Saving Money for Analytical Workloads in the Cloud
<|reference_start|>Saving Money for Analytical Workloads in the Cloud: As users migrate their analytical workloads to cloud databases, it is becoming just as important to reduce monetary costs as it is to optimize query runtime. In the cloud, a query is billed based on either its compute time or the amount of data it processes. We observe that analytical queries are either compute- or IO-bound and each query type executes cheaper in a different pricing model. We exploit this opportunity and propose methods to build cheaper execution plans across pricing models that complete within user-defined runtime constraints. We implement these methods and produce execution plans spanning multiple pricing models that reduce the monetary cost for workloads by as much as 56%. We reduce individual query costs by as much as 90%. The prices chosen by cloud vendors for cloud services also impact savings opportunities. To study this effect, we simulate our proposed methods with different cloud prices and observe that multi-cloud savings are robust to changes in cloud vendor prices. These results indicate the massive opportunity to save money by executing workloads across multiple pricing models.<|reference_end|>
@article{srivastava2024saving,
    title={Saving Money for Analytical Workloads in the Cloud},
    author={Tapan Srivastava, Raul Castro Fernandez},
    journal={arXiv preprint arXiv:2408.00253},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00253},
    primaryClass={cs.DB}
}