A bi-objective cost model for optimizing database queries in a multi-cloud environment

Cost models are broadly used in query processing to drive the query optimization process, accurately predict the query execution time, schedule database query tasks, apply admission control and derive resource requirements to name a few applications. The main role of cost models is to estimate the time needed to run the query on a specific machine. In a multi-cloud environment, cost models should be easily calibrated for a wide range of different physical machines, and time estimates need to be complemented with monetary cost information, since both the economic cost and the performance are of primary importance. This work aims to serve as the first proposal for a bi-objective query cost model suitable for queries executed over resources provided by potentially multiple cloud providers. We leverage existing calibrating modeling techniques for time estimates and we couple such estimates with monetary cost information covering the main charging options for using cloud resources. Moreover, we explain how the cost model can become part of an optimizer. Our approach is applicable to more generic data flow graphs, the execution plans of which do not necessarily comprise relational operators. Finally, we give a concrete example about the usage of our proposal and we validate its accuracy through real case studies.