analysis

Theodolite Analysis

This directory contains Jupyter notebooks for analyzing and visualizing benchmark execution results and plotting. The following notebooks are provided:

• demand-metric.ipynb: Create CSV files describing scalability according to the Theodolite demand metric.
• demand-metric-plot.ipynb: Create plots based on such CSV files of the demand metric.
• capacity-metric-plot.ipynb: Create plots based on such CSV files of the capacity metric.

For legacy reasons, we also provide the following notebooks, which, however, are not documented:

• scalability-graph.ipynb: Creates a scalability graph for a certain benchmark execution.
• scalability-graph-plotter.ipynb: Combines the scalability graphs of multiple benchmarks executions (e.g. for comparing different configuration).
• lag-trend-graph.ipynb: Visualizes the consumer lag evaluation over time along with the computed trend.

Usage

In general, the Theodolite Analysis Jupyter notebooks should be runnable by any Jupyter server. To make it a bit easier, we provide introductions for running notebooks with Docker and with Visual Studio Code. These intoduction may also be a good starting point for using another service.

For analyzing and visualizing benchmark results, either Docker or a Jupyter installation with Python 3.10 is required (e.g., in a virtual environment).

Running with Docker

This option requires Docker to be installed. You can build and run a container using the following commands. Make sure to set the results volume to the directory with your execution results and results-inst to a directory where the final scalability graphs should be placed. The output of the run command gives you an URL of the form http://127.0.0.1:8888/?token=..., which you should open in your webbrowser. From there you can access all notebooks. You can stop the Jupyter server with Crtl + C.

docker build . -t theodolite-analysis
docker run --rm -p 8888:8888 -v "$PWD/../results":/home/jovyan/results -v "$PWD/../results-inst":/home/jovyan/results-inst theodolite-analysis

Running with Visual Studio Code

The Visual Studio Code Documentation shows to run Jupyter notebooks with Visual Studio Code. For our notebooks, Python 3.10 or newer is required (e.g., in a virtual environment). Moreover, they require some Python libraries, which can be installed by:

pip install -r requirements.txt