tsnkit 0.3.0

A simple scheduling toolkit for time-sensitive network in Python.

tsnkit

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

@inproceedings{xue2024real,
  title={Real-time scheduling for 802.1 Qbv time-sensitive networking (TSN): A systematic review and experimental study},
  author={Xue, Chuanyu and Zhang, Tianyu and Zhou, Yuanbin and Nixon, Mark and Loveless, Andrew and Han, Song},
  booktitle={2024 IEEE 30th Real-Time and Embedded Technology and Applications Symposium (RTAS)},
  pages={108--121},
  year={2024},
  organization={IEEE}
}

Paper link: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10568056

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

Install

Install from source (recommended):

git clone https://github.com/ChuanyuXue/tsnkit
cd tsnkit
pip install .

From pip:

pip install -U tsnkit

Usage

Testing:

python3 -m tsnkit.algorithms.[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:

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

Related projects:

• OMNeT_TSNkit: Integrating TSNkit into OMNeT++ for simulation.
• VisTSN: Displaying TSN real-world testbed status when TSNKit results applies.

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 tsnkit/algorithms/__init__.py to implement the required interface and benchmark entrance.