Resource Elasticity for Large-Scale Machine Learning
Summary: Resource elasticity for declarative large-scale ML using memory-aware optimization and runtime plan migration. Introduces a memory-optimizer for near-optimal memory configurations and dynamic plan migration under YARN-style resource negotiation; up to 21x gains with low overhead and no static provisioning. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Botong Huang
- 2. Matthias Boehm
- 3. Yuanyuan Tian
- 4. Berthold Reinwald
- 5. Shirish Tatikonda
- 6. Frederick R. Reiss
Incoming Citations (Sorted by Pagerank)
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