def _reschedule(self):
new_tasks = []
for task_id in self.order_list:
if self.mfss_allows_execution(task_id) and task_id in self.tasks_to_do.keys():
new_task = self.allocate_resources(self.tasks_to_do[task_id])
if new_task is not None:
new_tasks.append(new_task)
del self.tasks_to_do[task_id]
else:
break
if len(new_tasks) == 0 and len(self.currently_assigned_resoruces) == 0 and len(self.tasks_to_do) is not 0:
if self.ignore_infeasible_schedules == False:
raise UnfeasibleScheduleException()
else:
Logger.warning("unfeasible schedule encountered. Ignoring mfss")
for task_id in self.tasks_to_do.keys():
new_task = self.allocate_resources(self.tasks_to_do[task_id])
if new_task is not None:
new_tasks.append(new_task)
del self.tasks_to_do[task_id]
break
return new_tasks
def _reschedule(self):
new_tasks = []
tasks_to_remove = []
self.eligble_to_run = self.get_tasks_eligible_to_run()
for task_id in self.order_list:
if self.bottleneck_allows_execution(task_id, self.eligble_to_run):
new_task = self.allocate_resources(self.tasks_to_do[task_id])
self.eligble_to_run = self.get_tasks_eligible_to_run()
if new_task == None:
pass
else:
new_tasks.append(new_task)
del self.tasks_to_do[task_id]
tasks_to_remove.append(task_id)
self.already_executed.append(task_id)
[self.order_list.remove(task) for task in tasks_to_remove]
if len(new_tasks) == 0 and len(self.currently_assigned_resoruces) == 0 and len(self.tasks_to_do) > 0:
if not self.ignore_infeasible_schedules:
raise UnfeasibleScheduleException()
else:
Logger.warning("Unfeasible schedule encountered. Ignoring mfss")
for task_id in self.order_list:
new_task = self.allocate_resources(self.tasks_to_do[task_id])
if new_task == None:
pass
else:
new_tasks.append(new_task)
del self.tasks_to_do[task_id]
tasks_to_remove.append(task_id)
self.already_executed.append(task_id)
[self.order_list.remove(task) for task in tasks_to_remove]
return new_tasks
def _reschedule(self):
new_tasks = []
tasks_to_remove = []
for task_id in self.order_list:
if self.mfss_allows_execution(task_id):
new_task = self.allocate_resources(self.tasks_to_do[task_id])
if new_task == None:
pass
else:
new_tasks.append(new_task)
del self.tasks_to_do[task_id]
tasks_to_remove.append(task_id)
self.already_executed.append(task_id)
[self.order_list.remove(task) for task in tasks_to_remove]
if len(new_tasks) == 0 and len(self.currently_assigned_resoruces) == 0 and len(self.tasks_to_do) > 0:
if not self.ignore_infeasible_schedules:
raise UnfeasibleScheduleException()
else:
Logger.warning("Unfeasible schedule encountered. Ignoring mfss")
for task_id in self.order_list:
new_task = self.allocate_resources(self.tasks_to_do[task_id])
if new_task == None:
pass
else:
new_tasks.append(new_task)
del self.tasks_to_do[task_id]
tasks_to_remove.append(task_id)
self.already_executed.append(task_id)
[self.order_list.remove(task) for task in tasks_to_remove]
return new_tasks
def process_job_parallel(scheduler, job, nr_cores, nr_iter, parameters = None):
Logger.log_level = 2
processes = []
manager = Manager()
return_values = manager.dict()
extremes = manager.dict()
start_time = datetime.datetime.now()
for i in range(nr_cores):
p = Process(target=worker, args=(i, nr_cores, scheduler, job, nr_iter, return_values, extremes, parameters,))
processes.append(p)
p.start()
for process in processes:
process.join()
#reduce
results = []
for value in return_values.values():
for entry in value:
results.append(entry)
min = None
max = None
for extreme in extremes.values():
if min is None or extreme[0].total_time < min.total_time:
min = extreme[0]
if max is None or extreme[1].total_time > max.total_time:
max = extreme[1]
Logger.warning("Min: %s" % min.total_time)
Logger.warning("Max: %s" % max.total_time)
duration = datetime.datetime.now() - start_time
Logger.warning("Simulation complete. Duration: %s" % (duration))
return results, (min,max)
def run_multiple_opt(args, parameter_sets):
for idx, param in enumerate(parameter_sets):
Logger.warning("running parameter set %s of %s" %
(idx, len(parameter_sets)))
run_optimization_process(args, param)
def run_multiple_opt(args, parameter_sets):
for idx, param in enumerate(parameter_sets):
Logger.warning("running parameter set %s of %s" % (idx, len(parameter_sets)))
run_optimization_process(args, param)