# -*- encoding: utf-8 -*- # # Authors: Vojtech CIMA # Bruno GRAZIOLI # Sean MURPHY # # 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 from unittest 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, audit_scope=None): if config is None: config = mock.Mock() super(FakerModelCollector, self).__init__(config) @property def notification_endpoints(self): return [] def get_audit_scope_handler(self, audit_scope): return None 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 FakeGnocchiMetrics(object): def __init__(self, model): self.model = model def mock_get_statistics(self, resource=None, resource_type=None, meter_name=None, period=300, aggregate='mean', granularity=300): if meter_name == 'host_cpu_usage': return self.get_compute_node_cpu_util( resource, period, aggregate, granularity) elif meter_name == 'host_ram_usage': return self.get_compute_node_ram_util( resource, period, aggregate, granularity) elif meter_name == 'instance_cpu_usage': return self.get_instance_cpu_util( resource, period, aggregate, granularity) elif meter_name == 'instance_ram_usage': return self.get_instance_ram_util( resource, period, aggregate, granularity) elif meter_name == 'instance_root_disk_size': return self.get_instance_disk_root_size( resource, period, aggregate, granularity) def get_compute_node_cpu_util(self, resource, period, aggregate, granularity): """Calculates node utilization dynamically. node CPU utilization should consider and correlate with actual instance-node mappings provided within a cluster model. Returns relative node CPU utilization <0, 100>. :param r_id: resource id """ node = self.model.get_node_by_uuid(resource.uuid) instances = self.model.get_node_instances(node) util_sum = 0.0 for instance in instances: total_cpu_util = instance.vcpus * self.get_instance_cpu_util( instance, period, aggregate, granularity) util_sum += total_cpu_util / 100.0 util_sum /= node.vcpus return util_sum * 100.0 def get_compute_node_ram_util(self, resource, period, aggregate, granularity): # Returns mock host ram usage in KB based on the allocated # instances. node = self.model.get_node_by_uuid(resource.uuid) instances = self.model.get_node_instances(node) util_sum = 0.0 for instance in instances: util_sum += self.get_instance_ram_util( instance, period, aggregate, granularity) return util_sum / 1024 @staticmethod def get_instance_cpu_util(resource, period, aggregate, granularity): 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(resource.uuid)] @staticmethod def get_instance_ram_util(resource, period, aggregate, granularity): 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(resource.uuid)] @staticmethod def get_instance_disk_root_size(resource, period, aggregate, granularity): 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(resource.uuid)] # TODO(lpetrut): consider dropping Ceilometer support, it was deprecated # in Ocata. class FakeCeilometerMetrics(FakeGnocchiMetrics): pass