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testing_with_san_traces.py
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testing_with_san_traces.py
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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.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
from policies.fc_categorical_policy import FCCategoricalPolicyWithValue
from baselines.critic_network_baseline import CriticNetworkBaseline
from policies.q_network import QNetwork
from algorithms.dracm import DRACM
from dracm_trainer import Trainer
from algorithms.mab_ts import MABTSGuassianServiceMigration
import os
os.environ["CUDA_VISIBLE_DEVICES"]="-1"
class Tester(object):
def __init__(self,
testing_env,
policy,
fc_policy,
rnn_q_net_policy,
q_net_policy,
eval_sampler,
fc_eval_sampler,
rnn_q_net_eval_sampler,
q_net_eval_sampler):
self.eval_env = testing_env
self.policy = policy
self.fc_policy = fc_policy
self.rnn_q_net_policy = rnn_q_net_policy
self.q_net_policy = q_net_policy
self.eval_sampler = eval_sampler
self.fc_eval_sampler = fc_eval_sampler
self.rnn_q_net_eval_sampler = rnn_q_net_eval_sampler
self.q_net_eval_sampler = q_net_eval_sampler
self.mab_ts_algo = MABTSGuassianServiceMigration(testing_env)
def run_test(self, rnn_policy=True):
avg_ppo_rewards = 0.0
avg_fc_ppo_rewards = 0.0
avg_random_rewards = 0.0
avg_always_migrate_rewards = 0.0
avg_optimal_rewards = 0.0
avg_no_migration_rewards = 0.0
avg_ppo_sample_rewards = 0.0
avg_drqn_rewards = 0.0
avg_dqn_rewards = 0.0
avg_mab_ts_rewards = 0.0
iter_number = 1
for i in range(iter_number):
dqn_rewards_collects, _ = self.q_net_eval_sampler.obtain_samples(is_rnn=False)
drqn_rewards_collects, _ = self.rnn_q_net_eval_sampler.obtain_samples(is_rnn=rnn_policy)
sample_reward_collects, _ = self.eval_sampler.obtain_samples(is_rnn=rnn_policy, is_greedy_sample=False)
fc_reward_collects, _ = self.fc_eval_sampler.obtain_samples(is_rnn=False, is_greedy_sample=False)
reward_collects, system_info_collects = self.eval_sampler.obtain_samples(is_rnn=rnn_policy,
is_greedy_sample=True)
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))
random_rewards = random_solution(self.eval_sampler.env, system_info_collects)
always_migrate_rewards = always_migration_solution(self.eval_sampler.env, system_info_collects)
optimal_rewards = optimal_solution_for_batch_system_infos(self.eval_sampler.env, system_info_collects)
# optimal_rewards = 0.0
no_migration_rewards = no_migration_solution(self.eval_sampler.env, system_info_collects)
self.eval_env.reset()
totoal_rewards = self.mab_ts_algo.train(num_iteration=3)
# totoal_rewards = np.array([0.0])
mab_ts_rewards = totoal_rewards[-1]
logger.log("---- round " + str(i) + " ----")
logger.log("eval sample reward", ppo_sample_rewards)
logger.log("eval fc ppo reward", fc_ppo_rewards)
logger.log("eval dqn reward", dqn_rewards)
logger.log("eval drqn reward", drqn_rewards)
logger.log("eval reward", ppo_rewards)
logger.log("eval random reward", -random_rewards)
logger.log("eval always migration reward", -always_migrate_rewards)
logger.log("eval optimal reward", -optimal_rewards)
logger.log("eval mab-ts reward", -mab_ts_rewards)
logger.log("no_migration_solution", -no_migration_rewards)
avg_dqn_rewards += dqn_rewards
avg_ppo_rewards += ppo_rewards
avg_fc_ppo_rewards += fc_ppo_rewards
avg_drqn_rewards += drqn_rewards
avg_random_rewards += random_rewards
avg_always_migrate_rewards += always_migrate_rewards
avg_optimal_rewards += optimal_rewards
avg_no_migration_rewards += no_migration_rewards
avg_ppo_sample_rewards += ppo_sample_rewards
avg_mab_ts_rewards += mab_ts_rewards
logger.logkv("eval sample reward", avg_ppo_sample_rewards / iter_number)
logger.logkv("eval reward", avg_ppo_rewards / iter_number)
logger.logkv("eval fc ppo reward", avg_fc_ppo_rewards / iter_number)
logger.logkv("eval dqn reward", avg_dqn_rewards / iter_number)
logger.logkv("eval drqn reward", avg_drqn_rewards / iter_number)
logger.logkv("eval random reward", -(avg_random_rewards / iter_number))
logger.logkv("eval always migration reward", -(avg_always_migrate_rewards / iter_number))
logger.logkv("eval optimal reward", -(avg_optimal_rewards / iter_number))
logger.logkv("eval no migration reward", -(avg_no_migration_rewards / iter_number))
logger.logkv("eval mab-ts reward", -(avg_mab_ts_rewards / iter_number))
logger.dumpkvs()
if __name__ == "__main__":
from environment.batch_migration_env import EnvironmentParameters
from environment.batch_migration_env import BatchMigrationEnv
from policies.rnn_q_network import RNNQNetwork
from policies.q_network import QNetwork
from policies.rnn_policy_with_action_input import RNNPolicyWithValue
from algorithms.mab_ts import MABTSGuassianServiceMigration
logger.configure(dir="./log/test_with_trace_number_30", 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 = [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/san_traces_coordinate.txt',
transmission_rates=[60.0, 48.0, 36.0, 24.0, 12.0], # Mbps
trace_length=100,
trace_interval=3,
is_full_observation=False,
is_full_action=True)
env = BatchMigrationEnv(env_eval_parameters)
eval_sample_size = 30
print("action dim of the environment: ", env._action_dim)
rnn_policy = RNNPolicyWithValue(observation_dim=env._state_dim,
action_dim=env._action_dim,
rnn_parameter=256,
embbeding_size=2)
vf_baseline = RNNCriticNetworkBaseline(rnn_policy)
sampler = MigrationSampler(env,
policy=rnn_policy,
batch_size=400,
num_environment_per_core=2,
max_path_length=100,
parallel=True,
num_process=5,
is_norm_reward=False)
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_sampler = MigrationSampler(env,
policy=fc_policy,
batch_size=4800,
num_environment_per_core=2,
max_path_length=100,
parallel=True,
num_process=8,
is_norm_reward=True) # 2 * 4 * 30
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)
trainer = Trainer(train_env=env,
eval_env=env,
algo=algo,
sampler=sampler,
sample_processor=sampler_process,
update_batch_size=100,
policy=rnn_policy,
n_itr=10,
save_interval=5,
eval_sampler=eval_sampler,
test_interval=10,
save_path=None)
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)
tester = Tester(testing_env=env,
policy=rnn_policy,
fc_policy=fc_policy,
rnn_q_net_policy=rnn_q_network,
q_net_policy=q_network,
eval_sampler=eval_sampler,
fc_eval_sampler=fc_eval_sampler,
rnn_q_net_eval_sampler=rnn_q_net_sampler,
q_net_eval_sampler=q_network_eval_sampler)
dracm_model_path = "./checkpoints_san/checkpoints_ppo_64-bs-new-2/model_checkpoint_epoch_115"
fc_dracm_model_path = "./checkpoints_san/checkpoints_ppo_64-bs-new-2-no-rnn/model_checkpoint_epoch_115"
drqn_model_path = "./checkpoints_san/checkpoints_drqn_san_64-bs-new/model_checkpoint_3800"
dqn_model_path = "./checkpoints_san/checkpoints_dqn_san_64-bs-new/model_checkpoint_3800"
if dracm_model_path != None:
rnn_policy.load_weights(dracm_model_path)
logger.log("Load rnn model successfully....")
if fc_dracm_model_path != None:
fc_policy.load_weights(fc_dracm_model_path)
logger.log("Load fc model successfully....")
if drqn_model_path != None:
rnn_q_network.load_weights(drqn_model_path)
logger.log("Load rnn q network model successfully ....")
if dqn_model_path != None:
q_network.load_weights(dqn_model_path)
logger.log("Load q network model successfully ....")
migration_set = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
for migration_coeff in migration_set:
env.migration_coefficient_high = migration_coeff
env.migration_coefficient_low = migration_coeff
tester.run_test(rnn_policy=True)