tsnkit

A scheduling and benchmark toolkit for Time-Sensitive Networking in Python

@inproceedings{xue2024real,
  title={Real-Time Scheduling for 802.1Qbv Time-Sensitive Networking (TSN): A Systematic Review and Experimental Study},
  author={Chuanyu Xue and Tianyu Zhang and Yuanbin Zhou and Mark Nixon and Andrew Loveless and Song Han},
  booktitle={Proceedings of the 30th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS)},
  year={2024},
  organization={IEEE}
}

Paper link: https://arxiv.org/abs/2305.16772

Documentation (work-in-progress): https://tsnkit.readthedocs.io

Install

Install from source (recommended):

git clone https://github.com/ChuanyuXue/tsnkit
cd tsnkit
python setup.py install

From pip:

pip install -U tsnkit

Usage

Testing:

python3 -m tsnkit.models.[METHOD] [STREAM PATH] [NETWORK PATH]

Reproducing benchmark paper results:

1. Check out to legacy branch.
2. Download data.gz from git-lfs, and unzip it to data folder. (Or generate it using data/input/generate_data.ipynb)
3. Go src foder and run python main.py --method=ALL --start=0 --end=38400.

Both main and legacy branches use the same logic (models & algorithms). However, we have refined the organization in the main branch by introducing a unified interface and standardized type notation to enhance maintainability and simplify the efforts to add new methods. The legacy branch houses the source code record used in the paper.

Code structure:

• src/tsnkit/models: Inplementations of all supported scheduling methods.
• src/tsnkit/simulation: TSN simulator to validate the scheduling output.
• src/tsnkit/utils: Data structures and helper functions.

Contribute

Contributions are welcome! Feel free to add your own scheduling algorithm in this toolkit. And contact me to update your new scheduling method into our benchmark paper!

Refer to src/tsnkit/models/__init__.py to implement the required interface and benchmark entrance.