Towards Green Query Processing - Auditing Power Before Deploying
Simon Pierre Dembele, Ladjel Bellatreche, CARLOS R. ORDÓÑEZ · 2020
Nowadays, energy reduction has become a critical and urgent issue for the database community. A lot of initiatives have been launched on energy-efficiency for intensive-workload computation covering individual hardware components, system software, to applications. This computation is mainly ensured by query optimizers. Their current versions minimize inputs-outputs operations and try to exploit RAM as much as possible, by ignoring energy. A couple of studies proposed the integration of energy into query optimizers that can be classified into hardware and software solutions. Several researchers have the idea that the operating systems and firmware manage energy and put software solutions in the second plan. This does not distinguish between tasks of operating systems and DBMSs. In this paper, we claim that building from scratch a green query processors and revisiting existing ones pass through 4-steps procedure: (1) establishment of a deep audit that allows understanding the query processor functioning, (2) identification of relevant energy-sensitive parameters belonging to hardware and software components, (3) elaboration of mathematical cost models estimating consumed energy when executing a query on a target DBMS and (4) setting of values of the energy-sensitive parameters using a nonlinear regression technique. To show the effectiveness of this procedure, we apply it on two open-source DBMSs with different functioning policies: PostgreSQL and MonetDB and compared them using the dataset and the workload of the TPC-H benchmark.