Moneyball: Proactive Auto-Scaling in Microsoft Azure SQL Database Serverless
Summary: Moneyball introduces proactive auto-scaling for Azure SQL Database Serverless by predicting pause/resume patterns to pre-warm resources, reducing wake-up latency. It avoids short idle reclamation to balance QoS and cost, enabling global adoption. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Olga Poppe (Microsoft)
- 2. Qun Guo (Microsoft)
- 3. Willis Lang (Microsoft)
- 4. Pankaj Arora (Microsoft)
- 5. Morgan Oslake (Microsoft)
- 6. Shize Xu (Microsoft)
- 7. Ajay Kalhan (Microsoft)
BibTeX Citation
@article{poppe_vldb22,
title = {{Moneyball: Proactive Auto-Scaling in Microsoft Azure SQL Database Serverless}},
author = {Poppe, Olga and Guo, Qun and Lang, Willis and Arora, Pankaj and Oslake, Morgan and Xu, Shize and Kalhan, Ajay},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {6},
pages = {1279--1287},
doi = {10.14778/3514061.3514073},
url = {https://doi.org/10.14778/3514061.3514073},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 234 | Self-Driving Database Management Systems | 2017 | CIDR | 0.00023810722 |
| 1,092 | Automated Demand-driven Resource Scaling in Relational Database-as-a-Service | 2016 | SIGMOD | 0.00012221946 |
| 1,218 | P-Store: An Elastic Database System with Predictive Provisioning | 2018 | SIGMOD | 0.00011621066 |
| 1,224 | Dhalion: Self-Regulating Stream Processing in Heron | 2017 | VLDB | 0.00011596911 |
| 5,681 | Survivability of Cloud Databases - Factors and Prediction | 2018 | SIGMOD | 6.1237253e-05 |
| 6,781 | Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation | 2021 | VLDB | 5.7764885e-05 |
| 10,098 | Not for the Timid: On the Impact of Aggressive Over-booking in the Cloud | 2016 | VLDB | 5.1498503e-05 |
| 11,685 | Toto - Benchmarking the Efficiency of a Cloud Service | 2021 | SIGMOD | 5.093636e-05 |
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|---|---|---|---|---|
| 1 | 6,518 | Tenant Placement in Over-subscribed Database-as-a-Service Clusters | 2022 | VLDB |
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| 3 | 641 | Relational Cloud: A Database-as-a-Service for the Cloud | 2011 | CIDR |
| 4 | 1,023 | Extreme Scale with Full SQL Language Support in Microsoft SQL Azure | 2010 | SIGMOD |
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| 7 | 10,098 | Not for the Timid: On the Impact of Aggressive Over-booking in the Cloud | 2016 | VLDB |
| 8 | 1,092 | Automated Demand-driven Resource Scaling in Relational Database-as-a-Service | 2016 | SIGMOD |
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| 10 | 11,150 | Proactive Resume and Pause of Resources for Microsoft Azure SQL Database Serverless | 2024 | SIGMOD |