Shaved Ice Compute Resource Commitment Dataset
Bibliographic record
Abstract
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},}
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".