formal datasource interface implementation
Changes to the baseclass for datasources so strategies can be made compatible with every datasource. Baseclass methods clearly describe expected values and types for both parameters and for method returns. query_retry has been added as base method since every current datasource implements it. Ceilometer is updated to work with the new baseclass. Several methods which are not part of the baseclass and are not used by any strategies are removed. The signature of these methods would have to be changed to fit with the new base class while it would limit strategies to only work with Ceilometer. Gnocchi is updated to work with the new baseclass. Gnocchi and Ceilometer will perform a transformation for the host_airflow metric as it retrieves 1/10 th of the actual CFM Monasca is updated to work with the new baseclass. FakeMetrics for Gnocchi, Monasca and Ceilometer are updated to work with the new method signatures of the baseclass. FakeClusterAndMetrics for Ceilometer and Gnocchi are updated to work with the new method signatures of the baseclass. The strategies workload_balance, vm_workload_consolidation, workload_stabilization, basic_consolidation, noisy_neighbour, outlet_temp_control and uniform_airflow are updated to work with the new datasource baseclass. This patch will break compatibility with plugin strategies and datasources due to the changes in signatures. Depends-on: I7aa52a9b82f4aa849f2378d4d1c03453e45c0c78 Change-Id: Ie30ca3dbf01062cbb20d3be5d514ec6b5155cd7c Implements: blueprint formal-datasource-interface
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Dantali0n
@@ -21,7 +21,6 @@ import copy
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import itertools
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import math
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import random
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import re
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import oslo_cache
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from oslo_config import cfg
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@@ -61,7 +60,7 @@ class WorkloadStabilization(base.WorkloadStabilizationBaseStrategy):
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MEMOIZE = _set_memoize(CONF)
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DATASOURCE_METRICS = ['host_cpu_usage', 'instance_cpu_usage',
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'instance_ram_usage', 'host_memory_usage']
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'instance_ram_usage', 'host_ram_usage']
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def __init__(self, config, osc=None):
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"""Workload Stabilization control using live migration
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@@ -109,27 +108,28 @@ class WorkloadStabilization(base.WorkloadStabilizationBaseStrategy):
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"type": "array",
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"items": {
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"type": "string",
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"enum": ["cpu_util", "memory.resident"]
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"enum": ["instance_cpu_usage", "instance_ram_usage"]
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},
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"default": ["cpu_util"]
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"default": ["instance_cpu_usage"]
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},
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"thresholds": {
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"description": "Dict where key is a metric and value "
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"is a trigger value.",
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"type": "object",
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"properties": {
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"cpu_util": {
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"instance_cpu_usage": {
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"type": "number",
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"minimum": 0,
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"maximum": 1
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},
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"memory.resident": {
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"instance_ram_usage": {
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"type": "number",
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"minimum": 0,
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"maximum": 1
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}
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},
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"default": {"cpu_util": 0.1, "memory.resident": 0.1}
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"default": {"instance_cpu_usage": 0.1,
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"instance_ram_usage": 0.1}
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},
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"weights": {
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"description": "These weights used to calculate "
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@@ -137,26 +137,26 @@ class WorkloadStabilization(base.WorkloadStabilizationBaseStrategy):
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" contains meter name and _weight suffix.",
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"type": "object",
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"properties": {
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"cpu_util_weight": {
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"instance_cpu_usage_weight": {
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"type": "number",
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"minimum": 0,
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"maximum": 1
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},
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"memory.resident_weight": {
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"instance_ram_usage_weight": {
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"type": "number",
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"minimum": 0,
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"maximum": 1
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}
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},
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"default": {"cpu_util_weight": 1.0,
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"memory.resident_weight": 1.0}
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"default": {"instance_cpu_usage_weight": 1.0,
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"instance_ram_usage_weight": 1.0}
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},
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"instance_metrics": {
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"description": "Mapping to get hardware statistics using"
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" instance metrics",
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"type": "object",
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"default": {"cpu_util": "compute.node.cpu.percent",
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"memory.resident": "hardware.memory.used"}
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"default": {"instance_cpu_usage": "host_cpu_usage",
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"instance_ram_usage": "host_ram_usage"}
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},
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"host_choice": {
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"description": "Method of host's choice. There are cycle,"
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@@ -189,12 +189,12 @@ class WorkloadStabilization(base.WorkloadStabilizationBaseStrategy):
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"type": "integer",
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"minimum": 0
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},
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"node": {
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"compute_node": {
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"type": "integer",
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"minimum": 0
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},
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},
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"default": {"instance": 720, "node": 600}
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"default": {"instance": 720, "compute_node": 600}
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},
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"aggregation_method": {
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"description": "Function used to aggregate multiple "
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@@ -209,12 +209,12 @@ class WorkloadStabilization(base.WorkloadStabilizationBaseStrategy):
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"type": "string",
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"default": 'mean'
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},
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"node": {
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"compute_node": {
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"type": "string",
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"default": 'mean'
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},
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},
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"default": {"instance": 'mean', "node": 'mean'}
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"default": {"instance": 'mean', "compute_node": 'mean'}
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},
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"granularity": {
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"description": "The time between two measures in an "
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@@ -234,7 +234,7 @@ class WorkloadStabilization(base.WorkloadStabilizationBaseStrategy):
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:param host_vcpus: int
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:return: float value
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"""
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return (instance_load['cpu_util'] *
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return (instance_load['instance_cpu_usage'] *
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(instance_load['vcpus'] / float(host_vcpus)))
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@MEMOIZE
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@@ -248,17 +248,15 @@ class WorkloadStabilization(base.WorkloadStabilizationBaseStrategy):
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instance_load = {'uuid': instance.uuid, 'vcpus': instance.vcpus}
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for meter in self.metrics:
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avg_meter = self.datasource_backend.statistic_aggregation(
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instance.uuid, meter, self.periods['instance'],
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self.granularity,
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aggregation=self.aggregation_method['instance'])
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instance, 'instance', meter, self.periods['instance'],
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self.aggregation_method['instance'], self.granularity)
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if avg_meter is None:
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LOG.warning(
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"No values returned by %(resource_id)s "
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"for %(metric_name)s", dict(
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resource_id=instance.uuid, metric_name=meter))
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return
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# cpu_util has been deprecated since Stein.
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if meter == 'cpu_util':
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if meter == 'instance_cpu_usage':
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avg_meter /= float(100)
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LOG.debug('Load of %(metric)s for %(instance)s is %(value)s',
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{'metric': meter,
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@@ -270,9 +268,10 @@ class WorkloadStabilization(base.WorkloadStabilizationBaseStrategy):
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def normalize_hosts_load(self, hosts):
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normalized_hosts = copy.deepcopy(hosts)
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for host in normalized_hosts:
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if 'memory.resident' in normalized_hosts[host]:
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if 'instance_ram_usage' in normalized_hosts[host]:
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node = self.compute_model.get_node_by_uuid(host)
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normalized_hosts[host]['memory.resident'] /= float(node.memory)
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normalized_hosts[host]['instance_ram_usage'] \
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/= float(node.memory)
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return normalized_hosts
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@@ -290,29 +289,21 @@ class WorkloadStabilization(base.WorkloadStabilizationBaseStrategy):
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hosts_load[node_id]['vcpus'] = node.vcpus
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LOG.debug('Getting load for %s', node_id)
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for metric in self.metrics:
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resource_id = ''
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avg_meter = None
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meter_name = self.instance_metrics[metric]
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if re.match('^compute.node', meter_name) is not None:
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resource_id = "%s_%s" % (node.uuid, node.hostname)
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else:
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resource_id = node_id
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avg_meter = self.datasource_backend.statistic_aggregation(
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resource_id, self.instance_metrics[metric],
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self.periods['node'], self.granularity,
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aggregation=self.aggregation_method['node'])
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node, 'compute_node', self.instance_metrics[metric],
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self.periods['compute_node'],
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self.aggregation_method['compute_node'], self.granularity)
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if avg_meter is None:
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LOG.warning('No values returned by node %s for %s',
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node_id, meter_name)
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del hosts_load[node_id]
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break
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else:
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if meter_name == 'hardware.memory.used':
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if meter_name == 'host_ram_usage':
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avg_meter /= oslo_utils.units.Ki
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if meter_name == 'compute.node.cpu.percent':
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avg_meter /= 100
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# hardware.cpu.util has been deprecated since Stein.
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if meter_name == 'hardware.cpu.util':
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if meter_name == 'host_cpu_usage':
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avg_meter /= 100
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LOG.debug('Load of %(metric)s for %(node)s is %(value)s',
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{'metric': metric,
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@@ -369,7 +360,7 @@ class WorkloadStabilization(base.WorkloadStabilizationBaseStrategy):
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s_host_vcpus = new_hosts[src_node.uuid]['vcpus']
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d_host_vcpus = new_hosts[dst_node.uuid]['vcpus']
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for metric in self.metrics:
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if metric == 'cpu_util':
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if metric == 'instance_cpu_usage':
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new_hosts[src_node.uuid][metric] -= (
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self.transform_instance_cpu(instance_load, s_host_vcpus))
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new_hosts[dst_node.uuid][metric] += (
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