Shaved Ice Compute Resource Commitment Dataset

To support further research into cloud compute forecasting, commitment optimization, and capacity planning, we present a data artifact of normalized Virtual Machine (VM) demand for 12 different machine types in 4 different regions over a 3-year period of time from the Snowflake Data Cloud, which includes data warehousing, data lakes, data science, data engineering, and other workloads across multiple clouds. The data artifact has been used in the paper Murray Stokely, Neel Nadgir, Jack Peele, and Orestis Kostakis. 2025. Shaved Ice: Optimal Compute Resource Commitments for Dynamic Multi-Cloud Workloads. In Proceedings of the 16th ACM/SPEC International Conference on Performance Engineering (ICPE ’25), May 5–9, 2025, Toronto, ON, Canada. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3676151.3719353 @inproceedings {snowflake-icpe25, author = {Murray Stokely and Orestis Kostakis and Neel Nadgir}, title = {Shaved Ice: Optimal Compute Resource Commitments for Dynamic Multi-Cloud Workloads}, booktitle = {Proceedings of the ACM/SPEC International Conference on Performance Engineering}, year = {2025}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA},}