Intelligent Pooling: Proactive Resource Provisioning in Large-scale Cloud Service
Summary: Proactive provisioning for Spark: hybrid low-latency ML predicts cluster/session demand and drives dynamic pool-size optimization to eliminate expensive startup overheads. Auto-tuned tradeoff between latency and COGS yields up to 43% idle-time reduction at 99% hit rate and is deployed in production. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Deepak Ravikumar
- 2. Alex Yeo
- 3. Yiwen Zhu
- 4. Aditya Lakra
- 5. Harsha Nagulapalli
- 6. Santhosh Ravindran
- 7. Steve Suh
- 8. Niharika Dutta
- 9. Andrew Fogarty
- 10. Yoonjae Park
- 11. Sumeet Khushalani
- 12. Arijit Tarafdar
- 13. Kunal Parekh
- 14. Subru Krishnan
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,081 | Dhalion: Self-Regulating Stream Processing in Heron | 2017 | VLDB | 0.00014201838 |
| 2,376 | Moneyball: Proactive Auto-Scaling in Microsoft Azure SQL Database Serverless | 2022 | VLDB | 8.9367999e-05 |
| 7,044 | Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation | 2021 | VLDB | 4.8475963e-05 |
| 7,099 | KEA: Tuning an Exabyte-Scale Data Infrastructure | 2021 | SIGMOD | 4.8263529e-05 |
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