env_args = dict(topology=topology,
seed=10,
allow_rejection=True,
load=load,
mean_service_holding_time=25,
episode_length=episode_length,
num_service_classes=num_service_classes,
classes_arrival_probabilities=classes_arrival_probabilities,
classes_reward=classes_reward,
num_spectrum_resources=num_spectrum_resources,
k_paths=k_paths)
env_rnd = gym.make('QoSConstrainedRA-v0', **env_args)
mean_reward_rnd, std_reward_rnd = evaluate_heuristic(env_rnd,
random_policy,
n_eval_episodes=episodes)
print('Rnd:', mean_reward_rnd, std_reward_rnd)
env_sp = gym.make('QoSConstrainedRA-v0', **env_args)
mean_reward_sp, std_reward_sp = evaluate_heuristic(env_sp,
shortest_path,
n_eval_episodes=episodes)
print('SP:', mean_reward_sp, std_reward_sp, env_sp.actions_output)
env_sap = gym.make('QoSConstrainedRA-v0', **env_args)
mean_reward_sap, std_reward_sap = evaluate_heuristic(env_sap,
shortest_available_path,
n_eval_episodes=episodes)
print('SAP:', mean_reward_sap, std_reward_sap, env_sap.actions_output)
])
env_args = dict(topology=topology,
seed=10,
allow_rejection=False,
mean_service_holding_time=7.5,
mean_service_inter_arrival_time=1. / 12.,
j=1,
episode_length=50,
node_request_probabilities=node_request_probabilities)
print('STR'.ljust(5), 'REW'.rjust(7), 'STD'.rjust(7))
init_env = gym.make('DeepRMSA-v0', **env_args)
env_rnd = init_env
mean_reward_rnd, std_reward_rnd = evaluate_heuristic(env_rnd,
random_policy,
n_eval_episodes=episodes)
print('Rnd:'.ljust(5), f'{mean_reward_rnd:.4f} {std_reward_rnd:>7.4f}')
env_sp = gym.make('DeepRMSA-v0', **env_args)
mean_reward_sp, std_reward_sp = evaluate_heuristic(env_sp,
shortest_path_first_fit,
n_eval_episodes=episodes)
print('SP:'.ljust(5), f'{mean_reward_sp:.4f} {std_reward_sp:>7.4f}')
env_sap = gym.make('DeepRMSA-v0', **env_args)
mean_reward_sap, std_reward_sap = evaluate_heuristic(
env_sap, shortest_available_path_first_fit, n_eval_episodes=episodes)
print('SAP:'.ljust(5), f'{mean_reward_sap:.4f} {std_reward_sap:>7.4f}')
# env_llp = gym.make('DeepRMSA-v0', **env_args)
env_args = dict(topology=topology,
seed=10,
allow_rejection=True,
load=load,
mean_service_holding_time=25,
episode_length=episode_length,
num_spectrum_resources=64)
print('STR'.ljust(5), 'REW'.rjust(7), 'STD'.rjust(7))
# Random Policy
init_env = gym.make('PowerAwareRMSA-v0', **env_args)
env_rnd = SimpleMatrixObservation(init_env)
mean_reward_rnd, std_reward_rnd = evaluate_heuristic(env_rnd,
random_policy,
n_eval_episodes=episodes)
print('Rnd:'.ljust(8), f'{mean_reward_rnd:.4f} {std_reward_rnd:>7.4f}')
print('Bit rate blocking:', (init_env.episode_bit_rate_requested -
init_env.episode_bit_rate_provisioned) /
init_env.episode_bit_rate_requested)
print(
'Request blocking:',
(init_env.episode_services_processed - init_env.episode_services_accepted)
/ init_env.episode_services_processed)
print('Total power:', 10 * np.log10(init_env.total_power))
print('Average power:',
10 * np.log10(init_env.total_power / init_env.services_accepted))
# Shortest Available Path First Fit Fixed Power
env_sap_ff_fp = gym.make('PowerAwareRMSA-v0', **env_args)
episode_length=episode_length,
num_spectrum_resources=64,
num_spatial_resources=num_spatial_resources)
# print('STR'.ljust(5), 'REW'.rjust(7), 'STD'.rjust(7))
#
# init_env = gym.make('RMCSA-v0', **env_args)
# env_rnd = SimpleMatrixObservation(init_env)
# mean_reward_rnd, std_reward_rnd = evaluate_heuristic(env_rnd, random_policy, n_eval_episodes=episodes)
# print('Rnd:'.ljust(8), f'{mean_reward_rnd:.4f} {std_reward_rnd:>7.4f}')
# print('Bit rate blocking:', (init_env.episode_bit_rate_requested - init_env.episode_bit_rate_provisioned) / init_env.episode_bit_rate_requested)
# print('Request blocking:', (init_env.episode_services_processed - init_env.episode_services_accepted) / init_env.episode_services_processed)
# print(init_env.topology.graph['throughput'])
env_sap = gym.make('RMCSA-v0', **env_args)
mean_reward_sap, std_reward_sap = evaluate_heuristic(
env_sap, shortest_available_first_core_first_fit, n_eval_episodes=episodes)
print('STR'.ljust(5), 'REW'.rjust(7), 'STD'.rjust(7))
# Initial Metrics for Environment
print('SAP-FF:'.ljust(8), f'{mean_reward_sap:.4f} {std_reward_sap:.4f}')
print(
'Bit rate blocking:',
(env_sap.episode_bit_rate_requested - env_sap.episode_bit_rate_provisioned)
/ env_sap.episode_bit_rate_requested)
print(
'Request blocking:',
(env_sap.episode_services_processed - env_sap.episode_services_accepted) /
env_sap.episode_services_processed)
# Additional Metrics For Environment