BTSPrediction

The BTS Prediction Machine Learning Units

These units are used to predict the failure event at base transceiver stations applying 4 ML models.

Data

The description of raw data is in bts data and a small pre-processing data file is within this directory. The pre-processing data was transformed from the raw data for the ML.

Many months of raw data are not shared here, available based on the discussion with the author.

Models

• DNN Multi Regression
• DNN Single Regression
• Multi Var LR
• Single Var RL
• LSTM single series

The ML unit relies on ML models trained with the data mentioned above.

TODO: a short info to indicate where is the code for training

LSTM single series

This model is

• applied to a single station for a single parameter (e.g., Load of Power Grid)

Further trained information can be found in the QoA4ML paper.

Simple test case

A simple test case includes:

• A message broker, using RabbitMQ, are used for sending and receiving requests and results
• A client-v1 sends normalized data for prediction and gets results
• A ML Unit as a service loads trained models from exported format (in TensorFlowLite), obtains requests from the the broker, performs the prediction and returns the predicted value

TODO:

• A client-v2 just sends raw sensoring data to a ML service v2 which performs data preprocessing and other data processing tasks and serving

Requirements

• Python3
• Pika
• Docker
• TensorFlow/TensorFlowLite

Running the experiment

• Start RabbitMQ using docker composed by running the script (run.sh) in server folder
• Start server/bts_prediction_server.py
• Use the example client code with our data

Authors/Contributions