c.append(l)
c.append(t)
return c
if __name__ == '__main__':
from sys import argv
from workflow import Workflow
from pool import AWS
from emo_tool import get_info, evaluate
from log import get_details
from plot import stat, trans, show
wf = Workflow(argv[1])
pool = AWS("aws.info")
info = get_info(wf, pool)
_, loc, ts = heft2(*info[2:])
c = get_chromosome(loc, ts)
makespan, cost = evaluate(c,
*info[2:-1],
n_tasks=len(loc),
n_nodes=len(loc),
n_types=len(info[1]))
scheme = get_details(c,
*info[:-1],
n_tasks=len(loc),
n_nodes=len(loc),
n_types=len(info[1]))
scheme = [(n, ) + v for n, v in scheme.iteritems()]
break
if len(types) > n_tasks:
types = types[:n_tasks]
c = array.array("I", [])
for l, t in itertools.izip_longest(loc, types, fillvalue=0):
c.append(l)
c.append(t)
return c
if __name__ == '__main__':
from sys import argv
from workflow import Workflow
from pool import AWS
from emo_tool import get_info, evaluate
from log import get_details
from plot import stat, trans, show
wf = Workflow(argv[1])
pool = AWS("aws.info")
info = get_info(wf, pool)
_, loc, ts = heft2(*info[2:])
c = get_chromosome(loc, ts)
makespan, cost = evaluate(c, *info[2:-1], n_tasks=len(loc), n_nodes=len(loc), n_types=len(info[1]))
scheme = get_details(c, *info[:-1], n_tasks=len(loc), n_nodes=len(loc), n_types=len(info[1]))
scheme = [(n,)+v for n,v in scheme.iteritems()]
print "Makespan: %.0fs\tCost: $%.2f" % (makespan, cost)
# stat(scheme)
# show(*trans(scheme))
if tuple(i.fitness) not in s:
s.add(tuple(i.fitness))
ar.append(i)
return ar
if __name__ == '__main__':
from sys import argv
from workflow import Workflow
from pool import AWS
import hotshot, hotshot.stats
wf = Workflow(argv[1])
pool = AWS("aws.info")
ar = nsga_2(*get_info(wf, pool))
# prof = hotshot.Profile("emo3.prof")
# archive = prof.runcall(nsga_2, *get_info(wf, pool))
# prof.close()
# prof.close()
# stats = hotshot.stats.load("emo3.prof")
# stats.strip_dirs()
# stats.sort_stats('time', 'calls')
# stats.print_stats()
ar.sort(key=lambda x: x.fitness[0])
for i in range(len(ar)):
print "[%d]: Makespan: %.0fs\tCost: $%.2f" % (i, ar[i].fitness[0],
ar[i].fitness[1])
s = set()
ar = []
for i in pop:
if tuple(i.fitness.values) not in s:
s.add(tuple(i.fitness.values))
ar.append(i)
ar.sort(key=lambda x: x.fitness.values[0])
if archive_name:
dump(archive_name, ar, task_names, type_names,
type_info_price, type_info_ecu, task_base_time,
task_preds, comm_speeds, comm_sizes,
n_tasks, n_nodes, n_types)
return ar
if __name__ == '__main__':
from sys import argv
from workflow import Workflow
from pool import AWS
import os
wf = Workflow(argv[1])
pool = AWS("aws.info")
ar = nsga_2(*(get_info(wf, pool)), archive_name="archive/%s.ar" % os.path.split(argv[1])[1][:-4])
for i in range(len(ar)):
print "[%d]: Makespan: %.0fs\tCost: $%.2f" % (i, ar[i].fitness.values[0], ar[i].fitness.values[1])
if tuple(i.fitness) not in s:
s.add(tuple(i.fitness))
ar.append(i)
return ar
if __name__ == '__main__':
from sys import argv
from workflow import Workflow
from pool import AWS
import hotshot, hotshot.stats
wf = Workflow(argv[1])
pool = AWS("aws.info")
ar = nsga_2(*get_info(wf, pool))
# prof = hotshot.Profile("emo3.prof")
# archive = prof.runcall(nsga_2, *get_info(wf, pool))
# prof.close()
# prof.close()
# stats = hotshot.stats.load("emo3.prof")
# stats.strip_dirs()
# stats.sort_stats('time', 'calls')
# stats.print_stats()
ar.sort(key=lambda x: x.fitness[0])
for i in range(len(ar)):
print "[%d]: Makespan: %.0fs\tCost: $%.2f" % (i, ar[i].fitness[0], ar[i].fitness[1])
