SkyPIE: A Fast & Accurate Oracle for Object Placement
Summary: SkyPIE precomputes an oracle of object-placement policies as a cost-hyperplane matrix via convex optimization. Online queries are 1–8 orders of magnitude faster and ILP-equivalent, enabling exact optimization on large workloads with >10x savings vs heuristics. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tiemo Bang (University of California Berkeley)
- 2. Chris Douglas (University of California Berkeley)
- 3. Natacha Crooks (University of California Berkeley)
- 4. Joseph M. Hellerstein (University of California Berkeley)
BibTeX Citation
@inproceedings{bang_sigmod24,
title = {{SkyPIE: A Fast \& Accurate Oracle for Object Placement}},
author = {Bang, Tiemo and Douglas, Chris and Crooks, Natacha and Hellerstein, Joseph M.},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639310},
url = {https://dl.acm.org/doi/10.1145/3639310},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,912 | ArrayMorph: Optimizing Hyperslab Queries on the Cloud for Machine Learning Pipelines | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 42 | The R+-Tree: A Dynamic Multi-Dimensional Index for Objects | 1987 | VLDB | 0.00046170812 |
| 939 | Starling: A Scalable Query Engine on Cloud Functions | 2020 | SIGMOD | 0.00013083524 |
| 1,512 | On the Analysis of Indexing Schemes | 1997 | PODS | 0.00010536001 |
| 3,071 | Choosing A Cloud DBMS: Architectures and Tradeoffs | 2019 | VLDB | 7.7885156e-05 |
| 3,931 | Take me to your leader! Online Optimization of Distributed Storage Configurations | 2015 | VLDB | 7.0093991e-05 |
| 4,103 | Exploiting Cloud Object Storage for High-Performance Analytics | 2023 | VLDB | 6.8993506e-05 |
| 4,558 | Where in the World is My Data? | 2011 | VLDB | 6.6328303e-05 |
| 8,089 | LEGOStore: A Linearizable Geo-Distributed Store Combining Replication and Erasure Coding | 2022 | VLDB | 5.4894353e-05 |
| 8,091 | Parallelism-Optimizing Data Placement for Faster Data-Parallel Computations | 2023 | VLDB | 5.4889128e-05 |
| 8,861 | Optimizing the cloud? Don't train models. Build oracles! | 2024 | CIDR | 5.355716e-05 |
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| 3 | 9,320 | QSkycube: Efficient Skycube Computation Using Point-Based Space Partitioning | 2011 | VLDB |
| 4 | 4,987 | Releasing Cloud Databases from the Chains of Performance Prediction Models | 2017 | CIDR |
| 5 | 7,589 | Cost-Intelligent Data Analytics in the Cloud | 2024 | CIDR |
| 6 | 2,344 | Towards Cost-Optimal Query Processing in the Cloud | 2021 | VLDB |
| 7 | 2,822 | Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings | 2020 | SIGMOD |
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| 9 | 10,000 | Saving Money for Analytical Workloads in the Cloud | 2024 | VLDB |
| 10 | 13,319 | SkyStore: Cost-Optimized Object Storage Across Regions and Clouds | 2025 | VLDB |