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Cloud Optimization & FinOps

Workload-aware methods for improving performance, resource efficiency, and cost across cloud data warehouses and infrastructure.

Keebo

Autonomously optimizes cloud data-warehouse performance and cost in production.

Research question

How can cloud data warehouses and infrastructure deliver better performance at lower cost under changing workloads?

Approach

Keebo turns workload learning into autonomous resource sizing and control for cloud data warehouses. Related query and pipeline optimization work appears under Self-Improving Data Systems.

Commercialization and customers

Keebo developed the first autonomous cloud data-warehouse optimization platform and was used by Fortune 100 companies.

Publications

Making Data Clouds Smarter at Keebo: Automated Warehouse Optimization using Data Learning

Barzan Mozafari, Radu Alexandru Burcuta, Alan Cabrera, Andrei Constantin, Derek Francis, David Grömling, Alekh Jindal, Maciej Konkolowicz, Valentin Marian Spac, Yongjoo Park, Russell Razo Carranzo, Nicholas Richardson, Abhishek Roy, Aayushi Srivastava, Isha Tarte, Brian Westphal, Chi Zhang

SIGMOD Demo 2023 · ACM SIGMOD International Conference on Management of Data — Demonstration

Paper ↗Project ↗
Cite
@inproceedings{8ede012b-09b5-4196-bc20-95033ee64807,
  title = {Making Data Clouds Smarter at Keebo: Automated Warehouse Optimization using Data Learning},
  author = {Barzan Mozafari and Radu Alexandru Burcuta and Alan Cabrera and Andrei Constantin and Derek Francis and David Grömling and Alekh Jindal and Maciej Konkolowicz and Valentin Marian Spac and Yongjoo Park and Russell Razo Carranzo and Nicholas Richardson and Abhishek Roy and Aayushi Srivastava and Isha Tarte and Brian Westphal and Chi Zhang},
  booktitle = {ACM SIGMOD International Conference on Management of Data — Demonstration},
  year = {2023},
  doi = {10.1145/3555041.3589681}
}

SlabCity: Whole-Query Optimization using Program Synthesis

Rui Dong, Jie Liu, Yuxuan Zhu, Cong Yan, Barzan Mozafari, Xinyu Wang

PVLDB 2023 · Proceedings of the VLDB Endowment

Paper ↗Project ↗
Cite
@article{def837ec-1591-44a4-a36d-929442c789f3,
  title = {SlabCity: Whole-Query Optimization using Program Synthesis},
  author = {Rui Dong and Jie Liu and Yuxuan Zhu and Cong Yan and Barzan Mozafari and Xinyu Wang},
  journal = {Proceedings of the VLDB Endowment},
  year = {2023},
  doi = {10.14778/3611479.3611515}
}