This project has been developed by
- Esteban Carisimo (Universidad de Buenos Aires & CONICET)
- Julian M. Del Fiore (Universite de Strasbourg)
- Diego Dujovne (Universidad Diego Portales)
- Cristel Pelsser (Universite de Strasbourg)
- J. Ignacio Alvarez-Hamelin (Universidad de Buenos Aires & CONICET)
One of our main goals is allowed other researchers to reproduce and replicate our analyses.
The following instruction will clearly explain how to set up your system, enable a working space and clone this repo.
In particular, we have divided our code into two pieces: data acquisition (fetch_data.sh) and analyses (run_notebooks.sh).
sudo apt-get -y install python3-pip
sudo pip3 install jupyter
More about JupyterLab: (https://www.digitalocean.com/community/tutorials/how-to-set-up-jupyter-notebook-with-python-3-on-ubuntu-18-04)
BGPdump home page: (https://bitbucket.org/ripencc/bgpdump/wiki/Home)
sudo apt-get install autoconf zlib1g-dev libbz2-dev liblzma-dev
sudo apt install mercurial
hg clone https://bitbucket.org/ripencc/bgpdump
cd bgpdump
sh ./bootstrap.sh
make
sudo make install
# Remember to go back to whatever directory where you’d like to create the working space
cd ..
sudo apt-get install python3-venv
It might be necessary to upgrade pip3
sudo pip3 install --upgrade pip
Time to finally clone this repo
git clone git@github.com:CoNexDat/latam-ixp-obs.git
To the ones who are not familiar with, venv the following lines will create a local copy of Python 3 as well as all libraries necessary to run our code (scripts and notebooks).
Having a local copy of Python's library will isolate our working space from the rest of the OS, which prevents dealing with broken package dependencies.
More info: (https://docs.python.org/3/library/venv.html)
cd latam-ixp-obs
python3 -m venv latam-ixp-obs
source latam-ixp-obs/bin/activate
pip3 install ipykernel
ipython kernel install --user --name=latam-ixp-obs
pip3 install -r requirements.txt
Our entire research was documented in Jupyter Notebooks, where code is displayed along with results.
However, in case you would like to rerun our code, we provide two scripts fetch_data.sh and run_notebooks.sh.
The former script invokes several python script to fetch a wide variety of datasets (BGP table dumps, delegation files, etc) from different data providers (PCH, RouteViews, etc.).
We decided to take a non-aggresive download strategy to avoid overwhealming data repositories as well as users connection.
Hence, it could take long (even more than a day) to download the entire dataset.
The latter script will rerun our analyses.
To avoid overwritting source files, after latest results will appear on new notebooks in notebooks/v4/RERUN_*.ipynb
To download our dataset (whether you are interested or not in rerunning the notebooks)
sh fetch_data.sh
We provide a link to our LG data collection In case you would like to use our dumps: https://cnet.fi.uba.ar/latam-ixp-obs/lg-ribs/
To rerun our notebooks
sh run_notebooks.sh
To open any of our notebooks (either source notebooks or rerun ones) from repo's root
jupyter notebook notebooks/v4/NOTEBOOK_FILE_NAME