Vertically Autoscaling Monolithic Applications with CaaSPER: Scalable Container-as-a-Service Performance Enhanced Resizing Algorithm for the Cloud

Summary: CaaSPER is a vertical autoscaling algorithm for monolithic DBaaS, blending reactive and proactive CPU resizing to reduce resource slack. App- and platform-agnostic; enables cost–performance trade-offs and maintains CPU utilization at target. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6848
Venue: SIGMOD
Year: 2024
Pagerank: 5.3483178e-05
Overall Rank: 8,910 | 38.87%
DOI: 10.1145/3626246.3653378

Incoming Non-self Citations Over Time

Authors

1. Anna Pavlenko (Microsoft)
2. Joyce Cahoon (Microsoft)
3. Yiwen Zhu (Microsoft)
4. Brian Kroth (Microsoft)
5. Michael Nelson (Microsoft)
6. Andrew Carter (Microsoft)
7. David Liao (Microsoft)
8. Travis Wright (Microsoft)
9. Jesús Camacho-Rodríguez (Microsoft)
10. Karla Saur (Microsoft)

BibTeX Citation

@inproceedings{pavlenko_sigmod24,
        title = {{Vertically Autoscaling Monolithic Applications with CaaSPER: Scalable Container-as-a-Service Performance Enhanced Resizing Algorithm for the Cloud}},
        author = {Pavlenko, Anna and Cahoon, Joyce and Zhu, Yiwen and Kroth, Brian and Nelson, Michael and Carter, Andrew and Liao, David and Wright, Travis and Camacho-Rodríguez, Jesús and Saur, Karla},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3626246.3653378},
        url = {https://dl.acm.org/doi/10.1145/3626246.3653378},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,695	Oceanus: Enable SLO-Aware Vertical Autoscaling for Cloud-Native Streaming Services in Tencent	2025	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
164	The Dangers of Replication and a Solution	1996	SIGMOD	0.00027698879
226	OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases	2014	VLDB	0.00024027277
584	Workload-Aware Database Monitoring and Consolidation	2011	SIGMOD	0.00016139925
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
1,092	Automated Demand-driven Resource Scaling in Relational Database-as-a-Service	2016	SIGMOD	0.00012221946
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
2,226	Moneyball: Proactive Auto-Scaling in Microsoft Azure SQL Database Serverless	2022	VLDB	8.9174082e-05
3,026	Database Replication: a Tale of Research across Communities	2010	VLDB	7.8384865e-05
3,236	Sharing Buffer Pool Memory in Multi-Tenant Relational Database-as-a-Service	2015	VLDB	7.6108467e-05
3,259	Characterizing Tenant Behavior for Placement and Crisis Mitigation in Multitenant DBMSs	2013	SIGMOD	7.5892257e-05
3,950	MagicScaler: Uncertainty-aware, Predictive Autoscaling	2023	VLDB	6.9980354e-05
4,816	A Top-Down Approach to Achieving Performance Predictability in Database Systems	2017	SIGMOD	6.4949878e-05
5,355	Solver-In-The-Loop Cluster Resource Management for Database-as-a-Service	2023	VLDB	6.2500992e-05
5,591	Flexible Resource Allocation for Relational Database-as-a-Service	2023	VLDB	6.1556906e-05
6,177	Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud	2022	VLDB	5.9496208e-05

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