1. Experimentation Tools
for Networking
Research
Mathieu Lacage
INRIA, Planète
Nov 15th 2010
Lacage (INRIA) Experimentation Tools & Network Research Nov 2010 1 / 55

2. Outline
Motivation
Simulated Packets
Direct Code Execution
NEPI
Conclusion
Perspectives
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3. Protocol evaluation
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4. Protocol evaluation
Analytical
analysis
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5. Protocol evaluation
Analytical
Simulator
analysis
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6. Protocol evaluation
Analytical
Simulator Testbed
analysis
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7. Protocol evaluation
Analytical
Simulator Testbed
analysis
Small
scale
field
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8. Protocol evaluation
Analytical
Simulator Testbed
analysis
Large Small
scale scale
field field
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9. Protocol evaluation
Analytical
Simulator Testbed
analysis
Large Small
In the
scale scale
wild
field field
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10. Protocol evaluation
Analytical
Simulator Testbed
analysis
Large Small
In the
scale scale
wild
field field
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11. Experimentation context
Experimentation
Realism
Time
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12. Why ?
One protocol isolated
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13. Why ?
One protocol isolated -> Multiple protocol
interaction
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14. Why ?
One protocol isolated -> Multiple protocol
interaction
Steady state
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15. Why ?
One protocol isolated -> Multiple protocol
interaction
Steady state -> Transient behavior
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16. Cheaper hardware
Hardware
Cost
Time
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17. Consequences
More testbeds
More Field tests
Time
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18. Consequences
Less More testbeds
Simulations More Field tests
Alone
Time
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19. Consequences
More Testbed+
Simulation
Time
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20. Downsides
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21. Downsides
A lot more work
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22. Downsides
A lot more work
Must master many experimentation
environments
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23. Downsides
A lot more work
Must master many experimentation
environments
Must implement protocols twice
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24. Objectives
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25. Objectives
Smooth transition simulations/testbed/field tests
Simulation Field
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26. Objectives
Smooth transition simulations/testbed/field tests
Run real protocol implementation in simulation
Real
Code
Simulation Field
Simulation
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27. Objectives
Smooth transition simulations/testbed/field tests
Run real protocol implementation in simulation
Use simulation as realtime emulator
Real RT
Code Sim
Simulation Field
Simulation Field
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28. Problems
Run real protocol implementation in simulation
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29. Problems
Run real protocol implementation in simulation
Virtualization of execution environment
Real Code
Simulation
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30. Problems
Run real protocol implementation in simulation
Virtualization of execution environment
Use simulation as realtime emulator
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31. Problems
Run real protocol implementation in simulation
Virtualization of execution environment
Use simulation as realtime emulator
Setup, deployment, of emulation platforms
RT RT
Field Field
Sim Sim
Deployment
Control
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32. Problems
Run real protocol implementation in simulation
Virtualization of execution environment
Use simulation as realtime emulator
Setup, deployment, of emulation platforms
In both cases
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33. Problems
Run real protocol implementation in simulation
Virtualization of execution environment
Use simulation as realtime emulator
Setup, deployment, of emulation platforms
In both cases
Transparency with other simulation models
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34. Problems
Run real protocol implementation in simulation
Virtualization of execution environment
Direct Code Execution
Use simulation as realtime emulator
Setup, deployment, of emulation platforms
In both cases
Transparency with other simulation models
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35. Problems
Run real protocol implementation in simulation
Virtualization of execution environment
Direct Code Execution
Use simulation as realtime emulator
Setup, deployment, of emulation platforms
NEPI
In both cases
Transparency with other simulation models
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36. Problems
Run real protocol implementation in simulation
Virtualization of execution environment
Direct Code Execution
Use simulation as realtime emulator
Setup, deployment, of emulation platforms
NEPI
In both cases
Transparency with other simulation models
Simulated Packets
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37. ns-3 Contributions
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38. ns-3 Contributions
ns-3: 300 KLOC
AODV OLSR
UDP/IPv4/ARP TCP UDP/Ipv6
Csma PointToPoint Uan Bridge
Spectrum Wifi Mesh Wimax
Core Mobility MPI
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39. ns-3 Contributions
ns-3: 300 KLOC
Wrote about 80 KLOC
AODV OLSR
UDP/IPv4/ARP TCP UDP/Ipv6
Csma PointToPoint Uan Bridge
Spectrum Wifi Mesh Wimax
Core Mobility MPI
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40. Scientific Contributions
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41. Scientific Contributions
Simulated packets
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42. Scientific Contributions
Simulated packets
More CPU efficient than other simulators
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43. Scientific Contributions
Simulated packets
More CPU efficient than other simulators
Automatic conversion simulation/network format
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44. Scientific Contributions
Simulated packets
More CPU efficient than other simulators
Automatic conversion simulation/network format
Direct Code Execution
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45. Scientific Contributions
Simulated packets
More CPU efficient than other simulators
Automatic conversion simulation/network format
Direct Code Execution
10x more CPU efficient than other DCE
frameworks
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46. Scientific Contributions
Simulated packets
More CPU efficient than other simulators
Automatic conversion simulation/network format
Direct Code Execution
10x more CPU efficient than other DCE
frameworks
Large applicability scope
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47. Scientific Contributions
Simulated packets
More CPU efficient than other simulators
Automatic conversion simulation/network format
Direct Code Execution
10x more CPU efficient than other DCE
frameworks
Large applicability scope
NEPI
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48. Scientific Contributions
Simulated packets
More CPU efficient than other simulators
Automatic conversion simulation/network format
Direct Code Execution
10x more CPU efficient than other DCE
frameworks
Large applicability scope
NEPI
Unified experiment description
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49. Scientific Contributions
Simulated packets
More CPU efficient than other simulators
Automatic conversion simulation/network format
Direct Code Execution
10x more CPU efficient than other DCE
frameworks
Large applicability scope
NEPI
Unified experiment description
Automated deployment
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50. Outline
Motivation
Simulated Packets
Direct Code Execution
NEPI
Conclusion
Perspectives
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51. Requirements
Transparent conversion to/from real bytes
CPU and memory efficiency
Fragmentation/Reassembly
Simulation-only data
Pretty printing
Extensibility
Robust Application Programming Interface (API)
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52. Related Work
Two approaches
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53. Related Work
Two approaches
Packet is list of headers: GTNetS, OMNeT++,
SSFNet
MAC IP TCP Payload
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54. Related Work
Two approaches
Packet is list of headers: GTNetS, OMNeT++,
SSFNet
MAC IP TCP Payload
Packet is buffer of bytes: Yans, GloMoSim
MAC IP TCP Payload
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55. Pros and Cons
List Buffer
Fragmentation, Reassembly
Conversion real bytes
Simulation-only data
Pretty printing
CPU, memory efficiency
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56. Our solution
Packet as buffer of bytes
Fragmentation, Reassembly
Automatic conversion to/from real bytes
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57. Our solution
Packet as buffer of bytes
Fragmentation, Reassembly
Automatic conversion to/from real bytes
Tags
Simulation-only data
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58. Our solution
Packet as buffer of bytes
Fragmentation, Reassembly
Automatic conversion to/from real bytes
Tags
Simulation-only data
Metadata
Pretty printing
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59. Our solution
Packet as buffer of bytes
Fragmentation, Reassembly
Automatic conversion to/from real bytes
Tags
Simulation-only data
Metadata
Pretty printing
Copy On Write (COW)
CPU, memory efficiency
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60. Performance evaluation
Micro-benchmark scenarios
Reception
Forwarding
Transmission
Re-transmission
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61. Relative speedup
ns-3 OMNet++ GTNetS Yans
Reception 1.00 1.98 2.25 3.07
Transmission 1.00 2.13 1.78 2.16
Forwarding 1.00 2.83 1.92 2.44
Retransmission 1.00 1.92 1.70 4.76
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62. Contributions
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63. Contributions
More CPU efficient than other simulators
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64. Contributions
More CPU efficient than other simulators
Transparent support for real network bytes
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65. Outline
Motivation
Simulated Packets
Direct Code Execution
NEPI
Conclusion
Perspectives
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66. The manual approach
Global variables
static int g_var; static int [100]g_var_array;
g_var ++; g_var_array[current_id ()] ++;
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67. The manual approach
Global variables
static int g_var; static int [100]g_var_array;
g_var ++; g_var_array[current_id ()] ++;
Redirect system calls
clock (); dce_clock ();
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68. The manual approach
Global variables
static int g_var; static int [100]g_var_array;
g_var ++; g_var_array[current_id ()] ++;
Redirect system calls
clock (); dce_clock ();
Re-implement all system calls
clock_t dce_clock (void) {
return Simulator::Now ().GetMicroSeconds ();
}
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69. The problem
Manual modifications: does not scale
Painful to do once
Impossible to do for software updates
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70. Related work
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71. Related work
Network Simulation Cradle
Automated source modifications for C code
Hard to extend to C++
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72. Related work
Network Simulation Cradle
Automated source modifications for C code
Hard to extend to C++
Weaves
Automated textual assembly modifications
Invalid assumptions about compiler-generated
code
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73. Related work
Network Simulation Cradle
Automated source modifications for C code
Hard to extend to C++
Weaves
Automated textual assembly modifications
Invalid assumptions about compiler-generated
code
COOJA
Automated memory virtualization
Slow
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74. ns-3 DCE
Executable and Linkable Format (ELF) loader
Fast
Automated memory virtualization
Automated system call redirection
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75. ns-3 DCE
Executable and Linkable Format (ELF) loader
Fast
Automated memory virtualization
Automated system call redirection
Userspace system calls
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76. ns-3 DCE
Executable and Linkable Format (ELF) loader
Fast
Automated memory virtualization
Automated system call redirection
Userspace system calls
Kernelspace system calls
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77. Loader Performance
Scenario:
udp-perf udp-perf
userspace DCE userspace DCE
Linux Linux Linux
UDP/IP UDP/IP UDP/IP
kernelspace DCE kernelspace DCE kernelspace DCE
PointToPointLink PointToPointLink
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78. Loader Performance
100000
Cooja
ns-3
Packets per
10000
wall clock
second
vs
1000
Number of
nodes
100
0 10 20 30 40 50 60 70
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79. Loader Performance
35000
Cooja
ns-3
30000
25000
Memory(bytes)
20000
vs
Number of 15000
nodes 10000
5000
0
0 10 20 30 40 50 60 70
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80. System Performance
1e+06
dce-none
dce-user
dce-user+kernel
Packets per
100000
wall clock
second
vs
10000
Number of
nodes
1000
0 2 4 6 8 10 12 14 16 18 20
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81. Tested programs
Binary Missing functions
/sbin/ip 24
/bin/ping 2
/bin/traceroute 5
/usr/sbin/zebra 42
/usr/sbin/ospfd 43
/usr/sbin/mip6d 38
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82. Untested programs
Binary Missing functions
ccnd 11
/usr/sbin/httpd 18
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83. Contributions
10x more efficient than existing alternatives
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84. Contributions
10x more efficient than existing alternatives
Larger scope than existing alternatives
Userspace: ping, traceroute, quagga, etc.
Kernelspace: Linux IP, TCP, etc.
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85. Contributions
10x more efficient than existing alternatives
Larger scope than existing alternatives
Userspace: ping, traceroute, quagga, etc.
Kernelspace: Linux IP, TCP, etc.
Potential usecases:
Debugging platform: single debugger controls
all protocol instances
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86. Contributions
10x more efficient than existing alternatives
Larger scope than existing alternatives
Userspace: ping, traceroute, quagga, etc.
Kernelspace: Linux IP, TCP, etc.
Potential usecases:
Debugging platform: single debugger controls
all protocol instances
Development platform
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87. Contributions
10x more efficient than existing alternatives
Larger scope than existing alternatives
Userspace: ping, traceroute, quagga, etc.
Kernelspace: Linux IP, TCP, etc.
Potential usecases:
Debugging platform: single debugger controls
all protocol instances
Development platform
Test platform
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88. Outline
Motivation
Simulated Packets
Direct Code Execution
NEPI
Conclusion
Perspectives
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89. Objective Scenario
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90. Objective Scenario
VLC Wifi
Router
Server STA
Wifi VLC
Router
AP Client
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91. Objective Scenario
VLC Wifi
Simulated links/networks
Router
Server STA
ns-3 simulation models
Realtime scheduler
Wifi VLC
Router
AP Client Tap device
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92. Objective Scenario
VLC Wifi
Simulated links/networks
Router
Server STA
ns-3 simulation models
Realtime scheduler
Wifi VLC
Router
AP Client Tap device
Light weight Virtual Machines
Linux Network Namespaces
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93. Easy Deployment
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94. Easy Deployment
Problem
Tap/VM creation and setup
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95. Easy Deployment
Problem
Tap/VM creation and setup
Coherent IP address assignment across
simulation and VMs
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96. Easy Deployment
Problem
Tap/VM creation and setup
Coherent IP address assignment across
simulation and VMs
Coherent IP forwarding tables across
simulation and VMs
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97. Easy Deployment
Problem
Tap/VM creation and setup
Coherent IP address assignment across
simulation and VMs
Coherent IP forwarding tables across
simulation and VMs
Solution
Automate everything
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98. Easy Deployment
Problem
Tap/VM creation and setup
Coherent IP address assignment across
simulation and VMs
Coherent IP forwarding tables across
simulation and VMs
Solution
Automate everything
BUT
Need global view of experiment topology
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99. Related Work
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100. Related Work
Emulab Tcl: ad hoc, hard to generalize
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101. Related Work
Emulab Tcl: ad hoc, hard to generalize
OMF Ruby: unclear how to extend it to model
complex topologies
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102. Related Work
Emulab Tcl: ad hoc, hard to generalize
OMF Ruby: unclear how to extend it to model
complex topologies
OMNeT++ NED: hard to ensure correctness
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103. Related Work
Emulab Tcl: ad hoc, hard to generalize
OMF Ruby: unclear how to extend it to model
complex topologies
OMNeT++ NED: hard to ensure correctness
SSF DML: hard to ensure correctness
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104. Related Work
Emulab Tcl: ad hoc, hard to generalize
OMF Ruby: unclear how to extend it to model
complex topologies
OMNeT++ NED: hard to ensure correctness
SSF DML: hard to ensure correctness
Geni RSPEC: hard to ensure correctness
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105. Related Work
Emulab Tcl: ad hoc, hard to generalize
OMF Ruby: unclear how to extend it to model
complex topologies
OMNeT++ NED: hard to ensure correctness
SSF DML: hard to ensure correctness
Geni RSPEC: hard to ensure correctness
Geni Omnispec: hard to ensure correctness
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106. NEPI Object Model
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107. NEPI Object Model
Functional unit / Box Example:
IP stack
TCP stack
Ethernet card
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108. NEPI Object Model
Functional unit / Box Example:
Attributes IP checksum
IP forwarding
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109. NEPI Object Model
Functional unit / Box Example:
Attributes Out packets
Trace sources
In packets
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110. NEPI Object Model
Functional unit / Box IP
Attributes
Trace sources dev
Connectors
node
Ethernet
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111. NEPI Object Model
Functional unit / Box IP
Attributes
Trace sources dev app
Connectors
Connection checking node node
Ethernet Ethernet
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112. NEPI Object Model
Functional unit / Box Node
Attributes
dev
Trace sources
Connectors cable
node node
cable
Connection checking Ethernet Ethernet
Hierarchical
Switch
port0 port1
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113. Objective Scenario
VLC Wifi
Router
Server STA
Wifi VLC
Router
AP Client
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114. NEPI Representation
NetNs IP ICMP ARP ns-3 IP ICMP ARP NetNs
Vlc Vlc
Node Mobility Mobility Node
Node Node
Wifi Loss Delay Wifi
TapNode FDNet Manager Manager FDNet TapNode
Net Net
Interface Device YansWifiChannel Device Interface
ApMac Device Device StaMac
YansWifiPhy YansWifiPhy
Nist Nist
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115. Global IP topology
n0 n1 n2 n3
Tap net0 Fd Wifi Ap net1 Wifi Sta Fd net2 Tap
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116. Demo screenshot
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117. Contributions
An integrated experimentation environment
Unified experiment description
Entire workflow support
Automated deployment
Strong coherency checking
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118. Outline
Motivation
Simulated Packets
Direct Code Execution
NEPI
Conclusion
Perspectives
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119. Papers Published
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120. Papers Published
“Yet another network simulator”, Proceedings of
the 2006 workshop on ns-2
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121. Papers Published
“Yet another network simulator”, Proceedings of
the 2006 workshop on ns-2
“NEPI: using independent simulators,
emulators, and testbeds for easy
experimentation”, SIGOPS Operating
Systems Review
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122. Papers in progress
“The ns-3 network simulator: experience
learned”, Software Practice & Experience
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123. Papers in progress
“The ns-3 network simulator: experience
learned”, Software Practice & Experience
“Direct Code Execution”, Networked Systems
Design and Implementation
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124. Activities
Chair
NSTools 2007
TPC
WNS3 2009, 2010, 2011
Simutools 2008,2009
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125. Simulated Packets
More CPU efficient than other simulators
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126. Simulated Packets
More CPU efficient than other simulators
Transparent support for real network bytes
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127. Direct Code Execution
10x more CPU efficient than other DCE frameworks
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128. Direct Code Execution
10x more CPU efficient than other DCE frameworks
A robust implementation
Userspace and kernelspace protocols
C,C++ protocols
ABI compatibility for userspace protocols
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129. NEPI
An integrated experimentation environment
Unified experiment description
Entire workflow support
Automated deployment
Strong coherency checking
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130. Impact
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131. Impact
Instrumental in creating an
Active
Growing
Open source
Simulation community
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132. Impact
Instrumental in creating an 0.6
0.5
Active
0.4
Growing
0.3
Open source
0.2
Simulation community
0.1
Contributors > 80
0
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133. Impact
Instrumental in creating an 800
700
Active 600
Growing 500
400
Open source
300
Simulation community 200
Contributors > 80 100
0
Hundreds of users
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134. Impact
Instrumental in creating an
Active
Growing
Open source
Simulation community
Contributors > 80
Hundreds of users
> 30 papers by users
2 sigcomm’09
1 sigcomm’10
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135. Outline
Motivation
Simulated Packets
Direct Code Execution
NEPI
Conclusion
Perspectives
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136. Tracing with DCE
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137. Tracing with DCE
Need to recompile for tracing changes
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138. Tracing with DCE
Need to recompile for tracing changes
Ideal workflow:
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139. Tracing with DCE
Need to recompile for tracing changes
Ideal workflow:
Trace function foo in bar.cc
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140. Tracing with DCE
Need to recompile for tracing changes
Ideal workflow:
Trace function foo in bar.cc
Trace line 122 in bar.cc
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141. Tracing with DCE
Need to recompile for tracing changes
Ideal workflow:
Trace function foo in bar.cc
Trace line 122 in bar.cc
Trace variable foo at line 144 in bar.cc
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142. Tracing with DCE
Need to recompile for tracing changes
Ideal workflow:
Trace function foo in bar.cc
Trace line 122 in bar.cc
Trace variable foo at line 144 in bar.cc
BUT, not Java: no introspection
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143. Dynamic instrumentation
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144. Dynamic instrumentation
Locate code in memory
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145. Dynamic instrumentation
Locate code in memory
Parse dynamic loader data structures
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146. Dynamic instrumentation
Locate code in memory
Parse dynamic loader data structures
Parse debugging information
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147. Dynamic instrumentation
Locate code in memory
Parse dynamic loader data structures
Parse debugging information
Insert assembly hooks
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148. Dynamic instrumentation
Locate code in memory
Parse dynamic loader data structures
Parse debugging information
Insert assembly hooks
Simple case (not thread-safe)
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149. Dynamic instrumentation
Locate code in memory
Parse dynamic loader data structures
Parse debugging information
Insert assembly hooks
Simple case (not thread-safe)
General case harder
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150. Dynamic instrumentation
Locate code in memory
Parse dynamic loader data structures
Parse debugging information
Insert assembly hooks
Simple case (not thread-safe)
General case harder
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151. Multi-threaded Scheduler
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152. Multi-threaded Scheduler
Conservative algorithm implemented:
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153. Multi-threaded Scheduler
Conservative algorithm implemented:
Fully transparent for users
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154. Multi-threaded Scheduler
Conservative algorithm implemented:
Fully transparent for users
Measurable speedup
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155. Multi-threaded Scheduler
Conservative algorithm implemented:
Fully transparent for users
Measurable speedup
Thread-safety is easy
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156. Multi-threaded Scheduler
Conservative algorithm implemented:
Fully transparent for users
Measurable speedup
Thread-safety is easy
Efficient thread-safety is hard
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157. Multi-threaded Scheduler
Conservative algorithm implemented:
Fully transparent for users
Measurable speedup
Thread-safety is easy
Efficient thread-safety is hard
Can we build more efficient thread-safety ?
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158. Multi-threaded Scheduler
Conservative algorithm implemented:
Fully transparent for users
Measurable speedup
Thread-safety is easy
Efficient thread-safety is hard
Can we build more efficient thread-safety ?
Can we use optimistic algorithm transparently ?
Lacage (INRIA) Experimentation Tools & Network Research Nov 2010 54 / 55

159. Multi-threaded Scheduler
Conservative algorithm implemented:
Fully transparent for users
Measurable speedup
Thread-safety is easy
Efficient thread-safety is hard
Can we build more efficient thread-safety ?
Can we use optimistic algorithm transparently ?
Transparency is key !
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160. Thank you !
Questions ?
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