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#
# -------------------------------------------------------------------------
# Copyright (c) 2015-2018 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 conductor.solver.request import demand
# from conductor.solver.resource import region
# from conductor.solver.resource import service
class Objective(object):
def __init__(self):
self.goal = None
self.operation = None
self.operand_list = []
def compute(self, _decision_path, _request):
value = 0.0
for op in self.operand_list:
if self.operation == "sum":
value += op.compute(_decision_path, _request)
_decision_path.cumulated_value = value
_decision_path.total_value = \
_decision_path.cumulated_value + \
_decision_path.heuristic_to_go_value
class Operand(object):
def __init__(self):
self.operation = None
self.weight = 0
self.function = None
def compute(self, _decision_path, _request):
value = 0.0
cei = _request.cei
if self.function.func_type == "latency_between":
if isinstance(self.function.loc_a, demand.Location):
if self.function.loc_z.name in \
list(_decision_path.decisions.keys()): # Python 3 Conversion -- dict object to list object
resource = \
_decision_path.decisions[self.function.loc_z.name]
candidate_cost = resource.get('cost')
loc = None
# if isinstance(resource, region.Region):
# loc = resource.location
# elif isinstance(resource, service.Service):
# loc = resource.region.location
loc = cei.get_candidate_location(resource)
value = \
self.function.compute(self.function.loc_a.value, loc) \
+ candidate_cost
elif isinstance(self.function.loc_z, demand.Location):
if self.function.loc_a.name in \
list(_decision_path.decisions.keys()): # Python 3 Conversion -- dict object to list object
resource = \
_decision_path.decisions[self.function.loc_a.name]
candidate_cost = resource.get('cost')
loc = None
# if isinstance(resource, region.Region):
# loc = resource.location
# elif isinstance(resource, service.Service):
# loc = resource.region.location
loc = cei.get_candidate_location(resource)
value = \
self.function.compute(self.function.loc_z.value, loc) \
+ candidate_cost
else:
if self.function.loc_a.name in \
list(_decision_path.decisions.keys()) and \
self.function.loc_z.name in \
list(_decision_path.decisions.keys()): # Python 3 Conversion -- dict object to list object
resource_a = \
_decision_path.decisions[self.function.loc_a.name]
loc_a = None
# if isinstance(resource_a, region.Region):
# loc_a = resource_a.location
# elif isinstance(resource_a, service.Service):
# loc_a = resource_a.region.location
loc_a = cei.get_candidate_location(resource_a)
resource_z = \
_decision_path.decisions[self.function.loc_z.name]
loc_z = None
# if isinstance(resource_z, region.Region):
# loc_z = resource_z.location
# elif isinstance(resource_z, service.Service):
# loc_z = resource_z.region.location
loc_z = cei.get_candidate_location(resource_z)
value = self.function.compute(loc_a, loc_z)
elif self.function.func_type == "hpa_score":
# Currently only minimize objective goal is supported
# Higher the HPA score the better.
# Invert HPA Score if goal is minimize
invert = -1
#
# if self.function.goal == "max":
# invert = 1
for demand_name, candidate_info in _decision_path.decisions.items():
hpa_score = invert * float(candidate_info.get('hpa_score', 0))
value += hpa_score
elif self.function.func_type == "distance_between":
if isinstance(self.function.loc_a, demand.Location):
if self.function.loc_z.name in \
list(_decision_path.decisions.keys()): # Python 3 Conversion -- dict object to list object
resource = \
_decision_path.decisions[self.function.loc_z.name]
candidate_cost = resource.get('cost')
loc = None
# if isinstance(resource, region.Region):
# loc = resource.location
# elif isinstance(resource, service.Service):
# loc = resource.region.location
loc = cei.get_candidate_location(resource)
value = \
self.function.compute(self.function.loc_a.value, loc) \
+ candidate_cost
elif isinstance(self.function.loc_z, demand.Location):
if self.function.loc_a.name in \
list(_decision_path.decisions.keys()): # Python 3 Conversion -- dict object to list object
resource = \
_decision_path.decisions[self.function.loc_a.name]
candidate_cost = resource.get('cost')
loc = None
# if isinstance(resource, region.Region):
# loc = resource.location
# elif isinstance(resource, service.Service):
# loc = resource.region.location
loc = cei.get_candidate_location(resource)
value = \
self.function.compute(self.function.loc_z.value, loc) \
+ candidate_cost
else:
if self.function.loc_a.name in \
list(_decision_path.decisions.keys()) and \
self.function.loc_z.name in \
list(_decision_path.decisions.keys()): # Python 3 Conversion -- dict object to list object
resource_a = \
_decision_path.decisions[self.function.loc_a.name]
loc_a = None
# if isinstance(resource_a, region.Region):
# loc_a = resource_a.location
# elif isinstance(resource_a, service.Service):
# loc_a = resource_a.region.location
loc_a = cei.get_candidate_location(resource_a)
resource_z = \
_decision_path.decisions[self.function.loc_z.name]
loc_z = None
# if isinstance(resource_z, region.Region):
# loc_z = resource_z.location
# elif isinstance(resource_z, service.Service):
# loc_z = resource_z.region.location
loc_z = cei.get_candidate_location(resource_z)
value = self.function.compute(loc_a, loc_z)
if self.operation == "product":
value *= self.weight
return value
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