66 lines
2.4 KiB
Python
66 lines
2.4 KiB
Python
# Copyright 2012 Anton Beloglazov
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# Copyright 2015 Huawei Technologies Co., Ltd.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Functions for solving NLP problems using the bruteforce method.
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"""
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import nlp
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from terracotta.common import frange
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def solve2(objective, constraint, step, limit):
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"""Solve a maximization problem for 2 states.
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:param objective: The objective function.
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:param constraint: A tuple representing the constraint.
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:param step: The step size.
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:param limit: The maximum value of the variables.
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:return: The problem solution.
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"""
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res_best = 0
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solution = []
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for x in frange(0, limit, step):
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for y in frange(0, limit, step):
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try:
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res = objective(x, y)
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if res > res_best and \
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constraint[1](constraint[0](x, y), constraint[2]):
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res_best = res
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solution = [x, y]
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except ZeroDivisionError:
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pass
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return solution
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def optimize(step, limit, otf, migration_time, ls, p, state_vector,
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time_in_states, time_in_state_n):
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"""Solve a MHOD optimization problem.
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:param step: The step size for the bruteforce algorithm.
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:param limit: The maximum value of the variables.
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:param otf: The OTF parameter.
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:param migration_time: The VM migration time in time steps.
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:param ls: L functions.
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:param p: A matrix of transition probabilities.
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:param state_vector: A state vector.
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:param time_in_states: The total time in all the states in time steps.
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:param time_in_state_n: The total time in the state N in time steps.
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:return: The solution of the problem.
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"""
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objective = nlp.build_objective(ls, state_vector, p)
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constraint = nlp.build_constraint(otf, migration_time, ls, state_vector,
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p, time_in_states, time_in_state_n)
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return solve2(objective, constraint, step, limit)
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