% --------------------------X
15 75 1.3% ----------------------------X
16 77 1.4% -----------------------------X
17 92 1.6% -----------------------------------X
18 98 1.8% -------------------------------------X
19 105 1.9% ----------------------------------------X
20 108 1.9% -----------------------------------------X
21 113 2.0% -------------------------------------------X
22 124 2.2% -----------------------------------------------X
23 134 2.4% ---------------------------------------------------X
U Waterloo Data 24 Oct 2007
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65. 2. Self-Similar Traffic
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66. The traffic is bursty
regardless of time scale
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67. Wikipedia
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68. 3. Poisson Session
Arrival
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69. Remote logins, starting
FTP, beginning of web
surfing etc.
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70. (so are dead light bulbs,
spelling mistakes, etc.)
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71. 4. Log-normal Duration
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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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76. Change one parameter,quot;
fix the rest
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77. Explore a wide range of values
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78. 3. Use Traces
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79. e.g. collects traces of web
sessions, video files, VoIP traffic
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80. Use it to simulate the traffic
source
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81. But must be careful about traffic
shaping and user/application
adaptation.
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82. e.g. traces collected during non-
congested time should not be use to
simulate congested networks.
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83. 4. publish simulator script for
others to verify
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84. Conclusion
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85. Simulation is useful but needs to
do it properly
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86. Be careful about your simulation
model: you want it to be as simple
as possible, but not simpler.
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87. Be careful about your conclusion:
“A is 13.5% better than B” is
probably useless.
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88. “A is 13.5% better than B under
these environment”quot;
is better but not general
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89. Not really for quantitative results,
but more for
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90. understanding the dynamics,quot;
illustrate a point,quot;
explore unexpected behavior.
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