e55f3793b6
This patch removes 'audit_scope' from __init__ of BaseClusterDataModelCollector class, as it is a singleton class and cannot be instantiate more than once. A new method is defined in BaseClusterDataModelCollector in place of property audit_scope_handler, which takes audit_scope as argument. Change-Id: I0664c151d71a711c118d43c180d8b0760b1c81fa Closes-Bug: #1732849
246 lines
8.8 KiB
Python
246 lines
8.8 KiB
Python
# -*- encoding: utf-8 -*-
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#
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# Authors: Vojtech CIMA <cima@zhaw.ch>
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# Bruno GRAZIOLI <gaea@zhaw.ch>
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# Sean MURPHY <murp@zhaw.ch>
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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# implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import mock
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from watcher.decision_engine.model.collector import base
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from watcher.decision_engine.model import model_root as modelroot
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class FakerModelCollector(base.BaseClusterDataModelCollector):
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def __init__(self, config=None, osc=None, audit_scope=None):
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if config is None:
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config = mock.Mock()
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super(FakerModelCollector, self).__init__(config)
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@property
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def notification_endpoints(self):
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return []
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def get_audit_scope_handler(self, audit_scope):
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return None
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def execute(self):
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return self.generate_scenario_1()
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def load_data(self, filename):
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cwd = os.path.abspath(os.path.dirname(__file__))
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data_folder = os.path.join(cwd, "data")
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with open(os.path.join(data_folder, filename), 'rb') as xml_file:
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xml_data = xml_file.read()
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return xml_data
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def load_model(self, filename):
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return modelroot.ModelRoot.from_xml(self.load_data(filename))
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def generate_scenario_1(self):
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"""Simulates cluster with 2 nodes and 2 instances using 1:1 mapping"""
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return self.load_model('scenario_1_with_metrics.xml')
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def generate_scenario_2(self):
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"""Simulates a cluster
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With 4 nodes and 6 instances all mapped to a single node
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"""
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return self.load_model('scenario_2_with_metrics.xml')
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def generate_scenario_3(self):
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"""Simulates a cluster
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With 4 nodes and 6 instances all mapped to one node
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"""
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return self.load_model('scenario_3_with_metrics.xml')
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def generate_scenario_4(self):
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"""Simulates a cluster
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With 4 nodes and 6 instances spread on all nodes
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"""
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return self.load_model('scenario_4_with_metrics.xml')
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class FakeCeilometerMetrics(object):
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def __init__(self, model):
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self.model = model
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def mock_get_statistics(self, resource_id, meter_name, period=3600,
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aggregate='avg'):
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if meter_name == "compute.node.cpu.percent":
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return self.get_node_cpu_util(resource_id)
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elif meter_name == "cpu_util":
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return self.get_instance_cpu_util(resource_id)
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elif meter_name == "memory.resident":
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return self.get_instance_ram_util(resource_id)
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elif meter_name == "disk.root.size":
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return self.get_instance_disk_root_size(resource_id)
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def get_node_cpu_util(self, r_id):
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"""Calculates node utilization dynamicaly.
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node CPU utilization should consider
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and corelate with actual instance-node mappings
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provided within a cluster model.
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Returns relative node CPU utilization <0, 100>.
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:param r_id: resource id
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"""
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node_uuid = '%s_%s' % (r_id.split('_')[0], r_id.split('_')[1])
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node = self.model.get_node_by_uuid(node_uuid)
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instances = self.model.get_node_instances(node)
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util_sum = 0.0
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for instance_uuid in instances:
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instance = self.model.get_instance_by_uuid(instance_uuid)
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total_cpu_util = instance.vcpus * self.get_instance_cpu_util(
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instance.uuid)
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util_sum += total_cpu_util / 100.0
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util_sum /= node.vcpus
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return util_sum * 100.0
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@staticmethod
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def get_instance_cpu_util(r_id):
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instance_cpu_util = dict()
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instance_cpu_util['INSTANCE_0'] = 10
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instance_cpu_util['INSTANCE_1'] = 30
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instance_cpu_util['INSTANCE_2'] = 60
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instance_cpu_util['INSTANCE_3'] = 20
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instance_cpu_util['INSTANCE_4'] = 40
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instance_cpu_util['INSTANCE_5'] = 50
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instance_cpu_util['INSTANCE_6'] = 100
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instance_cpu_util['INSTANCE_7'] = 100
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instance_cpu_util['INSTANCE_8'] = 100
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instance_cpu_util['INSTANCE_9'] = 100
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return instance_cpu_util[str(r_id)]
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@staticmethod
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def get_instance_ram_util(r_id):
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instance_ram_util = dict()
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instance_ram_util['INSTANCE_0'] = 1
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instance_ram_util['INSTANCE_1'] = 2
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instance_ram_util['INSTANCE_2'] = 4
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instance_ram_util['INSTANCE_3'] = 8
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instance_ram_util['INSTANCE_4'] = 3
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instance_ram_util['INSTANCE_5'] = 2
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instance_ram_util['INSTANCE_6'] = 1
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instance_ram_util['INSTANCE_7'] = 2
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instance_ram_util['INSTANCE_8'] = 4
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instance_ram_util['INSTANCE_9'] = 8
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return instance_ram_util[str(r_id)]
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@staticmethod
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def get_instance_disk_root_size(r_id):
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instance_disk_util = dict()
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instance_disk_util['INSTANCE_0'] = 10
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instance_disk_util['INSTANCE_1'] = 15
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instance_disk_util['INSTANCE_2'] = 30
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instance_disk_util['INSTANCE_3'] = 35
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instance_disk_util['INSTANCE_4'] = 20
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instance_disk_util['INSTANCE_5'] = 25
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instance_disk_util['INSTANCE_6'] = 25
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instance_disk_util['INSTANCE_7'] = 25
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instance_disk_util['INSTANCE_8'] = 25
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instance_disk_util['INSTANCE_9'] = 25
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return instance_disk_util[str(r_id)]
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class FakeGnocchiMetrics(object):
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def __init__(self, model):
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self.model = model
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def mock_get_statistics(self, resource_id, metric, granularity,
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start_time, stop_time, aggregation='mean'):
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if metric == "compute.node.cpu.percent":
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return self.get_node_cpu_util(resource_id)
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elif metric == "cpu_util":
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return self.get_instance_cpu_util(resource_id)
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elif metric == "memory.resident":
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return self.get_instance_ram_util(resource_id)
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elif metric == "disk.root.size":
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return self.get_instance_disk_root_size(resource_id)
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def get_node_cpu_util(self, r_id):
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"""Calculates node utilization dynamicaly.
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node CPU utilization should consider
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and corelate with actual instance-node mappings
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provided within a cluster model.
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Returns relative node CPU utilization <0, 100>.
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:param r_id: resource id
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"""
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node_uuid = '%s_%s' % (r_id.split('_')[0], r_id.split('_')[1])
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node = self.model.get_node_by_uuid(node_uuid)
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instances = self.model.get_node_instances(node)
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util_sum = 0.0
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for instance_uuid in instances:
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instance = self.model.get_instance_by_uuid(instance_uuid)
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total_cpu_util = instance.vcpus * self.get_instance_cpu_util(
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instance.uuid)
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util_sum += total_cpu_util / 100.0
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util_sum /= node.vcpus
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return util_sum * 100.0
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@staticmethod
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def get_instance_cpu_util(r_id):
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instance_cpu_util = dict()
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instance_cpu_util['INSTANCE_0'] = 10
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instance_cpu_util['INSTANCE_1'] = 30
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instance_cpu_util['INSTANCE_2'] = 60
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instance_cpu_util['INSTANCE_3'] = 20
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instance_cpu_util['INSTANCE_4'] = 40
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instance_cpu_util['INSTANCE_5'] = 50
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instance_cpu_util['INSTANCE_6'] = 100
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instance_cpu_util['INSTANCE_7'] = 100
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instance_cpu_util['INSTANCE_8'] = 100
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instance_cpu_util['INSTANCE_9'] = 100
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return instance_cpu_util[str(r_id)]
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@staticmethod
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def get_instance_ram_util(r_id):
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instance_ram_util = dict()
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instance_ram_util['INSTANCE_0'] = 1
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instance_ram_util['INSTANCE_1'] = 2
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instance_ram_util['INSTANCE_2'] = 4
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instance_ram_util['INSTANCE_3'] = 8
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instance_ram_util['INSTANCE_4'] = 3
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instance_ram_util['INSTANCE_5'] = 2
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instance_ram_util['INSTANCE_6'] = 1
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instance_ram_util['INSTANCE_7'] = 2
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instance_ram_util['INSTANCE_8'] = 4
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instance_ram_util['INSTANCE_9'] = 8
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return instance_ram_util[str(r_id)]
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@staticmethod
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def get_instance_disk_root_size(r_id):
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instance_disk_util = dict()
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instance_disk_util['INSTANCE_0'] = 10
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instance_disk_util['INSTANCE_1'] = 15
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instance_disk_util['INSTANCE_2'] = 30
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instance_disk_util['INSTANCE_3'] = 35
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instance_disk_util['INSTANCE_4'] = 20
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instance_disk_util['INSTANCE_5'] = 25
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instance_disk_util['INSTANCE_6'] = 25
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instance_disk_util['INSTANCE_7'] = 25
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instance_disk_util['INSTANCE_8'] = 25
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instance_disk_util['INSTANCE_9'] = 25
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return instance_disk_util[str(r_id)]
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