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Internet Backbones in Space

This README contains all the information necessary to replicate the results in the paper "Internet Backbones in Space", submitted to ACM CCR (January 2020).

The simulations presented in the paper are based on an in-house satellite network routing simulator.

Contents

The files in this submission are organized in the following way:

├── analysis: python scripts for the generation of the results in the paper. 
├── data: data required for the experiments.
│   ├── preproc: data that results from the pre-processing steps.
│   └── raw: raw datasets, sourced from external authorities.
├── figures: output folder for the analysis.
├── lib: auxiliary libraries.
└── preproc: scripts to pre-process datasets before the analysis.

Installing the dependencies

The simulation scripts run on python 3.6. We provide a simplified way of installing the many dependencies by using a docker image. A manual installation is also possible.

Using Docker

The simplest way to set up and run the experiments is buy building a docker image and running a docker container. Bear in mind that each of the following steps may require considerable amounts of time and computing resources.

  1. Build the Docker image with the dependencies 
    docker build -t ccr .
  2. Run a container from the image just built 
    docker run -it ccr
  3. Activate the virtual environment 
    pipenv shell
  4. Now it is possible to run experiments.

Manual installation

The experiments use pipenv as a python virtual environment manager. To install pipenv see the main page.

There are some dependencies, however, that are external to the pipenv environment. In particular, the dependencies for cartopy and cfgrib have to be installed before the virtual environment.

To install the virtual environment and all the dependencies, run in the root project directory pipenv install. Then, to activate the environment: pipenv shell. Now experiments can be run.

Running the experiments

We now describe how to run the experiments presented in the paper. Before you start, make sure that all the dependencies are installed correctly and that the virtual environment is activated (pipenv shell in the root folder of the submission).

The output of the analysis steps is printed to terminal, and the resulting figures will be found in figures/.

The experiments are presented in the order they appear in the paper.

GST cost of deployment estimation

The analysis of the costs of deployment is specific to a number of ground stations and to a number of sampling rounds, as the location of the ground stations is sampled randomly from a GDP distribution.

For example, to estimate the deployment cost of 1000 ground stations, re-sampling their positions 100 times:

python analysis/cost_analysis.py -n 1000 -r 1000

The script also outputs the cost of deploying GSTs at the biggest cities.

Latency under GST placement

The simulation is self-contained: python analysis/ixp_cities_deployment.py

Latency under rain fade

This experiment requires a longer pipeline, as it needs to download historical weather data and to process them. The experiments in the paper requires weather data recorded globally twice a day, for every day for 1 year (2018 in this case).

NOTE: The following steps require hours running on a 70-cores machine, ~50 GB of disk space and >10 GB of memory. To ease the replication process, we also provide instructions for the experiment on a smaller set of inputs. It is presented after the full pipeline.

  1. The first step is to download the dataset with the historical weather data. It is possible to specify the desired time interval to download with the additional command line arguments. 
    python preproc/download_gfs_data.py --out data/raw/gfs_1_year
  2. Then, this dataset is used to compute the inactive ground stations for each time-instant in the dataset. 
    python preproc/rainfall_to_inactive_gst.py data/raw/gfs_1_year data/preproc/inactive_gst_1_year.pkl
  3. Given the different sets of inactive ground stations, re-routing and path-control algorithms are run on each of the topologies resulting from disconnecting the ground stations. It is possible to specify the number of computing cores to execute the experiment in parallel. For the results in the paper, it is sufficient to run this for path-control=3. 
    python preproc/rerouting_experiment.py 3 data/preproc/inactive_gst_1_year.pkl data/preproc/reroute_pc3/ --cores 1
  4. Finally, the results of these operations are summarized in the plots that are also found in the paper: 
    python analysis/paco_rero_compare.py data/preproc/reroute_pc3/

Restricted Case: Instead of running the experiment on every day for a year, in this restricted case we look at 2 days per month over the duration of 2 years. The intermediate results of the pipeline are also provided in the submission material, and are marked with DEFAULT_ as a filename prefix. Using these files will allow testing single steps of the pipeline independently. Of course the results will slightly deviate from the ones in the paper.

  1. Download the dataset with historical weather data for the selected period we externally hosted. 
    wget https://polybox.ethz.ch/index.php/s/9ZTkBII6jwJabP8/download -O data/raw/DEFAULT_gfs_2month.tar.gz
tar -xvzf data/raw/DEFAULT_gfs_2month.tar.gz -C data/raw
  2. Compute the inactive ground stations. 
    python preproc/rainfall_to_inactive_gst.py data/raw/DEFAULT_gfs data/preproc/inactive_gst_2month.pkl
  3. Run re-routing and path control on the topologies resulting from the inactive ground stations (default file output form the previous step: data/preproc/DEFAULT_inactive_gst_2month.pkl) 
    python preproc/rerouting_experiment.py 3 data/preproc/DEFAULT_inactive_gst_2month.pkl data/preproc/reroute_pc3_2month/ --cores 1
  4. Analyze the results (default directory of results: data/preproc/DEFAULT_reroute_pc3_2month) 
    python analysis/paco_rero_compare.py data/preproc/reroute_pc3_2month/