This project is part of my master thesis at the Technical University Munich and also part of the MaLSAMi project of the Chair for Operating Systems at the Department of Informatics.

Recurrent Neural Network (RNN) based Schedulability Analysis

Schedulability analysis (SA) using a recurrent neural network (RNN).

Data

The Task-Sets are given through a SQL-database with the following three tables:

• TaskSet: Set_ID, Successful, TASK1_ID, TASK2_ID, TASK3_ID, TASK4_ID
• Task: Task_ID, Priority, Deadline, Quota, CAPS, PKG, Arg, CORES, COREOFFSET, CRITICALTIME, Period, Number_of_Jobs, OFFSET
• Job: Set_ID, Task_ID, Job_ID, Start_Date, End_Date, Exit_Value

Installation and Start

Download or clone the hole project. Add the database as described above to the project directory. Change to the project directory and type

vagrant up

to start the vagrant machine. Then type

vagrant ssh

to ssh into the vagrant machine. Change the directory with

cd /vagrant

to the project directory. Then you can do different things:

• do hyperparameter exploration
• train and evaluate a single model
• plot experiment results

Hyperparameter Exploration

For hyperparameter exploration uncomment line 66 in main.py and specifiy a name and number for the experiment (also name of the resulting csv-file):

hyperparameter_exploration(data=data, name='This is the name of the experiment', num='This is the
 number of the experiment')

The hyperparameter, that are tested, are defined in params.py. Currently the following hyperparameters are included:

Hyperparameter	Description
batch_size	Size of the data batches (number of task-sets), for which the weights are updated
num_epochs	Number of epochs, in which the hole dataset is processed
keep_prob	Percentage of inputs that are forwarded; (1 - keep_prob) are the number of inputs that are not forwarded by the dropout layer
num_cells	number of cells (LSTM, GRU) or recurrent layers
hidden_layer_size	size of the layers (number of neurons per layer)
hidden_activation	activation function of the layers
optimizer	algorithm to optimize the weights

There are also some configuration parameters defined in this file, to specifiy the hyperparameter experiment:

Configuration Parameter	Description
use_checkpoint	if the ModelCheckpoint callback should be used (saves the model)
checkpoint_dir	directory where the model should be saved
checkpoint_verbose	how much information should be printed to the console during saving of the model
use_earlystopping	if the EarlyStopping callback should be used (stops training if no improvement)
use_tensorboard	if the TensorBoard callback should be used (collects information for TensorBoard)
tensorboard_log_dir	directory where the TensorBoard log-files should be saved
use_reduceLR	if the RecudeLROnPlateau callback should be used (adapts learning rate automatically)
verbose_training	how much infomration should be printed to the console during training
verbose_eval	how much information should be printed to the console during evaluation
time_steps	number of time steps = sequence length = maximum number of tasks per task-set
element_size	sequence vector length = number of attributes per task
num_classes	number of classes = number of bits for coding the classes

The hyperparameter exploration can then be started by typing

python3.6 main.py

in the console. The results of the experiment can be found in the csv-file created in the working directory.

Train and Evaluate a Single Model

To train and evaluate a single Keras model uncomment line 72 in main.py and start the program by typing

python3.6 main.py

in the console. The hyperparameters and configuration parameters can be specified in the file params.py.

Plot Experiment Results

The plotting functions are defined in the file plotting.py. Add the desired functions to the main()-function and start the programm by typing

python3.6 plotting.py

in the console. Currently there are the following plotting functions available:

• plot a single hyperparameter (single line plot)
• plot the confusion matrix
• plot the correlation matrix

Best Model

The result of my hyperparameter exploration, i.e. the best model (best hyperparameter combination) if found, is saved in the directory

\experiments\LSTM\checkpoints\weights.best.hdf5

The results of my hyperparameter experiments are saved in the csv-files in the directory

\experiments\LSTM\