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#!/usr/bin/env python
#
# -------------------------------------------------------------------------
# Copyright (c) 2015-2017 AT&T Intellectual Property
#
# 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 oslo_log import log
from conductor.solver.optimizer.constraints import constraint
from conductor.solver.utils import utils
LOG = log.getLogger(__name__)
class CloudDistance(constraint.Constraint):
def __init__(self, _name, _type, _demand_list, _priority=0,
_comparison_operator=operator.le, _threshold=None):
constraint.Constraint.__init__(
self, _name, _type, _demand_list, _priority)
self.distance_threshold = _threshold
self.comparison_operator = _comparison_operator
if len(_demand_list) <= 1:
LOG.debug("Insufficient number of demands.")
raise ValueError
def solve(self, _decision_path, _candidate_list, _request):
conflict_list = []
# get the list of candidates filtered from the previous demand
solved_demands = list() # demands that have been solved in the past
decision_list = list()
future_demands = list() # demands that will be solved in future
# LOG.debug("initial candidate list {}".format(_candidate_list.name))
# find previously made decisions for the constraint's demand list
for demand in self.demand_list:
# decision made for demand
if demand in _decision_path.decisions:
solved_demands.append(demand)
# only one candidate expected per demand in decision path
decision_list.append(
_decision_path.decisions[demand])
else: # decision will be made in future
future_demands.append(demand)
# placeholder for any optimization we may
# want to do for demands in the constraint's demand
# list that conductor will solve in the future
# LOG.debug("decisions = {}".format(decision_list))
# temp copy to iterate
# temp_candidate_list = copy.deepcopy(_candidate_list)
# for candidate in temp_candidate_list:
for candidate in _candidate_list:
# check if candidate satisfies constraint
# for all relevant decisions thus far
is_candidate = True
for filtered_candidate in decision_list:
cei = _request.cei
if not self.comparison_operator(
utils.compute_air_distance(
cei.get_candidate_location(candidate),
cei.get_candidate_location(filtered_candidate)),
self.distance_threshold):
is_candidate = False
if not is_candidate:
if candidate not in conflict_list:
conflict_list.append(candidate)
_candidate_list = \
[c for c in _candidate_list if c not in conflict_list]
# msg = "final candidate list for demand {} is "
# LOG.debug(msg.format(_decision_path.current_demand.name))
# for c in _candidate_list:
# LOG.debug(" " + c.name)
return _candidate_list
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