139 lines
5.8 KiB
Python
139 lines
5.8 KiB
Python
# Copyright (c) 2015 Cisco Systems, Inc.
|
|
# 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.
|
|
|
|
import operator
|
|
|
|
from nova.scheduler import solvers as scheduler_solver
|
|
|
|
|
|
class FastSolver(scheduler_solver.BaseHostSolver):
|
|
|
|
def __init__(self):
|
|
super(FastSolver, self).__init__()
|
|
self.cost_classes = self._get_cost_classes()
|
|
self.constraint_classes = self._get_constraint_classes()
|
|
|
|
def _get_cost_matrix(self, hosts, filter_properties):
|
|
num_hosts = len(hosts)
|
|
num_instances = filter_properties.get('num_instances', 1)
|
|
solver_cache = filter_properties['solver_cache']
|
|
# initialize cost matrix
|
|
cost_matrix = [[0 for j in xrange(num_instances)]
|
|
for i in xrange(num_hosts)]
|
|
solver_cache['cost_matrix'] = cost_matrix
|
|
cost_objects = [cost() for cost in self.cost_classes]
|
|
cost_objects.sort(key=lambda cost: cost.precedence)
|
|
precedence_level = 0
|
|
for cost_object in cost_objects:
|
|
if cost_object.precedence > precedence_level:
|
|
# update cost matrix in the solver cache
|
|
solver_cache['cost_matrix'] = cost_matrix
|
|
precedence_level = cost_object.precedence
|
|
cost_multiplier = cost_object.cost_multiplier()
|
|
this_cost_mat = cost_object.get_cost_matrix(hosts,
|
|
filter_properties)
|
|
if not this_cost_mat:
|
|
continue
|
|
cost_matrix = [[cost_matrix[i][j] +
|
|
this_cost_mat[i][j] * cost_multiplier
|
|
for j in xrange(num_instances)]
|
|
for i in xrange(num_hosts)]
|
|
# update cost matrix in the solver cache
|
|
solver_cache['cost_matrix'] = cost_matrix
|
|
|
|
return cost_matrix
|
|
|
|
def _get_constraint_matrix(self, hosts, filter_properties):
|
|
num_hosts = len(hosts)
|
|
num_instances = filter_properties.get('num_instances', 1)
|
|
solver_cache = filter_properties['solver_cache']
|
|
# initialize constraint_matrix
|
|
constraint_matrix = [[True for j in xrange(num_instances)]
|
|
for i in xrange(num_hosts)]
|
|
solver_cache['constraint_matrix'] = constraint_matrix
|
|
constraint_objects = [cons() for cons in self.constraint_classes]
|
|
constraint_objects.sort(key=lambda cons: cons.precedence)
|
|
precedence_level = 0
|
|
for constraint_object in constraint_objects:
|
|
if constraint_object.precedence > precedence_level:
|
|
# update constraint matrix in the solver cache
|
|
solver_cache['constraint_matrix'] = constraint_matrix
|
|
precedence_level = constraint_object.precedence
|
|
this_cons_mat = constraint_object.get_constraint_matrix(hosts,
|
|
filter_properties)
|
|
if not this_cons_mat:
|
|
continue
|
|
constraint_matrix = [[constraint_matrix[i][j] &
|
|
this_cons_mat[i][j] for j in xrange(num_instances)]
|
|
for i in xrange(num_hosts)]
|
|
# update constraint matrix in the solver cache
|
|
solver_cache['constraint_matrix'] = constraint_matrix
|
|
|
|
return constraint_matrix
|
|
|
|
def solve(self, hosts, filter_properties):
|
|
host_instance_combinations = []
|
|
|
|
num_instances = filter_properties['num_instances']
|
|
num_hosts = len(hosts)
|
|
|
|
instance_uuids = filter_properties.get('instance_uuids') or [
|
|
'(unknown_uuid)' + str(i) for i in xrange(num_instances)]
|
|
|
|
filter_properties.setdefault('solver_cache', {})
|
|
filter_properties['solver_cache'].update(
|
|
{'cost_matrix': [],
|
|
'constraint_matrix': []})
|
|
|
|
cost_matrix = self._get_cost_matrix(hosts, filter_properties)
|
|
constraint_matrix = self._get_constraint_matrix(hosts,
|
|
filter_properties)
|
|
|
|
placement_cost_tuples = []
|
|
for i in xrange(num_hosts):
|
|
for j in xrange(num_instances):
|
|
if constraint_matrix[i][j]:
|
|
host_idx = i
|
|
inst_num = j + 1
|
|
cost_val = cost_matrix[i][j]
|
|
placement_cost_tuples.append(
|
|
(host_idx, inst_num, cost_val))
|
|
|
|
sorted_placement_costs = sorted(placement_cost_tuples,
|
|
key=operator.itemgetter(2))
|
|
|
|
host_inst_alloc = [0 for i in xrange(num_hosts)]
|
|
allocated_inst_num = 0
|
|
for (host_idx, inst_num, cost_val) in sorted_placement_costs:
|
|
delta = inst_num - host_inst_alloc[host_idx]
|
|
|
|
if (delta <= 0) or (allocated_inst_num + delta > num_instances):
|
|
continue
|
|
|
|
host_inst_alloc[host_idx] += delta
|
|
allocated_inst_num += delta
|
|
|
|
if allocated_inst_num == num_instances:
|
|
break
|
|
|
|
instances_iter = iter(instance_uuids)
|
|
for i in xrange(len(host_inst_alloc)):
|
|
num = host_inst_alloc[i]
|
|
for n in xrange(num):
|
|
host_instance_combinations.append(
|
|
(hosts[i], instances_iter.next()))
|
|
|
|
return host_instance_combinations
|