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MEC_Env.py
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MEC_Env.py
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from Config import Config
import numpy as np
import random
import math
import queue
class MEC:
def __init__(self, num_ue, num_edge, num_time, num_component, max_delay):
# Initialize variables
self.n_ue = num_ue
self.n_edge = num_edge
self.n_time = num_time
self.n_component = num_component
self.max_delay = max_delay
self.duration = Config.DURATION
self.ue_p_comp = Config.UE_COMP_ENERGY
self.ue_p_tran = Config.UE_TRAN_ENERGY
self.ue_p_idle = Config.UE_IDLE_ENERGY
self.edge_p_comp = Config.EDGE_COMP_ENERGY
self.time_count = 0
self.task_count_ue = 0
self.task_count_edge = 0
self.n_actions = 1 + self.n_edge
self.n_features = 1 + 1 + 1 + 1 + self.n_edge
self.n_lstm_state = self.n_edge
self.drop_trans_count = 0
self.drop_edge_count = 0
self.drop_ue_count = 0
# Computation and transmission capacities
self.comp_cap_ue = Config.UE_COMP_CAP * np.ones(self.n_ue) * self.duration
self.comp_cap_edge = Config.EDGE_COMP_CAP * np.ones([self.n_edge]) * self.duration
self.tran_cap_ue = Config.UE_TRAN_CAP * np.ones([self.n_ue, self.n_edge]) * self.duration
self.n_cycle = 1
self.task_arrive_prob = Config.TASK_ARRIVE_PROB
self.max_arrive_size = Config.TASK_MAX_SIZE
self.min_arrive_size = Config.TASK_MIN_SIZE
self.arrive_task_size_set = np.arange(self.min_arrive_size, self.max_arrive_size, 0.1)
#self.energy_state_set = np.arange(0.25,1, 0.25)
self.ue_energy_state = [Config.UE_ENERGY_STATE[np.random.randint(0,len(Config.UE_ENERGY_STATE))] for ue in range(self.n_ue)]
self.arrive_task_size = np.zeros([self.n_time, self.n_ue])
self.arrive_task_dens = np.zeros([self.n_time, self.n_ue])
#print(self.energy_state_set)
#print(self.ue_energy_state)
#self.comp_density=0.297
self.n_task = int(self.n_time * self.task_arrive_prob)
# Task delay and energy-related arrays
self.process_delay = np.zeros([self.n_time, self.n_ue])
self.ue_bit_processed = np.zeros([self.n_time, self.n_ue])
self.edge_bit_processed = np.zeros([self.n_time, self.n_ue, self.n_edge])
self.ue_bit_transmitted = np.zeros([self.n_time, self.n_ue])
self.ue_comp_energy = np.zeros([self.n_time, self.n_ue])
self.edge_comp_energy = np.zeros([self.n_time, self.n_ue, self.n_edge])
self.ue_idle_energy = np.zeros([self.n_time, self.n_ue, self.n_edge])
self.ue_tran_energy = np.zeros([self.n_time, self.n_ue])
self.unfinish_task = np.zeros([self.n_time, self.n_ue])
self.process_delay_trans = np.zeros([self.n_time, self.n_ue])
self.edge_drop = np.zeros([self.n_ue, self.n_edge])
# Queue information initialization
self.t_ue_comp = -np.ones([self.n_ue])
self.t_ue_tran = -np.ones([self.n_ue])
self.b_edge_comp = np.zeros([self.n_ue, self.n_edge])
# Queue initialization
self.ue_computation_queue = [queue.Queue() for _ in range(self.n_ue)]
self.ue_transmission_queue = [queue.Queue() for _ in range(self.n_ue)]
self.edge_computation_queue = [[queue.Queue() for _ in range(self.n_edge)] for _ in range(self.n_ue)]
self.edge_ue_m = np.zeros(self.n_edge)
self.edge_ue_m_observe = np.zeros(self.n_edge)
# Task indicator initialization
self.local_process_task = [{'DIV': np.nan, 'UE_ID': np.nan, 'TASK_ID': np.nan, 'SIZE': np.nan,
'TIME': np.nan, 'EDGE': np.nan, 'REMAIN': np.nan} for _ in range(self.n_ue)]
self.local_transmit_task = [{'DIV': np.nan, 'UE_ID': np.nan, 'TASK_ID': np.nan, 'SIZE': np.nan,
'TIME': np.nan, 'EDGE': np.nan, 'REMAIN': np.nan} for _ in range(self.n_ue)]
self.edge_process_task = [[{'DIV': np.nan, 'UE_ID': np.nan, 'TASK_ID': np.nan, 'SIZE': np.nan,
'TIME': np.nan, 'REMAIN': np.nan} for _ in range(self.n_edge)] for _ in range(self.n_ue)]
self.task_history = [[] for _ in range(self.n_ue)]
def reset(self, arrive_task_size, arrive_task_dens):
self.drop_trans_count = 0
self.drop_edge_count = 0
self.drop_ue_count = 0
# Reset variables and queues
self.task_history = [[] for _ in range(self.n_ue)]
self.UE_TASK = [-1] * self.n_ue
self.drop_edge_count = 0
self.arrive_task_size = arrive_task_size
self.arrive_task_dens = arrive_task_dens
self.time_count = 0
self.local_process_task = []
self.local_transmit_task = []
self.edge_process_task = []
self.ue_computation_queue = [queue.Queue() for _ in range(self.n_ue)]
self.ue_transmission_queue = [queue.Queue() for _ in range(self.n_ue)]
self.edge_computation_queue = [[queue.Queue() for _ in range(self.n_edge)] for _ in range(self.n_ue)]
self.t_ue_comp = -np.ones([self.n_ue])
self.t_ue_tran = -np.ones([self.n_ue])
self.b_edge_comp = np.zeros([self.n_ue, self.n_edge])
self.process_delay = np.zeros([self.n_time, self.n_ue])
self.ue_bit_processed = np.zeros([self.n_time, self.n_ue])
self.edge_bit_processed = np.zeros([self.n_time, self.n_ue, self.n_edge])
self.ue_bit_transmitted = np.zeros([self.n_time, self.n_ue])
self.ue_comp_energy = np.zeros([self.n_time, self.n_ue])
self.edge_comp_energy = np.zeros([self.n_time, self.n_ue, self.n_edge])
self.ue_idle_energy = np.zeros([self.n_time, self.n_ue, self.n_edge])
self.ue_tran_energy = np.zeros([self.n_time, self.n_ue])
self.unfinish_task = np.zeros([self.n_time, self.n_ue])
self.process_delay_trans = np.zeros([self.n_time, self.n_ue])
self.edge_drop = np.zeros([self.n_ue, self.n_edge])
self.local_process_task = [{'DIV': np.nan, 'UE_ID': np.nan, 'TASK_ID': np.nan, 'SIZE': np.nan,
'TIME': np.nan, 'EDGE': np.nan, 'REMAIN': np.nan} for _ in range(self.n_ue)]
self.local_transmit_task = [{'DIV': np.nan, 'UE_ID': np.nan, 'TASK_ID': np.nan, 'SIZE': np.nan,
'TIME': np.nan, 'EDGE': np.nan, 'REMAIN': np.nan} for _ in range(self.n_ue)]
self.edge_process_task = [[{'DIV': np.nan, 'UE_ID': np.nan, 'TASK_ID': np.nan, 'SIZE': np.nan,
'TIME': np.nan, 'REMAIN': np.nan} for _ in range(self.n_edge)] for _ in range(self.n_ue)]
# Initial observation and LSTM state
UEs_OBS = np.zeros([self.n_ue, self.n_features])
for ue_index in range(self.n_ue):
if self.arrive_task_size[self.time_count, ue_index] != 0:
UEs_OBS[ue_index, :] = np.hstack([
self.arrive_task_size[self.time_count, ue_index], self.t_ue_comp[ue_index],
self.t_ue_tran[ue_index],
np.squeeze(self.b_edge_comp[ue_index, :]),
self.ue_energy_state[ue_index]])
UEs_lstm_state = np.zeros([self.n_ue, self.n_lstm_state])
return UEs_OBS, UEs_lstm_state
# perform action, observe state and delay (several steps later)
def step(self, action):
ue_action_local = np.zeros([self.n_ue], np.int32)
ue_action_offload = np.zeros([self.n_ue], np.int32)
for ue_index in range(self.n_ue):
ue_action = action[ue_index]
ue_action_offload[ue_index] = int(ue_action - 1)
if ue_action == 0:
ue_action_local[ue_index] = 1
#ue_action_offload = np.zeros([self.n_ue], np.int32)
#ue_action_component = np.zeros([self.n_ue], np.int32)-1
#random_list = []
#for i in range(self.n_component):
#random_list.append(i)
# COMPUTATION QUEUE UPDATE ===================
for ue_index in range(self.n_ue):
ue_comp_cap = np.squeeze(self.comp_cap_ue[ue_index])
ue_arrive_task_size = np.squeeze(self.arrive_task_size[self.time_count, ue_index])
ue_arrive_task_dens = np.squeeze(self.arrive_task_dens[self.time_count, ue_index])
tmp_dict = {
'DIV' : 0 ,
'UE_ID': ue_index,
'TASK_ID': self.UE_TASK[ue_index],
'SIZE': ue_arrive_task_size,
'DENS': ue_arrive_task_dens,
'TIME': self.time_count,
'EDGE': ue_action_offload[ue_index],
}
if ue_action_local[ue_index] == 1:
self.ue_computation_queue[ue_index].put(tmp_dict)
'''
component_list = np.zeros([self.n_component], np.int32)-1
state_list = np.zeros([self.n_component], np.int32)
ue_action = action[ue_index]
if ue_action == 0:
ue_action_local[ue_index] = 1
else:
ue_action_offload[ue_index] = 1
sample = random.sample(random_list, int(ue_action))
for i in range(len(sample)):
component_list[sample[i]] = np.random.randint(0, self.n_edge)
ue_action_component[ue_index] = action[ue_index]
ue_comp_cap = np.squeeze(self.comp_cap_ue[ue_index])
ue_arrive_task_size = np.squeeze(self.arrive_task_size[self.time_count, ue_index])
ue_arrive_task_dens = np.squeeze(self.arrive_task_dens[self.time_count, ue_index])
if ue_arrive_task_size > 0:
self.UE_TASK[ue_index] += 1
task_dic = {
'UE_ID': ue_index,
'TASK_ID': self.UE_TASK[ue_index],
'SIZE': ue_arrive_task_size,
'DENS': ue_arrive_task_dens,
'TIME': self.time_count,
'EDGE': component_list,
'd_state': state_list,
'state': np.nan
}
self.task_history[ue_index].append(task_dic)
for component in range(self.n_component):
temp_dic = {
'DIV': component,
'UE_ID': ue_index,
'TASK_ID': self.UE_TASK[ue_index],
'SIZE': ue_arrive_task_size / self.n_component,
'DENS': ue_arrive_task_dens,
'TIME': self.time_count,
'EDGE': component_list[component],
'd_state': state_list[component]
}
#if component_list[component] > -1:
#self.ue_transmission_queue[ue_index].put(temp_dic)
#else:
#self.ue_computation_queue[ue_index].put(temp_dic)
'''
for cycle in range(self.n_cycle):
# TASK ON PROCESS
if math.isnan(self.local_process_task[ue_index]['REMAIN']) \
and (not self.ue_computation_queue[ue_index].empty()):
while not self.ue_computation_queue[ue_index].empty():
get_task = self.ue_computation_queue[ue_index].get()
#print(get_task)
if get_task['SIZE'] != 0:
if self.time_count - get_task['TIME'] + 1 <= self.max_delay:
self.local_process_task[ue_index]['UE_ID'] = get_task['UE_ID']
self.local_process_task[ue_index]['TASK_ID'] = get_task['TASK_ID']
self.local_process_task[ue_index]['SIZE'] = get_task['SIZE']
self.local_process_task[ue_index]['DENS'] = get_task['DENS']
self.local_process_task[ue_index]['TIME'] = get_task['TIME']
self.local_process_task[ue_index]['REMAIN'] = self.local_process_task[ue_index]['SIZE']
self.local_process_task[ue_index]['DIV'] = get_task['DIV']
break
else:
#self.task_history[ue_index][get_task['TASK_ID']]['d_state'][get_task['DIV']] = -1
self.process_delay[get_task['TIME'], ue_index] = self.max_delay
self.unfinish_task[get_task['TIME'], ue_index] = 1
# PROCESS
if self.local_process_task[ue_index]['REMAIN'] > 0:
if self.local_process_task[ue_index]['REMAIN'] >= (ue_comp_cap / self.local_process_task[ue_index]['DENS']):
self.ue_bit_processed[self.local_process_task[ue_index]['TIME'], ue_index] += ue_comp_cap / self.local_process_task[ue_index]['DENS']
self.ue_comp_energy[self.local_process_task[ue_index]['TIME'], ue_index] += (ue_comp_cap / self.local_process_task[ue_index]['DENS']) * (1 ** (-27) * (ue_comp_cap / self.local_process_task[ue_index]['DENS']))
else:
self.ue_bit_processed[self.local_process_task[ue_index]['TIME'], ue_index] += self.local_process_task[ue_index]['REMAIN']/ self.local_process_task[ue_index]['DENS']
self.ue_comp_energy[self.local_process_task[ue_index]['TIME'], ue_index] += self.local_process_task[ue_index]['REMAIN']/ self.local_process_task[ue_index]['DENS'] * (1 ** (-27) * (ue_comp_cap / self.local_process_task[ue_index]['DENS']))
self.local_process_task[ue_index]['REMAIN'] = \
self.local_process_task[ue_index]['REMAIN'] - ue_comp_cap / self.local_process_task[ue_index]['DENS']
#print(self.local_process_task[ue_index]['REMAIN'])
#print(ue_comp_cap, self.local_process_task[ue_index]['DENS'])
# if no remain, compute processing delay
if self.local_process_task[ue_index]['REMAIN'] <= 0:
self.process_delay[self.local_process_task[ue_index]['TIME'], ue_index] \
= self.time_count - self.local_process_task[ue_index]['TIME'] + 1
self.local_process_task[ue_index]['REMAIN'] = np.nan
#print("hi")
#self.task_history[ue_index][self.local_process_task[ue_index]['TASK_ID']]['d_state'][self.local_process_task[ue_index]['DIV']] = 1
#if sum(self.task_history[ue_index][self.local_process_task[ue_index]['TASK_ID']]['d_state']) > self.n_component-1:
elif self.time_count - self.local_process_task[ue_index]['TIME'] + 1 == self.max_delay:
#self.task_history[ue_index][self.local_process_task[ue_index]['TASK_ID']]['d_state'][self.local_process_task[ue_index]['DIV']] = -1
self.local_process_task[ue_index]['REMAIN'] = np.nan
self.process_delay[self.local_process_task[ue_index]['TIME'], ue_index] = self.max_delay
self.unfinish_task[self.local_process_task[ue_index]['TIME'], ue_index] = 1
self.drop_ue_count = self.drop_ue_count + 1
# OTHER INFO self.t_ue_comp[ue_index]
# update self.t_ue_comp[ue_index] only when ue_bitrate != 0
if ue_arrive_task_size != 0:
tmp_tilde_t_ue_comp = np.max([self.t_ue_comp[ue_index] + 1, self.time_count])
self.t_ue_comp[ue_index] = np.min([tmp_tilde_t_ue_comp
+ math.ceil(ue_arrive_task_size * ue_action_local[ue_index]
/ (ue_comp_cap / ue_arrive_task_dens)) - 1,
self.time_count + self.max_delay - 1])
# edge QUEUE UPDATE =========================
for ue_index in range(self.n_ue):
#ue_comp_density = self.comp_density
for edge_index in range(self.n_edge):
edge_cap = self.comp_cap_edge[edge_index]/self.n_cycle
for cycle in range(self.n_cycle):
# TASK ON PROCESS
if math.isnan(self.edge_process_task[ue_index][edge_index]['REMAIN']) \
and (not self.edge_computation_queue[ue_index][edge_index].empty()):
while not self.edge_computation_queue[ue_index][edge_index].empty():
get_task = self.edge_computation_queue[ue_index][edge_index].get()
if self.time_count - get_task['TIME'] + 1 <= self.max_delay:
self.edge_process_task[ue_index][edge_index]['UE_ID'] = get_task['UE_ID']
self.edge_process_task[ue_index][edge_index]['TASK_ID'] = get_task['TASK_ID']
self.edge_process_task[ue_index][edge_index]['SIZE'] = get_task['SIZE']
self.edge_process_task[ue_index][edge_index]['DENS'] = get_task['DENS']
self.edge_process_task[ue_index][edge_index]['TIME'] = get_task['TIME']
self.edge_process_task[ue_index][edge_index]['REMAIN'] = self.edge_process_task[ue_index][edge_index]['SIZE']
self.edge_process_task[ue_index][edge_index]['DIV'] = get_task['DIV']
break
else:
#self.task_history[get_task['UE_ID']][get_task['TASK_ID']]['d_state'][get_task['DIV']] = -1
self.process_delay[get_task['TIME'], ue_index] = self.max_delay
self.unfinish_task[get_task['TIME'], ue_index] = 1
# print(self.edge_process_task[ue_index][edge_index], "f_________")
# PROCESS
self.edge_drop[ue_index, edge_index] = 0
if self.edge_process_task[ue_index][edge_index]['REMAIN'] > 0:
if self.edge_process_task[ue_index][edge_index]['REMAIN'] >= (edge_cap / self.edge_process_task[ue_index][edge_index]['DENS'] / self.edge_ue_m[edge_index]):
self.edge_comp_energy[self.edge_process_task[ue_index][edge_index]['TIME'], ue_index, edge_index] += (edge_cap/ self.edge_process_task[ue_index][edge_index]['DENS']) * (self.edge_p_comp * self.duration)
self.edge_bit_processed[self.edge_process_task[ue_index][edge_index]['TIME'], ue_index, edge_index] += (edge_cap/ self.edge_process_task[ue_index][edge_index]['DENS'] / self.edge_ue_m[edge_index])
self.ue_idle_energy[self.edge_process_task[ue_index][edge_index]['TIME'], ue_index, edge_index] += (edge_cap / self.edge_process_task[ue_index][edge_index]['DENS'] / self.edge_ue_m[edge_index])
else:
self.edge_bit_processed[self.edge_process_task[ue_index][edge_index]['TIME'], ue_index, edge_index] += self.edge_process_task[ue_index][edge_index]['REMAIN'] / self.edge_ue_m[edge_index]
self.edge_comp_energy[self.edge_process_task[ue_index][edge_index]['TIME'], ue_index, edge_index] += (self.edge_process_task[ue_index][edge_index]['REMAIN']) * (self.edge_p_comp * self.duration)
self.ue_idle_energy[self.edge_process_task[ue_index][edge_index]['TIME'], ue_index, edge_index] += (self.edge_process_task[ue_index][edge_index]['REMAIN'] / self.edge_ue_m[edge_index]) * self.ue_p_idle
self.edge_process_task[ue_index][edge_index]['REMAIN'] = self.edge_process_task[ue_index][edge_index]['REMAIN'] - edge_cap/ self.edge_process_task[ue_index][edge_index]['DENS'] / self.edge_ue_m[edge_index]
if self.edge_process_task[ue_index][edge_index]['REMAIN'] <= 0:
self.process_delay[self.edge_process_task[ue_index][edge_index]['TIME'],ue_index] \
= self.time_count - self.edge_process_task[ue_index][edge_index]['TIME'] + 1
#self.task_history[self.edge_process_task[ue_index][edge_index]['UE_ID']][self.edge_process_task[ue_index][edge_index]['TASK_ID']]['d_state'][self.edge_process_task[ue_index][edge_index]['DIV']] = 1
self.edge_process_task[ue_index][edge_index]['REMAIN'] = np.nan
'''
if sum(self.task_history[ue_index][self.edge_process_task[ue_index][edge_index]['TASK_ID']]['d_state']) > self.n_component-1:
self.process_delay[self.edge_process_task[ue_index][edge_index]['TIME'],ue_index] \
= self.time_count - self.edge_process_task[ue_index][edge_index]['TIME'] + 1
'''
elif self.time_count - self.edge_process_task[ue_index][edge_index]['TIME'] + 1 == self.max_delay:
#self.task_history[self.edge_process_task[ue_index][edge_index]['UE_ID']][self.edge_process_task[ue_index][edge_index]['TASK_ID']]['d_state'][self.edge_process_task[ue_index][edge_index]['DIV']] = -1
self.edge_drop[ue_index, edge_index] = self.edge_process_task[ue_index][edge_index]['REMAIN']
self.process_delay[self.edge_process_task[ue_index][edge_index]['TIME'], ue_index] = self.max_delay
self.unfinish_task[self.edge_process_task[ue_index][edge_index]['TIME'], ue_index] = 1
self.edge_process_task[ue_index][edge_index]['REMAIN'] = np.nan
self.drop_edge_count = self.drop_edge_count + 1
#self.TASK_log[ue_index][self.edge_process_task[ue_index][edge_index]['TASK_ID']]['state'] = 2
# OTHER INFO
if self.edge_ue_m[edge_index] != 0:
self.b_edge_comp[ue_index, edge_index] \
= np.max([self.b_edge_comp[ue_index, edge_index]
- self.comp_cap_edge[edge_index]/ ue_arrive_task_dens / self.edge_ue_m[edge_index]
- self.edge_drop[ue_index, edge_index], 0])
# TRANSMISSION QUEUE UPDATE ===================
for ue_index in range(self.n_ue):
#ue_tran_cap = np.squeeze(self.tran_cap_ue[ue_index,:])[1]/self.n_cycle
ue_tran_cap = np.squeeze(self.tran_cap_ue[ue_index,:])
ue_arrive_task_size = np.squeeze(self.arrive_task_size[self.time_count, ue_index])
ue_arrive_task_dens = np.squeeze(self.arrive_task_dens[self.time_count, ue_index])
tmp_dict = {
'DIV' : 0 ,
'UE_ID': ue_index,
'TASK_ID': self.UE_TASK[ue_index],
'SIZE': ue_arrive_task_size,
'DENS': ue_arrive_task_dens,
'TIME': self.time_count,
'EDGE': ue_action_offload[ue_index],
}
if ue_action_local[ue_index] == 0:
self.ue_transmission_queue[ue_index].put(tmp_dict)
for cycle in range(self.n_cycle):
# TASK ON PROCESS
if math.isnan(self.local_transmit_task[ue_index]['REMAIN']) \
and (not self.ue_transmission_queue[ue_index].empty()):
while not self.ue_transmission_queue[ue_index].empty():
get_task = self.ue_transmission_queue[ue_index].get()
#print("trans", get_task)
if get_task['SIZE'] != 0:
#self.TASK_log[ue_index][get_task['TASK_ID']] = get_task
#self.task_history[ue_index].append(get_task)
if self.time_count - get_task['TIME'] + 1 <= self.max_delay:
self.local_transmit_task[ue_index]['UE_ID'] = get_task['UE_ID']
self.local_transmit_task[ue_index]['TASK_ID'] = get_task['TASK_ID']
self.local_transmit_task[ue_index]['SIZE'] = get_task['SIZE']
self.local_transmit_task[ue_index]['DENS'] = get_task['DENS']
self.local_transmit_task[ue_index]['TIME'] = get_task['TIME']
self.local_transmit_task[ue_index]['EDGE'] = int(get_task['EDGE'])
self.local_transmit_task[ue_index]['REMAIN'] = self.local_transmit_task[ue_index]['SIZE']
self.local_transmit_task[ue_index]['DIV'] = get_task['DIV']
break
else:
#self.task_history[get_task['UE_ID']][get_task['TASK_ID']]['d_state'][get_task['DIV']] = -1
self.process_delay[get_task['TIME'], ue_index] = self.max_delay
self.unfinish_task[get_task['TIME'], ue_index] = 1
# PROCESS
if self.local_transmit_task[ue_index]['REMAIN'] > 0:
if self.local_transmit_task[ue_index]['REMAIN'] >= ue_tran_cap[self.local_transmit_task[ue_index]['EDGE']]:
self.ue_tran_energy[self.local_transmit_task[ue_index]['TIME'], ue_index] += ue_tran_cap[self.local_transmit_task[ue_index]['EDGE']] * self.ue_p_tran
self.ue_bit_transmitted[self.local_transmit_task[ue_index]['TIME'], ue_index] += self.local_transmit_task[ue_index]['REMAIN']
else:
self.ue_tran_energy[self.local_transmit_task[ue_index]['TIME'], ue_index] += ue_tran_cap[self.local_transmit_task[ue_index]['EDGE']] * self.ue_p_tran
self.ue_bit_transmitted[self.local_transmit_task[ue_index]['TIME'], ue_index] += self.local_transmit_task[ue_index]['REMAIN']
self.local_transmit_task[ue_index]['REMAIN'] = \
self.local_transmit_task[ue_index]['REMAIN'] \
- ue_tran_cap[self.local_transmit_task[ue_index]['EDGE']]
#print(ue_tran_cap)
# UPDATE edge QUEUE
if self.local_transmit_task[ue_index]['REMAIN'] <= 0:
tmp_dict = {'UE_ID': self.local_transmit_task[ue_index]['UE_ID'],
'TASK_ID': self.local_transmit_task[ue_index]['TASK_ID'],
'SIZE' : self.local_transmit_task[ue_index]['SIZE'],
'DENS' : self.local_transmit_task[ue_index]['DENS'],
'TIME' : self.local_transmit_task[ue_index]['TIME'],
'EDGE' : self.local_transmit_task[ue_index]['EDGE'],
'DIV' : self.local_transmit_task[ue_index]['DIV']}
self.edge_computation_queue[ue_index][self.local_transmit_task[ue_index]['EDGE']].put(tmp_dict)
#print("_+_+____", self.local_transmit_task[ue_index]['EDGE'])
self.task_count_edge = self.task_count_edge + 1
edge_index = self.local_transmit_task[ue_index]['EDGE']
self.b_edge_comp[ue_index, edge_index] = self.b_edge_comp[ue_index, edge_index] + self.local_transmit_task[ue_index]['SIZE']
self.process_delay_trans[self.local_transmit_task[ue_index]['TIME'], ue_index] = self.time_count - self.local_transmit_task[ue_index]['TIME'] + 1
self.local_transmit_task[ue_index]['REMAIN'] = np.nan
elif self.time_count - self.local_transmit_task[ue_index]['TIME'] + 1 == self.max_delay:
#self.task_history[self.local_transmit_task[ue_index]['UE_ID']][self.local_transmit_task[ue_index]['TASK_ID']]['d_state'][self.local_transmit_task[ue_index]['DIV']] = -1
self.local_transmit_task[ue_index]['REMAIN'] = np.nan
self.process_delay[self.local_transmit_task[ue_index]['TIME'], ue_index] = self.max_delay
self.unfinish_task[self.local_transmit_task[ue_index]['TIME'], ue_index] = 1
self.drop_trans_count = self.drop_trans_count + 1
# OTHER INFO
if ue_arrive_task_size != 0:
tmp_tilde_t_ue_tran = np.max([self.t_ue_tran[ue_index] + 1, self.time_count])
self.t_ue_comp[ue_index] = np.min([tmp_tilde_t_ue_tran
+ math.ceil(ue_arrive_task_size * (1 - ue_action_local[ue_index])
/ ue_tran_cap[ue_action_offload[ue_index]]) - 1,
self.time_count + self.max_delay - 1])
# COMPUTE CONGESTION (FOR NEXT TIME SLOT)
self.edge_ue_m_observe = self.edge_ue_m
self.edge_ue_m = np.zeros(self.n_edge)
for edge_index in range(self.n_edge):
for ue_index in range(self.n_ue):
if (not self.edge_computation_queue[ue_index][edge_index].empty()) \
or self.edge_process_task[ue_index][edge_index]['REMAIN'] > 0:
self.edge_ue_m[edge_index] += 1
# TIME UPDATE
self.time_count = self.time_count + 1
done = False
if self.time_count >= self.n_time:
done = True
# set all the tasks' processing delay and unfinished indicator
for time_index in range(self.n_time):
for ue_index in range(self.n_ue):
if self.process_delay[time_index, ue_index] == 0 and self.arrive_task_size[time_index, ue_index] != 0:
self.process_delay[time_index, ue_index] = (self.time_count - 1) - time_index + 1
self.unfinish_task[time_index, ue_index] = 1
# OBSERVATION
UEs_OBS_ = np.zeros([self.n_ue, self.n_features])
UEs_lstm_state_ = np.zeros([self.n_ue, self.n_lstm_state])
if not done:
for ue_index in range(self.n_ue):
# observation is zero if there is no task arrival
if self.arrive_task_size[self.time_count, ue_index] != 0:
# state [A, B^{comp}, B^{tran}, [B^{edge}]]
UEs_OBS_[ue_index, :] = np.hstack([
self.arrive_task_size[self.time_count, ue_index],
self.t_ue_comp[ue_index] - self.time_count + 1,
self.t_ue_tran[ue_index] - self.time_count + 1,
self.b_edge_comp[ue_index, :],
self.ue_energy_state[ue_index]])
UEs_lstm_state_[ue_index, :] = np.hstack(self.edge_ue_m_observe)
return UEs_OBS_, UEs_lstm_state_, done
'''
for ue in range(self.n_ue):
for task in range(len(self.task_history[ue])):
#print(self.task_history[ue][task], "\n")
for component in range(self.n_component):
if self.task_history[ue][task]['d_state'][component] < 0:
self.process_delay[self.task_history[ue][task]['TIME'], ue] = self.max_delay
self.unfinish_task[self.task_history[ue][task]['TIME'], ue] = 1
'''