[Bug 1602444] Fix merged to charm-glance (master)

[OpenStack Infra]

Reviewed:  https://review.openstack.org/341234
Committed: https://git.openstack.org/cgit/openstack/charm-glance/commit/?id=b825bd4747006125562de7660d86f978764ec44b
Submitter: Jenkins
Branch:    master

commit b825bd4747006125562de7660d86f978764ec44b
Author: Billy Olsen <billy.olsen at gmail.com>
Date:   Tue Jul 12 17:09:25 2016 -0700

    Change worker-multiplier to float
    
    Change the worker-multiplier to a floating point config option type
    instead of integer. This allows users to specify workers to be less
    than the number of CPUs, which is useful in deployments with multiple
    services deployed into containers on top of bare metal.
    
    The fix is to simply change the config option type and to sync in
    the necessary update from lp:charm-helpers.
    
    Partial-Bug: #1602444
    
    Change-Id: I5f7b24d53f35c4a7ec9e5de563afa5e232ca83af
    Signed-off-by: Billy Olsen <billy.olsen at gmail.com>

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https://bugs.launchpad.net/bugs/1602444

Title:
  worker-multiplier option does not allow floating point values

Status in Charm Helpers:
  Fix Released
Status in cinder package in Juju Charms Collection:
  In Progress
Status in glance package in Juju Charms Collection:
  In Progress
Status in keystone package in Juju Charms Collection:
  In Progress
Status in neutron-api package in Juju Charms Collection:
  In Progress
Status in nova-cloud-controller package in Juju Charms Collection:
  In Progress
Status in swift-storage package in Juju Charms Collection:
  In Progress

Bug description:
  The worker-multiplier option is a convenience option that lets the
  users not worry about the number of CPUs specifically. However, in
  deployments where there are many containers running api services on
  the hosts, this results in way too many processes leading to high
  load.

  Unfortunately, the worker-multiplier option is specified as an integer
  value which does not allow a user to reduce this below the number of
  CPUs detected.

  A work around is to further spread the load to multiple baremetal
  machines and do not place all API services on the same machines.

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