Automated Demand-driven Resource Scaling in Relational Database-as-a-Service

Summary: Automates demand-driven auto-scaling of relational DaaS resources via robust signals from database engine telemetry to predict workload needs. Azure SQL Database prototype yields 1.5–3× cost savings with latency on par with utilization-only methods, letting tenants budget by latency rather than fixed resources. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 5254
Venue: SIGMOD
Year: 2016
Pagerank: 0.00012221946
Overall Rank: 1,092 | 92.51%
DOI: 10.1145/2882903.2903733

Incoming Non-self Citations Over Time

Authors

1. Sudipto Das (Microsoft)
2. Feng Li (Microsoft)
3. Vivek R. Narasayya (Microsoft)
4. Arnd Christian König (Microsoft)

BibTeX Citation

@inproceedings{das_sigmod16,
        title = {{Automated Demand-driven Resource Scaling in Relational Database-as-a-Service}},
        author = {Das, Sudipto and Li, Feng and Narasayya, Vivek R. and König, Arnd Christian},
        series = {{SIGMOD} '16},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2882903.2903733},
        url = {https://dl.acm.org/doi/10.1145/2882903.2903733},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 26 of 26 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
234	Self-Driving Database Management Systems	2017	CIDR	0.00023810722
461	Query-based Workload Forecasting for Self-Driving Database Management Systems	2018	SIGMOD	0.00018068441
1,559	Cloud-Native Database Systems at Alibaba: Opportunities and Challenges	2019	VLDB	0.00010362456
2,226	Moneyball: Proactive Auto-Scaling in Microsoft Azure SQL Database Serverless	2022	VLDB	8.9174082e-05
2,888	AI Meets Database: AI4DB and DB4AI	2021	SIGMOD	7.9941489e-05
3,219	iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases	2019	VLDB	7.6283153e-05
3,586	Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation	2021	VLDB	7.2834069e-05
3,662	openGauss: An Autonomous Database System	2021	VLDB	7.2166682e-05
3,758	Nova-LSM: A Distributed, Component-based LSM-tree Key-value Store	2021	SIGMOD	7.1471346e-05
4,335	Adaptive HTAP through Elastic Resource Scheduling	2020	SIGMOD	6.7556868e-05
5,340	Machine Learning for Databases	2021	VLDB	6.2603359e-05
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
6,939	Multi-Tenant Cloud Data Services: State-of-the-Art, Challenges and Opportunities	2022	SIGMOD	5.7338637e-05
7,049	MgCrab: Transaction Crabbing for Live Migration in Deterministic Database Systems	2019	VLDB	5.7177768e-05
8,193	Towards Building Autonomous Data Services on Azure	2023	SIGMOD	5.4696038e-05
8,386	A system design for elastically scaling transaction processing engines in virtualized servers	2020	VLDB	5.436614e-05
8,910	Vertically Autoscaling Monolithic Applications with CaaSPER: Scalable Container-as-a-Service Performance Enhanced Resizing Algorithm for the Cloud	2024	SIGMOD	5.3483178e-05
9,536	Database Gyms	2023	CIDR	5.2529727e-05
9,562	Kora: A Cloud-Native Event Streaming Platform For Kafka	2023	VLDB	5.2528121e-05
10,033	Modeling Concurrency Control as a Learnable Function	2026	SIGMOD	5.173224e-05
10,695	Oceanus: Enable SLO-Aware Vertical Autoscaling for Cloud-Native Streaming Services in Tencent	2025	SIGMOD	5.093636e-05
11,150	Proactive Resume and Pause of Resources for Microsoft Azure SQL Database Serverless	2024	SIGMOD	5.093636e-05
11,180	Lorentz: Learned SKU Recommendation Using Profile Data (DMDS)	2024	SIGMOD	5.093636e-05
11,291	OptScaler: A Collaborative Framework for Robust Autoscaling in the Cloud	2024	VLDB	5.093636e-05
11,685	Toto - Benchmarking the Efficiency of a Cloud Service	2021	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
523	Adaptive Self-Tuning Memory in DB2	2006	VLDB	0.00017133451
584	Workload-Aware Database Monitoring and Consolidation	2011	SIGMOD	0.00016139925
624	Performance Prediction for Concurrent Database Workloads	2011	SIGMOD	0.00015683402
682	Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques	2012	VLDB	0.00015014887
861	Automatic Virtual Machine Configuration for Database Workloads	2008	SIGMOD	0.00013543212
1,335	SQLVM: Performance Isolation in Multi-Tenant Relational Database-as-a-Service	2013	CIDR	0.00011122853
1,931	Performance and Resource Modeling in Highly-Concurrent OLTP Workloads	2013	SIGMOD	9.4664741e-05
3,259	Characterizing Tenant Behavior for Placement and Crisis Mitigation in Multitenant DBMSs	2013	SIGMOD	7.5892257e-05
4,731	Automated and On-Demand Provisioning of Virtual Machines for Database Applications	2007	SIGMOD	6.5346682e-05

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1	8,785	Tao: Improving Resource Utilization while Guaranteeing SLO in Multi-tenant Relational Database-as-a-Service	2024	SIGMOD
2	1,335	SQLVM: Performance Isolation in Multi-Tenant Relational Database-as-a-Service	2013	CIDR
3	9,728	Towards Database Virtualization for Database as a Service	2013	VLDB
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5	2,226	Moneyball: Proactive Auto-Scaling in Microsoft Azure SQL Database Serverless	2022	VLDB
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9	641	Relational Cloud: A Database-as-a-Service for the Cloud	2011	CIDR
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