2c7eabc69d
This makes scheduling weights more plugin friendly and creates shared code that can be used by the host scheduler as well as the future cells scheduler. Weighing classes can now be specified much like you can specify scheduling host filters. The new weights code reverses the old behavior where lower weights win. Higher weights are now the winners. The least_cost module and configs have been deprecated, but are still supported for backwards compatibility. The code has moved to nova.scheduler.weights.least_cost and been modified to work with the new loadable-class code. If any of the least_cost related config options are specified, this least_cost weigher will be used. For those not overriding the default least_cost config values, the new RamWeigher class will be used. The default behavior of the RamWeigher class is the same default behavior as the old least_cost module. The new weights code introduces a new config option 'scheduler_weight_classes' which is used to specify which weigher classes to use. The default is 'all classes', but modified if least_cost deprecated config options are used, as mentioned above. The RamWeigher class introduces a new config option 'ram_weight_multiplier'. The default of 1.0 causes weights equal to the free memory in MB to be returned, thus hosts with more free memory are preferred (causes equal spreading). Changing this value to a negative number such as -1.0 will cause reverse behavior (fill first). DocImpact Change-Id: I1e5e5039c299db02f7287f2d33299ebf0b9732ce
144 lines
4.9 KiB
Python
144 lines
4.9 KiB
Python
# Copyright 2011-2012 OpenStack LLC.
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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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"""
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Tests For Least Cost functions.
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"""
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from nova import config
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from nova import context
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from nova.openstack.common import cfg
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from nova.scheduler import weights
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from nova.scheduler.weights import least_cost
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from nova import test
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from nova.tests.scheduler import fakes
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test_least_cost_opts = [
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cfg.FloatOpt('compute_fake_weigher1_weight',
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default=2.0,
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help='How much weight to give the fake_weigher1 function'),
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cfg.FloatOpt('compute_fake_weigher2_weight',
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default=1.0,
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help='How much weight to give the fake_weigher2 function'),
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]
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CONF = config.CONF
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CONF.import_opt('least_cost_functions', 'nova.scheduler.weights.least_cost')
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CONF.import_opt('compute_fill_first_cost_fn_weight',
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'nova.scheduler.weights.least_cost')
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CONF.register_opts(test_least_cost_opts)
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def compute_fake_weigher1(hostinfo, options):
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return hostinfo.free_ram_mb + 10000
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def compute_fake_weigher2(hostinfo, options):
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return hostinfo.free_ram_mb * 2
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class LeastCostTestCase(test.TestCase):
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def setUp(self):
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super(LeastCostTestCase, self).setUp()
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self.host_manager = fakes.FakeHostManager()
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self.weight_handler = weights.HostWeightHandler()
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def _get_weighed_host(self, hosts, weight_properties=None):
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weigher_classes = least_cost.get_least_cost_weighers()
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if weight_properties is None:
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weight_properties = {}
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return self.weight_handler.get_weighed_objects(weigher_classes,
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hosts, weight_properties)[0]
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def _get_all_hosts(self):
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ctxt = context.get_admin_context()
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fakes.mox_host_manager_db_calls(self.mox, ctxt)
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self.mox.ReplayAll()
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host_states = self.host_manager.get_all_host_states(ctxt)
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self.mox.VerifyAll()
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self.mox.ResetAll()
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return host_states
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def test_default_of_spread_first(self):
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# Default modifier is -1.0, so it turns out that hosts with
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# the most free memory win
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hostinfo_list = self._get_all_hosts()
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# host1: free_ram_mb=512
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# host2: free_ram_mb=1024
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# host3: free_ram_mb=3072
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# host4: free_ram_mb=8192
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# so, host1 should win:
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weighed_host = self._get_weighed_host(hostinfo_list)
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self.assertEqual(weighed_host.weight, 8192)
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self.assertEqual(weighed_host.obj.host, 'host4')
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def test_filling_first(self):
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self.flags(compute_fill_first_cost_fn_weight=1.0)
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hostinfo_list = self._get_all_hosts()
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# host1: free_ram_mb=-512
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# host2: free_ram_mb=-1024
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# host3: free_ram_mb=-3072
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# host4: free_ram_mb=-8192
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# so, host1 should win:
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weighed_host = self._get_weighed_host(hostinfo_list)
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self.assertEqual(weighed_host.weight, -512)
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self.assertEqual(weighed_host.obj.host, 'host1')
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def test_weighted_sum_provided_method(self):
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fns = ['nova.tests.scheduler.test_least_cost.compute_fake_weigher1',
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'nova.tests.scheduler.test_least_cost.compute_fake_weigher2']
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self.flags(least_cost_functions=fns)
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hostinfo_list = self._get_all_hosts()
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# host1: free_ram_mb=512
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# host2: free_ram_mb=1024
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# host3: free_ram_mb=3072
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# host4: free_ram_mb=8192
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# [offset, scale]=
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# [10512, 11024, 13072, 18192]
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# [1024, 2048, 6144, 16384]
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# adjusted [ 2.0 * x + 1.0 * y] =
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# [22048, 24096, 32288, 52768]
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# so, host1 should win:
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weighed_host = self._get_weighed_host(hostinfo_list)
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self.assertEqual(weighed_host.weight, 52768)
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self.assertEqual(weighed_host.obj.host, 'host4')
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def test_weighted_sum_single_function(self):
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fns = ['nova.tests.scheduler.test_least_cost.compute_fake_weigher1']
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self.flags(least_cost_functions=fns)
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hostinfo_list = self._get_all_hosts()
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# host1: free_ram_mb=0
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# host2: free_ram_mb=1536
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# host3: free_ram_mb=3072
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# host4: free_ram_mb=8192
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# [offset, ]=
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# [10512, 11024, 13072, 18192]
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# adjusted [ 2.0 * x ]=
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# [21024, 22048, 26144, 36384]
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# so, host1 should win:
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weighed_host = self._get_weighed_host(hostinfo_list)
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self.assertEqual(weighed_host.weight, 36384)
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self.assertEqual(weighed_host.obj.host, 'host4')
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