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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
- 6780
- Venue
- SIGMOD
- Year
- 2024
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,934 | 24.01%
- 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 |
| 101 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049778866 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036859633 |
| 237 |
An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server |
1997 |
VLDB |
0.00031727601 |
| 329 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027301488 |
| 371 |
Self-Driving Database Management Systems |
2017 |
CIDR |
0.00025382677 |
| 423 |
Tuning Database Configuration Parameters with iTuned |
2009 |
VLDB |
0.00023628474 |
| 517 |
AutoAdmin "What-if" Index Analysis Utility |
1998 |
SIGMOD |
0.00021193179 |
| 571 |
A Heuristic Approach to Attribute Partitioning |
1979 |
SIGMOD |
0.00019891907 |
| 661 |
Adaptive Self-Tuning Memory in DB2 |
2006 |
VLDB |
0.00018488168 |
| 679 |
Skew-Aware Automatic Database Partitioning in Shared-Nothing, Parallel OLTP Systems |
2012 |
SIGMOD |
0.00018211621 |
| 760 |
Automatic Performance Diagnosis and Tuning in Oracle |
2005 |
CIDR |
0.00017003914 |
| 1,018 |
Automatic Physical Database Tuning: A Relaxation-based Approach |
2005 |
SIGMOD |
0.00014626746 |
| 1,321 |
Automated Demand-driven Resource Scaling in Relational Database-as-a-Service |
2016 |
SIGMOD |
0.00012605455 |
| 1,498 |
P-Store: An Elastic Database System with Predictive Provisioning |
2018 |
SIGMOD |
0.00011660631 |
| 1,810 |
SQL Memory Management in Oracle9i |
2002 |
VLDB |
0.00010471365 |
| 2,153 |
The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models |
2018 |
SIGMOD |
9.418541e-05 |
| 2,376 |
Moneyball: Proactive Auto-Scaling in Microsoft Azure SQL Database Serverless |
2022 |
VLDB |
8.9367999e-05 |
| 2,410 |
Automated Partitioning Design in Parallel Database Systems |
2011 |
SIGMOD |
8.8643562e-05 |
| 2,955 |
Magpie: Python at Speed and Scale using Cloud Backends |
2021 |
CIDR |
7.8188583e-05 |
| 4,575 |
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox |
2015 |
CIDR |
6.0662145e-05 |
| 5,729 |
Survivability of Cloud Databases - Factors and Prediction |
2018 |
SIGMOD |
5.3499416e-05 |
| 6,113 |
Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud |
2022 |
VLDB |
5.2006495e-05 |
| 6,278 |
The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward |
2021 |
VLDB |
5.1241654e-05 |
| 6,702 |
Tenant Placement in Over-subscribed Database-as-a-Service Clusters |
2022 |
VLDB |
4.9518701e-05 |
| 7,044 |
Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation |
2021 |
VLDB |
4.8475963e-05 |
| 8,859 |
Pipemizer: An Optimizer for Analytics Data Pipelines |
2022 |
VLDB |
4.4301601e-05 |
| 9,948 |
Not for the Timid: On the Impact of Aggressive Over-booking in the Cloud |
2016 |
VLDB |
4.2391069e-05 |
| 11,491 |
Toto - Benchmarking the Efficiency of a Cloud Service |
2021 |
SIGMOD |
4.1905499e-05 |
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