Traffic forecasting study case of an application server (cluster, servers x4) using Machine Learning. The project consists of three parts, all presented in this README file:

• Data Preprocessing: Preprocessing of the input data (different levels of aggregation)
• Data Statistics: Statistical analysis of the input and preprocessed data (seasonality, trends)
• Traffic forecast using Machine Learning algorithms (time series forecasting)

The input data file contains traffic data per minute, on four application servers which they consist a single cluster. The traffic is disctributed to these four application servers in a load balanced way. The format of the input data is:

date	        host	requests
15/03/26 14:00	as-01	316
15/03/26 14:00	as-02	285
15/03/26 14:00	as-03	306
15/03/26 14:00	as-04	286
15/03/26 14:01	as-01	268
15/03/26 14:01	as-02	303
15/03/26 14:01	as-03	266
15/03/26 14:01	as-04	290
...

The available data are from 2015-03-26 14:00:00 till 2020-04-03 19:59:00.

• runForecast.py: Main script for the Traffic Forecast part. Use python runForecast.py -h for available options.
• utils.py: Utilities script for the Traffic Forecast part.
• dataFactory.py: Main script for the Data Preprocessing part.
• dataStatistics.py: Main script for the Data Statistics part.
• trafficForecast.py: Interface for the Traffic Forecast part.
• model.py: Abstract class for each model implementation.
• dnn.py: Deep Neural Network implementation.
• rnn.py: Recurrent Neural Network implementation. Not added to the repository yet.
• lstm.py: Long Short Term Memory Neural Network implementation. Not added to the repository yet.

• Data Preprocessing
• Data Statistics
• Traffic Forecast