def __build_modified_profiles(mix):
for handle in mix.handles:
print 'processing modified profile %s ...' % (handle.name)
# Load TS
ts_name = wmeta.times_name(handle, wmeta.MUTATION_PNORM, mix)
# Modify CPU normal profile
profile = profile_modifier.process_trace(tc.connection(), ts_name,
handle.modifier, handle.additive,
handle.scale, handle.shift)
# Attach profile to handle
handle.profile_frequency, handle.profile = profile
# Store profiles
for handle in mix.handles:
profile = np.array(handle.profile)
profile_frequency = handle.profile_frequency
for size in xrange(conf_domainsize.count_domain_sizes()):
size = conf_domainsize.get_domain_spec(size).max_user_demand()
user_profile = (profile / 100.0) * size.users
profile_frequency = profile_frequency / (wmeta.CYCLE_TIME / schedule_builder.EXPERIMENT_DURATION)
user_profile = np.array(user_profile)
user_profile += 5
if not DRY_RUN:
print 'storing modified profile %s ...' % (handle.name)
__write_profile(wmeta.times_name(handle, wmeta.MUTATION_PUSER, mix), user_profile,
profile_frequency)
def __store_profile_yarns(mix, handle, normalizing_value, profile, profile_frequency):
# 1) RAW profile (e.g. for plotting)
raw_profile = np.array(profile)
if not DRY_RUN:
__write_profile(wmeta.times_name(handle, wmeta.MUTATION_PRAW, mix), raw_profile, profile_frequency)
# 2) NORMALIZED profile (normalized with the set maximum, see above) (e.g. to feed into SSAPv)
maxval = float(normalizing_value[handle.htype.id])
profile /= maxval
norm_profile = np.array(profile)
norm_profile[norm_profile > 1] = 1
norm_profile *= 100 # Times does not support float values
if not DRY_RUN:
__write_profile(wmeta.times_name(handle, wmeta.MUTATION_PNORM, mix), norm_profile, profile_frequency)
# 3) Store USER profiles (-> e.g. for Rain workload driver)
for size in xrange(conf_domainsize.count_domain_sizes()):
size = conf_domainsize.get_domain_spec(size).max_user_demand()
user_profile = profile * size.users
user_profile = np.array(user_profile)
user_profile += 5
user_profile_frequency = profile_frequency / (wmeta.CYCLE_TIME / schedule_builder.EXPERIMENT_DURATION)
user_profile_name = wmeta.times_name(handle, wmeta.MUTATION_PUSER, mix, size)
print 'FREQ - USER YARN: %s @ %f' % (user_profile_name, user_profile_frequency)
if not DRY_RUN:
__write_profile(user_profile_name, user_profile, user_profile_frequency)
def __build_convolutions(mix, handles):
# Build profiles by convolution
for handle in handles:
print 'processing convolution: %s ...' % (handle.name)
profile = profile_convolution.process_trace(tc.connection(), wmeta.times_name(handle, wmeta.MUTATION_RAW),
wmeta.CYCLE_TIME)
# Attach profile to handle
handle.profile_frequency, handle.profile = profile
# Get normalizing value for each handle type
normalizing_value = __get_normalizing_value(handles)
# Store profiles
for handle in handles:
print 'storing convolution: %s ...' % (handle.name)
__store_profile_yarns(mix, handle, normalizing_value, handle.profile, handle.profile_frequency)
def __build_sampleday(mix, handles):
# Build profiles from sample days
for handle in handles:
print 'processing sample day %s ...' % (handle.name)
profile = profile_sampleday.process_trace(tc.connection(), wmeta.times_name(handle, wmeta.MUTATION_RAW),
wmeta.CYCLE_TIME, handle.htype.day)
# Attach profile to handle
handle.profile_frequency, handle.profile = profile
# Max value in each set of TS
normalizing_value = __get_normalizing_value(handles)
# Store profiles
for handle in handles:
print 'storing sample day %s ...' % (handle.name)
__store_profile_yarns(mix, handle, normalizing_value, handle.profile, handle.profile_frequency)
