def __init__(self, reliability, n=100):
self.reliability = reliability
##TODO: simplify this
# self.mainHeft = HeftExecutorRunner().main
self.mainHeft = ExecutorsFactory.default().run_heft_executor
# self.mainCloudHeft = CloudHeftExecutorRunner().main
self.mainCloudHeft = ExecutorsFactory.default().run_cloudheft_executor
self.n = n
def decoratee(tsk):
res = ExecutorsFactory.default().run_mpgaheftoldpop_executor(
# for this experiment it doesn't matter at all
reliability=0.95,
is_silent=True,
wf_name=wf_name,
logger=None,
key_for_save='small_run',
#task_id_to_fail="ID00005_000",
task_id_to_fail=tsk,
fixed_interval_for_ga=15,
migrCount=5,
emigrant_selection=None,
all_iters_count=300,
merged_pop_iters=100,
ga_params={
"population": pop_size,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 10
},
save_path=save_path,
check_evolution_for_stopping=False,
mixed_init_pop=False,
mpnewVSmpoldmode=True)
return res
def decoratee(tsk):
res = ExecutorsFactory.default().run_mpgaheftoldpop_executor(
# for this experiment it doesn't matter at all
reliability=0.95,
is_silent=True,
wf_name=wf_name,
logger=None,
key_for_save='small_run',
#task_id_to_fail="ID00005_000",
task_id_to_fail=tsk,
fixed_interval_for_ga=15,
migrCount=5,
emigrant_selection=None,
all_iters_count=300,
merged_pop_iters=100,
ga_params={
"population": pop_size,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 10
},
save_path=save_path,
check_evolution_for_stopping=False,
mixed_init_pop=False,
mpnewVSmpoldmode=True)
return res
def setUp(self):
all_iters_count = 30
self.mixed_mpgaheftoldpop_func = partial(
ExecutorsFactory.default().run_mpgaheftoldpop_executor,
# for this experiment it doesn't matter at all
reliability=self.RELIABLE,
is_silent=True,
wf_name=self.DEFAULT_WF_NAME,
logger=None,
key_for_save='small_run',
#task_id_to_fail="ID00005_000",
#task_id_to_fail=tsk,
#save_path=,
fixed_interval_for_ga=15,
migrCount=5,
emigrant_selection=None,
all_iters_count=all_iters_count,
ga_params={
"population": 10,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 5
},
mixed_init_pop=True)
def check_logbook(x):
#all_iters = [i for i in range(0, all_iters_count)]
for rec in x:
assert "iter" in rec, "iter isn't presented in record"
assert "worst" in rec, "worst isn't presented for iter {0}".format(
rec["iter"])
assert "best" in rec, "best isn't presented for iter {0}".format(
rec["iter"])
assert "avr" in rec, "avr isn't presented for iter {0}".format(
rec["iter"])
#all_iters.remove(rec["iter"])
pass
#assert len(all_iters) == 0, "Next iters wasn't be founded in results: {0}".format(all_iters)
return True
run_spec = {
"iter": lambda x: x is None or x < all_iters_count,
"makespan": lambda x: True,
"pop_aggr": check_logbook
}
self.stat_specification = {
"wf_name": lambda x: x == self.DEFAULT_WF_NAME,
"event_name":
lambda x: x == "NodeUp" or x == "NodeFailed" or x == "",
# p for param
"task_id": lambda x: len(x) > 0,
"with_old_pop": run_spec,
"with_random": run_spec
}
pass
def setUp(self):
all_iters_count = 30
self.mixed_mpgaheftoldpop_func = partial(ExecutorsFactory.default().run_mpgaheftoldpop_executor,
# for this experiment it doesn't matter at all
reliability=self.RELIABLE,
is_silent=True,
wf_name=self.DEFAULT_WF_NAME,
logger=None,
key_for_save='small_run',
#task_id_to_fail="ID00005_000",
#task_id_to_fail=tsk,
#save_path=,
fixed_interval_for_ga=15,
migrCount=5,
emigrant_selection=None,
all_iters_count=all_iters_count,
ga_params={
"population": 10,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 5
}, mixed_init_pop=True)
def check_logbook(x):
#all_iters = [i for i in range(0, all_iters_count)]
for rec in x:
assert "iter" in rec, "iter isn't presented in record"
assert "worst" in rec, "worst isn't presented for iter {0}".format(rec["iter"])
assert "best" in rec, "best isn't presented for iter {0}".format(rec["iter"])
assert "avr" in rec, "avr isn't presented for iter {0}".format(rec["iter"])
#all_iters.remove(rec["iter"])
pass
#assert len(all_iters) == 0, "Next iters wasn't be founded in results: {0}".format(all_iters)
return True
run_spec = {
"iter": lambda x: x is None or x < all_iters_count,
"makespan": lambda x: True,
"pop_aggr": check_logbook
}
self.stat_specification = {
"wf_name": lambda x: x == self.DEFAULT_WF_NAME,
"event_name": lambda x: x == "NodeUp" or x == "NodeFailed" or x == "",
# p for param
"task_id": lambda x: len(x) > 0,
"with_old_pop": run_spec,
"with_random": run_spec
}
pass
def fnc(tsk):
return ExecutorsFactory.default().run_gaheftoldpop_executor(
reliability=0.95,
is_silent=True,
wf_name=wf_name,
logger=None,
key_for_save='small_run',
#task_id_to_fail="ID00005_000",
task_id_to_fail=tsk,
fixed_interval_for_ga=6,
save_path=save_path,
ga_params={
"population": 30,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 100
},
check_evolution_for_stopping=False,
nodes_conf=[10, 15, 25, 30] + [10, 15, 25, 30])
def fnc(tsk):
return ExecutorsFactory.default().run_gaheftoldpop_executor(
reliability=0.95,
is_silent=True,
wf_name=wf_name,
logger=None,
key_for_save='small_run',
#task_id_to_fail="ID00005_000",
task_id_to_fail=tsk,
fixed_interval_for_ga=6,
save_path=save_path,
ga_params={
"population": 30,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 100
},
check_evolution_for_stopping=False,
nodes_conf=[10, 15, 25, 30] + [10, 15, 25, 30])
def decoratee(tsk):
res = ExecutorsFactory.default().run_oldpop_executor(
# for this experiment it doesn't matter at all
reliability=0.95,
is_silent=True,
wf_name=wf_name,
logger=None,
key_for_save='small_run',
task_id_to_fail=tsk,
fixed_interval_for_ga=6,
ga_params={
"population": pop_size,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 100
},
save_path=save_path,
check_evolution_for_stopping=False,
nodes_conf=[10, 15, 25, 30] + [10, 15, 25, 30])
return res
def decoratee(fl_percent):
res = ExecutorsFactory.default().run_oldpop_executor_with_weakestfailonce(
# for this experiment it doesn't matter at all
reliability=0.95,
is_silent=True,
wf_name=wf_name,
logger=None,
key_for_save='small_run',
# it doesn't matter at all for this experiment
task_id_to_fail="ID00005_000",
fixed_interval_for_ga=15,
ga_params={
"population": pop_size,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 10
},
fail_percent=fl_percent,
save_path=save_path,
check_evolution_for_stopping=False)
return res
def decoratee(fl_percent):
res = ExecutorsFactory.default(
).run_oldpop_executor_with_weakestfailonce(
# for this experiment it doesn't matter at all
reliability=0.95,
is_silent=True,
wf_name=wf_name,
logger=None,
key_for_save='small_run',
# it doesn't matter at all for this experiment
task_id_to_fail="ID00005_000",
fixed_interval_for_ga=15,
ga_params={
"population": pop_size,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 10
},
fail_percent=fl_percent,
save_path=save_path,
check_evolution_for_stopping=False)
return res
def __init__(self, n=25, time_koeff=0.1):
self.mainHeft = ExecutorsFactory.default().run_heft_executor
self.mainGaHeft = ExecutorsFactory.default().run_gaheft_executor
self.n = n
self.time_koeff = time_koeff
def __init__(self, reliability, n=100):
self.reliability = reliability
self.mainCloudHeft = ExecutorsFactory.default().run_cloudheft_executor
self.mainHeft = ExecutorsFactory.default().run_heft_executor
self.mainGA = ExecutorsFactory.default().run_ga_executor
self.n = n
def __init__(self, reliability, n=25):
self.reliability = reliability
self.mainHeft = ExecutorsFactory.default().run_heft_executor
self.mainGaHeft = ExecutorsFactory.default().run_gaheft_executor
self.n = n
def setUp(self):
self.ga_func = partial(ExecutorsFactory.default().run_ga_executor,
is_silent=True,
wf_name=self.DEFAULT_WF_NAME,
logger=None,
with_ga_initial=True)
self.heft_func = partial(ExecutorsFactory.default().run_heft_executor,
is_silent=True,
wf_name=self.DEFAULT_WF_NAME,
logger=None)
self.gaheft_func = partial(
ExecutorsFactory.default().run_gaheft_executor,
is_silent=True,
wf_name=self.DEFAULT_WF_NAME,
logger=None,
fixed_interval_for_ga=15,
ga_params={
"population": 10,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 10
})
self.cloudheft_func = partial(
ExecutorsFactory.default().run_cloudheft_executor,
is_silent=True,
wf_name=self.DEFAULT_WF_NAME,
logger=None,
)
self.oldpopga_func = partial(
ExecutorsFactory.default().run_oldpop_executor,
is_silent=True,
wf_name=self.DEFAULT_WF_NAME,
logger=None,
key_for_save='by_5',
task_id_to_fail="ID00005_000",
fixed_interval_for_ga=15,
ga_params={
"population": 10,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 10
})
self.oldpopgaheft_func = partial(
ExecutorsFactory.default().run_gaheftoldpop_executor,
is_silent=False,
wf_name=self.DEFAULT_WF_NAME,
logger=None,
key_for_save='test',
task_id_to_fail="ID00005_000",
fixed_interval_for_ga=15,
ga_params={
"population": 10,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 10
})
self.oldpopmpgaheft_func = partial(
ExecutorsFactory.default().run_mpgaheftoldpop_executor,
is_silent=True,
wf_name=self.DEFAULT_WF_NAME,
logger=None,
key_for_save='test',
task_id_to_fail="ID00005_000",
fixed_interval_for_ga=15,
migrCount=5,
emigrant_selection=None,
all_iters_count=10,
ga_params={
"population": 10,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 2
})
self.oldpopmpgaheft_mixed_init_pop_func = partial(
self.oldpopmpgaheft_func, mixed_init_pop=True)
self.oldpopmpgaheft_with_merge_pop_func = partial(
self.oldpopmpgaheft_func, merged_pop_iters=2)
self.oldpopmpgaheft_vs_mpgaheft_func = partial(
self.oldpopmpgaheft_func, mpnewVSmpoldmode=True)
self.oldpopga_with_weakestfailonce_func = partial(
ExecutorsFactory.default(
).run_oldpop_executor_with_weakestfailonce,
is_silent=True,
wf_name=self.DEFAULT_WF_NAME,
logger=None,
key_for_save='test',
task_id_to_fail="ID00005_000",
fixed_interval_for_ga=15,
ga_params={
"population": 10,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 10
})
pass
heft_path = base_dir + "[{0}]_[{1}]_[{2}].txt".format(wf_name, heft_name, ComparisonUtility.cur_time())
heft_f = open(heft_path, "w")
heft_f.write("alg_name: " + heft_name + "\n")
heft_f.write("wf_name: " + str(wf_name) + "\n")
# gaheft_name = "GAHEFT"
# gaheft_path = base_dir + "[{0}]_[{1}]_[{2}].txt".format(wf_name, gaheft_name, ComparisonUtility.cur_time())
# gaheft_f = open(gaheft_path, "w")
# gaheft_f.write("alg_name: " + gaheft_name + "\n")
# gaheft_f.write("wf_name: " + str(wf_name) + "\n")
n = 20
# ## reliability 0.95 doesn't matter anything in this case# for id in ids[6:7]:
failure_coeffs = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]
## reliability 0.95 doesn't matter anything in this case
heft_makespans = [ExecutorsFactory.default().run_singlefail_heft_executor(0.95, True, wf_name, None, ids[6], coeff) for coeff in failure_coeffs]
save_result(heft_f, heft_makespans, id)
# heft_makespans = [SingleFailHeftExecutorExample().main(0.95, True, wf_name, None, id) for i in range(n)]
# save_result(heft_f, heft_makespans, id)
#
# # gaheft_makespans = [SingleFailGaHeftExecutorExample().main(0.95, True, wf_name, None, id) for i in range(n)]
# # save_result(gaheft_f, gaheft_makespans, id)
# pass
heft_f.close()
# gaheft_f.close()
heft_f = open(heft_path, "w")
heft_f.write("alg_name: " + heft_name + "\n")
heft_f.write("wf_name: " + str(wf_name) + "\n")
# gaheft_name = "GAHEFT"
# gaheft_path = base_dir + "[{0}]_[{1}]_[{2}].txt".format(wf_name, gaheft_name, ComparisonUtility.cur_time())
# gaheft_f = open(gaheft_path, "w")
# gaheft_f.write("alg_name: " + gaheft_name + "\n")
# gaheft_f.write("wf_name: " + str(wf_name) + "\n")
n = 20
# ## reliability 0.95 doesn't matter anything in this case# for id in ids[6:7]:
failure_coeffs = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]
## reliability 0.95 doesn't matter anything in this case
heft_makespans = [
ExecutorsFactory.default().run_singlefail_heft_executor(
0.95, True, wf_name, None, ids[6], coeff) for coeff in failure_coeffs
]
save_result(heft_f, heft_makespans, id)
# heft_makespans = [SingleFailHeftExecutorExample().main(0.95, True, wf_name, None, id) for i in range(n)]
# save_result(heft_f, heft_makespans, id)
#
# # gaheft_makespans = [SingleFailGaHeftExecutorExample().main(0.95, True, wf_name, None, id) for i in range(n)]
# # save_result(gaheft_f, gaheft_makespans, id)
# pass
heft_f.close()
# gaheft_f.close()
def setUp(self):
self.ga_func = partial(ExecutorsFactory.default().run_ga_executor,
is_silent=True,
wf_name=self.DEFAULT_WF_NAME,
logger=None,
with_ga_initial=True)
self.heft_func = partial(ExecutorsFactory.default().run_heft_executor,
is_silent=True,
wf_name=self.DEFAULT_WF_NAME,
logger=None)
self.gaheft_func = partial(ExecutorsFactory.default().run_gaheft_executor,
is_silent=True,
wf_name=self.DEFAULT_WF_NAME,
logger=None,
fixed_interval_for_ga=15,
ga_params={
"population": 10,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 10
})
self.cloudheft_func = partial(ExecutorsFactory.default().run_cloudheft_executor,
is_silent=True,
wf_name=self.DEFAULT_WF_NAME,
logger=None,)
self.oldpopga_func = partial(ExecutorsFactory.default().run_oldpop_executor,
is_silent=True,
wf_name=self.DEFAULT_WF_NAME,
logger=None,
key_for_save='by_5',
task_id_to_fail="ID00005_000",
fixed_interval_for_ga=15,
ga_params={
"population": 10,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 10
})
self.oldpopgaheft_func = partial(ExecutorsFactory.default().run_gaheftoldpop_executor,
is_silent=False,
wf_name=self.DEFAULT_WF_NAME,
logger=None,
key_for_save='test',
task_id_to_fail="ID00005_000",
fixed_interval_for_ga=15,
ga_params={
"population": 10,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 10
})
self.oldpopmpgaheft_func = partial(ExecutorsFactory.default().run_mpgaheftoldpop_executor,
is_silent=True,
wf_name=self.DEFAULT_WF_NAME,
logger=None,
key_for_save='test',
task_id_to_fail="ID00005_000",
fixed_interval_for_ga=15,
migrCount=5,
emigrant_selection=None,
all_iters_count=10,
ga_params={
"population": 10,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 2
})
self.oldpopmpgaheft_mixed_init_pop_func = partial(self.oldpopmpgaheft_func, mixed_init_pop=True)
self.oldpopmpgaheft_with_merge_pop_func = partial(self.oldpopmpgaheft_func, merged_pop_iters=2)
self.oldpopmpgaheft_vs_mpgaheft_func = partial(self.oldpopmpgaheft_func, mpnewVSmpoldmode=True)
self.oldpopga_with_weakestfailonce_func = partial(ExecutorsFactory.default().run_oldpop_executor_with_weakestfailonce,
is_silent=True,
wf_name=self.DEFAULT_WF_NAME,
logger=None,
key_for_save='test',
task_id_to_fail="ID00005_000",
fixed_interval_for_ga=15,
ga_params={
"population": 10,
"crossover_probability": 0.8,
"replacing_mutation_probability": 0.5,
"sweep_mutation_probability": 0.4,
"generations": 10
}
)
pass