def gaheft_reschedule(wf_added_time):
copy_gaheft_schedule = Schedule({node:[item for item in items] for (node, items) in ga_initial_schedule.mapping.items()})
added_time = all_initial_wf_time * wf_added_time
mark_finished(copy_gaheft_schedule)
gaheft_added = DynamicHeft(added_wf, resource_manager, estimator)
gaheft_added.current_time = added_time
gaheft_added_schedule = gaheft_added.run(copy_gaheft_schedule)
new_ga = GAComputationManager(15,
added_wf,
resource_manager,
estimator)
gaheft_added_schedule = new_ga.run(gaheft_added_schedule, added_time, False)[2]
mark_finished(gaheft_added_schedule)
nodes_seq_validaty = Utility.validateNodesSeq(gaheft_added_schedule)
if nodes_seq_validaty is not True:
raise Exception("Check for nodes_seq_validaty didn't pass")
initial_wf_validaty = Utility.validateParentsAndChildren(gaheft_added_schedule, initial_wf)
if initial_wf_validaty is not True:
raise Exception("Check for initial_wf_validaty didn't pass")
added_wf_validaty = Utility.validateParentsAndChildren(gaheft_added_schedule, added_wf)
if added_wf_validaty is not True:
raise Exception("Check for added_wf_validaty didn't pass")
#print("All Ok!")
result = Utility.makespan(gaheft_added_schedule)
return result
def gaheft_reschedule(wf_added_time):
copy_gaheft_schedule = Schedule({
node: [item for item in items]
for (node, items) in ga_initial_schedule.mapping.items()
})
added_time = all_initial_wf_time * wf_added_time
mark_finished(copy_gaheft_schedule)
gaheft_added = DynamicHeft(added_wf, resource_manager, estimator)
gaheft_added.current_time = added_time
gaheft_added_schedule = gaheft_added.run(copy_gaheft_schedule)
new_ga = GAComputationManager(15, added_wf, resource_manager, estimator)
gaheft_added_schedule = new_ga.run(gaheft_added_schedule, added_time,
False)[2]
mark_finished(gaheft_added_schedule)
nodes_seq_validaty = Utility.validateNodesSeq(gaheft_added_schedule)
if nodes_seq_validaty is not True:
raise Exception("Check for nodes_seq_validaty didn't pass")
initial_wf_validaty = Utility.validateParentsAndChildren(
gaheft_added_schedule, initial_wf)
if initial_wf_validaty is not True:
raise Exception("Check for initial_wf_validaty didn't pass")
added_wf_validaty = Utility.validateParentsAndChildren(
gaheft_added_schedule, added_wf)
if added_wf_validaty is not True:
raise Exception("Check for added_wf_validaty didn't pass")
#print("All Ok!")
result = Utility.makespan(gaheft_added_schedule)
return result
def do_exp():
config = {
"interact_individuals_count": 500,
"generations": 1000,
"env": Env(_wf, rm, estimator),
"species": [Specie(name=MAPPING_SPECIE, pop_size=500,
cxb=0.9, mb=0.9,
mate=lambda env, child1, child2: tools.cxOnePoint(child1, child2),
# mutate=mapping_default_mutate,
# mutate=lambda ctx, mutant: mapping_k_mutate(ctx, 3, mutant)
mutate=mapping_all_mutate,
# mutate=mapping_improving_mutation,
select=selector,
initialize=mapping_default_initialize,
# initialize=lambda ctx, pop: mapping_heft_based_initialize(ctx, pop, heft_mapping, 3),
stat=lambda pop: {"hamming_distances": hamming_distances([to_seq(p) for p in pop], to_seq(ms_ideal_ind)),
"unique_inds_count": unique_individuals(pop),
"pcm": pcm(pop),
"gdm": gdm(pop)}
),
Specie(name=ORDERING_SPECIE, fixed=True,
representative_individual=ListBasedIndividual(os_representative))
],
"solstat": lambda sols: {"best_components": hamming_for_best_components(sols, ms_ideal_ind, os_ideal_ind),
"best_components_itself": best_components_itself(sols),
"best": -1*Utility.makespan(build_schedule(_wf, estimator, rm, max(sols, key=lambda x: x.fitness)))
},
"operators": {
# "choose": default_choose,
"build_solutions": default_build_solutions,
# "fitness": fitness_mapping_and_ordering,
"fitness": overhead_fitness_mapping_and_ordering,
# "assign_credits": default_assign_credits
# "assign_credits": max_assign_credits
"assign_credits": assign_from_transfer_overhead
}
}
return do_experiment(saver, config, _wf, rm, estimator)
initial_wf = ExecutorRunner.get_wf(initial_wf_name, "00")
added_wf = ExecutorRunner.get_wf(added_wf_name, "10")
bundle = Utility.get_default_bundle()
(estimator, resource_manager, initial_schedule) = ExecutorRunner.get_infrastructure(bundle, 1.0, False)
## planning for initial wf
heft = DynamicHeft(initial_wf, resource_manager, estimator)
empty_schedule = Schedule({node:[] for node in heft.get_nodes()})
ga = GAComputationManager(15,
initial_wf,
resource_manager,
estimator)
ga_initial_schedule = ga._get_ga_alg()(empty_schedule, None)[2]
all_initial_wf_time = Utility.makespan(ga_initial_schedule)
print("Initial time: " + str(all_initial_wf_time))
n = 5
## planning for added wf
def gaheft_reschedule(wf_added_time):
copy_gaheft_schedule = Schedule({node:[item for item in items] for (node, items) in ga_initial_schedule.mapping.items()})
added_time = all_initial_wf_time * wf_added_time
mark_finished(copy_gaheft_schedule)
gaheft_added = DynamicHeft(added_wf, resource_manager, estimator)
gaheft_added.current_time = added_time
gaheft_added_schedule = gaheft_added.run(copy_gaheft_schedule)
stat=lambda pop: {
"hamming_distances": hamming_distances(pop, os_ideal_ind),
"unique_inds_count": unique_individuals(pop),
"pcm": pcm(pop),
"gdm": gdm(pop)
})
],
"solstat":
lambda sols: {
"best_components":
hamming_for_best_components(sols, ms_ideal_ind, os_ideal_ind),
"best_components_itself":
best_components_itself(sols),
"best":
-1 * Utility.makespan(
build_schedule(_wf, estimator, rm,
max(sols, key=lambda x: x.fitness)))
},
"operators": {
# "choose": default_choose,
"build_solutions": default_build_solutions,
# "fitness": fitness_mapping_and_ordering,
"fitness": overhead_fitness_mapping_and_ordering,
# "assign_credits": default_assign_credits
# "assign_credits": max_assign_credits
"assign_credits": assign_from_transfer_overhead
}
}
saver = UniqueNameSaver("../../temp/cga_heft_mixin")
),
Specie(name=ORDERING_SPECIE, pop_size=50,
cxb=0.8, mb=0.5,
mate=ordering_default_crossover,
mutate=ordering_default_mutate,
select=ordering_selector,
initialize=ordering_default_initialize,
stat=lambda pop: {"hamming_distances": hamming_distances(pop, os_ideal_ind),
"unique_inds_count": unique_individuals(pop),
"pcm": pcm(pop),
"gdm": gdm(pop)}
)
],
"solstat": lambda sols: {"best_components": hamming_for_best_components(sols, ms_ideal_ind, os_ideal_ind),
"best_components_itself": best_components_itself(sols),
"best": -1* Utility.makespan(build_schedule(_wf, estimator, rm, max(sols, key=lambda x: x.fitness)))
},
"operators": {
# "choose": default_choose,
"build_solutions": default_build_solutions,
# "fitness": fitness_mapping_and_ordering,
"fitness": overhead_fitness_mapping_and_ordering,
# "assign_credits": default_assign_credits
# "assign_credits": max_assign_credits
"assign_credits": assign_from_transfer_overhead
}
}
saver = UniqueNameSaver("../../temp/cga_heft_mixin")
def do_exp():
added_wf_name = "Montage_25"
initial_wf = ExecutorRunner.get_wf(initial_wf_name, "00")
added_wf = ExecutorRunner.get_wf(added_wf_name, "10")
bundle = Utility.get_default_bundle()
(estimator, resource_manager,
initial_schedule) = ExecutorRunner.get_infrastructure(bundle, 1.0, False)
## planning for initial wf
heft = DynamicHeft(initial_wf, resource_manager, estimator)
empty_schedule = Schedule({node: [] for node in heft.get_nodes()})
ga = GAComputationManager(15, initial_wf, resource_manager, estimator)
ga_initial_schedule = ga._get_ga_alg()(empty_schedule, None)[2]
all_initial_wf_time = Utility.makespan(ga_initial_schedule)
print("Initial time: " + str(all_initial_wf_time))
n = 5
## planning for added wf
def gaheft_reschedule(wf_added_time):
copy_gaheft_schedule = Schedule({
node: [item for item in items]
for (node, items) in ga_initial_schedule.mapping.items()
})
added_time = all_initial_wf_time * wf_added_time
def do_exp():
config = {
"interact_individuals_count":
100,
"generations":
300,
"env":
Env(_wf, rm, estimator),
"species": [
Specie(
name=MAPPING_SPECIE,
pop_size=50,
cxb=0.9,
mb=0.9,
mate=lambda env, child1, child2: tools.cxOnePoint(
child1, child2),
# mutate=mapping_default_mutate,
# mutate=lambda ctx, mutant: mapping_k_mutate(ctx, 3, mutant)
mutate=mapping_all_mutate,
# mutate=OnlyUniqueMutant()(mapping_all_mutate),
select=selector,
# initialize=mapping_default_initialize,
initialize=lambda ctx, pop: mapping_heft_based_initialize(
ctx, pop, heft_mapping, 3),
stat=lambda pop: {
"hamming_distances":
hamming_distances([to_seq(p)
for p in pop], to_seq(ms_ideal_ind)),
"unique_inds_count":
unique_individuals(pop),
"pcm":
pcm(pop),
"gdm":
gdm(pop)
}),
Specie(name=ORDERING_SPECIE,
fixed=True,
representative_individual=ListBasedIndividual(
os_representative))
],
"solstat":
lambda sols: {
"best_components":
hamming_for_best_components(sols, ms_ideal_ind, os_ideal_ind),
"best_components_itself":
best_components_itself(sols),
"best":
-1 * Utility.makespan(
build_schedule(_wf, estimator, rm,
max(sols, key=lambda x: x.fitness)))
},
"operators": {
# "choose": default_choose,
"build_solutions": default_build_solutions,
"fitness": fitness_mapping_and_ordering,
# "fitness": overhead_fitness_mapping_and_ordering,
# "assign_credits": default_assign_credits
# "assign_credits": max_assign_credits
"assign_credits": assign_from_transfer_overhead
}
}
return do_experiment(saver, config, _wf, rm, estimator)
#wf_added_times = [0.1]
initial_wf_name = "Montage_30"
added_wf_name = "Montage_25"
initial_wf = ExecutorRunner.get_wf(initial_wf_name, "00")
added_wf = ExecutorRunner.get_wf(added_wf_name, "10")
bundle = Utility.get_default_bundle()
(estimator, resource_manager, initial_schedule) = ExecutorRunner.get_infrastructure(bundle, 1.0, False)
## planning for initial wf
heft = DynamicHeft(initial_wf, resource_manager, estimator)
empty_schedule = Schedule({node:[] for node in heft.get_nodes()})
heft_schedule = heft.run(empty_schedule)
all_initial_wf_time = Utility.makespan(heft_schedule)
print("Initial time: " + str(all_initial_wf_time))
n = 1
## planning for added wf
def heft_reschedule(wf_added_time):
copy_heft_schedule = Schedule({node:[item for item in items] for (node, items) in heft_schedule.mapping.items()})
added_time = all_initial_wf_time * wf_added_time
heft_added = DynamicHeft(added_wf, resource_manager, estimator)
heft_added.current_time = added_time
heft_added_schedule = heft_added.run(copy_heft_schedule)
mark_finished(heft_added_schedule)