Eco-DMW: Eco-design methodology for data warehouses

In the Big Data Era, the management of energy consumption by servers and data centers has become a challenging issue for companies, institutions, and countries. In datacentric applications, DBMS are one of the major energy consumers when executing complex queries involving very large databases. Some research has been devoted to this issue, covering both the hardware and software dimensions. Regarding software, several proposals have been outlined, focusing either on analytical cost models to predict energy when executing queries or techniques to save energy. To this date, no research has taken account of energy at the physical design level, a crucial phase in database design. In this paper, we propose a methodology, called Eco-DMW, that integrates the energy dimension into the physical design. To show this integration, we study the case of materialized views, a redundant optimization structure. We first show the place that energy takes throughout this stage of design. A multi-objective formalization of the problem of materialized view selection is given. A genetic algorithm is developed to solve the problem. Intensive experiments are conducted using a mathematical cost model and a real measurement tool dedicated to computing energy. Results show the interest of this proposal to save energy and optimize queries in the presence of the selected materialized views.