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Optimizing Resource Allocation in Hyperscale Datacenters: Scalability, Usability, and...

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Meta builds Rebalancer, a reusable resource‑allocation framework with a high‑level spec language of 28 specs that translates rules into a compact expression graph linear in input size. It solves problems with up to ~1 million objects and 100k bins in under 100 seconds, runs ~15 million rebalances per day, and uses a generic local search solver that is ~5× faster than MIP and within ~2% of optimal (98% on a pathological case).

AI-written summary · about

  1. 0:00Resource allocation challenges
  2. 2:08Common theme in placement
  3. 3:10Usability gap in modeling
  4. 5:17Specification API for usability
  5. 7:04Scalability challenges
  6. 8:08Expression graph for linear scalability
  7. 10:15Local search algorithm design
  8. 12:43Speed versus solution quality
  9. 14:07Rebalancer production impact
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