1 / 64

The Design of XML-Based Model and Experiment Description Languages for Network Simulation

The Design of XML-Based Model and Experiment Description Languages for Network Simulation. Andrew Hallagan Bucknell University Dept. of Computer Science Luiz Felipe Perrone , Advisor April 26, 2011. 3 General Network Types. Clockwise from top: hardwired, wireless, and ad-hoc networks.

harriet-wiley
Télécharger la présentation

The Design of XML-Based Model and Experiment Description Languages for Network Simulation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Design of XML-Based Model and Experiment Description Languages for Network Simulation Andrew Hallagan Bucknell University Dept. of Computer Science Luiz Felipe Perrone, Advisor April 26, 2011

  2. 3 General Network Types Clockwise from top: hardwired, wireless, and ad-hoc networks

  3. Nodes 3 General Network Types Clockwise from top: hardwired, wireless, and ad-hoc networks

  4. Channels 3 General Network Types Clockwise from top: hardwired, wireless, and ad-hoc networks

  5. Communication Between Nodes Breaking a message up into packets.

  6. Communication Between Nodes Breaking a message up into packets.

  7. Communication Between Nodes Breaking a message up into packets.

  8. System Experiment with a model of the system Experiment with the actual system Physical model Mathematical model Analytical solution Simulation Ways to Study a System Source: Averill M. Law, Simulation, Modeling & Analysis

  9. System Experiment with a model of the system Experiment with the actual system Costly, disruptive Physical model Mathematical model Analytical solution Simulation Ways to Study a System Source: Averill M. Law, Simulation, Modeling & Analysis

  10. System Experiment with a model of the system Experiment with the actual system Costly, disruptive Physical model Mathematical model Not applicable Analytical solution Simulation Ways to Study a System Source: Averill M. Law, Simulation, Modeling & Analysis

  11. d = v∙t System Experiment with a model of the system Experiment with the actual system Costly, disruptive Physical model Mathematical model Not applicable Analytical solution Simulation Ways to Study a System Source: Averill M. Law, Simulation, Modeling & Analysis

  12. System Experiment with a model of the system Experiment with the actual system Costly, disruptive Physical model Mathematical model Not applicable Analytical solution Simulation Ways to Study a System Difficult, if not impossible Source: Averill M. Law, Simulation, Modeling & Analysis

  13. X-position = f(SPEED, ANGLE) Y-position = g(SPEED, ANGLE) SPEED ~ N(SPEED_MEAN, 3.0) ANGLE ~ U(0, ANGLE_BOUND) Model Inputs • SPEED_MEAN • ANGLE_BOUND A Simple Mobility Model The horizontal and vertical positions of the node are a function of SPEED and ANGLE, which are random variables distributed Normally and Uniformly, respectively.

  14. Model Description X-position = f(SPEED, ANGLE) Y-position = g(SPEED, ANGLE) SPEED ~ N(SPEED_MEAN, 3.0) ANGLE ~ U(0, ANGLE_BOUND) Model Inputs • SPEED_MEAN • ANGLE_BOUND A Simple Mobility Model The horizontal and vertical positions of the node are a function of SPEED and ANGLE, which are random variables distributed Normally and Uniformly, respectively.

  15. Factor Levels ANGLE_BOUND 15 30 45 60 SPEED_MEAN 2.0 3.0 4.0

  16. Design Matrix ANGLE_BOUND 15 15 15 30 30 30 45 45 45 60 60 60 SPEED_MEAN 2.0 3.0 4.0 2.0 3.0 4.0 2.0 3.0 4.0 2.0 3.0 4.0

  17. Design Matrix ANGLE_BOUND 15 15 15 30 30 30 45 45 45 60 60 60 SPEED_MEAN 2.0 3.0 4.0 2.0 3.0 4.0 2.0 3.0 4.0 2.0 3.0 4.0 design point

  18. Design Matrix ANGLE_BOUND 15 15 15 30 30 30 45 45 45 60 60 60 SPEED_MEAN 2.0 3.0 4.0 2.0 3.0 4.0 2.0 3.0 4.0 2.0 3.0 4.0

  19. Design Matrix Experiment Description ANGLE_BOUND 15 15 15 30 30 30 45 45 45 60 60 60 SPEED_MEAN 2.0 3.0 4.0 2.0 3.0 4.0 2.0 3.0 4.0 2.0 3.0 4.0

  20. Network Complexities Bandwidth, physical distance, network traffic, etc.

  21. Network Complexities Scale.

  22. Network Complexities Physical surroundings.

  23. Various simulator engines Researchers have a number of simulator engines to choose from, including ns-2, ns-3, OPNET, CSIM, GloMoSim and MATLAB.

  24. ns-2 Various simulator engines Researchers have a number of simulator engines to choose from, including ns-2, ns-3, OPNET, CSIM, GloMoSim and MATLAB.

  25. ns-3 ns-2 Various simulator engines Researchers have a number of simulator engines to choose from, including ns-2, ns-3, OPNET, CSIM, GloMoSim and MATLAB.

  26. ns-3 ns-2 Various simulator engines Researchers have a number of simulator engines to choose from, including ns-2, ns-3, OPNET, CSIM, GloMoSim and MATLAB.

  27. ns-3 ns-2 GloMoSim Various simulator engines Researchers have a number of simulator engines to choose from, including ns-2, ns-3, OPNET, CSIM, GloMoSim and MATLAB.

  28. ns-3 CSIM ns-2 GloMoSim Various simulator engines Researchers have a number of simulator engines to choose from, including ns-2, ns-3, OPNET, CSIM, GloMoSim and MATLAB.

  29. ns-3 CSIM ns-2 SWAN GloMoSim Various simulator engines Researchers have a number of simulator engines to choose from, including ns-2, ns-3, OPNET, CSIM, GloMoSim and MATLAB.

  30. ns-3 CSIM ns-2 SWAN Matlab GloMoSim Various simulator engines Researchers have a number of simulator engines to choose from, including ns-2, ns-3, OPNET, CSIM, GloMoSim and MATLAB.

  31. Recap • Networks are complex. • Modeling is difficult. • Experimentation isn’t straight-forward. • Mistakes in this process have led to credibility issues in network simulation.

  32. Solution: Automation • Automation provides guidance that helps increase credibility of published research. • Automate the modeling and experiment design processes. • Validate experiments at multiple levels.

  33. “[The Department of Energy] must continue the development of next generation complex networked systems that are more secure, less brittle to unexpected events, and more controllable. For these emerging efforts to be successful, it is essential that a firm intellectual foundation be provided for understanding and simulating large-scale networks.” Motivation for Network Simulation Research Source: J. M. Brase and D. L. Brown, Modeling, Simulation and Analysis of Complex Networked Systems. White paper.

  34. SAFE Simulation Automation Framework for Experiments

  35. ns-3 CSIM ns-2 SWAN Matlab GloMoSim Various simulator engines Researchers have a number of simulator engines to choose from, including ns-2, ns-3, OPNET, CSIM, GloMoSim and MATLAB.

  36. High-level view of the SAFE architecture. My work is concerned with the Model Description and Experiment Description inputs to this framework (in the yellow boxes).

  37. High-level view of the SAFE architecture. My work is concerned with the Model Description and Experiment Description inputs to this framework (in the yellow boxes).

  38. <html:ahref="http://www.google.com">Link to Google</a> Attribute Element content Attribute value Closing tag Namespace prefix Opening tag An HTML “anchor” element. This piece of HTML is valid XML. It is an a tag with an attribute href. The value of the href attribute is a URL string and the content of the a tag is the “Link to Google” string.

  39. NEDL: Experiment Description

  40. ANGLE_BOUND SPEED_MEAN NEDL Functionality Provide ways to list experimental factors.

  41. <nedl:factorlist> <nedl:factor>SPEED_MEAN</nedl:factor> <nedl:factor>ANGLE_BOUND</nedl:factor> </nedl:factorlist> A NEDL “factorlist” element The “factorlist” element lists all of the factor names referenced throughout the rest of the NEDL document.

  42. ANGLE_BOUND 15 30 45 60 SPEED_MEAN 2.0 3.0 4.0 NEDL Functionality Provide ways to list associated level values for each factor.

  43. “Use the values 3, 17, and 9.” ANGLE_BOUND 15 30 45 60 SPEED_MEAN 2.0 3.0 4.0 NEDL Functionality Provide ways to list associated level values for each factor.

  44. <nedl:memberof> <nedl:factor>SPEED_MEAN</nedl:factor> <nedl:listid>speed-values</nedl:listid> </nedl:memberof> <nedl:levellistid="speed-values"> <nedl:level>3</nedl:level> <nedl:level>17</nedl:level> <nedl:level>9</nedl:level> </nedl:levellist> A NEDL “memberof” element The “memberof” element associates a factor with a list of level values. The name was inspired by the terminology used to describe mathematical sets.

  45. “Use values of the form 5∙x + 2, where 0 ≤ x ≤ 9” ANGLE_BOUND 15 30 45 60 SPEED_MEAN 2.0 3.0 4.0 NEDL Functionality Provide ways to list associated level values for each factor.

  46. <nedl:sequence> <nedl:factor>SPEED_MEAN</nedl:factor> <nedl:test>EQUALS</nedl:test> <nedl:lconst>2</nedl:lconst> <nedl:op>PLUS</nedl:op> <nedl:rexpr> <nedl:lconst>5</nedl:lconst> <nedl:op>MULT</nedl:op> <nedl:rvar>x</nedl:rvar> </nedl:rexpr> <nedl:where> <nedl:range> <nedl:var>x</nedl:var> <nedl:lo>0</nedl:lo> <nedl:hi>9</nedl:hi> <nedl:delta>1</nedl:delta> </nedl:range> </nedl:where> </nedl:sequence> A NEDL “sequence” element The “sequence” element associates a factor with a sequence of level values. The sequence expression is built up using constants, variables, and further expression elements.

  47. SPEED_MEAN 2.0 3.0 4.0 • 2.0 • 3.0 • 4.0 • 2.0 • 3.0 • 4.0 • 2.0 • 3.0 • 4.0 ANGLE_BOUND 15 15 15 30 30 30 45 45 45 60 60 60 NEDL Functionality Provide ways to “prune” design points from the experiment space.

  48. SPEED_MEAN 2.0 3.0 4.0 • 2.0 • 3.0 • 4.0 • 2.0 • 3.0 • 4.0 • 2.0 • 3.0 • 4.0 ANGLE_BOUND 15 15 15 30 30 30 45 45 45 60 60 60 “Remove the specific design point where ANGLE_BOUND = 15 and SPEED_MEAN = 3.0.” NEDL Functionality Provide ways to “prune” design points from the experiment space.

  49. <nedl:exclusionrestriction> <nedl:settingfactor="ANGLE_BOUND" level="15"/> <nedl:settingfactor="SPEED_MEAN" level="3"/> </nedl:exclusionrestriction> A NEDL “exclusionrestriction” element The “exclusionrestriction” element specifies a whole or partial design point which should not be included in the final experiment space.

  50. SPEED_MEAN 2.0 3.0 4.0 • 2.0 • 3.0 • 4.0 • 2.0 • 3.0 • 4.0 • 2.0 • 3.0 • 4.0 ANGLE_BOUND 15 15 15 30 30 30 45 45 45 60 60 60 “Remove any design points where SPEED_MEAN = 4.0.” NEDL Functionality Provide ways to “prune” design points from the experiment space.

More Related