Regenerating Networked Systems’ Monitoring Traces with Neural Networks (NOMS'22)

Algorithm for correcting sessions of users of large-scale networked systems based on deep learning.

Input parameters:

Arguments(run_NOMS22.py):
    
    -h, --help          Show this help message and exit
    --append, -a        Append output logging file with analysis results (default=False)
    --trials, -r        Number of trials (default=1)
    --start_trials, -s  Start trials (default=0)
    --skip_train, -t    Skip training of the machine learning model training?
    --campaign, -c      Campaign [demo, lstm, no-lstm, deterministic](default=demo)
    --verbosity, -v     Verbosity logging level (INFO=20 DEBUG=10)

--------------------------------------------------------------

Arguments(main.py):

    -h, --help                  Show this help message and exit
    --original_swarm_file       File of ground truth.
    --training_swarm_file       File of training samples
    --corrected_swarm_file      File of correction
    --validation_swarm_file     File of validation
    --failed_swarm_file         File of failed swarm
    --analyse_file              Analyse results with statistics
    --dense_layers              Number of dense layers (e.g. 1, 2, 3)
    --neurons NEURONS           Number neurons per layer
    --cells CELLS               Numbers cells(neurons) LSTM
    --num_sample_training       Number samples for training
    --num_epochs                Number epochs training
    --analyse_file_mode         Open mode (e.g. 'w' or 'a')
    --model_architecture_file   Full model architecture file
    --model_weights_file        Full model weights file
    --size_window_left          Left window size
    --size_window_right         Right window size
    --threshold                 i.e. alpha (e.g. 0.5 - 0.95)
    --pif PIF                   Pif (only for statistics)
    --dataset DATASET           Dataset (only for statistics)
    --seed SEED                 Seed (only for statistics)
    --lstm_mode                 Activate LSTM mode
    --no-lstm_mode              Deactivate LSTM mode
    --skip_train, -t            Skip training of the machine learning model
    --deterministic_mode        Set deterministic correction mode
    --skip_correct, -c          Skip correction of the dataset
    --skip_analyse, -a          Skip analysis of the results
    --verbosity, -v             Verbosity logging level (INFO=20 DEBUG=10)
    --mode MODE                 Mode

    --------------------------------------------------------------
    Full traces available at: https://github.com/ComputerNetworks-UFRGS/TraceCollection/tree/master/01_traces

Run (all F_prob experiments):

python3 run_nom22.py -c lstm

Run (only one F_prob scenario)

python3 main.py

Run (all F_mon experiments):

python3 run_mif.py -c lstm

Run (only one F_mon scenario)

python3 main_mif.py

Requirements:

matplotlib 3.4.1 tensorflow 2.4.1 tqdm 4.60.0 numpy 1.18.5

keras 2.4.3 setuptools 45.2.0 h5py 2.10.0

ACKNOWLEDGMENTS

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. We also received funding from Rio Grande do Sul Research Foundation (FAPERGS) - Grant ARD 10/2020 and Nvidia – Academic Hardware Grant