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INFOCOM 2024: Online Resource Allocation for Edge Intelligence with Colocated Model Retraining and Inference

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# Online Resource Allocation for Edge Intelligence with Colocated Model Retraining and Inference
# Prerequisites
- pytorch, numpy, thop
# Usage

This is the implementation of ORRIC using the trained model in folder `models`.

The corresponding dataset can be found at [https://zenodo.org/records/2535967](https://zenodo.org/records/2535967). Please decompress `CIFAR-10-C.tar` in the root directory to obtain the `CIFAR-10-C` folder.
```
# ORRIC implementation
python train_inference_two_model.py 
```

Others:
```
# teacher (resnet_50) training
python res50_teacher_training.py
# student (mobilenet_v2) training
python mobile_student_training.py

# measure the MACs
python MACs.py
# measure the time
python measure_time.py
# measure the performance
python performance.py
```

# Slides

[Slides](https://caihuaiguang.github.io/Publication/INFOCOM2024/Online_Resource_Allocation_for_Edge_Intelligence_with_Colocated_Model_Retraining_and_Inference_slides.pdf)

# Citation
```
@INPROCEEDINGS{cai2024ORRIC,
  author={Huaiguang Cai and
          Zhi Zhou and
          Qianyi Huang},
  booktitle={IEEE INFOCOM 2024 - IEEE Conference on Computer Communications}, 
  title={Online Resource Allocation for Edge Intelligence with Colocated Model Retraining and Inference}, 
  year={2024},
  pages={1900-1909},
  doi={10.1109/INFOCOM52122.2024.10621206}}
```

# License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details

ORRIC

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