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This repository contains the scripts used to run experiments to evaluate the Opencraft 2 Education project. The scripts found in das/ were adapted from Net-Celerity to run with this project as well.

Using this repository

Experiments are designed to be run on the DAS5/6 machines.

DAS Setup

  • Many of the scripts are dependent on your studentID, so change the value found in config.cfg

  • Move your linux build of Opencraft to /var/scratch/student_id/

  • Move the das/ folder to /home/student_id/

  • Follow the linux install instructions for miniconda, but change the install directory to be at /var/scratch/student_id/

  • Navigate to the das/ folder and create the conda environment with

    conda env create -f environment.yml

  • You can manually start it with

    conda activate experiment-env

  • Add the above line to the end of your .bashrc file so it is active on start-up for every node

Running experiments

There are 3 main experiment scripts: base, players and pareto. Each have different configuration options at the top of the file.

  • Potential terrain types include "Empty", "1-Layer", "2-Layer", "3-Layer", "4-Layer", "5-Layer", "RollingHills"
  • Active logic can either be "" or "-activeLogic"
  • CircuitX / CircuitZ / Radius specify the dimensions of circuit generation

Nodes on DAS should be reserved via preserve -np _ -t _ where np is the number of nodes and t is the duration they are reserved for. You may need to run module load prun beforehand, or you can add it to your .bashrc so it loads on startup. After that, you run preserve -llist to see which nodes you received and fill in config.cfg with the appropriate information.

Different experiments were run for different purposes and so loop over different parameters to run experiments in one go.

Logs from every client and the server are collected into opencraft_logs directories. The internal application statistics are collected into opencraft_stats. Each node's system statistics are collected into system_logs.

After running the experiments, you can then run the get_data.sh script locally and it will pull all the recent runs to the data/ folder. After doing so, you can then run clear_data.sh on DAS so you aren't pulling the same data again

Data

The shared_config file contains some parameters that most other scripts in the scripts directory are dependent on. The main one to consider is the experiment_name as that dictates which folder other scripts will look at when managing data.

Data sanitisation

The server statistics collected start from when the server is run, rather than when the experiment begins after clients are connected. The following 3 scripts can be run in order to format the data, sum it all together and finally give an averaged csv file that other plotters will use: data_formatter.py, data_summer.py and data_averager.py.

Plotting

All plots output to the plots directory. Plots were created and adapted for the specific experiments run, so may not be compatible for other variations.

Certain scripts are designed for specific experiments while others work for all. stacked_line_graph.py and stacked_bar_graph.py work for any experiment and have an optional command-line argument all to include more statistics in the output. fps_scatter.py works for most experiments.

bar_combined.py and fps_scatter_combined.py were made specifically for players experiments and pareto.py for the pareto experiment.

Overhead

resource_usage.py is used to plot graphs related to overhead but system_logs_extracter.py should be run first to move the relevant system_logs to the data/overhead/ directory.