Merge "Add NUMA topology constraint to solver scheduler"
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# Copyright (c) 2015 Cisco Systems Inc.
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# All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# 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, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import copy
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from oslo_log import log as logging
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from nova.scheduler.filters import numa_topology_filter
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from nova_solverscheduler.scheduler.solvers import constraints
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LOG = logging.getLogger(__name__)
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class NUMATopologyConstraint(constraints.BaseLinearConstraint):
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"""Constraint on requested NUMA topology."""
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def __init__(self):
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super(NUMATopologyConstraint, self).__init__()
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self.host_filter = numa_topology_filter.NUMATopologyFilter()
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def _get_acceptable_instance_num(self, host_state, filter_properties,
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max_num):
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instance = filter_properties['request_spec']['instance_properties']
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acceptable_num = 0
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while acceptable_num < max_num:
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if self.host_filter.host_passes(host_state, filter_properties):
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acceptable_num += 1
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host_state.consume_from_instance(instance)
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else:
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break
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return acceptable_num
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def get_constraint_matrix(self, hosts, filter_properties):
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num_hosts = len(hosts)
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num_instances = filter_properties.get('num_instances')
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constraint_matrix = [[True for j in xrange(num_instances)]
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for i in xrange(num_hosts)]
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for i in xrange(num_hosts):
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host_state = copy.deepcopy(hosts[i])
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acceptable_instance_num = self._get_acceptable_instance_num(
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host_state, filter_properties, num_instances)
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if acceptable_instance_num < num_instances:
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inacceptable_num = num_instances - acceptable_instance_num
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constraint_matrix[i] = (
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[True for j in xrange(acceptable_instance_num)] +
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[False for j in xrange(inacceptable_num)])
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LOG.debug("%(host)s can accept %(num)s requested instances "
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"according to NUMATopologyConstraint.",
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{'host': hosts[i],
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'num': acceptable_instance_num})
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numa_topology_limit = host_state.limits.get('numa_topology')
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if numa_topology_limit:
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hosts[i].limits['numa_topology'] = numa_topology_limit
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return constraint_matrix
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@ -0,0 +1,112 @@
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# Copyright (c) 2015 Cisco Systems Inc.
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# All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# 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, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import mock
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from oslo_serialization import jsonutils
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from nova import objects
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from nova.objects import base as obj_base
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from nova import test
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from nova.tests.unit import fake_instance
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from nova_solverscheduler.scheduler.solvers.constraints \
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import numa_topology_constraint
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from nova_solverscheduler.tests.scheduler import solver_scheduler_fakes \
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as fakes
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NUMA_TOPOLOGY = objects.NUMATopology(
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cells=[
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objects.NUMACell(id=0, cpuset=set([1, 2]), memory=512,
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cpu_usage=0, memory_usage=0, mempages=[],
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siblings=[], pinned_cpus=set([])),
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objects.NUMACell(id=1, cpuset=set([3, 4]), memory=512,
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cpu_usage=0, memory_usage=0, mempages=[],
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siblings=[], pinned_cpus=set([]))
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]
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)
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class TestNUMATopologyConstraint(test.NoDBTestCase):
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def setUp(self):
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super(TestNUMATopologyConstraint, self).setUp()
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self.constraint_cls = numa_topology_constraint.NUMATopologyConstraint
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def _gen_fake_hosts(self):
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host1 = fakes.FakeSolverSchedulerHostState('host1', 'node1',
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{
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'numa_topology': objects.NUMATopology(
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cells=[
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objects.NUMACell(id=0, cpuset=set([1, 2]),
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memory=1024, cpu_usage=0, memory_usage=0,
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mempages=[], siblings=[], pinned_cpus=set([])),
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objects.NUMACell(id=1, cpuset=set([3, 4]),
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memory=1024, cpu_usage=0, memory_usage=0,
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mempages=[], siblings=[], pinned_cpus=set([]))]),
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'pci_stats': None
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})
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host2 = fakes.FakeSolverSchedulerHostState('host2', 'node1',
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{
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'numa_topology': objects.NUMATopology(
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cells=[
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objects.NUMACell(id=0, cpuset=set([1, 2]),
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memory=1024, cpu_usage=0, memory_usage=0,
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mempages=[], siblings=[], pinned_cpus=set([])),
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objects.NUMACell(id=1, cpuset=set([3, 4]),
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memory=512, cpu_usage=0, memory_usage=0,
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mempages=[], siblings=[], pinned_cpus=set([]))]),
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'pci_stats': None
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})
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host3 = fakes.FakeSolverSchedulerHostState('host3', 'node1',
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{
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'numa_topology': objects.NUMATopology(
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cells=[
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objects.NUMACell(id=0, cpuset=set([1, 2]),
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memory=512, cpu_usage=0, memory_usage=0,
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mempages=[], siblings=[], pinned_cpus=set([])),
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objects.NUMACell(id=1, cpuset=set([3]),
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memory=512, cpu_usage=0, memory_usage=0,
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mempages=[], siblings=[], pinned_cpus=set([]))]),
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'pci_stats': None
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})
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hosts = [host1, host2, host3]
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return hosts
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def test_get_constraint_matrix(self):
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self.flags(ram_allocation_ratio=1)
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self.flags(cpu_allocation_ratio=2)
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instance_topology = objects.InstanceNUMATopology(cells=[
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objects.InstanceNUMACell(id=0, cpuset=set([1, 2]), memory=512),
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objects.InstanceNUMACell(id=1, cpuset=set([3, 4]), memory=512)]
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)
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instance = fake_instance.fake_instance_obj(mock.sentinel.ctx,
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root_gb=0, ephemeral_gb=0, memory_mb=0, vcpus=0,)
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instance.numa_topology = instance_topology
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filter_properties = {
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'request_spec': {
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'instance_properties': jsonutils.to_primitive(
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obj_base.obj_to_primitive(instance))},
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'num_instances': 2}
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fake_hosts = self._gen_fake_hosts()
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expected_cons_mat = [
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[True, True],
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[True, False],
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[False, False]]
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cons_mat = self.constraint_cls().get_constraint_matrix(
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fake_hosts, filter_properties)
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self.assertEqual(expected_cons_mat, cons_mat)
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