Saving Money for Analytical Workloads in the Cloud

TL;DR

This paper presents methods to optimize cloud analytical workloads by leveraging different pricing models, significantly reducing costs while respecting runtime constraints, and demonstrating robustness across cloud vendors.

Contribution

It introduces novel techniques for building cost-effective execution plans across multiple cloud pricing models, a new approach in cloud workload optimization.

Findings

Cost reductions up to 56% for workloads.

Individual query costs reduced by up to 90%.

Multi-cloud strategies remain effective despite price variations.

Abstract

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.