Towards Cost-Optimal Query Processing in the Cloud - Leis, Viktor; Kuschewski, Maximilian (47th International Conference on Very Large Data Bases, 2021)
Public cloud providers offer hundreds of heterogeneous hardware instances. For analytical query processing systems, this presents a major challenge: depending on the hardware configuration, performance and cost may differ by orders of magnitude. We propose a simple and intuitive model that takes the workload, hardware, and cost into account to determine the optimal instance configuration. We discuss how such a model-based approach can significantly reduce costs and also guide the evolution of cloud-native database systems to achieve our vision of cost-optimal query processing.<br
Keywords: database; OLAP; model; analytical; cloud; query processing

White-Box OLAP Performance Modeling for the Cloud - Kuschewski, Maximilian; Leis, Viktor (11th Annual Conference on Innovative Data Systems Research (CIDR ‘21), 2021)
Public cloud providers offer hundreds of heterogeneous hardware instances. For cloud-based analytical query processing systems, this presents a major challenge: depending on the hardware configuration chosen, performance and cost may differ by orders of magnitude. We explore this relationship by building a white-box model that takes the workload, hardware, and cost into account to determine the optimal instance configuration. We argue that such an approach can guide the evolution of cloud-native OLAP systems.<br
Keywords: cloud; database; model