def run_without_offloading(cell_list):
def run(cell_num):
config = Env_Config()
model_path = os.path.join(root_dir,
'offloading/output_model/deepQ_test.ckpt')
offloading = Run_Offloading(model_path, config, cell_num)
result_dict = offloading.run_test_without_RL(0)
result_array = dict_to_nparray(result_dict, cell_num)
return result_array
target_path = os.path.join(
root_dir, 'offloading/result/without_offloading_without_RL')
utility.check_path_exist(target_path)
store_path = os.path.join(target_path, 'loop_report.txt')
if os.path.exists(store_path):
os.remove(store_path)
cell_list_len = len(cell_list)
all_cell_result_array_list = []
for cell_num in cell_list:
logger.info('cell_num:{}'.format(cell_num))
result_array = run(cell_num)
all_cell_result_array_list.append(result_array)
save_report(result_array, store_path)
all_cell_result_array = np.stack(all_cell_result_array_list, axis=0)
du.save_array(all_cell_result_array,
os.path.join(target_path, 'all_cell_result_array.npy'))
all_cell_result_array = np.stack(all_cell_result_array_list, axis=0)
du.save_array(all_cell_result_array,
os.path.join(target_path, 'all_cell_result_array.npy'))
plt.ioff()
def run_ARIMA_prediction_RL(cell_list):
def run_offloading(cell_num):
config = Env_Config()
config.features_num = 3
config.base_dir = os.path.join(root_dir,
'offloading/npy/real_prediction_ARIMA')
model_path = os.path.join(
root_dir, 'offloading/output_model/deepQ_RL_ARIMA_prediction.ckpt')
offloading = Run_Offloading(model_path, config, cell_num)
without_RL_with_offloading_result_dict = offloading.run_test_without_RL(
10)
without_RL_offloading_result_dict = offloading.run_test_without_RL(0)
logger.info(
'macro load:{} offloading_without_RL:{} without_RL_without_offloading:{}'
.format(
np.mean(
without_RL_offloading_result_dict['macro_load'][-149:]),
np.mean(without_RL_with_offloading_result_dict['energy_effi']
[-149:]),
np.mean(
without_RL_offloading_result_dict['energy_effi'][-149:])))
offloading.RL_train()
with_RL_result_dict = offloading.run_test_with_RL(reload=False)
with_RL_result_array = dict_to_nparray(with_RL_result_dict, cell_num)
# without_RL_with_offloading_result_array = dict_to_nparray(without_RL_with_offloading_result_dict)
# without_RL_offloading_result_array = dict_to_nparray(without_RL_offloading_result_dict)
offloading_plot(with_RL_result_dict,
without_RL_with_offloading_result_dict,
without_RL_offloading_result_dict)
return with_RL_result_array
plt.ion()
target_path = os.path.join(root_dir,
'offloading/result/RL_real_ARIMA_prediction')
utility.check_path_exist(target_path)
store_path = os.path.join(target_path, 'loop_report.txt')
if os.path.exists(store_path):
os.remove(store_path)
cell_list_len = len(cell_list)
all_cell_result_array_list = []
for cell_num in cell_list:
with_RL_result_array = run_offloading(cell_num)
all_cell_result_array_list.append(with_RL_result_array)
logger.info('cell_num:{} average effi mean:{}'.format(
cell_num, np.mean(with_RL_result_array[-149:, 2])))
save_report(with_RL_result_array, store_path)
all_cell_result_array = np.stack(all_cell_result_array_list, axis=0)
du.save_array(all_cell_result_array,
os.path.join(target_path, 'all_cell_result_array.npy'))
all_cell_result_array = np.stack(all_cell_result_array_list, axis=0)
du.save_array(all_cell_result_array,
os.path.join(target_path, 'all_cell_result_array.npy'))
plt.ioff()