ce64b6abfc
Move it here so that it can be kept in sync with openstack-common using the new update.py script for code in openstack-common's incubation area. See here for more details: http://wiki.openstack.org/CommonLibrary#Incubation Note: this commit just moves the existing code in Nova with no other changes. A subsequent commit will sync it with latest openstack-common so that it is easier see the new changes. Change-Id: If88d678b1b9bad3d37117de7f7159d7fea8ab4c8
130 lines
4.6 KiB
Python
130 lines
4.6 KiB
Python
# Copyright (c) 2011 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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Least Cost is an algorithm for choosing which host machines to
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provision a set of resources to. The input is a WeightedHost object which
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is decided upon by a set of objective-functions, called the 'cost-functions'.
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The WeightedHost contains a combined weight for each cost-function.
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The cost-function and weights are tabulated, and the host with the least cost
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is then selected for provisioning.
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"""
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from nova import flags
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from nova.openstack.common import cfg
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from nova import log as logging
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LOG = logging.getLogger('nova.scheduler.least_cost')
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least_cost_opts = [
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cfg.ListOpt('least_cost_functions',
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default=[
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'nova.scheduler.least_cost.compute_fill_first_cost_fn'
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],
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help='Which cost functions the LeastCostScheduler should use'),
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cfg.FloatOpt('noop_cost_fn_weight',
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default=1.0,
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help='How much weight to give the noop cost function'),
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cfg.FloatOpt('compute_fill_first_cost_fn_weight',
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default=1.0,
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help='How much weight to give the fill-first cost function'),
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]
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FLAGS = flags.FLAGS
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FLAGS.add_options(least_cost_opts)
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# TODO(sirp): Once we have enough of these rules, we can break them out into a
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# cost_functions.py file (perhaps in a least_cost_scheduler directory)
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class WeightedHost(object):
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"""Reduced set of information about a host that has been weighed.
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This is an attempt to remove some of the ad-hoc dict structures
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previously used."""
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def __init__(self, weight, host_state=None, blob=None, zone=None):
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self.weight = weight
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self.blob = blob
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self.zone = zone
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# Local members. These are not returned outside of the Zone.
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self.host_state = host_state
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def to_dict(self):
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x = dict(weight=self.weight)
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if self.blob:
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x['blob'] = self.blob
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if self.host_state:
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x['host'] = self.host_state.host
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if self.zone:
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x['zone'] = self.zone
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return x
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def noop_cost_fn(host_state, weighing_properties):
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"""Return a pre-weight cost of 1 for each host"""
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return 1
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def compute_fill_first_cost_fn(host_state, weighing_properties):
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"""More free ram = higher weight. So servers will less free
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ram will be preferred."""
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return host_state.free_ram_mb
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def weighted_sum(weighted_fns, host_states, weighing_properties):
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"""Use the weighted-sum method to compute a score for an array of objects.
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Normalize the results of the objective-functions so that the weights are
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meaningful regardless of objective-function's range.
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host_list - [(host, HostInfo()), ...]
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weighted_fns - list of weights and functions like:
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[(weight, objective-functions), ...]
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weighing_properties is an arbitrary dict of values that can influence
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weights.
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Returns a single WeightedHost object which represents the best
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candidate.
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"""
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# Make a grid of functions results.
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# One row per host. One column per function.
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scores = []
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for weight, fn in weighted_fns:
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scores.append([fn(host_state, weighing_properties)
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for host_state in host_states])
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# Adjust the weights in the grid by the functions weight adjustment
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# and sum them up to get a final list of weights.
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adjusted_scores = []
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for (weight, fn), row in zip(weighted_fns, scores):
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adjusted_scores.append([weight * score for score in row])
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# Now, sum down the columns to get the final score. Column per host.
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final_scores = [0.0] * len(host_states)
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for row in adjusted_scores:
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for idx, col in enumerate(row):
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final_scores[idx] += col
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# Super-impose the host_state into the scores so
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# we don't lose it when we sort.
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final_scores = [(final_scores[idx], host_state)
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for idx, host_state in enumerate(host_states)]
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final_scores = sorted(final_scores)
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weight, host_state = final_scores[0] # Lowest score is the winner!
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return WeightedHost(weight, host_state=host_state)
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