class HeadDoubleEasyScheduler(EasyBackfillScheduler):
def __init__(self, num_processors):
super(HeadDoubleEasyScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
def _schedule_head_of_list(self, current_time):
"Overriding parent method"
result = []
while True:
if len(self.unscheduled_jobs) == 0:
break
# check if the first job can be scheduled at current time
if (
self.cpu_snapshot.free_processors_available_at(current_time)
>= self.unscheduled_jobs[0].num_required_processors
):
job = self.unscheduled_jobs.pop(0)
job.predicted_run_time = 2 * job.user_estimated_run_time # doubling is done here
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
else:
# first job can't be scheduled
break
return result
def __init__(self, options):
super(EasyPlusPlusScheduler, self).__init__(options)
self.init_predictor(options)
self.init_corrector(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
self.unscheduled_jobs = []
self.ff = open("times-epp-sgd.txt", 'w')
class FcfsScheduler(Scheduler):
def __init__(self, options):
super(FcfsScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
self.waiting_queue_of_jobs = []
def new_events_on_job_submission(self, job, current_time):
self.cpu_snapshot.archive_old_slices(current_time)
self.waiting_queue_of_jobs.append(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def new_events_on_job_termination(self, job, current_time):
self.cpu_snapshot.archive_old_slices(current_time)
self.cpu_snapshot.delTailofJobFromCpuSlices(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def _schedule_jobs(self, current_time):
result = []
while len(self.waiting_queue_of_jobs) > 0:
job = self.waiting_queue_of_jobs[0]
if self.cpu_snapshot.free_processors_available_at(
current_time) >= job.num_required_processors:
self.waiting_queue_of_jobs.pop(0)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
else:
break
return result
class FcfsScheduler(Scheduler):
def __init__(self, options):
super(FcfsScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
self.waiting_queue_of_jobs = []
def new_events_on_job_submission(self, job, current_time):
self.cpu_snapshot.archive_old_slices(current_time)
self.waiting_queue_of_jobs.append(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def new_events_on_job_termination(self, job, current_time):
self.cpu_snapshot.archive_old_slices(current_time)
self.cpu_snapshot.delTailofJobFromCpuSlices(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def _schedule_jobs(self, current_time):
result = []
while len(self.waiting_queue_of_jobs) > 0:
job = self.waiting_queue_of_jobs[0]
if self.cpu_snapshot.free_processors_available_at(current_time) >= job.num_required_processors:
self.waiting_queue_of_jobs.pop(0)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
else:
break
return result
def __init__(self, options):
super(EasyPlusPlusScheduler, self).__init__(options)
self.init_predictor(options)
self.init_corrector(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
self.unscheduled_jobs = []
class EasySJBFScheduler(EasyBackfillScheduler):
""" This algorithm implements the algorithm in the paper of Tsafrir, Etzion, Feitelson, june 2007?
"""
def __init__(self, num_processors):
super(EasySJBFScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
def _backfill_jobs(self, current_time):
"Overriding parent method"
if len(self.unscheduled_jobs) <= 1:
return []
result = []
first_job = self.unscheduled_jobs[0]
tail = list_copy(self.unscheduled_jobs[1:])
tail_of_jobs_by_sjf_order = sorted(tail, key=sjf_sort_key)
self.cpu_snapshot.assignJobEarliest(first_job, current_time)
for job in tail_of_jobs_by_sjf_order:
if self.cpu_snapshot.canJobStartNow(job, current_time):
self.unscheduled_jobs.remove(job)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
self.cpu_snapshot.delJobFromCpuSlices(first_job)
return result
class TailDoubleEasyScheduler(EasyBackfillScheduler):
""" This algorithm implements the algorithm in the paper of Tsafrir, Etzion, Feitelson, june 2007?
"""
def __init__(self, options):
super(TailDoubleEasyScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
def _backfill_jobs(self, current_time):
"Overriding parent method"
if len(self.unscheduled_jobs) <= 1:
return []
result = []
first_job = self.unscheduled_jobs[0]
tail = list_copy(self.unscheduled_jobs[1:])
self.cpu_snapshot.assignJobEarliest(first_job, current_time)
for job in tail:
job.predicted_run_time = 2 * job.user_estimated_run_time # doubling is done here
if self.cpu_snapshot.canJobStartNow(job, current_time): # if job can be backfilled
self.unscheduled_jobs.remove(job)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
else:
job.predicted_run_time = job.user_estimated_run_time # undoubling is done here
self.cpu_snapshot.delJobFromCpuSlices(first_job)
return result
class ReverseEasyScheduler(EasyBackfillScheduler):
def __init__(self, options):
super(ReverseEasyScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
def _backfill_jobs(self, current_time):
"Overriding parent method"
if len(self.unscheduled_jobs) <= 1:
return []
result = []
first_job = self.unscheduled_jobs[0]
tail = list_copy(self.unscheduled_jobs[1:])
tail_of_jobs_by_reverse_order = sorted(tail, key=latest_sort_key)
self.cpu_snapshot.assignJobEarliest(first_job, current_time)
for job in tail_of_jobs_by_reverse_order:
if self.cpu_snapshot.canJobStartNow(job, current_time):
self.unscheduled_jobs.remove(job)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
self.cpu_snapshot.delJobFromCpuSlices(first_job)
return result
class EasySjbfScheduler(EasyBackfillScheduler):
""" This algorithm implements the algorithm in the paper of Tsafrir, Etzion, Feitelson, june 2007?
"""
def __init__(self, options):
super(EasySjbfScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
def _backfill_jobs(self, current_time):
"Overriding parent method"
if len(self.unscheduled_jobs) <= 1:
return []
result = []
first_job = self.unscheduled_jobs[0]
tail = list_copy(self.unscheduled_jobs[1:])
tail_of_jobs_by_sjf_order = sorted(tail, key=sjf_sort_key)
self.cpu_snapshot.assignJobEarliest(first_job, current_time)
for job in tail_of_jobs_by_sjf_order:
if self.cpu_snapshot.canJobStartNow(job, current_time):
job.is_backfilled = 1
self.unscheduled_jobs.remove(job)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
self.cpu_snapshot.unAssignJob(first_job)
return result
def __init__(self, num_processors, threshold=0.2, window_size=150):
super(OrigProbabilisticEasyScheduler, self).__init__(num_processors)
self.threshold = threshold
self.window_size = window_size # a parameter for the distribution
self.cpu_snapshot = CpuSnapshot(num_processors)
self.user_distribution = {}
self.unscheduled_jobs = []
self.currently_running_jobs = []
#self.work_list = [[None for i in xrange(self.num_processors+1)] for j in xrange(self.num_processors+1)]
self.M = {}
for c in xrange(self.num_processors + 1):
for n in xrange(self.num_processors + 1):
self.M[c, n] = 0.0
self.max_user_rounded_estimated_run_time = 0
self.prev_max_user_rounded_estimated_run_time = 0
class ShrinkingEasyScheduler(EasyBackfillScheduler):
""" This "toy" algorithm follows an the paper of Tsafrir, Etzion, Feitelson, june 2007
"""
def __init__(self, num_processors):
super(ShrinkingEasyScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.unscheduled_jobs = []
def new_events_on_job_submission(self, job, current_time):
if job.user_estimated_run_time > 1:
job.predicted_run_time = int(job.user_estimated_run_time / 2)
else:
job.predicted_run_time = 1
return super(ShrinkingEasyScheduler, self).new_events_on_job_submission(job, current_time)
def new_events_on_job_under_prediction(self, job, current_time):
assert job.predicted_run_time <= job.user_estimated_run_time
self.cpu_snapshot.assignTailofJobToTheCpuSlices(job)
return []
class ShrinkingEasyScheduler(EasyBackfillScheduler):
""" This "toy" algorithm follows an the paper of Tsafrir, Etzion, Feitelson, june 2007
"""
def __init__(self, num_processors):
super(ShrinkingEasyScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.unscheduled_jobs = []
def new_events_on_job_submission(self, job, current_time):
if job.user_estimated_run_time > 1:
job.predicted_run_time = int(job.user_estimated_run_time / 2)
else:
job.predicted_run_time = 1
return super(ShrinkingEasyScheduler,
self).new_events_on_job_submission(job, current_time)
def new_events_on_job_under_prediction(self, job, current_time):
assert job.predicted_run_time <= job.user_estimated_run_time
self.cpu_snapshot.assignTailofJobToTheCpuSlices(job)
return []
class LogScheduler(Scheduler):
def __init__(self, num_processors):
super(LogScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.waiting_queue_of_jobs = []
def new_events_on_job_submission(self, job, current_time):
self.cpu_snapshot.archive_old_slices(current_time)
result = []
#self.waiting_queue_of_jobs.append(job)
result.append(JobStartEvent(current_time+job.actual_wait_time, job))
#return [
# JobStartEvent(current_time, job)
# for job in self._log_schedule_jobs(current_time)
#]
return result
def new_events_on_job_termination(self, job, current_time):
self.cpu_snapshot.archive_old_slices(current_time)
self.cpu_snapshot.delTailofJobFromCpuSlices(job)
return []
"""return [
JobStartEvent(current_time, job)
for job in self._log_schedule_jobs(current_time)
]"""
def _schedule_jobs(self, current_time):
result = []
while len(self.waiting_queue_of_jobs) > 0:
job = self.waiting_queue_of_jobs[0]
if self.cpu_snapshot.free_processors_available_at(current_time) >= job.num_required_processors:
self.waiting_queue_of_jobs.pop(0)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
else:
break
return result
def _log_schedule_jobs(self, current_time): #by Siddharth
result = []
return result
"""while len(self.waiting_queue_of_jobs) > 0:
class ShrinkingEasyScheduler(EasyBackfillScheduler):
""" This "toy" algorithm follows an the paper of Tsafrir, Etzion, Feitelson, june 2007
"""
I_NEED_A_PREDICTOR = True
def __init__(self, options):
super(ShrinkingEasyScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
self.unscheduled_jobs = []
def new_events_on_job_submission(self, job, current_time):
if job.user_estimated_run_time > 1:
job.predicted_run_time = int(job.user_estimated_run_time / 2)
else:
job.predicted_run_time = 1
return super(ShrinkingEasyScheduler, self).new_events_on_job_submission(job, current_time)
def new_events_on_job_under_prediction(self, job, current_time):
pass #assert job.predicted_run_time <= job.user_estimated_run_time
new_predicted_run_time = common_correctors.reqtime(job, current_time)
self.cpu_snapshot.assignTailofJobToTheCpuSlices(job, new_predicted_run_time)
job.predicted_run_time = new_predicted_run_time
return [JobStartEvent(current_time, job)]
class HeadDoubleEasyScheduler(EasyBackfillScheduler):
def __init__(self, options):
super(HeadDoubleEasyScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
def _schedule_head_of_list(self, current_time):
"Overriding parent method"
result = []
while True:
if len(self.unscheduled_jobs) == 0:
break
# check if the first job can be scheduled at current time
if self.cpu_snapshot.free_processors_available_at(
current_time
) >= self.unscheduled_jobs[0].num_required_processors:
job = self.unscheduled_jobs.pop(0)
job.predicted_run_time = 2 * job.user_estimated_run_time # doubling is done here
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
else:
# first job can't be scheduled
break
return result
class ShrinkingEasyScheduler(EasyBackfillScheduler):
""" This "toy" algorithm follows an the paper of Tsafrir, Etzion, Feitelson, june 2007
"""
I_NEED_A_PREDICTOR = True
def __init__(self, options):
super(ShrinkingEasyScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
self.unscheduled_jobs = []
def new_events_on_job_submission(self, job, current_time):
if job.user_estimated_run_time > 1:
job.predicted_run_time = int(job.user_estimated_run_time / 2)
else:
job.predicted_run_time = 1
return super(ShrinkingEasyScheduler, self).new_events_on_job_submission(job, current_time)
def new_events_on_job_under_prediction(self, job, current_time):
assert job.predicted_run_time <= job.user_estimated_run_time
new_predicted_run_time = common_correctors.reqtime(job, current_time)
self.cpu_snapshot.assignTailofJobToTheCpuSlices(job, new_predicted_run_time)
job.predicted_run_time = new_predicted_run_time
return [JobStartEvent(current_time, job)]
def __init__(self, num_processors, threshold = 0.2, window_size=150):
super(OrigProbabilisticEasyScheduler, self).__init__(num_processors)
self.threshold = threshold
self.window_size = window_size # a parameter for the distribution
self.cpu_snapshot = CpuSnapshot(num_processors)
self.user_distribution = {}
self.unscheduled_jobs = []
self.currently_running_jobs = []
#self.work_list = [[None for i in xrange(self.num_processors+1)] for j in xrange(self.num_processors+1)]
self.M = {}
for c in xrange(self.num_processors+1):
for n in xrange(self.num_processors+1):
self.M[c, n] = 0.0
self.max_user_rounded_estimated_run_time = 0
self.prev_max_user_rounded_estimated_run_time = 0
def __init__(self, num_processors):
super(EasyPlusPlusScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.unscheduled_jobs = []
self.user_run_time_prev = {}
self.user_run_time_last = {}
def __init__(self, num_processors):
super(ShrinkingEasyScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.unscheduled_jobs = []
def __init__(self, num_processors):
super(HeadDoubleEasyScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
class EasyBackfillScheduler(Scheduler):
def __init__(self, num_processors):
super(EasyBackfillScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.unscheduled_jobs = []
def new_events_on_job_submission(self, just_submitted_job, current_time):
""" Here we first add the new job to the waiting list. We then try to schedule
the jobs in the waiting list, returning a collection of new termination events """
# TODO: a probable performance bottleneck because we reschedule all the
# jobs. Knowing that only one new job is added allows more efficient
# scheduling here.
#print 'User submits', just_submitted_job
self.cpu_snapshot.archive_old_slices(current_time)
self.unscheduled_jobs.append(just_submitted_job)
#print 'At time', current_time, 'unscheduled:', self.unscheduled_jobs
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def new_events_on_job_termination(self, job, current_time):
""" Here we first delete the tail of the just terminated job (in case it's
done before user estimation time). We then try to schedule the jobs in the waiting list,
returning a collection of new termination events """
self.cpu_snapshot.archive_old_slices(current_time)
self.cpu_snapshot.delTailofJobFromCpuSlices(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def _schedule_jobs(self, current_time):
"Schedules jobs that can run right now, and returns them"
jobs = self._schedule_head_of_list(current_time)
jobs += self._backfill_jobs(current_time)
#print 'Currently schedulable jobs:', jobs
#print 'Currently schedulable jobs count:', len(jobs)
self.totalScheduledJobs += len(jobs)
return jobs
def _schedule_head_of_list(self, current_time):
result = []
while True:
if len(self.unscheduled_jobs) == 0:
break
# Try to schedule the first job
if self.cpu_snapshot.free_processors_available_at(
current_time
) >= self.unscheduled_jobs[0].num_required_processors:
job = self.unscheduled_jobs.pop(0)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
else:
# first job can't be scheduled
break
return result
def _backfill_jobs(self, current_time):
"""
Find jobs that can be backfilled and update the cpu snapshot.
"""
if len(self.unscheduled_jobs) <= 1:
return []
result = []
tail_of_waiting_list = list_copy(self.unscheduled_jobs[1:])
for job in tail_of_waiting_list:
if self.canBeBackfilled(job, current_time):
self.unscheduled_jobs.remove(job)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
return result
def canBeBackfilled(self, second_job, current_time):
assert len(self.unscheduled_jobs) >= 2
assert second_job in self.unscheduled_jobs[1:]
if self.cpu_snapshot.free_processors_available_at(
current_time) < second_job.num_required_processors:
return False
first_job = self.unscheduled_jobs[0]
temp_cpu_snapshot = self.cpu_snapshot.copy()
temp_cpu_snapshot.assignJobEarliest(first_job, current_time)
# if true, this means that the 2nd job is "independent" of the 1st, and thus can be backfilled
return temp_cpu_snapshot.canJobStartNow(second_job, current_time)
def __init__(self, options):
super(FcfsScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
self.waiting_queue_of_jobs = []
class ConservativeScheduler(Scheduler):
def __init__(self, options):
super(ConservativeScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
self.unfinished_jobs_by_submit_time = []
def new_events_on_job_submission(self, job, current_time):
self.cpu_snapshot.archive_old_slices(current_time)
self.unfinished_jobs_by_submit_time.append(job)
self.cpu_snapshot.assignJobEarliest(job, current_time)
return [JobStartEvent(job.start_to_run_at_time, job)]
def new_events_on_job_termination(self, job, current_time):
""" Here we delete the tail of job if it was ended before the duration declaration.
It then reschedules the remaining jobs and returns a collection of new termination events
(using the dictionary data structure) """
self.cpu_snapshot.archive_old_slices(current_time)
self.unfinished_jobs_by_submit_time.remove(job)
self.cpu_snapshot.delTailofJobFromCpuSlices(job)
return self._reschedule_jobs(current_time)
def _reschedule_jobs(self, current_time):
newEvents = []
for job in self.unfinished_jobs_by_submit_time:
if job.start_to_run_at_time <= current_time:
continue # job started to run before, so it cannot be rescheduled (preemptions are not allowed)
prev_start_to_run_at_time = job.start_to_run_at_time
self.cpu_snapshot.delJobFromCpuSlices(job)
self.cpu_snapshot.assignJobEarliest(job, current_time)
assert prev_start_to_run_at_time >= job.start_to_run_at_time
if prev_start_to_run_at_time != job.start_to_run_at_time:
newEvents.append(JobStartEvent(job.start_to_run_at_time, job))
return newEvents
def __init__(self, num_processors):
super(EasyBackfillScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.unscheduled_jobs = []
def __init__(self, options):
super(ConservativeScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
self.unfinished_jobs_by_submit_time = []
class OrigProbabilisticEasyScheduler(Scheduler):
""" This algorithm implements a version of Feitelson and Nissimov, June 2007
"""
def __init__(self, num_processors, threshold = 0.2, window_size=150):
super(OrigProbabilisticEasyScheduler, self).__init__(num_processors)
self.threshold = threshold
self.window_size = window_size # a parameter for the distribution
self.cpu_snapshot = CpuSnapshot(num_processors)
self.user_distribution = {}
self.unscheduled_jobs = []
self.currently_running_jobs = []
#self.work_list = [[None for i in xrange(self.num_processors+1)] for j in xrange(self.num_processors+1)]
self.M = {}
for c in xrange(self.num_processors+1):
for n in xrange(self.num_processors+1):
self.M[c, n] = 0.0
self.max_user_rounded_estimated_run_time = 0
self.prev_max_user_rounded_estimated_run_time = 0
def new_events_on_job_submission(self, job, current_time):
# print "arrived:", job
rounded_up_estimated_time = _round_time_up(job.user_estimated_run_time)
if rounded_up_estimated_time > self.max_user_rounded_estimated_run_time:
self.prev_max_user_rounded_estimated_run_time = self.max_user_rounded_estimated_run_time
self.max_user_rounded_estimated_run_time = rounded_up_estimated_time
if not self.user_distribution.has_key(job.user_id):
self.user_distribution[job.user_id] = Distribution(job, self.window_size)
self.user_distribution[job.user_id].touch(2*self.max_user_rounded_estimated_run_time)
if self.prev_max_user_rounded_estimated_run_time < self.max_user_rounded_estimated_run_time:
for tmp_job in self.currently_running_jobs:
self.user_distribution[tmp_job.user_id].touch(2*self.max_user_rounded_estimated_run_time)
self.cpu_snapshot.archive_old_slices(current_time)
self.unscheduled_jobs.append(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def new_events_on_job_termination(self, job, current_time):
self.user_distribution[job.user_id].add_job(job)
self.currently_running_jobs.remove(job)
self.cpu_snapshot.archive_old_slices(current_time)
self.cpu_snapshot.delTailofJobFromCpuSlices(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def _schedule_jobs(self, current_time):
"Schedules jobs that can run right now, and returns them"
jobs = self._schedule_head_of_list(current_time)
jobs += self._backfill_jobs(current_time)
return jobs
def _schedule_head_of_list(self, current_time):
result = []
while True:
if len(self.unscheduled_jobs) == 0:
break
# Try to schedule the first job
if self.cpu_snapshot.free_processors_available_at(current_time) >= self.unscheduled_jobs[0].num_required_processors:
job = self.unscheduled_jobs.pop(0)
self.currently_running_jobs.append(job)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
else:
# first job can't be scheduled
break
return result
def _backfill_jobs(self, current_time):
if len(self.unscheduled_jobs) <= 1:
return []
result = []
first_job = self.unscheduled_jobs[0]
tail = list_copy(self.unscheduled_jobs[1:])
for job in tail:
if self.can_be_probabilistically_backfilled(job, current_time):
self.unscheduled_jobs.remove(job)
self.currently_running_jobs.append(job)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
return result
def can_be_probabilistically_backfilled(self, job, current_time):
assert len(self.unscheduled_jobs) >= 2
assert job in self.unscheduled_jobs[1:]
if self.cpu_snapshot.free_processors_available_at(current_time) < job.num_required_processors:
return False
first_job = self.unscheduled_jobs[0]
prediction = 0.0
max_bottle_neck = 0.0
bottle_neck = 0.0
t = 1
while t < 2 * job.user_estimated_run_time:
job_probability_to_end_at_t = self.probability_to_end_at(t, job)
max_bottle_neck = max(max_bottle_neck, self.bottle_neck(t, job, first_job, current_time))
prediction += job_probability_to_end_at_t * max_bottle_neck
t = t * 2
if prediction <= self.threshold:
return True
return False
def bottle_neck(self, time, second_job, first_job, current_time):
C = first_job.num_required_processors + second_job.num_required_processors
K = min(self.num_processors, C)
# M[n,c] is the probability that the first n running jobs will release at least c processors at _time_
M = self.M
num_of_currently_running_jobs = len(self.currently_running_jobs)
for c in xrange(K + 1):
M[0, c] = 0.0
for n in xrange(1, num_of_currently_running_jobs+1):
M[n, 0] = 1.0
for n in xrange(1, num_of_currently_running_jobs+1):
job_n = self.currently_running_jobs[n-1] # the n'th job: recall that a list has a zero index
job_n_required_processors = job_n.num_required_processors
Pn = self.probability_of_running_job_to_end_upto(time, current_time, job_n)
for c in xrange (1, job_n_required_processors):
val = M[n-1, c]
M[n, c] = val + (1.0 - val) * Pn
for c in xrange (job_n_required_processors, K + 1):
val = M[n-1, c]
M[n, c] = val + (M[n, c - job_n_required_processors] - val) * Pn
last_row_index = num_of_currently_running_jobs
if C <= K:
result = M[last_row_index, first_job.num_required_processors] - M[last_row_index, C]
else:
result = M[last_row_index, first_job.num_required_processors]
if result < 0:
result = 0.0
elif result > 1:
reuslt = 1.0
assert 0 <= result <= 1
return result
def probability_of_running_job_to_end_upto(self, time, current_time, job):
run_time = current_time - job.start_to_run_at_time
rounded_down_run_time = _round_time_down(run_time)
rounded_up_estimated_remaining_duration = _round_time_up(job.user_estimated_run_time - rounded_down_run_time)
if time >= rounded_up_estimated_remaining_duration:
return 1.0
num_of_jobs_in_first_bins = 0
num_of_jobs_in_middle_bins = 0.0
num_of_jobs_in_last_bins = 0
job_distribution = self.user_distribution[job.user_id]
for (key,value) in job_distribution.bins.iteritems():
if key > rounded_up_estimated_remaining_duration:
num_of_jobs_in_last_bins += value
elif key <= rounded_down_run_time:
num_of_jobs_in_first_bins += value
elif key <= time + rounded_down_run_time:
num_of_jobs_in_middle_bins += value
elif time + rounded_down_run_time > key/2 :
num_of_jobs_in_middle_bins += float(value*(time+rounded_down_run_time-(key/2))) / (key/2)
# at the tail of the middle bin, it won't terminate because of conditional probability
#else: pass
num_of_irrelevant_jobs = num_of_jobs_in_first_bins + num_of_jobs_in_last_bins
num_of_relevant_jobs = job_distribution.number_of_jobs_added - num_of_irrelevant_jobs
assert 0 <= num_of_jobs_in_middle_bins <= num_of_relevant_jobs, \
str(num_of_jobs_in_middle_bins)+str(" ")+str(num_of_relevant_jobs)
result = num_of_jobs_in_middle_bins / (num_of_relevant_jobs + 0.1)
return result
def probability_to_end_at(self, time, job):
job_distribution = self.user_distribution[job.user_id]
assert job_distribution.bins.has_key(time) == True
num_of_jobs_in_last_bins = 0
rounded_up_user_estimated_run_time = 2 * job.user_estimated_run_time - 1
for key in job_distribution.bins.keys():
if key > rounded_up_user_estimated_run_time:
num_of_jobs_in_last_bins += job_distribution.bins[key]
num_of_relevant_jobs = job_distribution.number_of_jobs_added - num_of_jobs_in_last_bins
assert 0 <= job_distribution.bins[time] <= num_of_relevant_jobs,\
str(time)+str(" ")+ str(job_distribution.bins[time])+str(" ")+str(num_of_relevant_jobs)
result = float(job_distribution.bins[time]) / (num_of_relevant_jobs + 0.1)
return result
def __init__(self, num_processors):
super(LogScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.waiting_queue_of_jobs = []
class EasyBackfillScheduler(Scheduler):
def __init__(self, num_processors):
super(EasyBackfillScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.unscheduled_jobs = []
def new_events_on_job_submission(self, just_submitted_job, current_time):
""" Here we first add the new job to the waiting list. We then try to schedule
the jobs in the waiting list, returning a collection of new termination events """
# TODO: a probable performance bottleneck because we reschedule all the
# jobs. Knowing that only one new job is added allows more efficient
# scheduling here.
#print 'User submits', just_submitted_job
self.cpu_snapshot.archive_old_slices(current_time)
self.unscheduled_jobs.append(just_submitted_job)
#print 'At time', current_time, 'unscheduled:', self.unscheduled_jobs
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def new_events_on_job_termination(self, job, current_time):
""" Here we first delete the tail of the just terminated job (in case it's
done before user estimation time). We then try to schedule the jobs in the waiting list,
returning a collection of new termination events """
self.cpu_snapshot.archive_old_slices(current_time)
self.cpu_snapshot.delTailofJobFromCpuSlices(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def _schedule_jobs(self, current_time):
"Schedules jobs that can run right now, and returns them"
jobs = self._schedule_head_of_list(current_time)
jobs += self._backfill_jobs(current_time)
#print 'Currently schedulable jobs:', jobs
#print 'Currently schedulable jobs count:', len(jobs)
self.totalScheduledJobs += len(jobs)
return jobs
def _schedule_head_of_list(self, current_time):
result = []
while True:
if len(self.unscheduled_jobs) == 0:
break
# Try to schedule the first job
if self.cpu_snapshot.free_processors_available_at(current_time) >= self.unscheduled_jobs[0].num_required_processors:
job = self.unscheduled_jobs.pop(0)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
else:
# first job can't be scheduled
break
return result
def _backfill_jobs(self, current_time):
"""
Find jobs that can be backfilled and update the cpu snapshot.
"""
if len(self.unscheduled_jobs) <= 1:
return []
result = []
tail_of_waiting_list = list_copy(self.unscheduled_jobs[1:])
for job in tail_of_waiting_list:
if self.canBeBackfilled(job, current_time):
self.unscheduled_jobs.remove(job)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
return result
def canBeBackfilled(self, second_job, current_time):
assert len(self.unscheduled_jobs) >= 2
assert second_job in self.unscheduled_jobs[1:]
if self.cpu_snapshot.free_processors_available_at(current_time) < second_job.num_required_processors:
return False
first_job = self.unscheduled_jobs[0]
temp_cpu_snapshot = self.cpu_snapshot.copy()
temp_cpu_snapshot.assignJobEarliest(first_job, current_time)
# if true, this means that the 2nd job is "independent" of the 1st, and thus can be backfilled
return temp_cpu_snapshot.canJobStartNow(second_job, current_time)
class EasyBackfillScheduler(Scheduler):
def __init__(self, options):
super(EasyBackfillScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
self.unscheduled_jobs = []
def new_events_on_job_submission(self, just_submitted_job, current_time):
""" Here we first add the new job to the waiting list. We then try to schedule
the jobs in the waiting list, returning a collection of new termination events """
# TODO: a probable performance bottleneck because we reschedule all the
# jobs. Knowing that only one new job is added allows more efficient
# scheduling here.
self.cpu_snapshot.archive_old_slices(current_time)
self.unscheduled_jobs.append(just_submitted_job)
retl = []
if (self.cpu_snapshot.free_processors_available_at(current_time) >= just_submitted_job.num_required_processors):
for job in self._schedule_jobs(current_time):
retl.append(JobStartEvent(current_time, job))
return retl
def new_events_on_job_termination(self, job, current_time):
""" Here we first delete the tail of the just terminated job (in case it's
done before user estimation time). We then try to schedule the jobs in the waiting list,
returning a collection of new termination events """
self.cpu_snapshot.archive_old_slices(current_time)
self.cpu_snapshot.delTailofJobFromCpuSlices(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def _schedule_jobs(self, current_time):
"Schedules jobs that can run right now, and returns them"
jobs = self._schedule_head_of_list(current_time)
jobs += self._backfill_jobs(current_time)
return jobs
def _schedule_head_of_list(self, current_time):
result = []
while True:
if len(self.unscheduled_jobs) == 0:
break
# Try to schedule the first job
if self.cpu_snapshot.free_processors_available_at(current_time) >= self.unscheduled_jobs[0].num_required_processors:
job = self.unscheduled_jobs.pop(0)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
else:
# first job can't be scheduled
break
return result
def _backfill_jobs(self, current_time):
"""
Find jobs that can be backfilled and update the cpu snapshot.
DEPRECATED FUNCTION !!!!!!
"""
if len(self.unscheduled_jobs) <= 1:
return []
result = []
tail_of_waiting_list = list_copy(self.unscheduled_jobs[1:])
first_job = self.unscheduled_jobs[0]
self.cpu_snapshot.assignJobEarliest(first_job, current_time)
for job in tail_of_waiting_list:
if self.cpu_snapshot.canJobStartNow(job, current_time):
job.is_backfilled = 1
self.unscheduled_jobs.remove(job)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
self.cpu_snapshot.unAssignJob(first_job)
return result
class EasyPlusPlusScheduler(Scheduler):
""" This algorithm implements the algorithm in the paper of Tsafrir, Etzion, Feitelson, june 2007?
"""
I_NEED_A_PREDICTOR = True
def __init__(self, options):
super(EasyPlusPlusScheduler, self).__init__(options)
self.init_predictor(options)
self.init_corrector(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
self.unscheduled_jobs = []
def new_events_on_job_submission(self, job, current_time):
self.cpu_snapshot.archive_old_slices(current_time)
self.predictor.predict(job, current_time, self.running_jobs)
if not hasattr(job,"initial_prediction"):
job.initial_prediction=job.predicted_run_time
self.unscheduled_jobs.append(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def new_events_on_job_termination(self, job, current_time):
self.predictor.fit(job, current_time)
if self.corrector.__name__=="ninetynine":
self.pestimator.fit(job.actual_run_time/job.user_estimated_run_time)
self.cpu_snapshot.archive_old_slices(current_time)
self.cpu_snapshot.delTailofJobFromCpuSlices(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def new_events_on_job_under_prediction(self, job, current_time):
pass #assert job.predicted_run_time <= job.user_estimated_run_time
if not hasattr(job,"num_underpredict"):
job.num_underpredict = 0
else:
job.num_underpredict += 1
if self.corrector.__name__=="ninetynine":
new_predicted_run_time = self.corrector(self.pestimator,job,current_time)
else:
new_predicted_run_time = self.corrector(job, current_time)
#set the new predicted runtime
self.cpu_snapshot.assignTailofJobToTheCpuSlices(job, new_predicted_run_time)
job.predicted_run_time = new_predicted_run_time
return [JobStartEvent(current_time, job)]
def _schedule_jobs(self, current_time):
"Schedules jobs that can run right now, and returns them"
jobs = self._schedule_head_of_list(current_time)
jobs += self._backfill_jobs(current_time)
return jobs
def _schedule_head_of_list(self, current_time):
result = []
while True:
if len(self.unscheduled_jobs) == 0:
break
# Try to schedule the first job
if self.cpu_snapshot.free_processors_available_at(current_time) >= self.unscheduled_jobs[0].num_required_processors:
job = self.unscheduled_jobs.pop(0)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
else:
# first job can't be scheduled
break
return result
def _backfill_jobs(self, current_time):
if len(self.unscheduled_jobs) <= 1:
return []
result = []
first_job = self.unscheduled_jobs[0]
tail = list_copy(self.unscheduled_jobs[1:])
tail_of_jobs_by_sjf_order = sorted(tail, key=sjf_sort_key)
self.cpu_snapshot.assignJobEarliest(first_job, current_time)
for job in tail_of_jobs_by_sjf_order:
if self.cpu_snapshot.canJobStartNow(job, current_time):
job.is_backfilled = 1
self.unscheduled_jobs.remove(job)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
self.cpu_snapshot.delJobFromCpuSlices(first_job)
return result
def __init__(self, options):
super(ConservativeScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
self.unfinished_jobs_by_submit_time = []
class EasyPlusPlusScheduler(Scheduler):
""" This algorithm implements the algorithm in the paper of Tsafrir, Etzion, Feitelson, june 2007?
"""
def __init__(self, num_processors):
super(EasyPlusPlusScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.unscheduled_jobs = []
self.user_run_time_prev = {}
self.user_run_time_last = {}
def new_events_on_job_submission(self, job, current_time):
if not self.user_run_time_last.has_key(job.user_id):
self.user_run_time_prev[job.user_id] = None
self.user_run_time_last[job.user_id] = None
self.cpu_snapshot.archive_old_slices(current_time)
self.unscheduled_jobs.append(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def new_events_on_job_termination(self, job, current_time):
assert self.user_run_time_last.has_key(job.user_id) == True
assert self.user_run_time_prev.has_key(job.user_id) == True
self.user_run_time_prev[job.user_id] = self.user_run_time_last[
job.user_id]
self.user_run_time_last[job.user_id] = job.actual_run_time
self.cpu_snapshot.archive_old_slices(current_time)
self.cpu_snapshot.delTailofJobFromCpuSlices(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def new_events_on_job_under_prediction(self, job, current_time):
assert job.predicted_run_time <= job.user_estimated_run_time
self.cpu_snapshot.assignTailofJobToTheCpuSlices(job)
job.predicted_run_time = job.user_estimated_run_time
return []
def _schedule_jobs(self, current_time):
"Schedules jobs that can run right now, and returns them"
for job in self.unscheduled_jobs:
if self.user_run_time_prev[job.user_id] != None:
average = int((self.user_run_time_last[job.user_id] +
self.user_run_time_prev[job.user_id]) / 2)
job.predicted_run_time = min(job.user_estimated_run_time,
average)
jobs = self._schedule_head_of_list(current_time)
jobs += self._backfill_jobs(current_time)
return jobs
def _schedule_head_of_list(self, current_time):
result = []
while True:
if len(self.unscheduled_jobs) == 0:
break
# Try to schedule the first job
if self.cpu_snapshot.free_processors_available_at(
current_time
) >= self.unscheduled_jobs[0].num_required_processors:
job = self.unscheduled_jobs.pop(0)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
else:
# first job can't be scheduled
break
return result
def _backfill_jobs(self, current_time):
if len(self.unscheduled_jobs) <= 1:
return []
result = []
first_job = self.unscheduled_jobs[0]
tail = list_copy(self.unscheduled_jobs[1:])
tail_of_jobs_by_sjf_order = sorted(tail, key=sjf_sort_key)
self.cpu_snapshot.assignJobEarliest(first_job, current_time)
for job in tail_of_jobs_by_sjf_order:
if self.cpu_snapshot.canJobStartNow(job, current_time):
self.unscheduled_jobs.remove(job)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
self.cpu_snapshot.delJobFromCpuSlices(first_job)
return result
def __init__(self, options):
super(TailDoubleEasyScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
class ConservativeScheduler(Scheduler):
def __init__(self, options):
super(ConservativeScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
self.unfinished_jobs_by_submit_time = []
def new_events_on_job_submission(self, job, current_time):
self.cpu_snapshot.archive_old_slices(current_time)
self.unfinished_jobs_by_submit_time.append(job)
self.cpu_snapshot.assignJobEarliest(job, current_time)
return [ JobStartEvent(job.start_to_run_at_time, job) ]
def new_events_on_job_termination(self, job, current_time):
""" Here we delete the tail of job if it was ended before the duration declaration.
It then reschedules the remaining jobs and returns a collection of new termination events
(using the dictionary data structure) """
self.cpu_snapshot.archive_old_slices(current_time)
self.unfinished_jobs_by_submit_time.remove(job)
self.cpu_snapshot.delTailofJobFromCpuSlices(job)
return self._reschedule_jobs(current_time)
def _reschedule_jobs(self, current_time):
newEvents = []
for job in self.unfinished_jobs_by_submit_time:
if job.start_to_run_at_time <= current_time:
continue # job started to run before, so it cannot be rescheduled (preemptions are not allowed)
prev_start_to_run_at_time = job.start_to_run_at_time
self.cpu_snapshot.delJobFromCpuSlices(job)
self.cpu_snapshot.assignJobEarliest(job, current_time)
assert prev_start_to_run_at_time >= job.start_to_run_at_time
if prev_start_to_run_at_time != job.start_to_run_at_time:
newEvents.append( JobStartEvent(job.start_to_run_at_time, job) )
return newEvents
def __init__(self, num_processors):
super(ConservativeScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.unfinished_jobs_by_submit_time = []
class EasyPlusPlusScheduler(Scheduler):
""" This algorithm implements the algorithm in the paper of Tsafrir, Etzion, Feitelson, june 2007?
"""
I_NEED_A_PREDICTOR = True
def __init__(self, options):
super(EasyPlusPlusScheduler, self).__init__(options)
self.init_predictor(options)
self.init_corrector(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
self.unscheduled_jobs = []
self.ff = open("times-epp-sgd.txt", 'w')
def new_events_on_job_submission(self, job, current_time):
self.cpu_snapshot.archive_old_slices(current_time)
self.predictor.predict(job, current_time, self.running_jobs)
self.ff.write("%d\t%d\n" %
(job.actual_run_time, job.predicted_run_time))
self.ff.flush()
if not hasattr(job, "initial_prediction"):
job.initial_prediction = job.predicted_run_time
self.unscheduled_jobs.append(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def new_events_on_job_termination(self, job, current_time):
self.predictor.fit(job, current_time)
if self.corrector.__name__ == "ninetynine":
self.pestimator.fit(job.actual_run_time /
job.user_estimated_run_time)
self.cpu_snapshot.archive_old_slices(current_time)
self.cpu_snapshot.delTailofJobFromCpuSlices(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def new_events_on_job_under_prediction(self, job, current_time):
assert job.predicted_run_time <= job.user_estimated_run_time
if not hasattr(job, "num_underpredict"):
job.num_underpredict = 0
else:
job.num_underpredict += 1
if self.corrector.__name__ == "ninetynine":
new_predicted_run_time = self.corrector(self.pestimator, job,
current_time)
else:
new_predicted_run_time = self.corrector(job, current_time)
#set the new predicted runtime
self.cpu_snapshot.assignTailofJobToTheCpuSlices(
job, new_predicted_run_time)
job.predicted_run_time = new_predicted_run_time
return [JobStartEvent(current_time, job)]
def _schedule_jobs(self, current_time):
"Schedules jobs that can run right now, and returns them"
jobs = self._schedule_head_of_list(current_time)
jobs += self._backfill_jobs(current_time)
return jobs
def _schedule_head_of_list(self, current_time):
result = []
while True:
if len(self.unscheduled_jobs) == 0:
break
# Try to schedule the first job
if self.cpu_snapshot.free_processors_available_at(
current_time
) >= self.unscheduled_jobs[0].num_required_processors:
job = self.unscheduled_jobs.pop(0)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
else:
# first job can't be scheduled
break
return result
def _backfill_jobs(self, current_time):
if len(self.unscheduled_jobs) <= 1:
return []
result = []
first_job = self.unscheduled_jobs[0]
tail = list_copy(self.unscheduled_jobs[1:])
tail_of_jobs_by_sjf_order = sorted(tail, key=sjf_sort_key)
self.cpu_snapshot.assignJobEarliest(first_job, current_time)
for job in tail_of_jobs_by_sjf_order:
if self.cpu_snapshot.canJobStartNow(job, current_time):
job.is_backfilled = 1
self.unscheduled_jobs.remove(job)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
self.cpu_snapshot.delJobFromCpuSlices(first_job)
return result
def __init__(self, num_processors):
super(ConservativeScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.unfinished_jobs_by_submit_time = []
def __init__(self, options):
super(FcfsScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
self.waiting_queue_of_jobs = []
def __init__(self, num_processors):
super(TailDoubleEasyScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
def __init__(self, num_processors):
super(EasyBackfillScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.unscheduled_jobs = []
def __init__(self, num_processors):
super(ShrinkingEasyScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.unscheduled_jobs = []
class EasyPlusPlusScheduler(Scheduler):
""" This algorithm implements the algorithm in the paper of Tsafrir, Etzion, Feitelson, june 2007?
"""
def __init__(self, num_processors):
super(EasyPlusPlusScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.unscheduled_jobs = []
self.user_run_time_prev = {}
self.user_run_time_last = {}
def new_events_on_job_submission(self, job, current_time):
if not self.user_run_time_last.has_key(job.user_id):
self.user_run_time_prev[job.user_id] = None
self.user_run_time_last[job.user_id] = None
self.cpu_snapshot.archive_old_slices(current_time)
self.unscheduled_jobs.append(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def new_events_on_job_termination(self, job, current_time):
assert self.user_run_time_last.has_key(job.user_id) == True
assert self.user_run_time_prev.has_key(job.user_id) == True
self.user_run_time_prev[job.user_id] = self.user_run_time_last[job.user_id]
self.user_run_time_last[job.user_id] = job.actual_run_time
self.cpu_snapshot.archive_old_slices(current_time)
self.cpu_snapshot.delTailofJobFromCpuSlices(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def new_events_on_job_under_prediction(self, job, current_time):
assert job.predicted_run_time <= job.user_estimated_run_time
self.cpu_snapshot.assignTailofJobToTheCpuSlices(job)
job.predicted_run_time = job.user_estimated_run_time
return []
def _schedule_jobs(self, current_time):
"Schedules jobs that can run right now, and returns them"
for job in self.unscheduled_jobs:
if self.user_run_time_prev[job.user_id] != None:
average = int((self.user_run_time_last[job.user_id] + self.user_run_time_prev[job.user_id])/ 2)
job.predicted_run_time = min (job.user_estimated_run_time, average)
jobs = self._schedule_head_of_list(current_time)
jobs += self._backfill_jobs(current_time)
return jobs
def _schedule_head_of_list(self, current_time):
result = []
while True:
if len(self.unscheduled_jobs) == 0:
break
# Try to schedule the first job
if self.cpu_snapshot.free_processors_available_at(current_time) >= self.unscheduled_jobs[0].num_required_processors:
job = self.unscheduled_jobs.pop(0)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
else:
# first job can't be scheduled
break
return result
def _backfill_jobs(self, current_time):
if len(self.unscheduled_jobs) <= 1:
return []
result = []
first_job = self.unscheduled_jobs[0]
tail = list_copy(self.unscheduled_jobs[1:])
tail_of_jobs_by_sjf_order = sorted(tail, key=sjf_sort_key)
self.cpu_snapshot.assignJobEarliest(first_job, current_time)
for job in tail_of_jobs_by_sjf_order:
if self.cpu_snapshot.canJobStartNow(job, current_time):
self.unscheduled_jobs.remove(job)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
self.cpu_snapshot.delJobFromCpuSlices(first_job)
return result
def __init__(self, num_processors):
super(EasyPlusPlusScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.unscheduled_jobs = []
self.user_run_time_prev = {}
self.user_run_time_last = {}
def __init__(self, options):
super(EasyBackfillScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
self.unscheduled_jobs = []
class OrigProbabilisticEasyScheduler(Scheduler):
""" This algorithm implements a version of Feitelson and Nissimov, June 2007
"""
def __init__(self, num_processors, threshold=0.2, window_size=150):
super(OrigProbabilisticEasyScheduler, self).__init__(num_processors)
self.threshold = threshold
self.window_size = window_size # a parameter for the distribution
self.cpu_snapshot = CpuSnapshot(num_processors)
self.user_distribution = {}
self.unscheduled_jobs = []
self.currently_running_jobs = []
#self.work_list = [[None for i in xrange(self.num_processors+1)] for j in xrange(self.num_processors+1)]
self.M = {}
for c in xrange(self.num_processors + 1):
for n in xrange(self.num_processors + 1):
self.M[c, n] = 0.0
self.max_user_rounded_estimated_run_time = 0
self.prev_max_user_rounded_estimated_run_time = 0
def new_events_on_job_submission(self, job, current_time):
# print "arrived:", job
rounded_up_estimated_time = _round_time_up(job.user_estimated_run_time)
if rounded_up_estimated_time > self.max_user_rounded_estimated_run_time:
self.prev_max_user_rounded_estimated_run_time = self.max_user_rounded_estimated_run_time
self.max_user_rounded_estimated_run_time = rounded_up_estimated_time
if not self.user_distribution.has_key(job.user_id):
self.user_distribution[job.user_id] = Distribution(
job, self.window_size)
self.user_distribution[job.user_id].touch(
2 * self.max_user_rounded_estimated_run_time)
if self.prev_max_user_rounded_estimated_run_time < self.max_user_rounded_estimated_run_time:
for tmp_job in self.currently_running_jobs:
self.user_distribution[tmp_job.user_id].touch(
2 * self.max_user_rounded_estimated_run_time)
self.cpu_snapshot.archive_old_slices(current_time)
self.unscheduled_jobs.append(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def new_events_on_job_termination(self, job, current_time):
self.user_distribution[job.user_id].add_job(job)
self.currently_running_jobs.remove(job)
self.cpu_snapshot.archive_old_slices(current_time)
self.cpu_snapshot.delTailofJobFromCpuSlices(job)
return [
JobStartEvent(current_time, job)
for job in self._schedule_jobs(current_time)
]
def _schedule_jobs(self, current_time):
"Schedules jobs that can run right now, and returns them"
jobs = self._schedule_head_of_list(current_time)
jobs += self._backfill_jobs(current_time)
return jobs
def _schedule_head_of_list(self, current_time):
result = []
while True:
if len(self.unscheduled_jobs) == 0:
break
# Try to schedule the first job
if self.cpu_snapshot.free_processors_available_at(
current_time
) >= self.unscheduled_jobs[0].num_required_processors:
job = self.unscheduled_jobs.pop(0)
self.currently_running_jobs.append(job)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
else:
# first job can't be scheduled
break
return result
def _backfill_jobs(self, current_time):
if len(self.unscheduled_jobs) <= 1:
return []
result = []
first_job = self.unscheduled_jobs[0]
tail = list_copy(self.unscheduled_jobs[1:])
for job in tail:
if self.can_be_probabilistically_backfilled(job, current_time):
self.unscheduled_jobs.remove(job)
self.currently_running_jobs.append(job)
self.cpu_snapshot.assignJob(job, current_time)
result.append(job)
return result
def can_be_probabilistically_backfilled(self, job, current_time):
assert len(self.unscheduled_jobs) >= 2
assert job in self.unscheduled_jobs[1:]
if self.cpu_snapshot.free_processors_available_at(
current_time) < job.num_required_processors:
return False
first_job = self.unscheduled_jobs[0]
prediction = 0.0
max_bottle_neck = 0.0
bottle_neck = 0.0
t = 1
while t < 2 * job.user_estimated_run_time:
job_probability_to_end_at_t = self.probability_to_end_at(t, job)
max_bottle_neck = max(
max_bottle_neck,
self.bottle_neck(t, job, first_job, current_time))
prediction += job_probability_to_end_at_t * max_bottle_neck
t = t * 2
if prediction <= self.threshold:
return True
return False
def bottle_neck(self, time, second_job, first_job, current_time):
C = first_job.num_required_processors + second_job.num_required_processors
K = min(self.num_processors, C)
# M[n,c] is the probability that the first n running jobs will release at least c processors at _time_
M = self.M
num_of_currently_running_jobs = len(self.currently_running_jobs)
for c in xrange(K + 1):
M[0, c] = 0.0
for n in xrange(1, num_of_currently_running_jobs + 1):
M[n, 0] = 1.0
for n in xrange(1, num_of_currently_running_jobs + 1):
job_n = self.currently_running_jobs[
n - 1] # the n'th job: recall that a list has a zero index
job_n_required_processors = job_n.num_required_processors
Pn = self.probability_of_running_job_to_end_upto(
time, current_time, job_n)
for c in xrange(1, job_n_required_processors):
val = M[n - 1, c]
M[n, c] = val + (1.0 - val) * Pn
for c in xrange(job_n_required_processors, K + 1):
val = M[n - 1, c]
M[n,
c] = val + (M[n, c - job_n_required_processors] - val) * Pn
last_row_index = num_of_currently_running_jobs
if C <= K:
result = M[last_row_index,
first_job.num_required_processors] - M[last_row_index,
C]
else:
result = M[last_row_index, first_job.num_required_processors]
if result < 0:
result = 0.0
elif result > 1:
reuslt = 1.0
assert 0 <= result <= 1
return result
def probability_of_running_job_to_end_upto(self, time, current_time, job):
run_time = current_time - job.start_to_run_at_time
rounded_down_run_time = _round_time_down(run_time)
rounded_up_estimated_remaining_duration = _round_time_up(
job.user_estimated_run_time - rounded_down_run_time)
if time >= rounded_up_estimated_remaining_duration:
return 1.0
num_of_jobs_in_first_bins = 0
num_of_jobs_in_middle_bins = 0.0
num_of_jobs_in_last_bins = 0
job_distribution = self.user_distribution[job.user_id]
for (key, value) in job_distribution.bins.iteritems():
if key > rounded_up_estimated_remaining_duration:
num_of_jobs_in_last_bins += value
elif key <= rounded_down_run_time:
num_of_jobs_in_first_bins += value
elif key <= time + rounded_down_run_time:
num_of_jobs_in_middle_bins += value
elif time + rounded_down_run_time > key / 2:
num_of_jobs_in_middle_bins += float(
value * (time + rounded_down_run_time -
(key / 2))) / (key / 2)
# at the tail of the middle bin, it won't terminate because of conditional probability
#else: pass
num_of_irrelevant_jobs = num_of_jobs_in_first_bins + num_of_jobs_in_last_bins
num_of_relevant_jobs = job_distribution.number_of_jobs_added - num_of_irrelevant_jobs
assert 0 <= num_of_jobs_in_middle_bins <= num_of_relevant_jobs, \
str(num_of_jobs_in_middle_bins)+str(" ")+str(num_of_relevant_jobs)
result = num_of_jobs_in_middle_bins / (num_of_relevant_jobs + 0.1)
return result
def probability_to_end_at(self, time, job):
job_distribution = self.user_distribution[job.user_id]
assert job_distribution.bins.has_key(time) == True
num_of_jobs_in_last_bins = 0
rounded_up_user_estimated_run_time = 2 * job.user_estimated_run_time - 1
for key in job_distribution.bins.keys():
if key > rounded_up_user_estimated_run_time:
num_of_jobs_in_last_bins += job_distribution.bins[key]
num_of_relevant_jobs = job_distribution.number_of_jobs_added - num_of_jobs_in_last_bins
assert 0 <= job_distribution.bins[time] <= num_of_relevant_jobs,\
str(time)+str(" ")+ str(job_distribution.bins[time])+str(" ")+str(num_of_relevant_jobs)
result = float(
job_distribution.bins[time]) / (num_of_relevant_jobs + 0.1)
return result
def __init__(self, options):
super(HeadDoubleEasyScheduler, self).__init__(options)
self.cpu_snapshot = CpuSnapshot(self.num_processors, options["stats"])
def __init__(self, num_processors):
super(FcfsScheduler, self).__init__(num_processors)
self.cpu_snapshot = CpuSnapshot(num_processors)
self.waiting_queue_of_jobs = []
