Proactive Resume and Pause of Resources for Microsoft Azure SQL Database Serverless

Summary: Proactive resource allocation for millions of serverless Azure SQL databases using demand forecasting to resume/pause resources. Near-optimal policy balances high availability, low cost, and low overhead, backed by cross-team architecture principles enabling transfer to other relational cloud DBs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6841
Venue: SIGMOD
Year: 2024
Pagerank: 5.093636e-05
Overall Rank: 11,150 | 23.51%
DOI: 10.1145/3626246.3653371

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Olga Poppe (Microsoft)
2. Pankaj Arora (Microsoft)
3. Sakshi Sharma (Microsoft)
4. Jie Chen (Microsoft)
5. Sachin Pandit (Microsoft)
6. Rahul Sawhney (Microsoft)
7. Vaishali Jhalani (Microsoft)
8. Willis Lang (Microsoft)
9. Qun Guo (Microsoft)
10. Anupriya Inumella (Microsoft)
11. Sanjana Dulipeta Sridhar (Microsoft)
12. Dheren Gala (Microsoft)
13. Nilesh Rathi (Microsoft)
14. Morgan Oslake (Microsoft)
15. Alexandru Chirica (Microsoft)
16. Sarika Iyer (Microsoft)
17. Prateek Goel (Microsoft)
18. Ajay Kalhan (Microsoft)

BibTeX Citation

@inproceedings{poppe_sigmod24,
        title = {{Proactive Resume and Pause of Resources for Microsoft Azure SQL Database Serverless}},
        author = {Poppe, Olga and Arora, Pankaj and Sharma, Sakshi and Chen, Jie and Pandit, Sachin and Sawhney, Rahul and Jhalani, Vaishali and Lang, Willis and Guo, Qun and Inumella, Anupriya and Sridhar, Sanjana Dulipeta and Gala, Dheren and Rathi, Nilesh and Oslake, Morgan and Chirica, Alexandru and Iyer, Sarika and Goel, Prateek and Kalhan, Ajay},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3626246.3653371},
        url = {https://dl.acm.org/doi/10.1145/3626246.3653371},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 28 of 28 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
43	The Case for Learned Index Structures	2018	SIGMOD	0.00046060254
86	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00035316107
154	Neo: A Learned Query Optimizer	2019	VLDB	0.00028726181
156	An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server	1997	VLDB	0.00028636811
234	Self-Driving Database Management Systems	2017	CIDR	0.00023810722
347	Tuning Database Configuration Parameters with iTuned	2009	VLDB	0.00020651582
387	AutoAdmin "What-if" Index Analysis Utility	1998	SIGMOD	0.00019442332
471	Skew-Aware Automatic Database Partitioning in Shared-Nothing, Parallel OLTP Systems	2012	SIGMOD	0.0001793564
523	Adaptive Self-Tuning Memory in DB2	2006	VLDB	0.00017133451
625	Automatic Performance Diagnosis and Tuning in Oracle	2005	CIDR	0.0001566968
768	Automatic Physical Database Tuning: A Relaxation-based Approach	2005	SIGMOD	0.00014173242
996	A Heuristic Approach to Attribute Partitioning	1979	SIGMOD	0.00012758481
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
1,616	The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models	2018	SIGMOD	0.00010213691
1,706	SQL Memory Management in Oracle9i	2002	VLDB	9.9628446e-05
1,852	Automated Partitioning Design in Parallel Database Systems	2011	SIGMOD	9.6134443e-05
2,226	Moneyball: Proactive Auto-Scaling in Microsoft Azure SQL Database Serverless	2022	VLDB	8.9174082e-05
2,651	Magpie: Python at Speed and Scale using Cloud Backends	2021	CIDR	8.2918086e-05
4,214	The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox	2015	CIDR	6.8276099e-05
5,681	Survivability of Cloud Databases - Factors and Prediction	2018	SIGMOD	6.1237253e-05
6,121	The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward	2021	VLDB	5.9688569e-05
6,177	Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud	2022	VLDB	5.9496208e-05
6,518	Tenant Placement in Over-subscribed Database-as-a-Service Clusters	2022	VLDB	5.8538324e-05
6,781	Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation	2021	VLDB	5.7764885e-05
8,864	Pipemizer: An Optimizer for Analytics Data Pipelines	2022	VLDB	5.355022e-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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