def start_simulation_sim_time(no_of_jobs_server_1, no_of_jobs_server_2,
arrival_rate, job_distribution, sim_time,
scheduler):
list_of_servers = init_servers(2, scheduler, job_distribution)
policy_i = ShortestQueue() #Join shortest queue policy
dispatcher = Dispatcher(
policy_i, list_of_servers) #Create a dispatcher with JSQ policy
statistics = Statistics()
world = Global(statistics)
p_arrivals = PoissonArrival(float(arrival_rate))
init_server_to_state(no_of_jobs_server_1, list_of_servers[0], scheduler,
world)
init_server_to_state(no_of_jobs_server_2, list_of_servers[1], scheduler,
world)
init_first_jobs(world, [dispatcher], p_arrivals)
world.number_of_arr_dep = 0 #resetting the number of events before we start
for x in range(1, 11):
# Now that each dispatcher has an arrival, we can start looping through events
while world.next_event() <= float(sim_time) * (
x * 0.1
): # 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 * 10))
#for loop to step between while loops (every 10%)while world.next
total_no_jobs = world._stats.number_of_jobs
print(total_no_jobs)
world._stats.print_stats()
save_stats(world)
def start_simulation_less_than_n(no_of_jobs_server_1, no_of_jobs_server_2,
arrival_rate, job_distribution, sim_time,
scheduler):
list_of_servers = init_servers(3, scheduler, job_distribution)
global jobs_ran
global final_data
stopping_n = no_of_jobs_server_2
# Create dispatcher
policy_i = ShortestQueue() #Join shortest queue policy
dispatcher = Dispatcher(
policy_i, list_of_servers) #Create a dispatcher with JSQ policy
statistics = Statistics()
world = Global(statistics)
p_arrivals = PoissonArrival(float(arrival_rate))
arrival = 0
init_server_to_state(no_of_jobs_server_1, list_of_servers[0], scheduler,
world)
init_server_to_state(no_of_jobs_server_2, list_of_servers[1], scheduler,
world)
init_server_to_state(no_of_jobs_server_2, list_of_servers[2], scheduler,
world)
initial_arrival = random.expovariate(p_arrivals._rate)
params = [dispatcher, world]
world.schedule_event(p_arrivals.generate_arrival, initial_arrival,
params) #Schedule first arrival to start chain
last_event = 0
world.number_of_arr_dep = 0 #resetting the number of events before we start
# Now we need to schedule the initial arrivals to start the chain of events.
# Now that each dispatcher has an arrival, we can start looping through events
while world.next_event() <= float(
sim_time
): # while the virtual time of next event is less than our simulation time..
if (list_of_servers[0]._total_jobs <= stopping_n
and list_of_servers[1]._total_jobs <= stopping_n
and list_of_servers[2]._total_jobs <= stopping_n):
break
last_event = world.next_event()
world.process_event(
) # We take the event and process it (running the function(s))
#for loop to step between while loops (every 10%)while world.next
#We've reached a stopping state. Record event parameters and print to file
jobs_ran += world._stats.number_of_jobs # We stopped, we add the number of jobs ran this time to global variable
recorded_x = list_of_servers[0]._total_jobs
recorded_y = list_of_servers[1]._total_jobs
recorded_T = last_event #Last event that happened (e.g. departure that caused the total jobs to be < 4)
recorded_N = world.number_of_arr_dep #Get the number of events that happened'
final_data.append((recorded_x, recorded_y, recorded_T, recorded_N))