Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Convergence of Communities: OKD = f(Kubernetes++)

Convergence of Communities: OKD = f(Kubernetes++)

Conference: DevConf CZ 2019
Link: https://sched.co/JciN
Authors: Diane Mueller and Daniel Izquierdo

This session will discuss joint research findings from Bitergia on the inter-relatedness of contributions to the Kubernetes, OpenShift/ OKD communities developing around distributions and share new approaches to OS community development. The inter-dependency of open source projects such as OpenShift(OKD) with upstream projects (Kubernetes), downstream services, and related initiatives (Operator Framework) has changed nature of open source community development. As communities converge, release schedules and priorities collides, project leaders need to adjust OS models, re-think interactions with multiple release cycles and juggle the divergent agendas. This session will cover lessons learned, best practices developed and the new realities of open source community development.

Daniel Izquierdo Cortazar

January 25, 2019
Tweet

More Decks by Daniel Izquierdo Cortazar

Other Decks in Business

Transcript

  1. Convergence of Communities: OKD = f(Kubernetes++) Diane Mueller Director, Community

    Development Red Hat Cloud Platform [email protected] @openshiftcommon January 25th, 2019 - Devconf.cz - Brno Daniel Izquierdo Chief Data Officer Bitergia [email protected] @dizquierdo / @bitergia
  2. • Origin to OKD - how the Project Shifted ◦

    OKD = f(Kubernetes++) • Reality Check ◦ A Short History Lesson • Dynamic Community Personas • Adapting the Model and the Tools ◦ OpenShift Commons Model Agenda
  3. OPEN SOURCE IS THE SOURCE OF TECHNOLOGICAL INNOVATION 1M+ projects

    KVM GNOME Apache Project OpenShift Origin OpenStack® Linux® kernel node.js Kubernetes Fedora OpenJDK TensorFlow Hyperledger 96M* REPOSITORIES 31M* DEVELOPERS 2.1M* BUSINESSES *GitHub Oct 2018
  4. KUBERNETES SIGs & WGs- ENGINEERING LEADERSHIP API MACHINERY AZURE DOCS

    OPENSTACK STORAGE CONTAINER IDENTITY AWS BIG DATA INSTRUMENTATION PRODUCT MANAGEMENT TESTING KUBEADM ADOPTION APPS CLI MULTI CLUSTER RELEASE UI RESOURCE MANAGEMENT ARCHITECTURE CLUSTER LIFECYCLE NETWORK SCALABILITY WINDOWS AUTH CLUSTER OPS NODE SCHEDULING APP DEF AUTO SCALING CONTRIBUTOR EXPERIENCE ON-PREM SERVICE CATALOG CLUSTER API 17 of 40 GROUPS RED HAT LEAD or CO-LEAD This slide is General Distribution/customer facing IOT EDGE POLICY *
  5. Chart Details Example • Dots are contributors • Blue Rectangles

    are projects or repositories • Dot size = # repos • Edge thick = # commits • Coloured dots are organizations
  6. OKD Project Atomic Prometheus Knative Project Lead Persona: SmarterClayton Kubernetes

    How he shows up: Kubernetes, OKD, Prometheus, Knative, Project Atomic, Solum (OpenStack) OpenStack (Solum)
  7. OpenStack Kubernetes OKD Organizational Persona: CERN How they show up:

    OKD, Kubernetes, OpenStack and more contributors
  8. Jaeger OpenStack Kubernetes OKD Individual Persona: Rackspace’s Greg Swift How

    they show up: OKD, Kubernetes, OpenStack contributors plus Slack, Commons, and more
  9. m3db Jaeger Kubernetes Operator Framework Tangential Persona: Uber’s Yuri Shkuro

    OKD Community Contribution: https://youtu.be/fjYAU3jayVo How they show up: Jaeger, OpenTracing, Operators, M3DB contributors OpenStack
  10. OpenStack Kubernetes OKD Corporate Persona: Amadeus’ Lénaïc Huard How they

    show up: Kubernetes, OKD, OCP on Azure, OCP on OpenStack
  11. OpenStack OpenStack OKD Upstream Persona: Alipay’s Kim Min Kubernetes How

    they show up: Kubernetes, OKD, Saltstack, Spinaker
  12. Commons as a Cross-Community Collaboration Model New Community Model Commons

    Briefings & Gatherings Code Contributions Mailing Lists SIGs Promote Peer-to-Peer Interactions https://commons.openshift.org/#join
  13. • No Company is Working on ‘Just One’ Thing •

    Upstream Coordination is Essential • Relationships Matter • Domain knowledge • Community Development vs. Community Management • Inclusivity over Exclusivity • Data Matters (clean, curated data and good tools!) • Anonymity is Dead What’s Next? Predictive Analysis (perhaps using IBM Watson ;-) Convergence of Conclusions