def main(mu,
target,
strategy,
nodes,
mode,
deadline,
seed,
sim_duration,
distribution_on=None,
distribution_off=None):
SIM_DURATION = sim_duration
env = simpy.Environment()
deadline_max = deadline
arrival_rate = 0.5
arrival_dist = "exponential"
d_on = 100
d_off = 600
dir = "traces/ue_scheduling/" + str(
mu
) + '-' + distribution_on + '_' + distribution_off + '-' + target + '-' + strategy + '-' + str(
nodes) + '-' + str(deadline_max) + '-' + mode + '-' + str(seed)
args = str(
mu
) + '-' + distribution_on + '_' + distribution_off + '-' + target + '-' + strategy + '-' + str(
nodes) + '-' + str(deadline_max)
try:
os.makedirs(dir)
except OSError:
pass
np.random.seed(seed)
monitor = Monitor(env, dir)
scheduler = strategy_mapping[target][strategy](env, monitor)
def traff_gen(mode, id):
if mode == "on_off":
traffic_generator = traffic_mapping[mode][arrival_dist](
env, id, arrival_rate, (distribution_on, d_on),
(distribution_off, d_off))
elif mode == "continuous":
traffic_generator = traffic_mapping[mode][arrival_dist](
env, id, arrival_rate)
return traffic_generator
channel = Channel(env, monitor)
cable = [Cable(env, mu), Cable(env, mu)]
cloud = Cloud(env, cable, scheduler)
rbs = RBS(env, channel, cable, scheduler, mu)
# traffic_generator = traff_gen(mode)
deadlines = np.random.uniform(1, deadline_max, nodes)
ue = []
for i in range(nodes):
ue.append(UE(env, i, channel, 0, deadlines[i], traff_gen(mode, i)))
env.run(until=SIM_DURATION)
# rho = (1/0.5 * 50 /250) * 10 * 10000
# print("estimate nbr. packet: {}".format(rho))
# print(channel.get_queue_len())
arrival_array = np.array(channel.get_arrivals())
print(len(arrival_array))
end = arrival_array[-1]
# print(end)
start = 500
index = 0
for i in range(len(arrival_array)):
if arrival_array[i] >= start:
break
total_arrivals = len(arrival_array[i:-1])
print(total_arrivals)
load = total_arrivals / (end - start) / 24
pd.DataFrame(arrival_array,
columns=["arrival_time"]).to_pickle(dir + "/arrivals.pkl")
monitor.dump_to_file()
loss_array = monitor.get_loss()
# print(loss_array)
waste_array = monitor.get_waste()
# print(waste_array)
loss = 0
for l in loss_array:
if l[0] >= start:
loss += l[1]
loss_rate = loss / total_arrivals
waste = 0
pilots = 0
for w in waste_array:
if w[0] >= start:
waste += w[1]
pilots += 12
waste = waste / pilots
return args, loss_rate, waste
def main_ue_edf(mu, nodes, mode, beta, seed, sim_duration, traffic_props):
SIM_DURATION = sim_duration
env = simpy.Environment()
deadline_max = traffic_props['deadline']
traffic_type = traffic_props['traffic_type']
arrival_dist = traffic_props['arrival_dist']
print(traffic_props)
np.random.seed(seed)
if traffic_type == 'homogeneous':
arrival_rates = np.ones(nodes) * traffic_props['arrival_rate']
deadline_min = traffic_props['arrival_rate']
arrival_mean = traffic_props['arrival_rate']
elif traffic_type == 'heterogeneous':
arrival_rates = np.random.uniform(traffic_props['arrival_rate_min'],
traffic_props['arrival_rate_max'],
size=nodes)
arrival_mean = 0.5 * (traffic_props['arrival_rate_min'] +
traffic_props['arrival_rate_max'])
deadline_min = traffic_props['arrival_rate_min']
# print('heterogeneous')
# print(arrival_rates)
print(np.mean(arrival_rates))
if mode == "on_off":
distribution_on = traffic_props['distribution_on']
distribution_off = traffic_props['distribution_off']
d_on = traffic_props['d_on']
d_off = traffic_props['d_off']
traffic_profile = mode + '_' + distribution_on + '-' + traffic_type
else:
traffic_profile = mode + '-' + traffic_type
trace_dir = "traces/softcom_lowrate"
dir = os.path.join(
trace_dir,
str(mu) + '-' + str(beta) + '-' + str(nodes) + '-' +
str(arrival_mean) + '-' + str(deadline_max) + '-' + traffic_profile +
'-' + str(seed))
args = str(mu) + '-' + str(beta) + '-' + str(nodes) + '-' + str(
arrival_mean) + '-' + str(deadline_max) + '-' + traffic_profile
try:
os.makedirs(dir)
except OSError:
pass
monitor = Monitor(env, dir)
scheduler = us.edf(env, beta, monitor)
def traff_gen(mode, id):
if mode == "on_off":
traffic_generator = traffic_mapping[mode][arrival_dist](
env, id, arrival_rates[i], (distribution_on, d_on),
(distribution_off, d_off))
elif mode == "continuous":
traffic_generator = traffic_mapping[mode][arrival_dist](
env, id, arrival_rates[i])
return traffic_generator
channel = Channel(env, monitor)
loglaplace_base = 0.46699687428378805
if mu == 0:
mu_loc = 0
else:
mu_loc = mu - loglaplace_base
cable = [Cable(env, mu_loc), Cable(env, mu_loc)]
cloud = Cloud(env, cable, scheduler)
rbs = RBS(env, channel, cable, scheduler, mu, beta)
# traffic_generator = traff_gen(mode)
deadlines = np.random.uniform(deadline_min, deadline_max, nodes)
ue = []
for i in range(nodes):
ue.append(UE(env, i, channel, 0, deadlines[i], traff_gen(mode, i)))
env.run(until=SIM_DURATION)
# rho = (1/0.5 * 50 /250) * 10 * 10000
# print("estimate nbr. packet: {}".format(rho))
# print(channel.get_queue_len())
arrival_array = np.array(channel.get_arrivals())
print(len(arrival_array))
end = arrival_array[-1]
# print(end)
start = 500
index = 0
for i in range(len(arrival_array)):
if arrival_array[i] >= start:
break
total_arrivals = len(arrival_array[i:-1])
print(total_arrivals)
# load = total_arrivals / (end-start) / 24
pd.DataFrame(arrival_array,
columns=["arrival_time"]).to_pickle(dir + "/arrivals.pkl")
monitor.dump_to_file()
loss_array = monitor.get_loss()
# print(loss_array)
waste_array = monitor.get_waste()
# print(waste_array)
loss = 0
for l in loss_array:
if l[0] >= start:
loss += l[1]
loss_probability = loss / total_arrivals
waste = 0
pilots = 0
for w in waste_array:
if w[0] >= start:
waste += w[1]
pilots += 12 * beta
waste = waste / pilots
return args, loss_probability, waste
