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#
# -------------------------------------------------------------------------
# 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_config import cfg
from oslo_log import log
import copy
import time
from conductor import service
# from conductor.solver.optimizer import decision_path as dpath
# from conductor.solver.optimizer import best_first
# from conductor.solver.optimizer import greedy
from conductor.solver.optimizer import fit_first
from conductor.solver.optimizer import random_pick
from conductor.solver.request import demand
from conductor.solver.triage_tool.triage_data import TriageData
LOG = log.getLogger(__name__)
CONF = cfg.CONF
SOLVER_OPTS = [
]
CONF.register_opts(SOLVER_OPTS, group='solver')
class Optimizer(object):
# FIXME(gjung): _requests should be request (no underscore, one item)
def __init__(self, conf, _requests=None, _begin_time=None):
self.conf = conf
# start time of solving the plan
if _begin_time is not None:
self._begin_time = _begin_time
# self.search = greedy.Greedy(self.conf)
self.search = None
# self.search = best_first.BestFirst(self.conf)
if _requests is not None:
self.requests = _requests
# Were the 'simulators' ever used? It doesn't look like this.
# Since solver/simulator code needs cleansing before being moved to ONAP,
# I see no value for having this piece of code which is not letting us do
# that cleanup. Also, Shankar has confirmed solver/simulators folder needs
# to go away. Commenting out for now - may be should be removed permanently.
# Shankar (TODO).
# else:
# ''' for simulation '''
# req_sim = request_simulator.RequestSimulator(self.conf)
# req_sim.generate_requests()
# self.requests = req_sim.requests
def get_solution(self, num_solutions):
LOG.debug("search start")
for rk in self.requests:
request = self.requests[rk]
LOG.debug("--- request = {}".format(rk))
decision_list = list()
LOG.debug("1. sort demands")
demand_list = self._sort_demands(request)
for d in demand_list:
LOG.debug(" demand = {}".format(d.name))
LOG.debug("2. search")
while (num_solutions == 'all' or num_solutions > 0):
LOG.debug("searching for the solution {}".format(len(decision_list) + 1))
st = time.time()
_copy_demand_list = copy.deepcopy(demand_list)
if not request.objective.goal:
LOG.debug("No objective function is provided. "
"Random pick algorithm is used")
self.search = random_pick.RandomPick(self.conf)
best_path = self.search.search(demand_list, request)
else:
LOG.debug("Fit first algorithm is used")
self.search = fit_first.FitFirst(self.conf)
best_path = self.search.search(demand_list,
request.objective, request)
if best_path is not None:
self.search.print_decisions(best_path)
else:
LOG.debug("no solution found")
break
LOG.debug("search delay = {} sec".format(time.time() - st))
# add the current solution to decision_list
decision_list.append(best_path.decisions)
#remove the candidate with "uniqueness = true"
demand_list = copy.deepcopy(_copy_demand_list)
self._remove_unique_candidate(request, best_path, demand_list)
if num_solutions != 'all':
num_solutions -= 1
self.search.triageSolver.getSolution(decision_list)
return decision_list
def _remove_unique_candidate(self, _request, current_decision, demand_list):
# This method is to remove previous solved/used candidate from consideration
# when Conductor needs to provide multiple solutions to the user/client
for demand_name, candidate_attr in current_decision.decisions.items():
candidate_uniqueness = candidate_attr.get('uniqueness')
if candidate_uniqueness and candidate_uniqueness == 'true':
# if the candidate uniqueness is 'false', then remove
# that solved candidate from the translated candidates list
_request.demands[demand_name].resources.pop(candidate_attr.get('candidate_id'))
# update the demand_list
for demand in demand_list:
if(getattr(demand, 'name') == demand_name):
demand.resources = _request.demands[demand_name].resources
def _sort_demands(self, _request):
LOG.debug(" _sort_demands")
demand_list = []
# first, find loc-demand dependencies
# using constraints and objective functions
open_demand_list = []
for key in _request.constraints:
c = _request.constraints[key]
if c.constraint_type == "access_distance":
for dk in c.demand_list:
if _request.demands[dk].sort_base != 1:
_request.demands[dk].sort_base = 1
open_demand_list.append(_request.demands[dk])
for op in _request.objective.operand_list:
if op.function.func_type == "latency_between": #TODO do i need to include the region_group here?
if isinstance(op.function.loc_a, demand.Location):
if _request.demands[op.function.loc_z.name].sort_base != 1:
_request.demands[op.function.loc_z.name].sort_base = 1
open_demand_list.append(op.function.loc_z)
elif isinstance(op.function.loc_z, demand.Location):
if _request.demands[op.function.loc_a.name].sort_base != 1:
_request.demands[op.function.loc_a.name].sort_base = 1
open_demand_list.append(op.function.loc_a)
elif op.function.func_type == "distance_between":
if isinstance(op.function.loc_a, demand.Location):
if _request.demands[op.function.loc_z.name].sort_base != 1:
_request.demands[op.function.loc_z.name].sort_base = 1
open_demand_list.append(op.function.loc_z)
elif isinstance(op.function.loc_z, demand.Location):
if _request.demands[op.function.loc_a.name].sort_base != 1:
_request.demands[op.function.loc_a.name].sort_base = 1
open_demand_list.append(op.function.loc_a)
if len(open_demand_list) == 0:
init_demand = self._exist_not_sorted_demand(_request.demands)
open_demand_list.append(init_demand)
# second, find demand-demand dependencies
while True:
d_list = self._get_depended_demands(open_demand_list, _request)
for d in d_list:
demand_list.append(d)
init_demand = self._exist_not_sorted_demand(_request.demands)
if init_demand is None:
break
open_demand_list.append(init_demand)
return demand_list
def _get_depended_demands(self, _open_demand_list, _request):
demand_list = []
while True:
if len(_open_demand_list) == 0:
break
d = _open_demand_list.pop(0)
if d.sort_base != 1:
d.sort_base = 1
demand_list.append(d)
for key in _request.constraints:
c = _request.constraints[key]
# FIXME(snarayanan): "aic" only to be known by conductor-data
if c.constraint_type == "aic_distance":
if d.name in c.demand_list:
for dk in c.demand_list:
if dk != d.name and \
_request.demands[dk].sort_base != 1:
_request.demands[dk].sort_base = 1
_open_demand_list.append(
_request.demands[dk])
for op in _request.objective.operand_list:
if op.function.func_type == "latency_between": #TODO
if op.function.loc_a.name == d.name:
if op.function.loc_z.name in \
_request.demands.keys():
if _request.demands[
op.function.loc_z.name].sort_base != 1:
_request.demands[
op.function.loc_z.name].sort_base = 1
_open_demand_list.append(op.function.loc_z)
elif op.function.loc_z.name == d.name:
if op.function.loc_a.name in \
_request.demands.keys():
if _request.demands[
op.function.loc_a.name].sort_base != 1:
_request.demands[
op.function.loc_a.name].sort_base = 1
_open_demand_list.append(op.function.loc_a)
elif op.function.func_type == "distance_between":
if op.function.loc_a.name == d.name:
if op.function.loc_z.name in \
_request.demands.keys():
if _request.demands[
op.function.loc_z.name].sort_base != 1:
_request.demands[
op.function.loc_z.name].sort_base = 1
_open_demand_list.append(op.function.loc_z)
elif op.function.loc_z.name == d.name:
if op.function.loc_a.name in \
_request.demands.keys():
if _request.demands[
op.function.loc_a.name].sort_base != 1:
_request.demands[
op.function.loc_a.name].sort_base = 1
_open_demand_list.append(op.function.loc_a)
return demand_list
def _exist_not_sorted_demand(self, _demands):
not_sorted_demand = None
for key in _demands:
demand = _demands[key]
if demand.sort_base != 1:
not_sorted_demand = demand
break
return not_sorted_demand
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