225 lines
9.5 KiB
Python
225 lines
9.5 KiB
Python
# Copyright (c) 2014 Cisco Systems Inc.
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# All Rights Reserved.
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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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"""
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A reference solver implementation that models the scheduling problem as a
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Linear Programming (LP) problem using the PULP modeling framework. This
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implementation includes disk and memory constraints, and uses the free ram as
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a cost metric to maximize or minimize for the LP problem.
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"""
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from oslo.config import cfg
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from pulp import constants
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from pulp import pulp
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from nova.openstack.common.gettextutils import _
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from nova.openstack.common import log as logging
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from nova.scheduler import solvers as novasolvers
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LOG = logging.getLogger(__name__)
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CONF = cfg.CONF
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CONF.import_opt('disk_allocation_ratio', 'nova.scheduler.filters.disk_filter')
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CONF.import_opt('ram_allocation_ratio', 'nova.scheduler.filters.ram_filter')
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CONF.import_opt('ram_weight_multiplier', 'nova.scheduler.weights.ram')
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class HostsPulpSolver(novasolvers.BaseHostSolver):
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"""A LP based constraint solver implemented using PULP modeler."""
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def host_solve(self, hosts, instance_uuids, request_spec,
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filter_properties):
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"""This method returns a list of tuples - (host, instance_uuid)
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that are returned by the solver. Here the assumption is that
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all instance_uuids have the same requirement as specified in
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filter_properties
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"""
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host_instance_tuples_list = []
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if instance_uuids:
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num_instances = len(instance_uuids)
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else:
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num_instances = request_spec.get('num_instances', 1)
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instance_uuids = ['unset_uuid%s' % i
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for i in xrange(num_instances)]
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num_hosts = len(hosts)
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host_ids = ['Host%s' % i for i in range(num_hosts)]
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LOG.debug(_("All Hosts: %s") % [h.host for h in hosts])
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for host in hosts:
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LOG.debug(_("Host state: %s") % host)
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host_id_dict = dict(zip(host_ids, hosts))
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instances = ['Instance%s' % i for i in range(num_instances)]
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instance_id_dict = dict(zip(instances, instance_uuids))
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# supply is a dictionary for the number of units of
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# resource for each Host.
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# Currently using only the disk_mb and memory_mb
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# as the two resources to satisfy. Need to eventually be able to
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# plug-in different resources. An example supply dictionary:
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# supply = {"Host1": [1000, 1000],
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# "Host2": [4000, 1000]}
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supply = dict((host_ids[i],
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[self._get_usable_disk_mb(hosts[i]),
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self._get_usable_memory_mb(hosts[i]), ])
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for i in range(len(host_ids)))
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number_of_resource_types_per_host = 2
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required_disk_mb = self._get_required_disk_mb(filter_properties)
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required_memory_mb = self._get_required_memory_mb(filter_properties)
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# demand is a dictionary for the number of
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# units of resource required for each Instance.
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# An example demand dictionary:
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# demand = {"Instance0":[200, 300],
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# "Instance1":[900, 100],
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# "Instance2":[1800, 200],
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# "Instance3":[200, 300],
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# "Instance4":[700, 800], }
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# However for the current scenario, all instances to be scheduled
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# per request have the same requirements. Need to eventually
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# to support requests to specify different instance requirements
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demand = dict((instances[i],
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[required_disk_mb, required_memory_mb, ])
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for i in range(num_instances))
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# Creates a list of costs of each Host-Instance assignment
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# Currently just like the nova.scheduler.weights.ram.RAMWeigher,
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# using host_state.free_ram_mb * ram_weight_multiplier
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# as the cost. A negative ram_weight_multiplier means to stack,
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# vs spread.
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# An example costs list:
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# costs = [ # Instances
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# # 1 2 3 4 5
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# [2, 4, 5, 2, 1], # A Hosts
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# [3, 1, 3, 2, 3] # B
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# ]
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# Multiplying -1 as we want to use the same behavior of
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# ram_weight_multiplier as used by ram weigher.
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costs = [[-1 * host.free_ram_mb *
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CONF.ram_weight_multiplier
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for i in range(num_instances)]
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for host in hosts]
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costs = pulp.makeDict([host_ids, instances], costs, 0)
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# The PULP LP problem variable used to add all the problem data
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prob = pulp.LpProblem("Host Instance Scheduler Problem",
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constants.LpMinimize)
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all_host_instance_tuples = [(w, b)
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for w in host_ids
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for b in instances]
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vars = pulp.LpVariable.dicts("IA", (host_ids, instances),
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0, 1, constants.LpInteger)
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# The objective function is added to 'prob' first
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prob += (pulp.lpSum([vars[w][b] * costs[w][b]
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for (w, b) in all_host_instance_tuples]),
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"Sum_of_Host_Instance_Scheduling_Costs")
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# The supply maximum constraints are added to
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# prob for each supply node (Host)
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for w in host_ids:
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for i in range(number_of_resource_types_per_host):
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prob += (pulp.lpSum([vars[w][b] * demand[b][i]
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for b in instances])
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<= supply[w][i],
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"Sum_of_Resource_%s" % i + "_provided_by_Host_%s" % w)
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# The number of Hosts required per Instance, in this case it is only 1
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for b in instances:
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prob += (pulp.lpSum([vars[w][b] for w in host_ids])
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== 1, "Sum_of_Instance_Assignment%s" % b)
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# The demand minimum constraints are added to prob for
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# each demand node (Instance)
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for b in instances:
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for j in range(number_of_resource_types_per_host):
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prob += (pulp.lpSum([vars[w][b] * demand[b][j]
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for w in host_ids])
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>= demand[b][j],
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"Sum_of_Resource_%s" % j + "_required_by_Instance_%s" % b)
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# The problem is solved using PuLP's choice of Solver
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prob.solve()
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if pulp.LpStatus[prob.status] == 'Optimal':
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for v in prob.variables():
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if v.name.startswith('IA'):
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(host_id, instance_id) = v.name.lstrip('IA').lstrip(
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'_').split('_')
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if v.varValue == 1.0:
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host_instance_tuples_list.append(
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(host_id_dict[host_id],
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instance_id_dict[instance_id]))
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return host_instance_tuples_list
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def _get_usable_disk_mb(self, host_state):
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"""This method returns the usable disk in mb for the given host.
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Takes into account the disk allocation ratio.
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(virtual disk to physical disk allocation ratio).
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"""
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free_disk_mb = host_state.free_disk_mb
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total_usable_disk_mb = host_state.total_usable_disk_gb * 1024
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disk_allocation_ratio = CONF.disk_allocation_ratio
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disk_mb_limit = total_usable_disk_mb * disk_allocation_ratio
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used_disk_mb = total_usable_disk_mb - free_disk_mb
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usable_disk_mb = disk_mb_limit - used_disk_mb
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return usable_disk_mb
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def _get_required_disk_mb(self, filter_properties):
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"""This method returns the required disk in mb from
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the given filter_properties dictionary object.
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"""
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requested_disk_mb = 0
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instance_type = filter_properties.get('instance_type')
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if instance_type is not None:
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requested_disk_mb = 1024 * (instance_type.get('root_gb', 0) +
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instance_type.get('ephemeral_gb', 0))
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return requested_disk_mb
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def _get_usable_memory_mb(self, host_state):
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"""This method returns the usable memory in mb for the given host.
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Takes into account the ram allocation ratio.
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(Virtual ram to physical ram allocation ratio).
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"""
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free_ram_mb = host_state.free_ram_mb
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total_usable_ram_mb = host_state.total_usable_ram_mb
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ram_allocation_ratio = CONF.ram_allocation_ratio
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memory_mb_limit = total_usable_ram_mb * ram_allocation_ratio
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used_ram_mb = total_usable_ram_mb - free_ram_mb
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usable_ram_mb = memory_mb_limit - used_ram_mb
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return usable_ram_mb
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def _get_required_memory_mb(self, filter_properties):
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"""This method returns the required memory in mb from
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the given filter_properties dictionary object
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"""
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required_ram_mb = 0
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instance_type = filter_properties.get('instance_type')
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if instance_type is not None:
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required_ram_mb = instance_type.get('memory_mb', 0)
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return required_ram_mb
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