def test_sed_make_decision_heterogeneous_choose_server_1(self):
heterogeneous_servers = simutil.init_servers(2, [1, 1], [3, 1])
heterogeneous_servers[0]._total_jobs = 1 # Job to server 0
self.assertEqual(heterogeneous_servers[0]._service_rate, 3)
self.assertEqual(heterogeneous_servers[1]._service_rate, 1)
self.assertEqual(
self.sed_policy.make_decision(heterogeneous_servers, Job(0.2,
0.4)), 0)
def setUp(self):
self.random_policy = policies.RND()
self.shortest_queue_policy = policies.ShortestQueue()
self.sed_policy = policies.SED()
self.servers = simutil.init_servers(5, [1, 1, 1, 1, 1],
[2, 2, 2, 2, 2])
self.td_learn_policy = policies.TDlearning([4, 4], [], [], 100,
self.servers)
def setUp(self):
job_size_rate = 1
job_sizes = Expo(job_size_rate)
seed = 500
self.arrival = arrivals.PoissonArrival(2, job_sizes, arr_seed=seed)
self.statistics = stats.Statistics()
self.world = global_state.Global(self.statistics)
self.servers = simutil.init_servers(2, [1, 1], [2, 2]) # 2 servers, both using 1 (FIFO) and both run at 2 job units service rate.
self.dispatcher = simutil.init_dispatchers(1, [1], [self.servers], [], 725)
def setUp(self):
self.stats = stats.Statistics()
self.world = global_state.Global(self.stats)
job_size_rate = 1
self.job_sizes = Expo(job_size_rate)
arr_seed = 100
self.p_arrivals = arrivals.PoissonArrival(2, self.job_sizes, arr_seed)
self.list_of_servers = simutil.init_servers(1, [1], [2])
self.policy_i = policies.ShortestQueue()
self.d = simutil.init_dispatchers(1, [2], [self.list_of_servers], [],
150)
def q_testing_id(id):
sim_time = 2000000
list_of_servers = simutil.init_servers(2, [1, 1], [3, 1]) # 2 servers using FIFO and mui = 3, and 1
list_of_dispatchers = simutil.init_dispatchers(1, [3], [list_of_servers], [[4, 4], [], [], sim_time]) # 1 dispatcher, Qlearn, 5x5 box, nothing excluded.
statistics = Statistics()
world = Global(statistics)
job_sizes = Expo(2) # Lambda = 2, rho will be lambda / (3+1) = 0.5
p_arrivals = PoissonArrival(1, job_sizes) # Poisson arrivals with 1 arrival rate.
simutil.init_first_jobs(world, list_of_dispatchers, p_arrivals)
# Main loop
for x in range(1, 101):
while world.next_event() <= float(sim_time) * (x * 0.01): # while the virtual time of next event is less than our simulation time..
world.process_event() # We take the event and process it (running the function(s))
# world._stats.write_to_file_jobs()
print("thread id: {}, finised {}%".format(id, x))
if(x % 5 == 0):
v2 = list_of_dispatchers[0]._policy.v2
with open('../data_results/TD_learn_server_utilization/2Mjobs_TD_alpha_decay_rho_' + str(id) + '.txt', 'a') as f:
for key in v2:
f.write(str(key) + ': ' + str(v2[key]) + '\n')
f.write('\n')
def td_learn_server_utilization(rho, round_nr):
sim_time = 5000000
list_of_servers = simutil.init_servers(2, [1, 1], [3, 1]) # 2 servers using FIFO and mui = 3, and 1
list_of_dispatchers = simutil.init_dispatchers(1, [3], [list_of_servers], [[12, 4], [], [], sim_time]) # 1 dispatcher, Qlearn, 5x5 box, nothing excluded.
statistics = Statistics()
world = Global(statistics)
# arrival_rate = 2 # get the arrival rate by using given rho
job_sizes = Expo(1) # Lambda = 2, rho will be lambda / (3+1) = 0.5
p_arrivals = PoissonArrival(3.92, job_sizes) # Poisson arrivals with 1 arrival rate.
simutil.init_first_jobs(world, list_of_dispatchers, p_arrivals)
# Main loop
for x in range(1, 101):
while world.next_event() <= float(sim_time) * (x * 0.01): # while the virtual time of next event is less than our simulation time..
world.process_event() # We take the event and process it (running the function(s))
# world._stats.write_to_file_jobs()
print("round: {}, rho: {}, finised {}%".format(round_nr, rho, x))
v2 = list_of_dispatchers[0]._policy.v2
with open('../data_results/TD_learn_server_utilization/500kjobs_SED_rho_' + str(rho) + '-3.txt', 'a') as f:
for key in v2:
f.write(str(key) + ': ' + str(v2[key]) + '\n')
f.write('\n')
def TD_testing(st,
servers,
dispatchers,
arrival_rate,
job_size_rate,
job_distribution,
qlearn,
arrivals_seed=None,
policyseed=None,
file_name=None,
td_matrix=None):
sim_time = st
list_of_servers = simutil.init_servers(
len(servers[0]), servers[0],
servers[1]) # List of servers for 1 dispatcher
servers_for_dispatchers = []
servers_for_dispatchers.append(list_of_servers)
list_of_dispatchers = simutil.init_dispatchers(
dispatchers[0],
dispatchers[1],
servers_for_dispatchers,
qlearn,
policyseed,
td_matrix_and_backup=[
td_matrix, SED(policyseed)
]) # 1 dispatcher, Qlearn, 5x5 box, nothing excluded.
statistics = Statistics()
world = Global(statistics)
if job_distribution == 1:
job_sizes = Expo(job_size_rate)
if file_name is not None:
print("file name used")
f = open(file_name, 'r')
data = f.read().splitlines()
sim_time = int(data[-1])
print(sim_time)
data2 = [
tuple(float(x) for x in item.split(',')) for item in data[:-1]
]
p_arrivals = PoissonArrival(
arrival_rate, job_sizes, arrivals_seed,
file=data2) # Poisson arrivals with arrival rate 1.
else:
p_arrivals = PoissonArrival(
arrival_rate, job_sizes,
arrivals_seed) # Poisson arrivals with arrival rate 1.
simutil.init_first_jobs(world, list_of_dispatchers, p_arrivals)
# Main loop
start = timer()
for x in range(1, 101):
while world.next_event() <= float(sim_time) * (
x * 0.01
): # while the virtual time of next event is less than our simulation time..
world.process_event(
) # We take the event and process it (running the function(s))
print("{}%".format(x))
end = timer()
print("Total time: {}".format(end - start))
if dispatchers[1] == 3:
print('The state counter: ' +
str(list_of_dispatchers[0]._policy.state_counter))
total_no_jobs = world._stats.number_of_jobs
print(total_no_jobs)
# simutil.print_step_matrix(list_of_dispatchers[0])
world._stats.print_stats()
world._stats.save_run(sim_time)
simutil.save_stats(world) # Ask user if he wants to save stats to file
def setUp(self):
self.servers = simutil.init_servers(2, [1, 1], [2, 2]) # 2 servers, both using 1 (FIFO) and both run at 2 job units service rate.
self.dispatchers = simutil.init_dispatchers(1, [1], [self.servers], [])
self.dispatcher = self.dispatchers[0]