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watcher/watcher/tests/decision_engine/model/faker_cluster_and_metrics.py
suzhengwei d2f70f9d6f change ram util metric 
Metric 'memory.usage' depends on balloon driver,
and shows the memory usage inside guest-os,
while 'memory.resident' represents volume of RAM
used by the instance on the physical machine
So, it is more reasonable to use 'memory.resident'
when calculating node utilization
by gathering instances utilization of the node.

Change-Id: I12dff5176bcf6cb103aa44cafd54f9ecd7170864
2017-08-07 16:04:19 +08:00

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8.7 KiB
Python
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# -*- encoding: utf-8 -*-
#
# Authors: Vojtech CIMA <cima@zhaw.ch>
# Bruno GRAZIOLI <gaea@zhaw.ch>
# Sean MURPHY <murp@zhaw.ch>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import mock
from watcher.decision_engine.model.collector import base
from watcher.decision_engine.model import model_root as modelroot
class FakerModelCollector(base.BaseClusterDataModelCollector):
def __init__(self, config=None, osc=None):
if config is None:
config = mock.Mock()
super(FakerModelCollector, self).__init__(config)
@property
def notification_endpoints(self):
return []
def execute(self):
return self.generate_scenario_1()
def load_data(self, filename):
cwd = os.path.abspath(os.path.dirname(__file__))
data_folder = os.path.join(cwd, "data")
with open(os.path.join(data_folder, filename), 'rb') as xml_file:
xml_data = xml_file.read()
return xml_data
def load_model(self, filename):
return modelroot.ModelRoot.from_xml(self.load_data(filename))
def generate_scenario_1(self):
"""Simulates cluster with 2 nodes and 2 instances using 1:1 mapping"""
return self.load_model('scenario_1_with_metrics.xml')
def generate_scenario_2(self):
"""Simulates a cluster
With 4 nodes and 6 instances all mapped to a single node
"""
return self.load_model('scenario_2_with_metrics.xml')
def generate_scenario_3(self):
"""Simulates a cluster
With 4 nodes and 6 instances all mapped to one node
"""
return self.load_model('scenario_3_with_metrics.xml')
def generate_scenario_4(self):
"""Simulates a cluster
With 4 nodes and 6 instances spread on all nodes
"""
return self.load_model('scenario_4_with_metrics.xml')
class FakeCeilometerMetrics(object):
def __init__(self, model):
self.model = model
def mock_get_statistics(self, resource_id, meter_name, period=3600,
aggregate='avg'):
if meter_name == "compute.node.cpu.percent":
return self.get_node_cpu_util(resource_id)
elif meter_name == "cpu_util":
return self.get_instance_cpu_util(resource_id)
elif meter_name == "memory.resident":
return self.get_instance_ram_util(resource_id)
elif meter_name == "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)]
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.resident":
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)]
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