The document discusses several challenges and strategies for upgrading OpenStack deployments from one release to another. It notes that OpenStack has a rapid release cycle and emphasizes the importance of designing systems that can support rolling, continuous upgrades of components through multiple versions. Effective upgrade strategies require coordination between operations and development teams to ensure new versions maintain backward compatibility and upgrades can be tested in a controlled manner.

1. Rob Hirschfeld
Dell, Distinguished Engineer
http://lifeatthebar.com

2. • This session could repeat a lot from last summit
• http://www.openstack.org/summit/san-diego-2012/openstack-summit-
sessions/presentation/getting-from-folsom-to-grizzly-a-devops-upgrade-
pattern
• Interoperability & Reference Architecture
• Reference Architecture w/ Heat (Tuesday @ 11:00)
• Interop Panel (Tuesday @ 5:20)
• Upgrade Projects
• https://wiki.openstack.org/wiki/Upgrade-with-minimal-downtime
• https://wiki.openstack.org/wiki/Grenade

3. • The “Problem“ with Migration
• Paths to Nirvana (or Roads to Perdition)
• Alternatives
• An Opinion
• Discussion
F G
H
http://learn.genetics.utah.edu/content/begin/cells/organelles/

4. • OpenStack has 3 month release major/minor cycle
• Major version every 6 months
• Minor version (but important) 3 & 6 months after release
• Lots of Changes
• Bugs are fixed
• Operating Systems upgrade
• New technologies appear
• Whole projects are split off
• We expect operators to
• Keep systems running
• Never loose data
• And… Stay up to date http://cdn2.arkive.org
sockeye-salmon-predated-by-grizzly-bear-on-migration-upstream.jpg

5. • What are we upgrading?
• OpenStack - Yes!
• Dependent packages - Probably?
• Base OS - Maybe?
• What is the state during the "in-between" time?
• Infrastructure downtime?
• VM downtime? VM Reboot? Controlled/Informed?
• Availability Windows?
• What contingency plans?
• Dry run? Maybe.
• Recover by going backwards? Maybe.
• What level of safety and trust do you need?
• Assure data integrity?
• Assure Infrastructure Integrity?
• Maintain Security?
• How long can the migration take?
• Big bang move or gradual migrate?
• How will my API consumers/ecosystem cope?
• Can Keystone Grizzly work with Folsom Nova???
• What about futures? G.1 to G.2? H to I?
• Can I skip versions? Jump from G to I?
http://www.publicdomainpictures.net
Steep Steps by Peter Griffin

6. • Beginning Answers
• Distros will manage dependencies and packaging
• We can’t lose data or compromise security
• Infrastructure state and integrity will vary by solution
• Assumption of Staging
• Some managed environment (not a manual deploy)
• Staging/test environment to get "familiar" with the problem.
• Maintenance window for production - limits scope of change
• Step-wise changes are OK (big bang is not required)
• We can make trade-offs to defray expensive requirements
• Beyond Assumptions… Paradigm Shifts
• There are shared best practices
• Upgrades can be automated in a sharable way
http://www.theemailadmin.com/wp-content/uploads/2012/09/GFI229-hot-water-migration.jpg

7. All the nodes update to the latest code
in a short time window
• Details:
1. Cookbooks include update (instead of install) directives.
2. Control upstream package point (e.g. apt-update when appropriate)
3. Force chef-client run
4. Now at new level
• Considerations
• Pros: Potentially fast, continuous operation
• Cons: Don't mess up, it is your production environment
• Scope: Security updates
• Code Assumptions:
• System can function through service restarts.
• Underlying data models don't change or migrate appropriately.

8. Nodes migrate in staged groups
• Details:
1. Choose subset of machines and quiesce them.
2. Update set
3. Freeze state (by tenant)
4. Migrate service/tenant content
5. Repurpose after complete.
• Considerations
• Pros: Safer, more controlled, and can move tenants as needed
• Cons: Takes longer, still has cut-over point, but less open
http://allgodscrittersgotrhythm.blogspot.com/2010_08_01_archive.html

9. Nodes changed individually by a system-wide
orchestration that supports components of multiple versions
• Details
1. Components must be able to straddle versions
2. Orchestration updates core components to new version
3. System as a whole queiseces and is validated (requires self test)
4. Orchestration individually migrates components (return to step 3)
• Considerations
• Pros: Creates a highly resilient system that handles higher rate of change
• Cons: More complex to create and maintain
http://www.grizzlycentral.com/forum/grizzly-tire-wheel-combos/1204-upgrade-tires-grizzly.html

10. • Orchestration (not just deployment automation)
• Awareness of physical layout is required
• Must respect fault zones to sustain HA
• Proximity of resources matters for migration
• Networking transitions are essential
• Collaboration with development teams is essential
• Components must support current and previous
• Upgrade plan must be baked into configuration and tested
• Upgrade dependencies must be 1) clear and 2) minimized
• HA complicates upgrades
• Upgrade can be detected as a failure
• HA system must be able to bridge versions

12. • Partial features were confusing
• We wanted to get ahead on upgrade
• It looked like dev jumped to Grizzly
• Good news:
• Some testing of upgrade
• Folsom to Grizzly ops was pretty smooth
• Bad news:
• Grizzly is more complex (more moving parts)
• Missing multi-node upgrade validation

13. DB Oslo
Keystone
Msg Bus
Client Glance
Nova Compute
Dashboard Cinder Celimeter
Quantum

14. • Fault Tolerance on BOTH SIDES AND VERSIONS
• Same Version = EASY
• Backwards Version = HARD
• Forward Version = IMPOSSIBLE
Keystone
Grizzly
Keystone Easy Nova
Havana Havana

15. • We want to limit need to sustain old services
• New versions should support past APIs
• API consumers can migrate in steps Nova
Grizzly
Keystone
Step 2
Grizzly
API
Keystone Nova
Havana Step 3
Havana
Ideally, we’d server AND client would be multi-version

16. • Size Matters
• Big Steps = Release Based
• Small Steps = Commit Based G H
• Small steps are digest
• Easier to test small steps
• Incur less technical debt
• Expose issues to developers while code is fresh
• Large steps create risk
• More combinations to test
• More changes at one time
• Difficult to fix design issues

17. Forced Client
Big Bang!
Migration
Protocol Protocol
Driven Stepping
Rolling
Upgrade
Server vs Client
Parallel
Operation
Continuous Small Step vs Large Staged
Deploy Upgrade

18. Forced Client
Big Bang!
Migration
Protocol Protocol
Driven Stepping
Rolling
Upgrade
Server vs Client
Parallel
Operation
Continuous Small Step vs Large Staged
Deploy Upgrade

19. Forced Client
Big Bang!
Migration
Protocol Protocol
Driven Stepping
Rolling
Upgrade
Server vs Client
Parallel
Operation
Continuous Small Step vs Large Staged
Deploy Upgrade

20. • Servers & agents must be version tolerant
• Clients protocols must be testable and documented
• Ensure non-destructive migration
• Fast-fail on client, but version tolerant on server
• Require Expectation that servers will migrate need to be built
into the system! Servers must be adopting latest protocols or
clients will not follow.
• Servers must test legacy clients/protocols! We must have tests!
• We must be able to find and upgrade legacy clients

21. • Deployment Upstream Cookbooks/Modules
• Best Practice Discussions
• Code for Upgradeability
• Crowbar Collaboration
• Upgrade is a FEATURE!
• Orchestration + Chef
• Pull from Source Deployments
• System Discovery
• Networking Configuration
• Operating System Install
http://farm3.static.flickr.com/2561/3891653055_262410bc31.jpg