~/panel

Panel Discussion: Fragmentation of the Batch Ecosystem in Kubernetes, Challenges and Solutions

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name:Albin S
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Panelists compare concrete systems and limits: Armada, Unicorn, MCAD and Flux (qflux/fluence) build schedulers, queuing, quotas and hierarchical borrowing because etcd chokes at ~100k simultaneous job submissions and Kubernetes events lack ordering or delivery guarantees. Armada reports scaling two orders of magnitude beyond etcd by buffering, while the Flux operator instantiates a mini cluster inside Kubernetes to run tens‑to‑hundreds of thousands of tasks. Remaining gaps: gang scheduling fragmentation, multi‑cluster federation, multi‑tenant quotas and shared filesystem health.

AI-written summary · about

  1. 0:00Batch workloads on Kubernetes
  2. 2:06Declarative vs imperative batch models
  3. 5:47Challenges in batch workloads
  4. 7:51Missing batch features vs YARN
  5. 10:00Kubernetes for batch workloads?
  6. 13:52Solutions for batch scheduling gaps
  7. 15:34Batch vs Kubernetes adaptation
  8. 18:11Power user needs vs batch efficiency
  9. 21:01Multi-cluster batch federation
  10. 25:34Mathematical accuracy in batch workloads
  11. 27:02UI and identity for batch systems
  12. 31:54Multi-tenancy in batch Kubernetes
  13. 34:01Scaling etcd and dependencies
  14. 36:26Collaboration with traditional schedulers
  15. 38:09Improving etcd for batch frameworks
  16. 41:49Educating users for batch submission
  17. 43:15User workload correlation in Kubernetes
  18. 45:22Storage challenges for batch workloads
  19. 46:24Defending etcd and tools
  20. 47:50Common APIs for batch workloads
  21. 50:18Forums for batch Kubernetes discussions
  22. 51:05Closing remarks
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