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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
- 6779
- Venue
- SIGMOD
- Year
- 2024
- Pagerank
- 4.1945683e-05
- Overall Rank
- 10,931 | 23.96%
- DOI
-
10.1145/3626246.3653371
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
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 |
| 102 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049545203 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036721403 |
| 237 |
An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server |
1997 |
VLDB |
0.00031726304 |
| 333 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027206884 |
| 359 |
Self-Driving Database Management Systems |
2017 |
CIDR |
0.0002592783 |
| 424 |
Tuning Database Configuration Parameters with iTuned |
2009 |
VLDB |
0.00023616398 |
| 516 |
AutoAdmin "What-if" Index Analysis Utility |
1998 |
SIGMOD |
0.00021196031 |
| 592 |
A Heuristic Approach to Attribute Partitioning |
1979 |
SIGMOD |
0.00019547845 |
| 663 |
Adaptive Self-Tuning Memory in DB2 |
2006 |
VLDB |
0.00018469455 |
| 679 |
Skew-Aware Automatic Database Partitioning in Shared-Nothing, Parallel OLTP Systems |
2012 |
SIGMOD |
0.00018215154 |
| 765 |
Automatic Performance Diagnosis and Tuning in Oracle |
2005 |
CIDR |
0.00017016449 |
| 1,017 |
Automatic Physical Database Tuning: A Relaxation-based Approach |
2005 |
SIGMOD |
0.00014634307 |
| 1,322 |
Automated Demand-driven Resource Scaling in Relational Database-as-a-Service |
2016 |
SIGMOD |
0.00012610912 |
| 1,501 |
P-Store: An Elastic Database System with Predictive Provisioning |
2018 |
SIGMOD |
0.00011664869 |
| 1,810 |
SQL Memory Management in Oracle9i |
2002 |
VLDB |
0.0001047003 |
| 2,157 |
The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models |
2018 |
SIGMOD |
9.416022e-05 |
| 2,375 |
Moneyball: Proactive Auto-Scaling in Microsoft Azure SQL Database Serverless |
2022 |
VLDB |
8.9452359e-05 |
| 2,413 |
Automated Partitioning Design in Parallel Database Systems |
2011 |
SIGMOD |
8.8672223e-05 |
| 2,954 |
Magpie: Python at Speed and Scale using Cloud Backends |
2021 |
CIDR |
7.8262582e-05 |
| 4,576 |
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox |
2015 |
CIDR |
6.0721464e-05 |
| 5,719 |
Survivability of Cloud Databases - Factors and Prediction |
2018 |
SIGMOD |
5.3550742e-05 |
| 6,110 |
Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud |
2022 |
VLDB |
5.2056003e-05 |
| 6,261 |
The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward |
2021 |
VLDB |
5.1350714e-05 |
| 6,699 |
Tenant Placement in Over-subscribed Database-as-a-Service Clusters |
2022 |
VLDB |
4.9566258e-05 |
| 7,047 |
Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation |
2021 |
VLDB |
4.8521181e-05 |
| 8,859 |
Pipemizer: An Optimizer for Analytics Data Pipelines |
2022 |
VLDB |
4.4344107e-05 |
| 9,946 |
Not for the Timid: On the Impact of Aggressive Over-booking in the Cloud |
2016 |
VLDB |
4.2431724e-05 |
| 11,487 |
Toto - Benchmarking the Efficiency of a Cloud Service |
2021 |
SIGMOD |
4.1945683e-05 |
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