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#!/bin/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.
#
# -------------------------------------------------------------------------
#
from oslo_log import log
import sys
from conductor.solver.optimizer import decision_path as dpath
from conductor.solver.optimizer import search
LOG = log.getLogger(__name__)
class FitFirst(search.Search):
def __init__(self, conf):
search.Search.__init__(self, conf)
def search(self, _demand_list, _objective, _request):
decision_path = dpath.DecisionPath()
decision_path.set_decisions({})
# Begin the recursive serarch
return self._find_current_best(
_demand_list, _objective, decision_path, _request)
def _find_current_best(self, _demand_list, _objective,
_decision_path, _request):
# _demand_list is common across all recursions
if len(_demand_list) == 0:
LOG.debug("search done")
return _decision_path
# get next demand to resolve
demand = _demand_list.pop(0)
LOG.debug("demand = {}".format(demand.name))
_decision_path.current_demand = demand
# call constraints to whittle initial candidates
# candidate_list meets all constraints for the demand
candidate_list = self._solve_constraints(_decision_path, _request)
# find the best candidate among the list
# bound_value keeps track of the max value discovered
# thus far for the _decision_path. For every demand
# added to the _decision_path bound_value will be set
# to a really large value to begin with
bound_value = 0.0
version_value = "0.0"
if "min" in _objective.goal:
bound_value = sys.float_info.max
# Start recursive search
while True:
best_resource = None
# Find best candidate that optimizes the cost for demand.
# The candidate list can be empty if the constraints
# rule out all candidates
for candidate in candidate_list:
_decision_path.decisions[demand.name] = candidate
_objective.compute(_decision_path, _request)
# this will set the total_value of the _decision_path
# thus far up to the demand
if _objective.goal is None:
best_resource = candidate
elif _objective.goal == "min_cloud_version":
# convert the unicode to string
candidate_version = candidate \
.get("cloud_region_version").encode('utf-8')
if _decision_path.total_value < bound_value or \
(_decision_path.total_value == bound_value and
self._compare_version(candidate_version,
version_value) > 0):
bound_value = _decision_path.total_value
version_value = candidate_version
best_resource = candidate
elif _objective.goal == "min":
# if the path value is less than bound value
# we have found the better candidate
if _decision_path.total_value < bound_value:
# relax the bound_value to the value of
# the path - this will ensure a future
# candidate will be picked only if it has
# a value lesser than the current best candidate
bound_value = _decision_path.total_value
best_resource = candidate
# Rollback if we don't have any candidate picked for
# the demand.
if best_resource is None:
LOG.debug("no resource, rollback")
# Put the current demand (which failed to find a
# candidate) back in the list so that it can be picked
# up in the next iteration of the recursion
_demand_list.insert(0, demand)
return None # return None back to the recursion
else:
# best resource is found, add to the decision path
_decision_path.decisions[demand.name] = best_resource
_decision_path.total_value = bound_value
# Begin the next recursive call to find candidate
# for the next demand in the list
decision_path = self._find_current_best(
_demand_list, _objective, _decision_path, _request)
# The point of return from the previous recursion.
# If the call returns no candidates, no solution exists
# in that path of the decision tree. Rollback the
# current best_resource and remove it from the list
# of potential candidates.
if decision_path is None:
candidate_list.remove(best_resource)
# reset bound_value to a large value so that
# the next iteration of the current recursion
# will pick the next best candidate, which
# will have a value larger than the current
# bound_value (proof by contradiction:
# it cannot have a smaller value, if it wasn't
# the best_resource.
if _objective.goal == "min":
bound_value = sys.float_info.max
else:
# A candidate was found for the demand, and
# was added to the decision path. Return current
# path back to the recursion.
return decision_path
def _compare_version(self, version1, version2):
version1 = version1.split('.')
version2 = version2.split('.')
for i in range(max(len(version1), len(version2))):
v1 = int(version1[i]) if i < len(version1) else 0
v2 = int(version2[i]) if i < len(version2) else 0
if v1 > v2:
return 1
elif v1 < v2:
return -1
return 0
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