supunab/netty-performace-tuning

Scripts for running performance tests and tuning algorithm implementations.

Netty Performance Tuning

Scripts for running performance tests and tuning algorithm implementations. This is particularly targeted to use with https://github.com/smb564/adaptive-concurrency-control.

Installing python packages

The most easiest way to install packages for this is to create a virtual environment inside.

Create a virtual environment using the following command. (If you are not familiar just google "python virtual environments") (The below will create a virtual environment named venv)

python3 -m venv venv

Now avtivate the virtual environment using the following command.

source venv/bin/activate

After activating the environment, you should install the packages you will be using. Following packages are required for this project. (You can use pip to install packages)

pip install requests matplotlib sklearn numpy scikit-optimize scipy hyperopt

Automation Scripts

At the time of writing, there were two test automation scripts.

netty_script_dataset.sh runs a set of experiments and collects results for different use cases with dynamic, and several fixed thread pool sizes

netty_script.sh runs a simple bayesian tuning case. You can improve this to run tuning case against the default case and generate comparison plots using the plots (like in TPC-W case).