conductor/conductor/solver/optimizer/greedy.py

   1 #!/bin/python
   2 #
   3 # -------------------------------------------------------------------------
   4 #   Copyright (c) 2015-2017 AT&T Intellectual Property
   5 #   Copyright (C) 2020 Wipro Limited.
   6 #
   7 #   Licensed under the Apache License, Version 2.0 (the "License");
   8 #   you may not use this file except in compliance with the License.
   9 #   You may obtain a copy of the License at
  10 #
  11 #       http://www.apache.org/licenses/LICENSE-2.0
  12 #
  13 #   Unless required by applicable law or agreed to in writing, software
  14 #   distributed under the License is distributed on an "AS IS" BASIS,
  15 #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  16 #   See the License for the specific language governing permissions and
  17 #   limitations under the License.
  18 #
  19 # -------------------------------------------------------------------------
  20 #
  21
  22
  23 from oslo_log import log
  24 import sys
  25
  26 from conductor.solver.optimizer import decision_path as dpath
  27 from conductor.solver.optimizer import search
  28
  29 LOG = log.getLogger(__name__)
  30
  31
  32 class Greedy(search.Search):
  33
  34     def __init__(self, conf):
  35         search.Search.__init__(self, conf)
  36
  37     def search(self, _demand_list, _objective):
  38         decision_path = dpath.DecisionPath()
  39         decision_path.set_decisions({})
  40
  41         for demand in _demand_list:
  42             LOG.debug("demand = {}".format(demand.name))
  43
  44             decision_path.current_demand = demand
  45             candidate_list = self._solve_constraints(decision_path)
  46
  47             bound_value = 0.0
  48             if _objective.goal == "min":
  49                 bound_value = sys.float_info.max
  50
  51             best_resource = None
  52             for candidate in candidate_list:
  53                 decision_path.decisions[demand.name] = candidate
  54                 _objective.compute(decision_path)
  55                 if _objective.goal == "min":
  56                     if decision_path.total_value < bound_value:
  57                         bound_value = decision_path.total_value
  58                         best_resource = candidate
  59                 elif _objective.goal == "max":
  60                     if decision_path.total_value > bound_value:
  61                         bound_value = decision_path.total_value
  62                         best_resource = candidate
  63
  64             if best_resource is not None:
  65                 decision_path.decisions[demand.name] = best_resource
  66                 decision_path.total_value = bound_value
  67             else:
  68                 return None
  69
  70         return decision_path