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test_different_arriving_rate_rome.py
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test_different_arriving_rate_rome.py
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from environment.batch_migration_env import BatchMigrationEnv
from environment.batch_migration_env import EnvironmentParameters
from sampler.migration_sampler import EvaluationSampler
from sampler.migration_sampler import MigrationSampler
from sampler.migration_sampler import MigrationSamplerProcess
from policies.always_migration_solution import always_migration_solution
from policies.optimal_solution import optimal_solution_for_batch_system_infos
from policies.no_migration_solution import no_migration_solution
import tensorflow as tf
import numpy as np
import utils.logger as logger
from sampler.migration_sampler import EvaluationSampler
from sampler.migration_sampler import EvaluationSamplerForDRQN
from sampler.migration_sampler import MigrationSampler
from sampler.migration_sampler import MigrationSamplerProcess
from baselines.rnn_critic_network_baseline import RNNCriticNetworkBaseline
from policies.random_solution import random_solution
from policies.optimal_solution import optimal_solution_for_batch_system_infos
from policies.no_migration_solution import no_migration_solution
from policies.fc_categorical_policy import FCCategoricalPolicyWithValue
from baselines.critic_network_baseline import CriticNetworkBaseline
from policies.always_migrate_policy import AlwaysMigratePolicy
from policies.q_network import QNetwork
from algorithms.dracm import DRACM
from dracm_trainer import Trainer
from algorithms.mab_ts import MABTSGuassianServiceMigration
from policies.rnn_q_network import RNNQNetwork
from policies.q_network import QNetwork
from policies.rnn_policy_with_action_input import RNNPolicyWithValue
logger.configure(dir="./log/test_arriving_rate_rome", format_strs=['stdout', 'log', 'csv'])
number_of_base_state = 64
x_base_state = 8
y_base_state = 8
# possion_rate_vector = np.random.randint(15, 31, size=number_of_base_state)
# print("possion_rate_vector is: ", repr(possion_rate_vector))
possion_rate_vector = [7, 10, 8, 14, 15, 6, 20, 18, 11, 17, 20, 9, 8, 14, 9, 15, 8, 17, 9, 9, 10, 7, 17, 10,
13, 12, 5, 8, 10, 13, 19, 15, 10, 9, 10, 18, 12, 13, 5, 11, 7, 8, 8, 19, 15, 15, 6, 10,
5, 20, 17, 5, 5, 16, 5, 19, 19, 19, 9, 20, 17, 14, 17, 17]
# possion_rate_vector = [11, 8, 20, 9, 18, 18, 9, 17, 12, 17, 9, 17, 14, 10, 5, 7, 12,
# 8, 20, 10, 14, 12, 20, 14, 8, 6, 15, 7, 18, 9, 8, 18, 17, 7,
# 11, 11, 13, 14, 8, 18, 13, 17, 6, 18, 17, 18, 18, 7, 9, 6, 12,
# 10, 9, 8, 20, 14, 11, 15, 14, 6, 6, 15, 16, 20]
# start point (41.856, 12.442), end point (41.928,12.5387), a region in Roman, Italy.
env_eval_parameters = EnvironmentParameters(trace_start_index=120,
num_traces=30,
server_frequency=128.0, # GHz
num_base_station=number_of_base_state,
optical_fiber_trans_rate=500.0,
backhaul_coefficient=0.02,
migration_coefficient_low=1.0,
migration_coefficient_high=3.0,
server_poisson_rate=possion_rate_vector,
client_poisson_rate=2,
server_task_data_lower_bound=(0.05 * 1000.0 * 1000.0 * 8),
server_task_data_higher_bound=(5 * 1000.0 * 1000.0 * 8),
client_task_data_lower_bound=(0.05 * 1000.0 * 1000.0 * 8),
client_task_data_higher_bound=(5 * 1000.0 * 1000.0 * 8),
migration_size_low=0.5,
migration_size_high=100.0,
ratio_lower_bound=200.0,
ratio_higher_bound=10000.0,
map_width=8000.0, map_height=8000.0,
num_horizon_servers=x_base_state, num_vertical_servers=y_base_state,
traces_file_path='./environment/rome_traces_coordinate.txt',
transmission_rates=[60.0, 48.0, 36.0, 24.0, 12.0], # Mbps
trace_length=100,
trace_interval=12,
is_full_observation=False,
is_full_action=True)
env = BatchMigrationEnv(env_eval_parameters)
eval_sample_size = 30
rnn_policy = RNNPolicyWithValue(observation_dim=env._state_dim,
action_dim=env._action_dim,
rnn_parameter=256,
embbeding_size=2)
vf_baseline = RNNCriticNetworkBaseline(rnn_policy)
eval_sampler = EvaluationSampler(env,
policy=rnn_policy,
batch_size=eval_sample_size,
max_path_length=100)
sampler_process = MigrationSamplerProcess(baseline=vf_baseline,
discount=0.99,
gae_lambda=0.95,
normalize_adv=True,
positive_adv=False)
fc_policy = FCCategoricalPolicyWithValue(observation_dim=env._state_dim,
action_dim=env._action_dim,
fc_parameters=[256])
vf_baseline = CriticNetworkBaseline(fc_policy)
fc_eval_sampler = EvaluationSampler(env,
policy=fc_policy,
batch_size=10,
max_path_length=100)
algo = DRACM(policy=rnn_policy,
value_function=rnn_policy,
policy_optimizer=tf.keras.optimizers.Adam(5e-4),
value_optimizer=tf.keras.optimizers.Adam(5e-4),
is_rnn=True,
is_shared_critic_net=True,
num_inner_grad_steps=4,
clip_value=0.2,
vf_coef=0.5,
max_grad_norm=0.5,
entropy_coef=0.01)
rnn_q_network = RNNQNetwork(observation_dim=env._state_dim,
action_dim=env._action_dim,
rnn_parameter=256,
fc_parameters=128,
epsilon=0.1)
rnn_q_net_sampler = EvaluationSamplerForDRQN(env,
policy=rnn_q_network,
batch_size=eval_sample_size,
max_path_length=100)
q_network = QNetwork(observation_dim=env._state_dim,
action_dim=env._action_dim,
hidden_parameter=256,
fc_parameters=128,
epsilon=0.1)
q_network_eval_sampler = EvaluationSamplerForDRQN(env,
policy=q_network,
batch_size=30,
max_path_length=100,
is_rnn=False)
dracm_model_path = "./checkpoints_rome/checkpoints_ppo_64-bs-new-2/model_checkpoint_epoch_115"
fc_dracm_model_path = "./checkpoints_rome/checkpoints_ppo_64-bs-new-no-rnn/model_checkpoint_epoch_115"
drqn_model_path = "./checkpoints_rome/checkpoints_drqn_rome_64-bs-new/model_checkpoint_3800"
dqn_model_path = "./checkpoints_rome/checkpoints_dqn_rome_64-bs-new/model_checkpoint_3800"
rnn_policy.load_weights(dracm_model_path)
logger.log("Load rnn model successfully....")
fc_policy.load_weights(fc_dracm_model_path)
logger.log("Load fc model successfully....")
rnn_q_network.load_weights(drqn_model_path)
logger.log("Load rnn q network model successfully ....")
q_network.load_weights(dqn_model_path)
logger.log("Load q network model successfully ....")
poisson_rate_set = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
for poisson_rate in poisson_rate_set:
logger.log("Client poisson rate: ", poisson_rate)
env.client_poisson_rate = poisson_rate
am_eval_sampler = EvaluationSampler(env,
policy=AlwaysMigratePolicy(env._state_dim,action_dim=env._action_dim),
batch_size=30,
max_path_length=100)
rewards, system_infos = am_eval_sampler.obtain_samples(is_rnn=False)
system_infos = np.array(system_infos)
logger.log("processing sample's system_info shape", system_infos.shape)
always_migration_latency = always_migration_solution(env, system_infos)
logger.log("always migration latency is: ", always_migration_latency)
never_migration_latency = no_migration_solution(env, system_infos)
logger.log("no migration latency is: ", never_migration_latency)
optimal_rewards = optimal_solution_for_batch_system_infos(env, system_infos)
logger.log("optimal latency is: ", optimal_rewards)
random_rewards = random_solution(env, system_infos)
logger.log("random latency is: ", random_rewards)
dqn_rewards_collects, _ = q_network_eval_sampler.obtain_samples(is_rnn=False)
drqn_rewards_collects, _ = rnn_q_net_sampler.obtain_samples(is_rnn=True)
fc_reward_collects, _ = fc_eval_sampler.obtain_samples(is_rnn=False, is_greedy_sample=False)
sample_reward_collects, _ = eval_sampler.obtain_samples(is_rnn=rnn_policy,
is_greedy_sample=False)
reward_collects, system_info_collects = eval_sampler.obtain_samples(is_rnn=rnn_policy,
is_greedy_sample=True)
env.reset()
mab_ts_algo = MABTSGuassianServiceMigration(env)
totoal_rewards = mab_ts_algo.train(num_iteration=3)
mab_ts_rewards = totoal_rewards[-1]
dqn_rewards = np.mean(np.sum(dqn_rewards_collects, axis=-1))
drqn_rewards = np.mean(np.sum(drqn_rewards_collects, axis=-1))
fc_ppo_rewards = np.mean(np.sum(fc_reward_collects, axis=-1))
ppo_rewards = np.mean(np.sum(reward_collects, axis=-1))
ppo_sample_rewards = np.mean(np.sum(sample_reward_collects, axis=-1))
logger.log("eval fc ppo latency ", -fc_ppo_rewards)
logger.log("eval dqn latency ", -dqn_rewards)
logger.log("eval drqn latency ", -drqn_rewards)
logger.log("eval sample latency ", -ppo_sample_rewards)
logger.log("eval latency ", -ppo_rewards)
logger.log("eval mab-ts reward ", -mab_ts_rewards)