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Diffstat (limited to 'openstack/common/scheduler/base_weight.py')
| -rw-r--r-- | openstack/common/scheduler/base_weight.py | 147 |
1 files changed, 0 insertions, 147 deletions
diff --git a/openstack/common/scheduler/base_weight.py b/openstack/common/scheduler/base_weight.py deleted file mode 100644 index d4f6a319..00000000 --- a/openstack/common/scheduler/base_weight.py +++ /dev/null @@ -1,147 +0,0 @@ -# Copyright (c) 2011-2012 OpenStack Foundation. -# All Rights Reserved. -# -# 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. - -""" -Pluggable Weighing support -""" - -import abc - -import six - -from openstack.common.scheduler import base_handler - - -def normalize(weight_list, minval=None, maxval=None): - """Normalize the values in a list between 0 and 1.0. - - The normalization is made regarding the lower and upper values present in - weight_list. If the minval and/or maxval parameters are set, these values - will be used instead of the minimum and maximum from the list. - - If all the values are equal, they are normalized to 0. - """ - - if not weight_list: - return () - - if maxval is None: - maxval = max(weight_list) - - if minval is None: - minval = min(weight_list) - - maxval = float(maxval) - minval = float(minval) - - if minval == maxval: - return [0] * len(weight_list) - - range_ = maxval - minval - return ((i - minval) / range_ for i in weight_list) - - -class WeighedObject(object): - """Object with weight information.""" - def __init__(self, obj, weight): - self.obj = obj - self.weight = weight - - def __repr__(self): - return "<WeighedObject '%s': %s>" % (self.obj, self.weight) - - -@six.add_metaclass(abc.ABCMeta) -class BaseWeigher(object): - """Base class for pluggable weighers. - - The attributes maxval and minval can be specified to set up the maximum - and minimum values for the weighed objects. These values will then be - taken into account in the normalization step, instead of taking the values - from the calculated weights. - """ - - minval = None - maxval = None - - def weight_multiplier(self): - """How weighted this weigher should be. - - Override this method in a subclass, so that the returned value is - read from a configuration option to permit operators specify a - multiplier for the weigher. - """ - return 1.0 - - @abc.abstractmethod - def _weigh_object(self, obj, weight_properties): - """Override in a subclass to specify a weight for a specific - object. - """ - - def weigh_objects(self, weighed_obj_list, weight_properties): - """Weigh multiple objects. - - Override in a subclass if you need access to all objects in order - to calculate weights. Do not modify the weight of an object here, - just return a list of weights. - """ - # Calculate the weights - weights = [] - for obj in weighed_obj_list: - weight = self._weigh_object(obj.obj, weight_properties) - - # Record the min and max values if they are None. If they anything - # but none we assume that the weigher has set them - if self.minval is None: - self.minval = weight - if self.maxval is None: - self.maxval = weight - - if weight < self.minval: - self.minval = weight - elif weight > self.maxval: - self.maxval = weight - - weights.append(weight) - - return weights - - -class BaseWeightHandler(base_handler.BaseHandler): - object_class = WeighedObject - - def get_weighed_objects(self, weigher_classes, obj_list, - weighing_properties): - """Return a sorted (descending), normalized list of WeighedObjects.""" - - if not obj_list: - return [] - - weighed_objs = [self.object_class(obj, 0.0) for obj in obj_list] - for weigher_cls in weigher_classes: - weigher = weigher_cls() - weights = weigher.weigh_objects(weighed_objs, weighing_properties) - - # Normalize the weights - weights = normalize(weights, - minval=weigher.minval, - maxval=weigher.maxval) - - for i, weight in enumerate(weights): - obj = weighed_objs[i] - obj.weight += weigher.weight_multiplier() * weight - - return sorted(weighed_objs, key=lambda x: x.weight, reverse=True) |