IIsy introcuces hybrid in-network ML deployment which employs a small in-network ML model on the network device and a large ML model over the end-point. This is the artifact for the paper "IIsy: Hybrid In-Network Classification Using Programmable Switches" in IEEE/ACM Transactions on Networking (with early arXiv version named "IIsy: Practical In-Network Classification"). 💡IIsy's model mapping for in-network ML is now integrated in Planter link.

• Change the dataset. Making sure the new data loader has the load_data function.

  vim src/<dataset name>_dataset.py
  

  Open the main file by using vim main.py and change the load data name code such as:

  from src.Iris_dataset import load_data
  ...
  features_subset = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']
  ...
  X_train, y_train, X_test, y_test, used_features = load_data(4, './Data')
  

• Change the parameters (e.g., model size, depth, and used features) for the small in-network ML model on the network device and a large ML model over the end-point.

  vim main.py
  

• Run the simulation.

  python3 main.py
  

  The algorithm will output the switch fraction and accuracy of both model and syatem under different confidence thresholds.

  ...
  Switch confidence th=0.64: AUC 0.981143 , Macro-F1  0.963309 , Accuracy 0.984522 , Precision 0.983752 , Recall 0.998776 , Precision normal switch 0.985413 , Precision normal server 0.999157  , Precision anomaly switch 0.000000 , Precision anomaly server 0.912983 , Switch Fraction 0.848257
  Switch confidence th=0.66: AUC 0.981143 , Macro-F1  0.963309 , Accuracy 0.984522 , Precision 0.983752 , Recall 0.998776 , Precision normal switch 0.985413 , Precision normal server 0.999157  , Precision anomaly switch 0.000000 , Precision anomaly server 0.912983 , Switch Fraction 0.848257
  Switch confidence th=0.68: AUC 0.981144 , Macro-F1  0.963354 , Accuracy 0.984540 , Precision 0.983774 , Recall 0.998774 , Precision normal switch 0.985442 , Precision normal server 0.999164  , Precision anomaly switch 0.000000 , Precision anomaly server 0.912990 , Switch Fraction 0.848227
  Switch confidence th=0.70: AUC 0.981144 , Macro-F1  0.963354 , Accuracy 0.984540 , Precision 0.983774 , Recall 0.998774 , Precision normal switch 0.985442 , Precision normal server 0.999164  , Precision anomaly switch 0.000000 , Precision anomaly server 0.912990 , Switch Fraction 0.848227
  ...
  

Please submit an issue with the appropriate label on Github.

The files are licensed under Apache License: LICENSE. The text of the license can also be found in the LICENSE file.

If you use this code, please cite our papers:

@article{zheng2024iisy,
  title={{IIsy: Hybrid In-Network Classification Using Programmable Switches}},
  author={Zheng, Changgang and Xiong, Zhaoqi and Bui, Thanh T and Kaupmees, Siim and Bensoussane, Riyad and Bernabeu, Antoine and Vargaftik, Shay and Ben-Itzhak, Yaniv and Zilberman, Noa},
  journal={IEEE/ACM Transactions on Networking},
  year={2024}
}

@article{zheng2024automating,
  title={{Planter: Rapid Prototyping of In-Network Machine Learning Inference}},
  author={Zheng, Changgang and Zang, Mingyuan and Hong, Xinpeng and Perreault, Liam and Bensoussane, Riyad and Vargaftik, Shay and Ben-Itzhak, Yaniv and Zilberman, Noa},
  journal={ACM SIGCOMM Computer Communication Review},
  year={2024}
}

We are also excited to introduce several IIsy related papers (Survey, DINC, and QCMP) in the link.

The following people contributed to this project: Changgang Zheng, Zhaoqi Xiong, Thanh T Bui, Siim Kaupmees, Riyad Bensoussane, Antoine Bernabeu, Shay Vargaftik, Yaniv Ben-Itzhak, and Noa Zilberman. This work was partly funded by VMware, EU Horizon SMARTEDGE (101092908, UKRI 10056403), Leverhulme Trust (ECF-2016-289) and Isaac Newton Trust. We acknowledge support from Intel.