Cost-effective data analytics across multiple cloud regions

We propose a cloud-native data analytics engine for processing data stored among geographically distributed cloud regions with reduced cost. A job is split into subtasks and placed across regions based on factors including prices of compute resources and data transmission. We present its architecture which leverages existing cloud infrastructures and discuss major challenges of its system design. Preliminary experiments show that the cost is reduced by 15.1% for a decision support query on a four-region public cloud setup.