OpenStack Block Storage (Cinder)
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# 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
from oslo_log import log as logging
from cinder.scheduler import base_handler
LOG = logging.getLogger(__name__)
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)
class BaseWeigher(object, metaclass=abc.ABCMeta):
"""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
LOG.debug("Weigher %(cls_name)s returned, "
"weigher value is {max: %(maxval)s, min: %(minval)s}",
{'cls_name': weigher_cls.__name__,
'maxval': weigher.maxval,
'minval': weigher.minval})
return sorted(weighed_objs, key=lambda x: x.weight, reverse=True)