What is this repository?

This repository contains an example of how you can use SEM to run your ns-3 simulations, analyze the results, and share them with the community.

Keep reading for an overview of the contents of this example repository, and clone the repo to follow along and experiment by yourself:

git clone --recurse-submodules https://github.com/signetlabdei/sem-example

The SEM workflow

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NOTE

This section describes the contents of this repo. If you want a step-by-step guide on how to initialize your own SEM project, follow this section.

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The file structure you see in this repo is meant to provide a reference on how to structure your SEM projects, and explain its components before you go on and create your own. What you see here is very similar to what you can expect to end up with when analyzing your very own ns-3 simulations using SEM: an ns-3 installation in the ns-3 folder, a results folder containing the outcomes of the simulations you ran through SEM, a set of tools to inspect what your simulation is doing in the visualization folder, a script to obtain and plot some results called analysis.py, and a report showing the results of your analysis in the report folder.

The ns-3 submodule

First of all, notice that we have an ns-3 folder - since the point of SEM is to run and analyze ns-3 simulations, you will always need to have an ns-3 folder to work on.

Interactive discovery with Jupyter notebooks

Since SEM runs on Python, it can be used as an interface between ns-3 and the data visualization facilities provided by Jupyter notebooks. To see how this can be done, either run your own Jupyter notebook server locally by issuing the jupyter notebook in a shell or launch a fully working online example on binder.

The analysis script

Jupyter notebooks can be handy for interactive sessions and for looking at your simulation results, however some users prefer more streamlined and REPL-based development. Check out the analysis.py file to get an idea of this workflow: the main steps to obtain results are the same as in the Jupyter notebook example, but this file also shows how to save the parsed data in external files.

Saving results and pulling them in from LaTeX

Inside the report folder you can find an example of a simple LaTeX report containing figures generated from data output by the analysis.py SEM script.

Kickstarting your own ns-3 simulation campaign

If you plan on creating your own simulation campaign using the workflow we just described, here's a set of commands and steps that will get you started in no time:

1. Create a folder for your analysis
2. Inside the folder, clone the ns-3 installation you will run simulations with
3. Create a Python script to perform the analysis in - here we will call it analysis.py, but you can choose whatever name best fits your purpose.