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 __write_profile(name, ts, frequency, overwrite=True):
# Check if the profile exists
if len(tc.connection().find(name)) > 0:
# Double check if it is allowed to overwrite profiles in Times
if overwrite:
tc.connection().remove(name)
else:
print 'WARNING - will not overwrite profile %s' % name
return
# Create a new ts
print ' writing profile handles %s' % (name)
tc.connection().create(name, frequency)
# Crete elements array
elements = []
for i in xrange(len(ts)):
item = ts[i]
element = ttypes.Element()
element.timestamp = i * frequency
element.value = item
elements.append(element)
# Append elements to ts
tc.connection().append(name, elements)
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)
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 createBenchmarkProfile(name, users, freq, duration):
# Check if the profile exists
if len(tc.connection().find(name)) > 0:
# Double check if it is allowed to overwrite profiles in Times
if overwrite:
tc.connection().remove(name)
else:
print 'WARNING - will not overwrite profile %s' % name
return
# Create a new ts
print ' writing profile handles %s' % (name)
tc.connection().create(name, freq)
# Crete elements array
elements = []
for i in xrange(duration / freq):
element = ttypes.Element()
element.timestamp = i * freq
element.value = users
elements.append(element)
# Append elements to ts
tc.connection().append(name, elements)
