Optimizing Aggregate Query Processing in Cloud Data Warehouses

In this paper, we study and optimize the aggregate query processing in a highly distributed Cloud Data Warehouse, where each database stores a subset of relational data in a star-schema. Existing aggregate query processing algorithms focus on optimizing various query operations but give less importance to communication cost overhead (Two-phase algorithm). However, in cloud architectures, the communication cost overhead is an important factor in query processing. Thus, we consider communication overhead to improve the distributed query processing in such cloud data warehouses. We then design query-processing algorithms by analyzing aggregate operation and eliminating most of the sort and group-by operations with the help of integrity constraints and our proposed storage structures, PK-map and Tuple-index-map. Extensive experiments on PlanetLab cloud machines validate the effectiveness of our proposed framework in improving the response time, reducing node-to-node interdependency, minimizing communication overhead, and reducing database table access required for aggregate query.