def get_maintenance_scheme_pulp_optimization(de_capacity, dm_capacity,
data_centers) -> tuple:
"""
Uses PuLP library to find minimal values of equations
"""
variables = list()
for i in range(len(data_centers) * 2):
variable = pulp.LpVariable('x{}'.format(i), lowBound=0, cat='Integer')
variables.append(variable)
managers = variables[::2]
engineers = variables[1::2]
problem = pulp.LpProblem("Minimize number of engineers", pulp.LpMinimize)
problem += sum(engineers), 'Z'
problem += sum(managers) == 1
for i, data_center in enumerate(data_centers):
problem += dm_capacity * managers[i] + de_capacity * engineers[
i] >= data_center['servers']
problem.solve()
for variable in managers:
if variable.value():
data_center_id = int(
int(re.search(r'\d+', str(variable)).group()) / 2)
engineer_list = [x.value() for x in engineers]
return int(sum(engineer_list)), data_centers[data_center_id]['name']
def host_solve(self, hosts, instance_uuids, request_spec,
filter_properties):
"""This method returns a list of tuples - (host, instance_uuid)
that are returned by the solver. Here the assumption is that
all instance_uuids have the same requirement as specified in
filter_properties.
"""
host_instance_tuples_list = []
if instance_uuids:
num_instances = len(instance_uuids)
else:
num_instances = request_spec.get('num_instances', 1)
#Setting a unset uuid string for each instance.
instance_uuids = [
'unset_uuid' + str(i) for i in xrange(num_instances)
]
num_hosts = len(hosts)
LOG.debug(_("All Hosts: %s") % [h.host for h in hosts])
for host in hosts:
LOG.debug(_("Host state: %s") % host)
# Create dictionaries mapping host/instance IDs to hosts/instances.
host_ids = ['Host' + str(i) for i in range(num_hosts)]
host_id_dict = dict(zip(host_ids, hosts))
instance_ids = ['Instance' + str(i) for i in range(num_instances)]
instance_id_dict = dict(zip(instance_ids, instance_uuids))
# Create the 'prob' variable to contain the problem data.
prob = pulp.LpProblem("Host Instance Scheduler Problem",
constants.LpMinimize)
# Create the 'variables' matrix to contain the referenced variables.
variables = [[
pulp.LpVariable("IA" + "_Host" + str(i) + "_Instance" + str(j), 0,
1, constants.LpInteger)
for j in range(num_instances)
] for i in range(num_hosts)]
# Get costs and constraints and formulate the linear problem.
self.cost_objects = [cost() for cost in self.cost_classes]
self.constraint_objects = [
constraint(variables, hosts, instance_uuids, request_spec,
filter_properties)
for constraint in self.constraint_classes
]
costs = [[0 for j in range(num_instances)] for i in range(num_hosts)]
for cost_object in self.cost_objects:
cost = cost_object.get_cost_matrix(hosts, instance_uuids,
request_spec, filter_properties)
cost = cost_object.normalize_cost_matrix(cost, 0.0, 1.0)
weight = float(self.cost_weights[cost_object.__class__.__name__])
costs = [[
costs[i][j] + weight * cost[i][j] for j in range(num_instances)
] for i in range(num_hosts)]
prob += (pulp.lpSum([
costs[i][j] * variables[i][j] for i in range(num_hosts)
for j in range(num_instances)
]), "Sum_of_Host_Instance_Scheduling_Costs")
for constraint_object in self.constraint_objects:
coefficient_vectors = constraint_object.get_coefficient_vectors(
variables, hosts, instance_uuids, request_spec,
filter_properties)
variable_vectors = constraint_object.get_variable_vectors(
variables, hosts, instance_uuids, request_spec,
filter_properties)
operations = constraint_object.get_operations(
variables, hosts, instance_uuids, request_spec,
filter_properties)
for i in range(len(operations)):
operation = operations[i]
len_vector = len(variable_vectors[i])
prob += (operation(
pulp.lpSum([
coefficient_vectors[i][j] * variable_vectors[i][j]
for j in range(len_vector)
])), "Costraint_Name_%s" %
constraint_object.__class__.__name__ + "_No._%s" % i)
# The problem is solved using PULP's choice of Solver.
prob.solve()
# Create host-instance tuples from the solutions.
if pulp.LpStatus[prob.status] == 'Optimal':
for v in prob.variables():
if v.name.startswith('IA'):
(host_id,
instance_id) = v.name.lstrip('IA').lstrip('_').split('_')
if v.varValue == 1.0:
host_instance_tuples_list.append(
(host_id_dict[host_id],
instance_id_dict[instance_id]))
return host_instance_tuples_list
def solve(self, hosts, filter_properties):
"""This method returns a list of tuples - (host, instance_uuid)
that are returned by the solver. Here the assumption is that
all instance_uuids have the same requirement as specified in
filter_properties.
"""
host_instance_combinations = []
num_instances = filter_properties['num_instances']
num_hosts = len(hosts)
instance_uuids = filter_properties.get('instance_uuids') or [
'(unknown_uuid)' + str(i) for i in xrange(num_instances)
]
filter_properties.setdefault('solver_cache', {})
filter_properties['solver_cache'].update({
'cost_matrix': [],
'constraint_matrix': []
})
cost_matrix = self._get_cost_matrix(hosts, filter_properties)
cost_matrix = self._adjust_cost_matrix(cost_matrix)
constraint_matrix = self._get_constraint_matrix(
hosts, filter_properties)
# Create dictionaries mapping temporary host/instance keys to
# hosts/instance_uuids. These temorary keys are to be used in the
# solving process since we need a convention of lp variable names.
host_keys = ['Host' + str(i) for i in xrange(num_hosts)]
host_key_map = dict(zip(host_keys, hosts))
instance_num_keys = [
'InstanceNum' + str(i) for i in xrange(num_instances + 1)
]
instance_num_key_map = dict(
zip(instance_num_keys, xrange(num_instances + 1)))
# create the pulp variables
variable_matrix = [[
pulp.LpVariable('HI_' + host_key + '_' + instance_num_key, 0, 1,
constants.LpInteger)
for instance_num_key in instance_num_keys
] for host_key in host_keys]
# create the 'prob' variable to contain the problem data.
prob = pulp.LpProblem("Host Instance Scheduler Problem",
constants.LpMinimize)
# add cost function to pulp solver
cost_variables = [
variable_matrix[i][j] for i in xrange(num_hosts)
for j in xrange(num_instances + 1)
]
cost_coefficients = [
cost_matrix[i][j] for i in xrange(num_hosts)
for j in xrange(num_instances + 1)
]
prob += (pulp.lpSum([
cost_coefficients[i] * cost_variables[i]
for i in xrange(len(cost_variables))
]), "Sum_Costs")
# add constraints to pulp solver
for i in xrange(num_hosts):
for j in xrange(num_instances + 1):
if constraint_matrix[i][j] is False:
prob += (variable_matrix[i][j] == 0,
"Cons_Host_%s" % i + "_NumInst_%s" % j)
# add additional constraints to ensure the problem is valid
# (1) non-trivial solution: number of all instances == that requested
prob += (pulp.lpSum([
variable_matrix[i][j] * j for i in xrange(num_hosts)
for j in xrange(num_instances + 1)
]) == num_instances, "NonTrivialCons")
# (2) valid solution: each host is assigned 1 num-instances value
for i in xrange(num_hosts):
prob += (pulp.lpSum([
variable_matrix[i][j] for j in xrange(num_instances + 1)
]) == 1, "ValidCons_Host_%s" % i)
# The problem is solved using PULP's choice of Solver.
prob.solve(
pulp_solver_classes.PULP_CBC_CMD(
maxSeconds=CONF.solver_scheduler.pulp_solver_timeout_seconds))
# Create host-instance tuples from the solutions.
if pulp.LpStatus[prob.status] == 'Optimal':
num_insts_on_host = {}
for v in prob.variables():
if v.name.startswith('HI'):
(host_key, instance_num_key
) = v.name.lstrip('HI').lstrip('_').split('_')
if v.varValue == 1:
num_insts_on_host[host_key] = (
instance_num_key_map[instance_num_key])
instances_iter = iter(instance_uuids)
for host_key in host_keys:
num_insts_on_this_host = num_insts_on_host.get(host_key, 0)
for i in xrange(num_insts_on_this_host):
host_instance_combinations.append(
(host_key_map[host_key], instances_iter.next()))
else:
LOG.warn(
_LW("Pulp solver didnot find optimal solution! "
"reason: %s"), pulp.LpStatus[prob.status])
host_instance_combinations = []
return host_instance_combinations
def host_solve(self, hosts, instance_uuids, request_spec,
filter_properties):
"""This method returns a list of tuples - (host, instance_uuid)
that are returned by the solver. Here the assumption is that
all instance_uuids have the same requirement as specified in
filter_properties
"""
host_instance_tuples_list = []
if instance_uuids:
num_instances = len(instance_uuids)
else:
num_instances = request_spec.get('num_instances', 1)
instance_uuids = [
'unset_uuid%s' % i for i in xrange(num_instances)
]
num_hosts = len(hosts)
host_ids = ['Host%s' % i for i in range(num_hosts)]
LOG.debug(_("All Hosts: %s") % [h.host for h in hosts])
for host in hosts:
LOG.debug(_("Host state: %s") % host)
host_id_dict = dict(zip(host_ids, hosts))
instances = ['Instance%s' % i for i in range(num_instances)]
instance_id_dict = dict(zip(instances, instance_uuids))
# supply is a dictionary for the number of units of
# resource for each Host.
# Currently using only the disk_mb and memory_mb
# as the two resources to satisfy. Need to eventually be able to
# plug-in different resources. An example supply dictionary:
# supply = {"Host1": [1000, 1000],
# "Host2": [4000, 1000]}
supply = dict((host_ids[i], [
self._get_usable_disk_mb(hosts[i]),
self._get_usable_memory_mb(hosts[i]),
]) for i in range(len(host_ids)))
number_of_resource_types_per_host = 2
required_disk_mb = self._get_required_disk_mb(filter_properties)
required_memory_mb = self._get_required_memory_mb(filter_properties)
# demand is a dictionary for the number of
# units of resource required for each Instance.
# An example demand dictionary:
# demand = {"Instance0":[200, 300],
# "Instance1":[900, 100],
# "Instance2":[1800, 200],
# "Instance3":[200, 300],
# "Instance4":[700, 800], }
# However for the current scenario, all instances to be scheduled
# per request have the same requirements. Need to eventually
# to support requests to specify different instance requirements
demand = dict((instances[i], [
required_disk_mb,
required_memory_mb,
]) for i in range(num_instances))
# Creates a list of costs of each Host-Instance assignment
# Currently just like the nova.scheduler.weights.ram.RAMWeigher,
# using host_state.free_ram_mb * ram_weight_multiplier
# as the cost. A negative ram_weight_multiplier means to stack,
# vs spread.
# An example costs list:
# costs = [ # Instances
# # 1 2 3 4 5
# [2, 4, 5, 2, 1], # A Hosts
# [3, 1, 3, 2, 3] # B
# ]
# Multiplying -1 as we want to use the same behavior of
# ram_weight_multiplier as used by ram weigher.
costs = [[
-1 * host.free_ram_mb * CONF.ram_weight_multiplier
for i in range(num_instances)
] for host in hosts]
costs = pulp.makeDict([host_ids, instances], costs, 0)
# The PULP LP problem variable used to add all the problem data
prob = pulp.LpProblem("Host Instance Scheduler Problem",
constants.LpMinimize)
all_host_instance_tuples = [(w, b) for w in host_ids
for b in instances]
vars = pulp.LpVariable.dicts("IA", (host_ids, instances), 0, 1,
constants.LpInteger)
# The objective function is added to 'prob' first
prob += (pulp.lpSum([
vars[w][b] * costs[w][b] for (w, b) in all_host_instance_tuples
]), "Sum_of_Host_Instance_Scheduling_Costs")
# The supply maximum constraints are added to
# prob for each supply node (Host)
for w in host_ids:
for i in range(number_of_resource_types_per_host):
prob += (pulp.lpSum(
[vars[w][b] * demand[b][i]
for b in instances]) <= supply[w][i],
"Sum_of_Resource_%s" % i + "_provided_by_Host_%s" % w)
# The number of Hosts required per Instance, in this case it is only 1
for b in instances:
prob += (pulp.lpSum([vars[w][b] for w in host_ids]) == 1,
"Sum_of_Instance_Assignment%s" % b)
# The demand minimum constraints are added to prob for
# each demand node (Instance)
for b in instances:
for j in range(number_of_resource_types_per_host):
prob += (
pulp.lpSum([vars[w][b] * demand[b][j]
for w in host_ids]) >= demand[b][j],
"Sum_of_Resource_%s" % j + "_required_by_Instance_%s" % b)
# The problem is solved using PuLP's choice of Solver
prob.solve()
if pulp.LpStatus[prob.status] == 'Optimal':
for v in prob.variables():
if v.name.startswith('IA'):
(host_id,
instance_id) = v.name.lstrip('IA').lstrip('_').split('_')
if v.varValue == 1.0:
host_instance_tuples_list.append(
(host_id_dict[host_id],
instance_id_dict[instance_id]))
return host_instance_tuples_list
from pulp import pulp
# Ejemplo del problema de transporte utilizando PuLP
# Variable prob que contiene los datos del problema
prob = pulp.LpProblem("Problema de distribución", pulp.LpMinimize)
# Creamos lista de Centros de Distribución o nodos de oferta
Centros_Distribucion = ["CEDI A", "CEDI B"]
# diccionario con la capacidad de oferta de cada CEDI
oferta = {"CEDI A": 10, "CEDI B": 20}
# Creamos la lista de los bares o nodos de demanda
plantas = ["Planta 1", "Planta 2", "Planta 3", "Planta 4", "Planta 5"]
# diccionario con la capacidad de demanda de cada Planta
demanda = {
"Planta 1": 3,
"Planta 2": 5,
"Planta 3": 6,
"Planta 4": 2,
"Planta 5": 7,
}
# Lista con los costos de transporte de cada nodo
costos = [ # Plantas
# 1 2 3 4 5
[26250, 73500, 106750, 105000, 32900], # A CEDIS
[28700, 18200, 40250, 129500, 37450] # B
]
def distribute(self, fairness=True, output=None):
"""
This method is responsible for actually solving the linear programming problem.
It uses the data in the instance variables.
The optional parameter **fairness** indicates if the solution should minimize individual effort. By default the solution will enforce this condition.
An error will be raised in case the data has inconsistencies.
"""
# Validate the problem variables
self._validate()
# State problem
problem = pulp.LpProblem("Fair Distribution Problem", pulp.LpMinimize)
# Prepare variables
targets_objects = {}
for (t, target) in enumerate(self._targets):
for (o, object) in enumerate(self._objects):
variable = pulp.LpVariable(
'x' + str(t) + str(o), lowBound=0, cat='Binary')
position = {'target': t, 'object': o}
targets_objects['x' + str(t) + str(o)] = (variable, position)
# Generate linear expression for self._weights (Summation #1)
weights = [(variable, self._weights[weight_position['target']][weight_position['object']])
for (variable, weight_position) in targets_objects.values()]
weight_expression = pulp.LpAffineExpression(weights)
# Generate linear expression for effort distribution (Summation #2)
weight_diff_vars = []
if fairness:
total_targets = len(self._targets)
for (t, target) in enumerate(self._targets):
weight_diff_aux_variable = pulp.LpVariable(
'weight_diff_'+str(t), lowBound=0)
weight_diff_vars.append(weight_diff_aux_variable)
negative_effort_diff_weights = []
positive_effort_diff_weights = []
negative_factor = -1 * total_targets
positive_factor = 1 * total_targets
for (o, object) in enumerate(self._objects):
id = 'x' + str(t) + str(o)
negative_effort_diff_weights.append(
(targets_objects[id][0], negative_factor * self._weights[t][o]))
positive_effort_diff_weights.append(
(targets_objects[id][0], positive_factor * self._weights[t][o]))
negative_effort_diff = pulp.LpAffineExpression(
negative_effort_diff_weights) + weight_expression
positive_effort_diff = pulp.LpAffineExpression(
positive_effort_diff_weights) - weight_expression
problem += negative_effort_diff <= weight_diff_aux_variable, 'abs negative effort diff ' + \
str(t)
problem += positive_effort_diff <= weight_diff_aux_variable, 'abs positive effort diff ' + \
str(t)
# Constraints - Each task must be done
for (o, object) in enumerate(self._objects):
constraints = []
for (t, target) in enumerate(self._targets):
constraints.append(targets_objects['x' + str(t) + str(o)][0])
problem += pulp.lpSum(constraints) == 1, 'Task ' + \
str(o) + ' must be done'
# Set objective function
problem += weight_expression + \
pulp.lpSum(weight_diff_vars), "obj"
if output:
problem.writeLP(output)
problem.solve()
# Build output
data = {}
for v in filter(lambda x: x.varValue > 0, problem.variables()):
if v.name not in targets_objects:
continue
position = targets_objects[v.name][1]
target = self._targets[position['target']]
object = self._objects[position['object']]
if target not in data:
data[target] = []
data[target].append(object)
return data
