Saving Money for Analytical Workloads in the Cloud

Summary: Exploit differences in cloud billing (compute-time vs bytes-scanned) by classifying analytical queries as compute- or IO-bound and selecting execution modalities that minimize monetary cost while meeting user runtime constraints. Implement cost-aware plan generation that mixes pricing models (and clouds), yielding up to 56% workload and 90% per-query savings and robust gains across vendor price variations. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13749
Venue: VLDB
Year: 2024
Pagerank: 5.1814573e-05
Overall Rank: 10,000 | 31.40%
DOI: 10.14778/3681954.3682018

Incoming Non-self Citations Over Time

Authors

1. Tapan Srivastava (University of Chicago)
2. Raul Castro Fernandez (University of Chicago)

BibTeX Citation

@article{srivastava_vldb24,
        title = {{Saving Money for Analytical Workloads in the Cloud}},
        author = {Srivastava, Tapan and Fernandez, Raul Castro},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {11},
        pages = {3524--3537},
        doi = {10.14778/3681954.3682018},
        url = {https://doi.org/10.14778/3681954.3682018},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,076	CloudGlide: Deconstructing the Landscape of Cloud-Based Analytics	2025	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 20 of 20 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
18	How Good Are Query Optimizers, Really?	2016	VLDB	0.00059284255
24	Spark SQL: Relational Data Processing in Spark	2015	SIGMOD	0.00054865648
54	On Random Sampling over Joins	1999	SIGMOD	0.00040810225
66	The Snowflake Elastic Data Warehouse	2016	SIGMOD	0.00038561587
401	Deep Unsupervised Cardinality Estimation	2020	VLDB	0.00019092557
409	Semantic Data Caching and Replacement	1996	VLDB	0.00018985783
445	Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources	2018	SIGMOD	0.00018336751
476	The Making of TPC-DS	2006	VLDB	0.00017860667
818	Amazon Redshift Re-invented	2022	SIGMOD	0.00013822916
943	DynaMat: A Dynamic View Management System for Data Warehouses	1999	SIGMOD	0.00013068595
1,675	Garlic: A New Flavor of Federated Query Processing for DB2	2002	SIGMOD	0.00010035333
1,925	POLARIS: The Distributed SQL Engine in Azure Synapse	2020	VLDB	9.4764398e-05
2,344	Towards Cost-Optimal Query Processing in the Cloud	2021	VLDB	8.7199754e-05
3,018	The LDBC Social Network Benchmark: Business Intelligence Workload	2023	VLDB	7.8473755e-05
3,071	Choosing A Cloud DBMS: Architectures and Tradeoffs	2019	VLDB	7.7885156e-05
3,330	A Formal Perspective on the View Selection Problem	2001	VLDB	7.5139396e-05
5,348	Crystal: A Unified Cache Storage System for Analytical Databases	2021	VLDB	6.2562688e-05
5,659	POP/FED: Progressive Query Optimization for Federated Queries in DB2	2006	VLDB	6.1311315e-05
6,206	Combining Aggregation and Sampling (Nearly) Optimally for Approximate Query Processing	2021	SIGMOD	5.9443409e-05
8,861	Optimizing the cloud? Don't train models. Build oracles!	2024	CIDR	5.355716e-05

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