1. Introduction
In this use case, we utilize the "Cell Metrics"(RRU.PrbUsedDl) dataset provided by the O-RAN SC SIM space, which includes synthetic data generated by a simulator, with all data recorded in Unix timestamp format.
The model training process is carried out on the O-RAN SC AI/ML Framework, including GPU support, and considers both traditional machine learning (ML) and deep learning (DL) approaches. For ML models, we use Random Forest and Support Vector Regression (SVR), while for DL models, we employ RNN, LSTM, and GRU architectures.
By managing the ON/OFF state of cells through traffic forecasting, we can reduce power consumption. Additionally, if the AI/ML models used for forecasting are operated in an eco-friendly manner, further power savings can be achieved. In this use case, we measure the carbon emissions and energy consumption during the cell traffic forecasting process using AI/ML to ensure that the forecasting model is not only effective but also environmentally sustainable.
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CellReports.csv
The file contains traffic data from Viavi.View file name insert.py The file processes the GPU_dataset and inserts the data into InfluxDB.
(Changed required: DATASET_PATH , INFLUX_IP , INFLUX_TOKEN)View file name pipeline.ipynb The file defines the model structure and training process.
View file name deploy.yaml The yaml file is used for deploying model inference service.
View file name predict.sh The script used for excuting the model prediction.
add inference files….(yaml, inference 실행되는 sh 파일, 예시 input 파일, 예시 output 파일)
5. Example
Contributors
Peter Moonki Hong - Samsung
Taewan Kim - Samsung
Corbin(Geon) Kim - Kyunghee Univ. MCL
Sungjin Lee - Kyunghee Univ. MCL
Hyuksun Kwon - Kyunghee Univ. MCL
Hoseong Choi - Kyunghee Univ. MCL
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