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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2. Requirements
Configuring GPU Usage in a Machine Learning Pipeline
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Refer to the image below, and make sure to set
_measurementto "cell_metrics"
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3. Upload AI/ML Pipeline Script (Jupyter Notebook)
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Set the TrainingJob name in lowercase
Configure the Feature Filter
For the query to work correctly, use backticks(`) to specify a specific cell site for filtering (e.g., `Viavi.Cell.Name` == "S1/B13/C1")
Refer to the image below
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5. Result
These logs can be reviewed through the logs of the Kubeflow pod generated during training execution, and the details that can be checked are as follows:
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