Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud
Summary: Doppler automates Azure SQL recommendations for migrations using a price–performance score from metrics, without access to sensitive data. Augmented by Azure-behavior priors, it nudges toward SKU, exposes cost savings, and is validated in DMS v5.5. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Joyce Cahoon (Microsoft)
- 2. Wenjing Wang (Microsoft)
- 3. Yiwen Zhu (Microsoft)
- 4. Katherine Lin (Microsoft)
- 5. Sean Liu (Microsoft)
- 6. Raymond Truong (Microsoft)
- 7. Neetu Singh (Microsoft)
- 8. Chengcheng Wan (University of Chicago)
- 9. Alexandra Ciortea (Microsoft)
- 10. Sreraman Narasimhan (Microsoft)
- 11. Subru Krishnan (Microsoft)
BibTeX Citation
@article{cahoon_vldb22,
title = {{Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud}},
author = {Cahoon, Joyce and Wang, Wenjing and Zhu, Yiwen and Lin, Katherine and Liu, Sean and Truong, Raymond and Singh, Neetu and Wan, Chengcheng and Ciortea, Alexandra and Narasimhan, Sreraman and Krishnan, Subru},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {12},
pages = {3509--3521},
doi = {10.14778/3554821.3554840},
url = {https://doi.org/10.14778/3554821.3554840},
year = {2022}
}
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,344 | Towards Cost-Optimal Query Processing in the Cloud | 2021 | VLDB | 8.7199754e-05 |
| 3,063 | Oracle Database Replay | 2008 | SIGMOD | 7.802088e-05 |
| 3,460 | DIAMetrics: Benchmarking Query Engines at Scale | 2020 | VLDB | 7.3939262e-05 |
| 6,102 | AutoExecutor: Predictive Parallelism for Spark SQL Queries | 2021 | VLDB | 5.976708e-05 |
| 6,946 | KEA: Tuning an Exabyte-Scale Data Infrastructure | 2021 | SIGMOD | 5.7309848e-05 |
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