Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads
Summary: ease.ml: declarative ML service for multi-tenant clusters; formalizes multi-tenant model selection to minimize global regret, balancing efficiency and fairness. A hybrid algorithm fusing bandits with Bayesian optimization (regret bound) yields up to 9.8x faster global accuracy than heuristics and 4.1x vs SOTA on synthetic data and DL/Azure ML Studio tasks. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 16 of 16 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 140 | The MADlib Analytics Library or MAD Skills, the SQL | 2012 | VLDB | 0.00042270404 |
| 557 | SystemML: Declarative Machine Learning on Spark | 2016 | VLDB | 0.00020197988 |
| 2,731 | CPU Sharing Techniques for Performance Isolation in Multi-tenant Relational Database-as-a-Service | 2014 | VLDB | 8.2108797e-05 |
| 2,818 | Implicit Parallelism through Deep Language Embedding | 2015 | SIGMOD | 8.0665558e-05 |
| 3,436 | Sharing Buffer Pool Memory in Multi-Tenant Relational Database-as-a-Service | 2015 | VLDB | 7.0948913e-05 |
| 6,099 | WOO: A Scalable and Multi-tenant Platform for Continuous Knowledge Base Synthesis | 2013 | VLDB | 5.2104516e-05 |
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