Eco-processing of OLAP complex queries

With the Era of Big Data and the spectacular development of High-Performance Computing, organizations and countries spend considerable efforts and money to control/reduce the energy consumption. In data-centric applications, DBMS are one of the major energy consumers when executing complex queries. As a consequence, integrating the energy aspects in the advanced database design becomes an economic necessity. To predict this energy, the development of mathematical cost models is one of the avenues worth exploring. In this paper, we propose a cost model for estimating the energy required to execute a workload. This estimation is obtained by the means of statistical regression techniques that consider three types of parameters related to the query execution strategies, the used deployment platform and the characteristics of the data warehouses. To evaluate the quality of our cost model, we conduct two types of experiments: one using our mathematical cost model and another using a real DBMS with dataset of TPC-H and TPC-DS benchmarks. The obtained results show the quality of our cost model.