Cloud Orchestration is Broken
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The document discusses the evolution of cloud orchestration from basic architectures to more complex environments, emphasizing the importance of tools, versioning, and management strategies. It critiques current cloud management practices as lacking holistic support and advocates for a more modular and reusable approach in cloud configurations. Additionally, it highlights the need for improved workflow and abstraction in cloud management, drawing parallels to software development principles.
Cloud Orchestration is Broken
- 1. The Cloud is Broken Those who ignore history are doomed to repeat it Edgar Román emroman@pbs.org March 3rd, 2015 DC Python Meetup
- 2. Caveats, Disclaimer, etc • These are my opinions • I am not yet omniscient so my knowledge of tools mentioned may be inaccurate • We’re really talking about Cloud Orchestration • For moderate to complex environments (my blog doesn’t count) – Beyond web app / db
- 3. Our Architecture – V1 • Web App tier – Runs code from git repo • DB Master with slaves – Hopefully managed by DDL in repo (i.e. Django Migrations) • Memcache/Redis layer – Simple and self-configuring • Celery Queue – Asynchronous jobs, persistent queue • Job worker pool
- 4. And more… • Web App tier – Lives in Auto-Scaling group – Allows inbound tcp connections on 80/443 via load balancer • DB Master with slaves – Only one inbound tcp port allowed – Defined set of network connection for replication • Memcache/Redis layer – Restricted access to this from Web Apps only • Celery Queue – Web App can queue jobs, works can pop • Job worker pool – No inbound access at all!
- 5. Then we evolve • V2 – Adds ElasticSearch tier • V3 – Adds nightly Hadoop batch
- 6. Add some environments… • Production, Staging, QA • Then the devs want a local copy to work on
- 7. The challenge • Production is on v1 • V2 is in QA • Devs working on V3 And I need to manage them all quickly and easily
- 8. Philosophy Shift • Olden days – Used Visio to track changes to the physical hardware • Now – Use tools to track multiple environments or tiers in the cloud now • Why not – Create the entire architecture as needed, preconfigured, and on-demand
- 9. If you create a single virtual entity in a cloud without a script, it is like writing a perl script on a server somewhere without telling anyone
- 10. We’ve learned so much from software development, why can’t we use this knowledge for cloud orchestration and management?
- 11. Modules / Decomposition Versioning Code Reuse / DRY Abstraction Compilations / Build Workflow
- 12. Modules / Decomposition • We know from software: – Grouping makes sense – Helps organize logical sets of things • What we have in cloud management: – Default view of chef management consoles is a flat list of nodes – Vast majority of tutorials and examples put all hosts in a single network – AWS EC2, Chef, Ansible supports optional groups by tagging • Conclusion: Poor holistic support
- 13. Versioning • We know from software: – Versioning is critical for tracking features and bugs – Allows recovery from errors, mistakes, and disasters – Versioning important not just at file level, but whole project • What we have in cloud management: – Ansible, Chef only version individual playbooks/cookbooks, not projects/environments/collections – Restoring a known state for cloud project is a manual process • Conclusion: Poor holistic support
- 14. Code Reuse / DRY • We know from software: – Repeating yourself causes bloat and often errors when refactoring / updating code – Updates in normalized code are easier and well understood • What we have in cloud management: – Minimal support for extra variables in Ansible/Chef/Cloudformation per class of server – Global variables for credentials – Generally would need to cut/paste extra variables in multiple places • Conclusion: We’re getting there
- 15. Abstraction • What we know from software: – Using abstractions like file i/o allow use on multiple platforms • What we have in cloud management: – Mostly tools support multiple clouds (AWS, Rackspace, etc) – OpenStack is closest analogy to cloud abstraction • Conclusion: Very Promising
- 16. Compilation / Workflow • What we know from software: – Compilation of code enables easy transport and packaging – Enables DRY capabilities • What we have in cloud management: – Workflow support is generally supported, but not necessarily holistically or with versioning of workflow support • Conclusion: Not Bad
- 17. So…we should extend tools… • to deal with not just servers, but networks and other entities (abstraction) • to manage collections of these entities (modules) • to manage versioning of these collections (versioning) • to allow configuration of these versioned collections per environments (dry) • to allow deployment (workflow) of these versioned collections with configurations to specific environments
- 18. Keep an eye on… • Apache CloudStack – http://cloudstack.apache.org/ • Cloudify – http://getcloudify.org/
- 19. Questions? Oh yeah, we’re hiring…