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Add gnocchi support in VM-Workload-Consolidation strategy

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This patch adds gnocchi support in VM-Workload-Consolidation strategy
and adds unit tests corresponding to that change.

Change-Id: I4aab158a6b7c92cb9fe8979bb8bd6338c4686b11
Partiallly-Implements: bp gnocchi-watcher
This commit is contained in:
Santhosh Fernandes
2017-03-28 15:33:10 +05:30
parent 4a3c15185c
commit 4642a92e78
3 changed files with 260 additions and 45 deletions
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83
watcher/tests/decision_engine/model/faker_cluster_and_metrics.py
View File

@@ -157,3 +157,86 @@ class FakeCeilometerMetrics(object):
instance_disk_util['INSTANCE_8'] = 25
instance_disk_util['INSTANCE_9'] = 25
return instance_disk_util[str(r_id)]
class FakeGnocchiMetrics(object):
def __init__(self, model):
self.model = model
def mock_get_statistics(self, resource_id, metric, granularity,
start_time, stop_time, aggregation='mean'):
if metric == "compute.node.cpu.percent":
return self.get_node_cpu_util(resource_id)
elif metric == "cpu_util":
return self.get_instance_cpu_util(resource_id)
elif metric == "memory.usage":
return self.get_instance_ram_util(resource_id)
elif metric == "disk.root.size":
return self.get_instance_disk_root_size(resource_id)
def get_node_cpu_util(self, r_id):
"""Calculates node utilization dynamicaly.
node CPU utilization should consider
and corelate with actual instance-node mappings
provided within a cluster model.
Returns relative node CPU utilization <0, 100>.
:param r_id: resource id
"""
node_uuid = '%s_%s' % (r_id.split('_')[0], r_id.split('_')[1])
node = self.model.get_node_by_uuid(node_uuid)
instances = self.model.get_node_instances(node)
util_sum = 0.0
for instance_uuid in instances:
instance = self.model.get_instance_by_uuid(instance_uuid)
total_cpu_util = instance.vcpus * self.get_instance_cpu_util(
instance.uuid)
util_sum += total_cpu_util / 100.0
util_sum /= node.vcpus
return util_sum * 100.0
@staticmethod
def get_instance_cpu_util(r_id):
instance_cpu_util = dict()
instance_cpu_util['INSTANCE_0'] = 10
instance_cpu_util['INSTANCE_1'] = 30
instance_cpu_util['INSTANCE_2'] = 60
instance_cpu_util['INSTANCE_3'] = 20
instance_cpu_util['INSTANCE_4'] = 40
instance_cpu_util['INSTANCE_5'] = 50
instance_cpu_util['INSTANCE_6'] = 100
instance_cpu_util['INSTANCE_7'] = 100
instance_cpu_util['INSTANCE_8'] = 100
instance_cpu_util['INSTANCE_9'] = 100
return instance_cpu_util[str(r_id)]
@staticmethod
def get_instance_ram_util(r_id):
instance_ram_util = dict()
instance_ram_util['INSTANCE_0'] = 1
instance_ram_util['INSTANCE_1'] = 2
instance_ram_util['INSTANCE_2'] = 4
instance_ram_util['INSTANCE_3'] = 8
instance_ram_util['INSTANCE_4'] = 3
instance_ram_util['INSTANCE_5'] = 2
instance_ram_util['INSTANCE_6'] = 1
instance_ram_util['INSTANCE_7'] = 2
instance_ram_util['INSTANCE_8'] = 4
instance_ram_util['INSTANCE_9'] = 8
return instance_ram_util[str(r_id)]
@staticmethod
def get_instance_disk_root_size(r_id):
instance_disk_util = dict()
instance_disk_util['INSTANCE_0'] = 10
instance_disk_util['INSTANCE_1'] = 15
instance_disk_util['INSTANCE_2'] = 30
instance_disk_util['INSTANCE_3'] = 35
instance_disk_util['INSTANCE_4'] = 20
instance_disk_util['INSTANCE_5'] = 25
instance_disk_util['INSTANCE_6'] = 25
instance_disk_util['INSTANCE_7'] = 25
instance_disk_util['INSTANCE_8'] = 25
instance_disk_util['INSTANCE_9'] = 25
return instance_disk_util[str(r_id)]

60
watcher/tests/decision_engine/strategy/strategies/test_vm_workload_consolidation.py
View File

@@ -18,6 +18,7 @@
# limitations under the License.
#
import datetime
import mock
from watcher.common import exception
@@ -29,6 +30,17 @@ from watcher.tests.decision_engine.model import faker_cluster_and_metrics
class TestVMWorkloadConsolidation(base.TestCase):
scenarios = [
("Ceilometer",
{"datasource": "ceilometer",
"fake_datasource_cls":
faker_cluster_and_metrics.FakeCeilometerMetrics}),
("Gnocchi",
{"datasource": "gnocchi",
"fake_datasource_cls":
faker_cluster_and_metrics.FakeGnocchiMetrics}),
]
def setUp(self):
super(TestVMWorkloadConsolidation, self).setUp()
@@ -41,11 +53,11 @@ class TestVMWorkloadConsolidation(base.TestCase):
self.m_model = p_model.start()
self.addCleanup(p_model.stop)
p_ceilometer = mock.patch.object(
strategies.VMWorkloadConsolidation, "ceilometer",
p_datasource = mock.patch.object(
strategies.VMWorkloadConsolidation, self.datasource,
new_callable=mock.PropertyMock)
self.m_ceilometer = p_ceilometer.start()
self.addCleanup(p_ceilometer.stop)
self.m_datasource = p_datasource.start()
self.addCleanup(p_datasource.stop)
p_audit_scope = mock.patch.object(
strategies.VMWorkloadConsolidation, "audit_scope",
@@ -57,13 +69,14 @@ class TestVMWorkloadConsolidation(base.TestCase):
self.m_audit_scope.return_value = mock.Mock()
# fake metrics
self.fake_metrics = faker_cluster_and_metrics.FakeCeilometerMetrics(
self.fake_metrics = self.fake_datasource_cls(
self.m_model.return_value)
self.m_model.return_value = model_root.ModelRoot()
self.m_ceilometer.return_value = mock.Mock(
self.m_datasource.return_value = mock.Mock(
statistic_aggregation=self.fake_metrics.mock_get_statistics)
self.strategy = strategies.VMWorkloadConsolidation(config=mock.Mock())
self.strategy = strategies.VMWorkloadConsolidation(
config=mock.Mock(datasource=self.datasource))
def test_exception_stale_cdm(self):
self.fake_cluster.set_cluster_data_model_as_stale()
@@ -81,7 +94,7 @@ class TestVMWorkloadConsolidation(base.TestCase):
instance_util = dict(cpu=1.0, ram=1, disk=10)
self.assertEqual(
instance_util,
self.strategy.get_instance_utilization(instance_0, model))
self.strategy.get_instance_utilization(instance_0))
def test_get_node_utilization(self):
model = self.fake_cluster.generate_scenario_1()
@@ -91,7 +104,7 @@ class TestVMWorkloadConsolidation(base.TestCase):
node_util = dict(cpu=1.0, ram=1, disk=10)
self.assertEqual(
node_util,
self.strategy.get_node_utilization(node_0, model))
self.strategy.get_node_utilization(node_0))
def test_get_node_capacity(self):
model = self.fake_cluster.generate_scenario_1()
@@ -301,10 +314,33 @@ class TestVMWorkloadConsolidation(base.TestCase):
strategies.VMWorkloadConsolidation, "ceilometer")
m_ceilometer = p_ceilometer.start()
self.addCleanup(p_ceilometer.stop)
p_gnocchi = mock.patch.object(
strategies.VMWorkloadConsolidation, "gnocchi")
m_gnocchi = p_gnocchi.start()
self.addCleanup(p_gnocchi.stop)
datetime_patcher = mock.patch.object(
datetime, 'datetime',
mock.Mock(wraps=datetime.datetime)
)
mocked_datetime = datetime_patcher.start()
mocked_datetime.utcnow.return_value = datetime.datetime(
2017, 3, 19, 18, 53, 11, 657417)
self.addCleanup(datetime_patcher.stop)
m_ceilometer.return_value = mock.Mock(
statistic_aggregation=self.fake_metrics.mock_get_statistics)
m_gnocchi.return_value = mock.Mock(
statistic_aggregation=self.fake_metrics.mock_get_statistics)
instance0 = model.get_instance_by_uuid("INSTANCE_0")
self.strategy.get_instance_utilization(instance0)
m_ceilometer.statistic_aggregation.assert_any_call(
aggregate='avg', meter_name='disk.root.size',
period=3600, resource_id=instance0.uuid)
if self.strategy.config.datasource == "ceilometer":
m_ceilometer.statistic_aggregation.assert_any_call(
aggregate='avg', meter_name='disk.root.size',
period=3600, resource_id=instance0.uuid)
elif self.strategy.config.datasource == "gnocchi":
stop_time = datetime.datetime.utcnow()
start_time = stop_time - datetime.timedelta(
seconds=int('3600'))
m_gnocchi.statistic_aggregation.assert_called_with(
resource_id=instance0.uuid, metric='disk.root.size',
granularity=300, start_time=start_time, stop_time=stop_time,
aggregation='mean')