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Optimizing the cloud? Don't train models. Build oracles!

Summary: Introduce cloud oracles: a non-ML approach for online cloud configuration that leverages parametric convex optimization to deliver guaranteed accuracy and explainable decisions. Validate empirically and outline research directions to extend the oracle approach beyond convex settings. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
516
Venue
CIDR
Year
2024
Pagerank
5.355716e-05
Overall Rank
8,861 | 39.21%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{bang_cidr24,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '24},
        title = {{Optimizing the cloud? Don't train models. Build oracles!}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Bang, Tiemo and Power, Conor and Ameli, Siavash and Crooks, Natacha and Hellerstein, Joseph M.},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
9,735 SkyPIE: A Fast & Accurate Oracle for Object Placement 2024 SIGMOD 5.227679e-05
9,913 Adaptive data transformations for QaaS 2025 CIDR 5.1955087e-05
10,000 Saving Money for Analytical Workloads in the Cloud 2024 VLDB 5.1814573e-05
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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
323 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021264788
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
568 SageDB: A Learned Database System 2019 CIDR 0.0001641553
818 Amazon Redshift Re-invented 2022 SIGMOD 0.00013822916
1,061 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012369764
1,218 P-Store: An Elastic Database System with Predictive Provisioning 2018 SIGMOD 0.00011621066
1,765 Selecting Subexpressions to Materialize at Datacenter Scale 2018 VLDB 9.8079546e-05
2,540 Multi-Objective Parametric Query Optimization 2015 VLDB 8.45187e-05
2,620 Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation 2021 VLDB 8.3363963e-05
2,844 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 8.0608767e-05
3,003 Configuration-Parametric Query Optimization for Physical Design Tuning 2008 SIGMOD 7.8620915e-05
3,614 Cloudy with High Chance of DBMS: A 10-year Prediction for Enterprise-Grade ML 2020 CIDR 7.2568185e-05
4,011 Towards Dynamic and Safe Configuration Tuning for Cloud Databases 2022 SIGMOD 6.959982e-05
4,470 Kepler: Robust Learning for Faster Parametric Query Optimization 2023 SIGMOD 6.6817353e-05
5,394 Leveraging Query Logs and Machine Learning for Parametric Query Optimization 2022 VLDB 6.2336084e-05
6,121 The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward 2021 VLDB 5.9688569e-05
6,946 KEA: Tuning an Exabyte-Scale Data Infrastructure 2021 SIGMOD 5.7309848e-05
8,091 Parallelism-Optimizing Data Placement for Faster Data-Parallel Computations 2023 VLDB 5.4889128e-05
8,193 Towards Building Autonomous Data Services on Azure 2023 SIGMOD 5.4696038e-05
9,224 Phoebe: A Learning-based Checkpoint Optimizer 2021 VLDB 5.3035811e-05
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