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Container Management Platform
Alberto Arias Maestro, CTO @ ElasticBox
 Generation 1
 Workflow Driven
 Flexibility > Reusability
 Configuration Management mixed with Infra management
 Generation 2
 Model Driven
 IaaS Models (1 per cloud) + Configuration Management Models
 Reusability < Flexibility
 Configuration Management separated from Infra management
 Generation 3 container driven
 Model Driven
 Configuration Management at build time
 Single abstract container infrastructure model
Cloud Management Platforms
Architecture
 Everything is a Model
 No need to orchestrate API calls
 No code to maintain
 Decoupled Binding Model
 Containers talk to Services
 Services forward traffic to POD
 Advanced Lifecycle Management
 Rolling updates and canary deployments
 POD Auto-scaling
 Container + Infrastructure
 Infrastructure Provisioning on demand
 Support for GCE, AWS, OpenStack
Why Kubernetes?
Cloud 3.0: Containers + Services
Demo
Roadmap
Roadmap
Roadmap
 Containers behave differently per environment
 Most deployment issues are related to configuration differences/incompatibilities.
 Not all containers work on all container management platforms.
 Make deployment commutative and test for it.
 Invest in a fast repo
 Pulling containers is the most time consuming.
 Add a test for the size of your containers.
 Understand network model of your container management system
 Network overlays, bridges, intra containers communication.
 Log everything
 Logs are the new ssh
What we learned?
Appendix
 Authentication/Authorization:
 Move towards a native Authorization model
 SAML Authentication
 SAML Claims for namespace membership
 Quota Management UI
 Management:
 Trigger for rolling updates and canary deployments
 Email Notifications for lifecycle events
 Integration:
 Audit Trail and event archiving
 Git authentication support
Roadmap

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ElasticKube

  • 1. Container Management Platform Alberto Arias Maestro, CTO @ ElasticBox
  • 2.  Generation 1  Workflow Driven  Flexibility > Reusability  Configuration Management mixed with Infra management  Generation 2  Model Driven  IaaS Models (1 per cloud) + Configuration Management Models  Reusability < Flexibility  Configuration Management separated from Infra management  Generation 3 container driven  Model Driven  Configuration Management at build time  Single abstract container infrastructure model Cloud Management Platforms
  • 4.  Everything is a Model  No need to orchestrate API calls  No code to maintain  Decoupled Binding Model  Containers talk to Services  Services forward traffic to POD  Advanced Lifecycle Management  Rolling updates and canary deployments  POD Auto-scaling  Container + Infrastructure  Infrastructure Provisioning on demand  Support for GCE, AWS, OpenStack Why Kubernetes?
  • 10.  Containers behave differently per environment  Most deployment issues are related to configuration differences/incompatibilities.  Not all containers work on all container management platforms.  Make deployment commutative and test for it.  Invest in a fast repo  Pulling containers is the most time consuming.  Add a test for the size of your containers.  Understand network model of your container management system  Network overlays, bridges, intra containers communication.  Log everything  Logs are the new ssh What we learned?
  • 12.  Authentication/Authorization:  Move towards a native Authorization model  SAML Authentication  SAML Claims for namespace membership  Quota Management UI  Management:  Trigger for rolling updates and canary deployments  Email Notifications for lifecycle events  Integration:  Audit Trail and event archiving  Git authentication support Roadmap

Editor's Notes

  1. 451 report on DevOps, Q1 2014 Gartner, Seven Steps to Start Your Devops Initiative, Sept 2014 451 report on DevOps, Q1 2014 Gartner, 2014 CIO Survey Cloud application lifecycle is broken More than 60% of businesses would like to deploy more frequently 51% of enterprises cite business and strategy demanding to reduce application cycles Only 16% of businesses are using model-driven automation tools
  2. Docker file contains enough info for you to create an image that contains the binaries of an application. At a low level, the inputs/outputs of that application. Docker file contains bare minimum to deploy. But it’s not rich enough to contain meta data. Not enough visibility of changes; rich parameterization. For example -
  3. Docker file contains enough info for you to create an image that contains the binaries of an application. At a low level, the inputs/outputs of that application. Docker file contains bare minimum to deploy. But it’s not rich enough to contain meta data. Not enough visibility of changes; rich parameterization. For example -
  4. Docker file contains enough info for you to create an image that contains the binaries of an application. At a low level, the inputs/outputs of that application. Docker file contains bare minimum to deploy. But it’s not rich enough to contain meta data. Not enough visibility of changes; rich parameterization. For example -
  5. Docker file contains enough info for you to create an image that contains the binaries of an application. At a low level, the inputs/outputs of that application. Docker file contains bare minimum to deploy. But it’s not rich enough to contain meta data. Not enough visibility of changes; rich parameterization. For example -