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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
h6a787376ec9c406b
Venue
SIGMOD
Year
2024
Pagerank
4.9769913e-05
Overall Rank
11,504 | 22.68%
DOI
10.1145/3626246.3653371

Incoming Non-self Citations Over Time

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Authors

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)

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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
40 The Case for Learned Index Structures 2018 SIGMOD 0.00046363107
78 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00036675568
144 Neo: A Learned Query Optimizer 2019 VLDB 0.00029090793
151 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00028664776
224 Self-Driving Database Management Systems 2017 CIDR 0.00024011047
322 Tuning Database Configuration Parameters with iTuned 2009 VLDB 0.00021034201
378 AutoAdmin "What-if" Index Analysis Utility 1998 SIGMOD 0.00019541534
473 Skew-Aware Automatic Database Partitioning in Shared-Nothing, Parallel OLTP Systems 2012 SIGMOD 0.00017684278
508 Adaptive Self-Tuning Memory in DB2 2006 VLDB 0.00017083245
598 Automatic Performance Diagnosis and Tuning in Oracle 2005 CIDR 0.0001575517
751 Automatic Physical Database Tuning: A Relaxation-based Approach 2005 SIGMOD 0.00014251362
1,013 A Heuristic Approach to Attribute Partitioning 1979 SIGMOD 0.00012513898
1,068 Automated Demand-driven Resource Scaling in Relational Database-as-a-Service 2016 SIGMOD 0.00012186505
1,160 P-Store: An Elastic Database System with Predictive Provisioning 2018 SIGMOD 0.00011763834
1,606 The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models 2018 SIGMOD 0.00010091937
1,660 SQL Memory Management in Oracle9i 2002 VLDB 9.9581502e-05
1,884 Automated Partitioning Design in Parallel Database Systems 2011 SIGMOD 9.4349703e-05
1,996 Moneyball: Proactive Auto-Scaling in Microsoft Azure SQL Database Serverless 2022 VLDB 9.213369e-05
2,208 Magpie: Python at Speed and Scale using Cloud Backends 2021 CIDR 8.8445332e-05
4,300 The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox 2015 CIDR 6.676283e-05
5,767 Survivability of Cloud Databases - Factors and Prediction 2018 SIGMOD 5.9997338e-05
5,838 Tenant Placement in Over-subscribed Database-as-a-Service Clusters 2022 VLDB 5.9726313e-05
5,938 Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud 2022 VLDB 5.9383582e-05
6,248 The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward 2021 VLDB 5.8345657e-05
6,903 Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation 2021 VLDB 5.6493582e-05
8,978 Pipemizer: An Optimizer for Analytics Data Pipelines 2022 VLDB 5.2446911e-05
10,326 Not for the Timid: On the Impact of Aggressive Over-booking in the Cloud 2016 VLDB 5.0327532e-05
11,998 Toto - Benchmarking the Efficiency of a Cloud Service 2021 SIGMOD 4.9769913e-05
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