config.yml

num_pods: 2500 # must be bigger than or equal to sim_steps
pods_per_step: 0 # set to 0 for the default value (num_pods/sim_steps)
sim_steps: 10
num_edge_devices: 35 # ignored if import_sample is True
num_fog_nodes: 10 # ignored if import_sample is True
num_cloud_servers: 5 # ignored if import_sample is True
behaviour_type: 'random_walk' # 'random_walk', 'aco', 'hba', or 'mlp' (for neural network based decision-making)
import_graph: True
graph_sample: EFC1
nn_sample: 'mlp_config15:31:57.462020.yaml' # needs to be specified for 'mlp' behaviour_type mode 'normal'
plot_all: True # takes effect if mode is not 'train'
gen_samples_num: 50 # takes effect if mode == 'gen'
mode: 'normal' # 'normal' for normal mode, 'gen' for generating samples, 'train' for training
path_to_dataset: 'generated/dataset'
frevo_port: 9990
candidate_evals: 10 # required number of inference requests to evaluate a candidate
training_results_path: "/home/madnad/Documents/Myrtus/Frevo/Frevo/Results/EFC_test" # to save the best trained model
evaluate_best_candidate: True # enables running simulation with the best trained model after training session 
randomized_pod_interval: True # if True, the pods will have a random arrival interval
mu: 0.5 # parameter µ ∈ (0, 1] defining the frequency of arriving pods