def add_to_aggregate(self, context, aggregate, host, slave_info=None):
"""Add a compute host to an aggregate."""
if not pool_states.is_hv_pool(aggregate.metadata):
return
if CONF.xenserver.independent_compute:
raise exception.NotSupportedWithOption(
operation='adding to a XenServer pool',
option='CONF.xenserver.independent_compute')
invalid = {
pool_states.CHANGING: _('setup in progress'),
pool_states.DISMISSED: _('aggregate deleted'),
pool_states.ERROR: _('aggregate in error')
}
if (aggregate.metadata[pool_states.KEY] in invalid.keys()):
raise exception.InvalidAggregateActionAdd(
aggregate_id=aggregate.id,
reason=invalid[aggregate.metadata[pool_states.KEY]])
if (aggregate.metadata[pool_states.KEY] == pool_states.CREATED):
aggregate.update_metadata({pool_states.KEY: pool_states.CHANGING})
if len(aggregate.hosts) == 1:
# this is the first host of the pool -> make it master
self._init_pool(aggregate.id, aggregate.name)
# save metadata so that we can find the master again
metadata = {
'master_compute': host,
host: self._host_uuid,
pool_states.KEY: pool_states.ACTIVE
}
aggregate.update_metadata(metadata)
else:
# the pool is already up and running, we need to figure out
# whether we can serve the request from this host or not.
master_compute = aggregate.metadata['master_compute']
if master_compute == CONF.host and master_compute != host:
# this is the master -> do a pool-join
# To this aim, nova compute on the slave has to go down.
# NOTE: it is assumed that ONLY nova compute is running now
self._join_slave(aggregate.id, host,
slave_info.get('compute_uuid'),
slave_info.get('url'), slave_info.get('user'),
slave_info.get('passwd'))
metadata = {
host: slave_info.get('xenhost_uuid'),
}
aggregate.update_metadata(metadata)
elif master_compute and master_compute != host:
# send rpc cast to master, asking to add the following
# host with specified credentials.
slave_info = self._create_slave_info()
self.compute_rpcapi.add_aggregate_host(context, host,
aggregate,
master_compute,
slave_info)
def invalid_option(option_name, recommended_value):
LOG.exception(_('Current value of '
'CONF.xenserver.%(option)s option incompatible with '
'CONF.xenserver.independent_compute=True. '
'Consider using "%(recommended)s"'),
{'option': option_name,
'recommended': recommended_value})
raise exception.NotSupportedWithOption(
operation=option_name,
option='CONF.xenserver.independent_compute')
def snapshot(self, context, instance, image_id, update_task_state):
"""Snapshots the specified instance.
:param context: security context
:param instance: nova.objects.instance.Instance
:param image_id: Reference to a pre-created image that will hold the
snapshot.
:param update_task_state: Callback function to update the task_state
on the instance while the snapshot operation progresses. The
function takes a task_state argument and an optional
expected_task_state kwarg which defaults to
nova.compute.task_states.IMAGE_SNAPSHOT. See
nova.objects.instance.Instance.save for expected_task_state usage.
"""
if not self.disk_dvr.capabilities.get('snapshot'):
raise exc.NotSupportedWithOption(
message=_("The snapshot operation is not supported in "
"conjunction with a [powervm]/disk_driver setting "
"of %s.") % CONF.powervm.disk_driver)
self._log_operation('snapshot', instance)
# Define the flow.
flow = tf_lf.Flow("snapshot")
# Notify that we're starting the process.
flow.add(
tf_img.UpdateTaskState(update_task_state,
task_states.IMAGE_PENDING_UPLOAD))
# Connect the instance's boot disk to the management partition, and
# scan the scsi bus and bring the device into the management partition.
flow.add(tf_stg.InstanceDiskToMgmt(self.disk_dvr, instance))
# Notify that the upload is in progress.
flow.add(
tf_img.UpdateTaskState(
update_task_state,
task_states.IMAGE_UPLOADING,
expected_state=task_states.IMAGE_PENDING_UPLOAD))
# Stream the disk to glance.
flow.add(
tf_img.StreamToGlance(context, self.image_api, image_id, instance))
# Disconnect the boot disk from the management partition and delete the
# device.
flow.add(tf_stg.RemoveInstanceDiskFromMgmt(self.disk_dvr, instance))
# Run the flow.
tf_base.run(flow, instance=instance)
