b1cef8c4a0
Implemented layering so it will allow to have multiple solver engines. Implements blueprint: dynamic-allocation Change-Id: I7ed1ec0216fb9778b4fa5be4fb4f6141a0e26fc9
332 lines
12 KiB
Python
332 lines
12 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright 2016 Mirantis, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# 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, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import itertools
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from bareon_allocator.sequences import CrossSumInequalitySequence
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from bareon_allocator.solvers.linear_program import LinearProgram
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class LinearProgramCreator(object):
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"""Creates LinearProgram based on DynamicSchema object."""
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NONE_ORDER_COEFFICIENT = 1
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SET_COEFFICIENT = 2
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def __init__(self,
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dynamic_schema,
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weight_sets_criteria=[
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'min_size',
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'max_size',
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'best_with_disks']):
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"""Initializes the object.
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:param dynamic_schema: :class:`DynamicSchema` object
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:param weight_sets_criteria: a list of strings, which represents
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attributes of spaces based on which sets will be created to
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make equations.
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"""
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self.weight_sets_criteria = weight_sets_criteria
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self.disks = dynamic_schema.disks
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self.spaces = dynamic_schema.spaces
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self.spaces_len = len(self.spaces)
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self.disks_len = len(self.disks)
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# For each space, x (size of the space) is represented
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# for each disk as separate variable, so for each
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# disk we have len(spaces) * len(disks) sizes
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self.x_amount = self.disks_len * self.spaces_len
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def linear_program(self):
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"""Returns linear program object
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:return: :class:`LinearProgram` linear program object
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"""
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space_size_equation = self._make_space_size_constraints()
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disk_size_equation = self._make_disk_size_constraints()
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equality_weight_equation = self._make_weight_constraints()
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# Merge both equality and constraint vectors into a single dictionary
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equations = self._merge_equations(space_size_equation,
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disk_size_equation)
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equations = self._merge_equations(equations,
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equality_weight_equation)
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objective_coefficients = self._make_objective_function_coefficient()
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return LinearProgram(
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x_amount=self.x_amount,
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optimization_type=LinearProgram.MAXIMIZE,
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objective_function_coefficients=objective_coefficients,
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**equations)
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def _make_space_size_constraints(self):
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"""Create min and max constraints for each space.
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In case of 2 disks and 2 spaces
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For first space min_size >= 10 and max_size <= 20
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1 * x1 + 0 * x2 + 1 * x3 + 0 * x4 >= 10
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1 * x1 + 0 * x2 + 1 * x3 + 0 * x4 <= 20
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For second space min_size >= 15 and max_size <= 30
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0 * x1 + 1 * x2 + 0 * x3 + 1 * x4 >= 15
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0 * x1 + 1 * x2 + 0 * x3 + 1 * x4 <= 30
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"""
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constraint_equation = {
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'lower_constraint_matrix': [],
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'lower_constraint_vector': [],
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'upper_constraint_matrix': [],
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'upper_constraint_vector': []}
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for space_idx, space in enumerate(self.spaces):
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row = self._make_matrix_row()
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for disk_idx in range(self.disks_len):
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row[disk_idx * self.spaces_len + space_idx] = 1
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if space.min_size is not None:
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constraint_equation['lower_constraint_matrix'].append(
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row)
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constraint_equation['lower_constraint_vector'].append(
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space.min_size)
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if space.max_size is not None:
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constraint_equation['upper_constraint_matrix'].append(
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row)
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constraint_equation['upper_constraint_vector'].append(
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space.max_size)
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return constraint_equation
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def _merge_equations(self, eq1, eq2):
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"""Merges two equations into a single dictionary of equations.
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:param eq1: equation dictionary, where key is a name of equation and
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value is a vector or matrix
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:param eq2: same as eq1
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:return: merged equation
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"""
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result = {}
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all_keys = set(eq1.keys() + eq2.keys())
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for key in all_keys:
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if eq2.get(key) and eq1.get(key):
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# Merge if both have values
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result[key] = eq1[key] + eq2[key]
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elif eq2.get(key):
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result[key] = eq2[key]
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elif eq1.get(key):
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result[key] = eq1[key]
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return result
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def _make_disk_size_constraints(self):
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"""Creates equations based on disk sizes.
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So solver will not allocate more then "disk size" space for each disk.
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In case of 2 spaces and 3 disks the result should be:
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[[1, 1, 0, 0, 0, 0],
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[0, 0, 1, 1, 0, 0],
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[0, 0, 0, 0, 1, 1]]
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Explanation of the first row
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[1, - x1 multiplier, size of space 1 on the first disk
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1, - x2 multiplier, size of space 2 on the first disk
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0, - x3 multiplier, size of space 1 on 2nd disk, 0 for the first
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0, - x4 multiplier, size of space 2 on 2nd disk, 0 for the first
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0, - x5 multiplier, size of space 1 on 3rd disk, 0 for the first
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0] - x6 multiplier, size of space 2 on 3rd disk, 0 for the first
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:return: equations, where key is a name of equation, value is a list
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or vector
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"""
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constraint_equation = {
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'upper_constraint_matrix': [],
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'upper_constraint_vector': []}
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for disk_idx in range(self.disks_len):
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row = self._make_matrix_row()
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for space_idx, space in enumerate(self.spaces):
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row[disk_idx * self.spaces_len + space_idx] = 1
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constraint_equation['upper_constraint_matrix'].append(row)
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constraint_equation['upper_constraint_vector'].append(
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self.disks[disk_idx].size)
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return constraint_equation
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def _make_weight_constraints(self):
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"""Refresh weight.
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Create weight constraints for spaces which have same
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max constraint or for those which don't have it at all.
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Lets say, second space is equal to the third, as the result
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we will have next equation:
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0 * x1 + (1 / weight) * x2 + (-1 / weight) * x3 +
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0 * x4 + (1 / weight) * x5 + (-1 / weight) * x6 = 0
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See "Weight" section in the documentation for details:
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http://bareon-allocator.readthedocs.org/en
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/latest/architecture.html#weight
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TODO(eli): it should be not equality, but inequality with some
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range, so we will not get fails every time exact constraint cannot be
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satisfied.
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"""
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weight_equations = {
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'equality_constraint_matrix': [],
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'equality_constraint_vector': []}
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weight_spaces_sets = self._get_spaces_sets_by(
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self.weight_sets_criteria)
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for spaces_set in weight_spaces_sets:
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# Don't set weight if there is less than one space in the set
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if len(spaces_set) < 2:
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continue
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first_weight = spaces_set[0].weight
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first_space_idx = self.spaces.index(spaces_set[0])
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for space in spaces_set[1:]:
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row = self._make_matrix_row()
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# If weight is 0, it doesn't make sense to set for such
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# space a weight
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if space.weight == 0:
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continue
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space_idx = self.spaces.index(space)
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for disk_idx in range(self.disks_len):
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row_i = disk_idx * len(self.spaces)
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row[row_i + first_space_idx] = 1.0 / first_weight
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row[row_i + space_idx] = -1.0 / space.weight
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weight_equations['equality_constraint_matrix'].append(row)
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weight_equations['equality_constraint_vector'].append(0)
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return weight_equations
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def _make_objective_function_coefficient(self):
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"""Creates objective function coefficients.
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We want spaces to be allocated on disks in order which user
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specified them in the schema. In order to do that, we set
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coefficients higher for those spaces which defined earlier in the
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list.
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:return: a vector of coefficients
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"""
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# Instead of just Integer sequence special type of sequence is being
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# used, see documentation [1] for details.
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# Every order coefficient should be between 0 and 1 (not included),
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# in order to aviod having 1st element equal to 1, sequence should be
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# started from 2nd element.
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#
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# [1] http://bareon-allocator.readthedocs.org/en
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# /latest/architecture.html#ordering
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seq = CrossSumInequalitySequence(self.x_amount + 1)
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next(seq, None)
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coefficients = [1.0 / i for i in seq]
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space_sets = self._get_spaces_sets_by(['best_with_disks'])
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no_best_disks = self._get_empty_sets_disks_ids(['best_with_disks'])
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for i_set, space_set in enumerate(space_sets):
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for space in space_set:
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s_i = self.spaces.index(space)
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for d_i, disk in enumerate(self.disks):
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c_i = self.spaces_len * d_i + s_i
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# Set constant for none_order spaces
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if space.none_order:
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coefficients[c_i] = self.NONE_ORDER_COEFFICIENT
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continue
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# If space does not belong to any set, order coefficient
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# will be left without any additional coefficients.
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if (space.best_with_disks and
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disk.id in space.best_with_disks):
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# If the space has "best disks" and current disk is
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# in best disks list, add coefficient.
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coefficients[c_i] += self.SET_COEFFICIENT
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elif (not space.best_with_disks and
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disk.id in no_best_disks):
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# If the space does *not* have "best disks" and
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# current disk is not in the list of "best disks" of
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# any space, add set coefficient.
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coefficients[c_i] += self.SET_COEFFICIENT
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# By default the algorithm tries to minimize the solution
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# we should invert sign, in order to make it a maximization
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# function, because we want disks to be maximally allocated.
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return [-c for c in coefficients]
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def _get_empty_sets_disks_ids(self, criteria):
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"""Get disks indexes which do not belong to set of any spaces.
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:param criteria: a list of strings, with criteria by which sets has
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to be created
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:return: a list of disks indexes
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"""
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all_disks_ids = [d.id for d in self.disks]
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used_disks_ids = []
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for k, space in self._get_sets_by(criteria):
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if k[0]:
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used_disks_ids.extend(list(k[0]))
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return list(set(all_disks_ids) - set(used_disks_ids))
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def _get_spaces_sets_by(self, criteria):
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"""Get all spaces which are used for sets.
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:param criteria: a list of strings with attributes by which sets has
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to be created
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:return: a list of spaces lists, where each list item is represents
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a set
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"""
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return [i[1] for i in self._get_sets_by(criteria)]
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def _get_sets_by(self, criteria):
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"""Makes sets based on criteria from space attributes.
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:param criteria: a list of strings with attributes by which sets has
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to be created
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:return: a list of tuples, where first item are criteria, second
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item is a list of spaces
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"""
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def get_values(space):
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return [getattr(space, c, None) for c in criteria]
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grouped_spaces = itertools.groupby(
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sorted(self.spaces, key=get_values),
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key=get_values)
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return [(k, list(v)) for k, v in grouped_spaces]
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def _make_matrix_row(self):
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"""Make a matrix row
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:return: a vector where all the items are 0
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"""
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return [0] * self.x_amount
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